WEBVTT

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And what that basically does is that it londers the

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transfection as an idea and as a methodology

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through this false pandemic and comes out on the other end

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as a rushed but perfect outcome.

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And it is frustrating to me now in some ways

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how many people are picking up on this idea so late.

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When I think the first time that I presented the idea

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that they were gonna do a bait and switch

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with the spike protein or the LNP

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or the RNA being impure already in the beginning of 2021

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when these ideas were first presented as problems

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especially the toxicity of the spike.

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It hit me almost immediately that there were too many people

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in this consensus that we're sure that the spike protein

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of the virus was dangerous.

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The spike protein of the virus was manipulated.

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But then when they used the spike protein in the shot

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most of these people didn't have the immediate

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well what the hell are they doing that for.

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And in fact for a very long time many of these people

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continue to focus on the spike of the virus

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rather than the spike of the transfections

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and you can look back and you can see them all do it.

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So they can go on their streams

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and they can go on their podcasts now

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and say that that's not what happened

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but the problem is is that there's a whole internet

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full of videos of these morons

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and the exact things that they said.

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And that's the part that's kind of getting me angry

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and why those words sometimes come out.

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I have to really center myself to think about

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how despicable it is that these people

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have absolutely spectacularly committed

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to keeping some of these lies intact.

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For their comfort, for their fame, for fortune, it's awful.

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But there is no question that that's what's going on right now.

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The novel virus narrative is under preservation

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by these people and a virus narrative

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about a bio-weapon being released

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is preserving the narrative.

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A discussion of gain of function is preserving the narrative.

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And a complete absence of the discussion

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of where the excess deaths actually came from

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is a complete preservation of the narrative.

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And almost every one of these people is guilty of that.

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And that's what I'm trying to sort out with this map.

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I've got a lot of people on here,

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I don't even clue down here, like John Cullen

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and people that we just can on the outset discard.

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You know, I'm not gonna put Adam Gardner on here

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or something like that.

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These people that were posers already in 2020,

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obviously like the Berenson guy.

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Why would I even put him on this map?

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He revealed himself has extremely manufactured construct already.

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They revealed him as that in the beginning of the pandemic

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by censoring him off Twitter

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and then letting him sue Twitter.

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I mean, this was all as much publicity

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as anyone could hope to have.

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Every one of these examples of censorship

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that anybody ever heard of,

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like Robert Malone's censorship ended up in what?

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Two more apps being populated,

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telegram and GAB

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and a whole bunch of people,

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including Brett Weinstein going over to those platforms

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at the same time that Robert Malone did

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under the pretense that censorship isn't cool.

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This was all behavior designed to drive the crowds around,

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to move the sheep into different paddocks

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and to see who would follow who were.

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And this game has been going on

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from the very, very beginning on social media

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to try and sort out who we were

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and who the problems were.

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And that's how they found me so early.

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That's why so many people have shown up wherever I am

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or go in places.

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Serendipitously, everybody wanted to go

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to the CHD conference all of a sudden

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once they found out Jay was going.

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So many people didn't even register for the conference,

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but suddenly when they found out Jay was going,

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they had to be there.

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It was kind of pathetic, actually.

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In retrospect, I should have seen it very clearly.

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So I'm still fighting that, but, you know, whatever.

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We're going to work on that a little less often now

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and adding people to that regularly is still,

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I think, really important.

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But remember, the idea is,

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is they're trying to make you feel like you're processing,

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you're experiencing this,

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and it's going by so fast

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and there are so many smart people involved

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that you don't need to concern yourself with it.

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I mean, why would you get all up in arms

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if Steve Kirsch and Robert Malone

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are working on your behalf, right?

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So the last couple of days we've been focused

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on these two companies, Epivax and Absolera,

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and we got there because of Peter Collis

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and a recommendation by no one other than Robert Malone.

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Robert Malone has been associated directly

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and indirectly with many of these companies

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based in Canada in Vancouver.

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And many of these companies seem to be entangled

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in a AI machine learning type software stack

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that is designed to bypass

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or to reinvigorate the antibody patent paradox.

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Now, it is not by myself that I did this.

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It's a lot of work.

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Most of the work done by Mark Koolack,

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who found a lot of these companies,

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did a lot of leg work on archiving all of the data from them

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and all of the information about them

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so that we can compare them across pages.

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And so I think we've discovered something here

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that's worth talking about because of the long-standing relationship

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between Moderna, Absolular, and Epivax.

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All of these people, especially, I think, Annie DeGroote,

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they're all interesting people that are probably underrated.

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Robert Malone, also, in his connections with these people,

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I think it's a pretty spot-on observation

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to think that George Webb, Boston Consulting,

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Pfizer, and Robert Malone are all kind of one show

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with Veritas and making us think that this kind of thing.

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And of course, the future is personalized medicine.

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And so we're being distracted actively by these people.

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We're being made to ask the wrong questions.

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That's their main job.

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And it's taken me a long time to figure out,

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or think I'm close to figuring out what it is their job is.

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I think because it's so flexible,

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their job is to make you ask the wrong question

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that if you won't ask that question,

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they can try and make you ask that one

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or put another shiny object in front of you for you to chase.

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And that's why they keep coming back.

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That's why you can't ever get rid of them,

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because if the last story didn't catch your attention,

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and it wasn't getting enough clicks,

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well, I got another one for you now.

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And that's how you can almost see

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how Robert Malone's evolution has occurred.

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You know, little bits and pieces here,

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and he only gives you as much as you need to keep you moving along.

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And a lot of these people play that game.

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And the reason why they're playing that game

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is because there is a time point we're at

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where we basically need the my kids age,

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the teenagers, we need to get them on board

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with the idea that there is a pandemic potential everywhere,

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and therefore, in order to protect you from it,

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we need vaccines, we need digital medical records,

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and we need to keep track of who's vaccinated.

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Otherwise, the potential for pandemics is just too great.

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And the real hidden message here

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is that they want to collect as much medical data

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and combine it with genotypic and phenotypic data

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as they possibly can.

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So that someday, in the near or far future,

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they can use AI to crack the human genome.

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That's the joke.

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They tell everybody that's the story they tell at the dinner table

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after these big conferences.

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When they take you to the secret meeting,

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that's what they tell you we're really up to.

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And so it's fine if the masses think

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we're trying to get rid of them,

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because we are trying to get rid of some of them,

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we don't need the adults.

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There's not enough time for us

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to collect anything meaningful for them,

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what we really need are babies.

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And that's the literal truth.

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They need to convince the next generation

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of parents that surrendering their kids

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genetic and medical data, it was normal,

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is part of what you owe to society as a parent.

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I can guarantee you 100%

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that that's where this is going.

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And they're not trying to convince me, I got kids.

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They're not trying to convince you, you got kids,

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and you don't know exactly what your responsibilities are

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for them and to them.

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But they're trying slowly

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to change how my son will think about his kids

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because it's my son's kids they want.

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And if you don't see

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that this is at least a generation ahead

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of what they're trying to get to, so 20 years from now,

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then you're not looking far enough ahead.

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That's how this game is played.

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It's a multi-generational game

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for all the marbles.

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It's not for 10 years from now.

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And although the election next year is important,

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their plans don't hinge on it.

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Because they're flexible.

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They can always bring something else.

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And so the point is is that we've got a few years

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for everybody to learn the biology well enough

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to declare their freedom.

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Or at some point they're just going to continue

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to push the physical manifestation

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of public health.

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They can keep releasing clones.

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They can keep making people sick.

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They can keep poisoning people

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and blaming it on mother nature.

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That is not a problem.

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And if they have to, they will.

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So I thought it would be really important

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as we're trying to track down

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this long-term timeline

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and we're trying to see the people in it.

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I thought it would be really instructive

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for you to see this video,

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which was just released today

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by the Huberman lab.

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Now I'm no fear familiar with the Huberman lab,

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but he's a neurobiologist at Stanford.

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And if you watch his YouTube about the brain

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and about motivation

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and about dopamine

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and about vision,

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he is one of the sharpest knives in the drawer.

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And I would dare to say

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that he's another one of these people

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that I'm in his field

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and I would humbly submit

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that that guy is a beast.

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So I would love to have a conversation with him.

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I'd love to talk to him about immunology.

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It wouldn't surprise me

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if he knew about as much about immunology as I do.

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He's a very, very bright guy.

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But what I want you to see here

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is how a guy who works at Stanford

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has ten year at Stanford

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and he's a baller.

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This guy is a beast.

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He's super, super on top of the literature.

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He's really in front

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of the main theories of neuroscience

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and I look up to him with the greatest respect

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with regard to that field.

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And as a successful academic,

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I mean, you know,

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he's as good as they get, I guess.

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I mean, he's not some Nobel prize dude.

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I mean, he's a solid Stanford professor.

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Let me say it like that, okay?

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What I want you to see here

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is that Dr. Priscilla Chan

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and Mark Zuckerberg

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are two rich people

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who do not defer to him

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as either smart

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or accomplished

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but rather defer to him

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as an interviewer

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or a podcaster.

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He might as well be

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me coming in there and saying

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hi, I'm a guy who works out of his garage

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and I'd like to talk to you.

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Tell us what you're going to do.

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And they tell him

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about their plan

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to cure

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all human diseases.

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Just want you to let that settle in.

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Android boy

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who says we're going to

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grill some meats.

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If you've never seen that Facebook video

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of him talking about smoking some meat

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in his backyard

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with his friends,

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please Google that

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and realize what we're dealing with here.

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This is a construct.

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This is some

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weird dude.

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I don't know what to tell you what he is

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but he's not just a guy with a company

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and that's not just his wife

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and you will see it

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as they talk.

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We are being ruled by these people.

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These unelected

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egomaniacs

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and I'm not talking about

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Huberman. I'm talking about these two

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and he went to their house

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so that's submission.

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Okay.

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I'm not saying that Huberman is submissive

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but I'm telling him

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if you're listening

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Mr. Dr. Huberman

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that's what you've done.

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You submitted to them

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and they show they act like you're

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submitting to them.

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The arrogance

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that Priscilla Chan speaks

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with here as somebody who's almost

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never been on camera in America

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she speaks as though she's already

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president or something

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and remember what they're talking about.

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They're talking about the future of health

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and technology and curing all

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human diseases.

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This is some serious

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serious talk.

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By the way, I think this is what happened

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thanks to Allison for this link.

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I think I had a vocal hemorrhage

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so I had something

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there's a little thin layer of

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fibrous tissue that surrounds

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the outside of your vocal cords

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and if you abuse your vocal cords

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you can bleed there and then that will

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fill up with blood and I think what happened

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actually is that happened a

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long time ago

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and it got worse and worse.

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To the point it was like a big bubble

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or something because it was blocking

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my sleep and blocking my breathing

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when I was playing basketball and it felt

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like a bubble or a thump in my throat

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and so the amount of blood that came out

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not to be graphic or anything like that but

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it was a lot more blood than I'm seeing in this picture

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I mean it had to have been

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I don't know what way it was a quantity

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though you know so

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whatever happened I'm sure

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needs a chance to heal

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and most of these hemorrhages apparently

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will heal with immediate voice rest

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and that means limited or entirely

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suspended for several days

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which I haven't done

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so I still may have to do that

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but I'm not talking usually

24:27.080 --> 24:29.080
so I sit here all the time

24:29.080 --> 24:31.080
by myself

24:31.080 --> 24:33.080
but I was well never am I going to say that

24:33.080 --> 24:35.080
here we go

24:35.080 --> 24:37.080
these guys are crazy

24:37.080 --> 24:39.080
it's just nuts

24:39.080 --> 24:41.080
I can't even believe it

24:41.080 --> 24:43.080
I'm going to put myself a little

24:43.080 --> 24:45.080
smaller

24:45.080 --> 24:47.080
please

24:47.080 --> 24:51.080
it's extraordinary

24:51.080 --> 24:53.080
I don't know how long we're going to watch it

24:53.080 --> 24:55.080
we're not going to watch the whole thing

24:55.080 --> 24:57.080
it's like two hours long so I'm not going to watch it

24:57.080 --> 24:59.080
what I really want you to do

24:59.080 --> 25:01.080
is the language that they're using

25:01.080 --> 25:03.080
and the lack of depth

25:03.080 --> 25:05.080
that they describe the concepts

25:05.080 --> 25:07.080
let me remember what we're saying here

25:07.080 --> 25:09.080
they're talking to a

25:09.080 --> 25:11.080
Stanford neurobiologist

25:11.080 --> 25:15.080
who podcasts and communicates

25:15.080 --> 25:19.080
about science as his part-time fun job

25:19.080 --> 25:21.080
and so

25:21.080 --> 25:23.080
he's a

25:23.080 --> 25:25.080
in-depth interviewer

25:25.080 --> 25:27.080
you can understand things right

25:27.080 --> 25:29.080
and

25:29.080 --> 25:31.080
to make the claim that you're going to cure all diseases

25:31.080 --> 25:33.080
by collecting data

25:33.080 --> 25:35.080
Huberman's the kind of guy

25:35.080 --> 25:37.080
who could in theory demand

25:37.080 --> 25:39.080
a better explanation

25:39.080 --> 25:41.080
so let's hold him to account as well

25:41.080 --> 25:43.080
as Huberman calling out

25:43.080 --> 25:46.080
the BS that he's hearing or not

25:46.080 --> 25:48.080
and how much BS are we hearing

25:48.080 --> 25:50.080
I'll try to take some notes

25:50.080 --> 25:52.080
but I'll probably just interrupt

25:52.080 --> 25:54.080
too

25:54.080 --> 25:56.080
anyway let me see

25:56.080 --> 25:58.080
maybe I'm just gonna speed it up just

25:58.080 --> 26:02.080
a tad okay not a lot just a tad

26:02.080 --> 26:04.080
Welcome to the Huberman Lab Podcast

26:04.080 --> 26:06.080
where we discuss science

26:06.080 --> 26:08.080
and science-based tools for everyday life

26:08.080 --> 26:11.080
I'm Andrew Huberman

26:11.080 --> 26:13.080
and I'm a professor of neurobiology

26:13.080 --> 26:15.080
and ophthalmology at Stanford School of Medicine

26:15.080 --> 26:17.080
My guest today are Mark Zuckerberg

26:17.080 --> 26:18.080
and Dr. Priscilla Chan

26:18.080 --> 26:20.080
Mark Zuckerberg, as everybody knows

26:20.080 --> 26:22.080
founded the company Facebook

26:22.080 --> 26:24.080
is now the CEO of Meta

26:24.080 --> 26:26.080
which includes Facebook, Instagram, WhatsApp

26:26.080 --> 26:28.080
and other technology platforms

26:28.080 --> 26:30.080
Dr. Priscilla Chan graduated from Harvard

26:30.080 --> 26:32.080
and went on to do her medical degree

26:32.080 --> 26:34.080
at the University of California San Francisco

26:34.080 --> 26:36.080
Mark Zuckerberg and Dr. Priscilla Chan

26:36.080 --> 26:38.080
are married and the co-founders

26:38.080 --> 26:40.080
of the CZI or Chan Zuckerberg Initiative

26:40.080 --> 26:42.080
a philanthropic organization

26:42.080 --> 26:44.080
whose stated goal is to cure all human diseases

26:44.080 --> 26:46.080
The Chan Zuckerberg Initiative is accomplishing that

26:46.080 --> 26:48.080
by providing critical funding

26:48.080 --> 26:49.080
not available elsewhere

26:49.080 --> 26:50.080
as well as a novel framework

26:50.080 --> 26:51.080
functioning of cells

26:51.080 --> 26:53.080
cataloging all the different human cell types

26:53.080 --> 26:56.080
as well as providing AI or artificial intelligence platforms

26:56.080 --> 26:57.080
to mine all of that data

26:57.080 --> 26:59.080
to discover new pathways and cures

26:59.080 --> 27:00.080
for all human diseases

27:00.080 --> 27:02.080
The first hour of today's discussion

27:02.080 --> 27:04.080
is held with both Dr. Priscilla Chan

27:04.080 --> 27:05.080
and Mark Zuckerberg

27:05.080 --> 27:06.080
during which we discuss the CZI

27:06.080 --> 27:08.080
I mean think about what he just said there

27:08.080 --> 27:10.080
I kind of agree with Jason M

27:10.080 --> 27:11.080
what a cartoon goal

27:11.080 --> 27:13.080
basically he says

27:13.080 --> 27:15.080
we're going to collect so much data

27:15.080 --> 27:16.080
that will understand it

27:16.080 --> 27:18.080
that's kind of like saying

27:18.080 --> 27:19.080
we're going to measure the weather

27:19.080 --> 27:21.080
in so many places around the world

27:21.080 --> 27:22.080
and feed it into a computer

27:22.080 --> 27:23.080
if we measure it in enough places

27:23.080 --> 27:26.080
we're going to know what the weather is forever

27:26.080 --> 27:29.080
it's a similar statement

27:29.080 --> 27:31.080
we're going to collect so much weather data

27:31.080 --> 27:33.080
that we're going to understand how storms work

27:33.080 --> 27:36.080
and we're going to be able to predict the weather better than ever

27:36.080 --> 27:40.080
except you're talking about a biological

27:40.080 --> 27:42.080
pattern integrity

27:42.080 --> 27:44.080
that's somehow governed by genetics

27:44.080 --> 27:45.080
but we don't know how

27:45.080 --> 27:47.080
and you're going to figure it out

27:47.080 --> 27:48.080
by collecting a bunch of data

27:48.080 --> 27:50.080
and that sounds great

27:50.080 --> 27:52.080
but the way that he's selling it

27:52.080 --> 27:54.080
and the way that he's accepting it

27:54.080 --> 27:57.080
I agree he's almost got to be a seller

27:57.080 --> 27:58.080
like this is

27:58.080 --> 27:59.080
you're going to see it

27:59.080 --> 28:00.080
I'll get to let him talk

28:00.080 --> 28:02.080
because at least he's got to give an introduction

28:02.080 --> 28:04.080
but it's brutal

28:04.080 --> 28:06.080
it really means to try and cure all human diseases

28:06.080 --> 28:08.080
we talk about the motivational backbone for the CZI

28:08.080 --> 28:11.080
that extends well into each of their personal histories

28:11.080 --> 28:12.080
indeed you'll learn quite a lot

28:12.080 --> 28:13.080
about Dr. Priscilla Chan

28:13.080 --> 28:14.080
who has

28:14.080 --> 28:16.080
I must say an absolutely incredible family story

28:16.080 --> 28:17.080
leading up to her role as a physician

28:17.080 --> 28:19.080
and her motivations for the CZI

28:19.080 --> 28:20.080
and beyond

28:20.080 --> 28:21.080
and you'll learn from Mark

28:21.080 --> 28:23.080
how he's bringing an engineering and AI perspective

28:23.080 --> 28:25.080
to the discovery of new cures for human disease

28:25.080 --> 28:27.080
the second half of today's discussion

28:27.080 --> 28:29.080
is just between Mark Zuckerberg and me

28:29.080 --> 28:31.080
during which we discuss various meta platforms

28:31.080 --> 28:33.080
including of course social media platforms

28:33.080 --> 28:35.080
and their effects on mental health in children and adults

28:35.080 --> 28:37.080
we also discuss VR, virtual reality

28:37.080 --> 28:39.080
as well as augmented and mixed reality

28:39.080 --> 28:41.080
and we discuss AI, artificial intelligence

28:41.080 --> 28:44.080
and how it stands to transform not just our online experiences

28:44.080 --> 28:46.080
with social media and other technologies

28:46.080 --> 28:48.080
and how it stands to potentially transform

28:48.080 --> 28:50.080
every aspect of everyday life

28:50.080 --> 28:52.080
before we begin I'd like to emphasize that this podcast

28:52.080 --> 28:54.080
is separate from my teaching and research roles at Stanford

28:54.080 --> 28:56.080
it is however part of my desire and effort

28:56.080 --> 28:58.080
to bring zero cost to consumer information about

28:58.080 --> 29:00.080
okay so now

29:00.080 --> 29:02.080
I'm gonna let his sponsors play out

29:02.080 --> 29:04.080
because I want you to hear who they are

29:04.080 --> 29:06.080
and then realize who else they sponsor

29:06.080 --> 29:08.080
a limit or no

29:08.080 --> 29:11.080
element is one of his sponsors

29:11.080 --> 29:13.080
element is this salt drink

29:13.080 --> 29:15.080
that a lot of these sports guys run

29:15.080 --> 29:17.080
so there's like five other podcasts

29:17.080 --> 29:19.080
that are kind of underneath Joe Rogan

29:19.080 --> 29:23.080
and they're all sponsored by that

29:25.080 --> 29:27.080
and you'll hear a couple other ones too

29:27.080 --> 29:29.080
maybe I don't know

29:29.080 --> 29:31.080
science and science related tools to the general public

29:31.080 --> 29:33.080
in keeping with that theme

29:33.080 --> 29:35.080
I'd like to thank the sponsors of today's podcast

29:35.080 --> 29:37.080
our first sponsor is Ate Sleep

29:37.080 --> 29:39.080
Ate Sleep makes smart mattress covers with cooling, heating

29:39.080 --> 29:41.080
and sleep tracking capacity

29:41.080 --> 29:42.080
really greatly improved

29:42.080 --> 29:44.080
this episode is also brought to us by Element

29:44.080 --> 29:46.080
Element is an electrolyte drink that has everything

29:46.080 --> 29:47.080
that's a lot of water

29:47.080 --> 29:48.080
I'll often also have

29:48.080 --> 29:50.080
www.lmnt.com.com

29:50.080 --> 29:52.080
I'm pleased to announce that we will be

29:52.080 --> 29:54.080
hosting four live events in Australia

29:54.080 --> 29:55.080
we've now scheduled

29:55.080 --> 29:56.080
oh yeah I forgot

29:56.080 --> 29:57.080
each of which is entitled

29:57.080 --> 29:58.080
www.blackcom.com

29:58.080 --> 30:00.080
to get a free sample pack with your purchase

30:00.080 --> 30:02.080
again that's www.drinklmnt.com

30:02.080 --> 30:04.080
it's also interesting that he's gonna host

30:04.080 --> 30:06.080
and he's to announce that we will be hosting four live events in Australia

30:06.080 --> 30:08.080
each of which is entitled The Brain Body Contract

30:08.080 --> 30:10.080
during which I will share science

30:10.080 --> 30:12.080
and science related tools for mental health

30:12.080 --> 30:13.080
physical health and performance

30:13.080 --> 30:15.080
so let's get him some world travel

30:15.080 --> 30:17.080
get him to Australia

30:17.080 --> 30:19.080
and then get him to talk about nothing

30:19.080 --> 30:21.080
to do with the last three years of life

30:21.080 --> 30:24.080
get him lots of hotel rooms

30:24.080 --> 30:26.080
and stuff like that

30:26.080 --> 30:28.080
I think you can see it

30:28.080 --> 30:30.080
he's definitely been co-opted

30:30.080 --> 30:31.080
he's got a great job at Stanford

30:31.080 --> 30:33.080
he's never gonna question the narrative

30:33.080 --> 30:36.080
even if he realizes that they're overextended

30:36.080 --> 30:39.080
it's obvious that he probably believes in the

30:39.080 --> 30:41.080
in the basic public health narrative

30:41.080 --> 30:43.080
so that's unfortunate

30:43.080 --> 30:45.080
let's go

30:45.080 --> 30:47.080
also be a live question and answer session

30:47.080 --> 30:49.080
we have limited tickets still available

30:49.080 --> 30:51.080
for the event in Melbourne on February 10th

30:51.080 --> 30:53.080
as well as the event in Brisbane on February 24th

30:53.080 --> 30:55.080
our event in Sydney at the Sydney Opera House

30:55.080 --> 30:57.080
sold out very quickly

30:57.080 --> 30:59.080
so as a consequence we've now scheduled

30:59.080 --> 31:01.080
if I learned about this a few years ago

31:01.080 --> 31:03.080
when my lab talked so great to meet you

31:03.080 --> 31:05.080
and thank you for having me here in your home

31:05.080 --> 31:07.080
thanks for having us on the podcast

31:07.080 --> 31:09.080
I'd like to talk about the CZI

31:09.080 --> 31:11.080
Zuckerberg initiative

31:11.080 --> 31:13.080
I learned about this a few years ago

31:13.080 --> 31:15.080
when my lab was and still is now at Stanford

31:15.080 --> 31:17.080
as a very exciting philanthropic effort

31:17.080 --> 31:19.080
that has a truly big mission

31:19.080 --> 31:21.080
I can't imagine a bigger mission

31:21.080 --> 31:23.080
so maybe you can tell us what that big mission is

31:23.080 --> 31:25.080
and then we can get into some of the mechanics of how that

31:25.080 --> 31:27.080
don't forget that the Chan Zuckerberg initiative

31:27.080 --> 31:29.080
is also responsible for BioArchive

31:29.080 --> 31:31.080
which was one of these

31:31.080 --> 31:33.080
preprint servers that really ruined

31:33.080 --> 31:35.080
the beginning of the pandemic with noise

31:35.080 --> 31:39.080
big mission can become a reality

31:39.080 --> 31:43.080
so like you're mentioning in 2015

31:43.080 --> 31:45.080
we launched the Chan Zuckerberg initiative

31:45.080 --> 31:47.080
and what we were hoping to do at CZI

31:47.080 --> 31:49.080
was think about how do we build a better future

31:49.080 --> 31:51.080
for everyone and looking for ways

31:51.080 --> 31:53.080
where we can contribute the resources

31:53.080 --> 31:55.080
that we have to bring philanthropically

31:55.080 --> 31:57.080
and the experiences that Mark and I have had

31:57.080 --> 31:59.080
for me as a physician and educator

31:59.080 --> 32:01.080
for Mark as an engineer

32:01.080 --> 32:03.080
and then our ability to bring teams together

32:03.080 --> 32:05.080
to build the builders

32:05.080 --> 32:07.080
you know Mark has been a builder throughout his career

32:07.080 --> 32:09.080
and what could we do if we actually put together a team

32:09.080 --> 32:11.080
to build tools

32:11.080 --> 32:13.080
do great science

32:13.080 --> 32:15.080
and so within our science portfolio

32:15.080 --> 32:17.080
we've really been focused on what

32:17.080 --> 32:19.080
some people think is either an incredibly

32:19.080 --> 32:21.080
audacious goal or

32:21.080 --> 32:23.080
an inevitable goal but I think about it

32:23.080 --> 32:25.080
as something that will happen if we sort of continue

32:25.080 --> 32:27.080
focusing on it which is to be able to

32:27.080 --> 32:29.080
cure prevent or manage all disease by the end of the century

32:29.080 --> 32:31.080
all disease so that's important

32:31.080 --> 32:33.080
right a lot of times people ask like which

32:33.080 --> 32:35.080
disease and the whole point is that there is

32:35.080 --> 32:37.080
not one disease and it's really about

32:37.080 --> 32:39.080
taking a step back to where I always

32:39.080 --> 32:41.080
found the most hope as a physician

32:41.080 --> 32:43.080
which is new discoveries

32:43.080 --> 32:45.080
and new opportunities and new ways of

32:45.080 --> 32:47.080
understanding how to keep people well

32:47.080 --> 32:49.080
come from basic science

32:49.080 --> 32:51.080
what she just said makes absolutely no sense

32:51.080 --> 32:53.080
and is no way related

32:53.080 --> 32:55.080
to this absolutely ridiculous

32:55.080 --> 32:57.080
goal of flying to the sun and

32:57.080 --> 32:59.080
touching it with your tongue

32:59.080 --> 33:01.080
I mean I don't even know what she's talking about

33:01.080 --> 33:03.080
we're going to cure all diseases

33:03.080 --> 33:05.080
and that's really important right it's all diseases

33:05.080 --> 33:07.080
mm-hmm

33:07.080 --> 33:09.080
do you mean diseases

33:09.080 --> 33:11.080
like infectious diseases

33:11.080 --> 33:13.080
or do you mean like genetic diseases

33:13.080 --> 33:15.080
or do you mean like bacterial

33:15.080 --> 33:17.080
infections or

33:17.080 --> 33:19.080
do you mean like

33:19.080 --> 33:21.080
screen addiction

33:21.080 --> 33:23.080
or

33:23.080 --> 33:25.080
or you see

33:25.080 --> 33:27.080
this is the kind of bamboozlement

33:27.080 --> 33:29.080
that gets done when you're trying to get

33:29.080 --> 33:31.080
the money from venture capitalists

33:31.080 --> 33:33.080
when you're trying to get money from

33:33.080 --> 33:35.080
people who don't understand

33:35.080 --> 33:37.080
who have been inside of the mythology

33:37.080 --> 33:39.080
for so long

33:39.080 --> 33:41.080
that this all sounds real

33:41.080 --> 33:43.080
this is one of the biggest

33:43.080 --> 33:45.080
snake oil salesman couples

33:45.080 --> 33:47.080
on the planet

33:47.080 --> 33:49.080
this is a non-elected

33:49.080 --> 33:51.080
I don't even know

33:51.080 --> 33:53.080
why she started out talking first

33:53.080 --> 33:55.080
have you ever heard her speak before

33:55.080 --> 33:57.080
why do I want to hear her speak?

33:57.080 --> 33:59.080
she's just a doctor

33:59.080 --> 34:01.080
she didn't even become rich

34:01.080 --> 34:03.080
for any particularly good reason

34:03.080 --> 34:05.080
other than for the fact

34:05.080 --> 34:07.080
that her husband apparently

34:07.080 --> 34:09.080
invented a platform

34:09.080 --> 34:11.080
that's being used to control most of America

34:11.080 --> 34:13.080
in the world

34:13.080 --> 34:15.080
you're right

34:15.080 --> 34:17.080
it's really annoying

34:17.080 --> 34:19.080
to me right now

34:19.080 --> 34:21.080
how many of these people presume to govern us

34:21.080 --> 34:23.080
just based on the fact

34:23.080 --> 34:25.080
that the system has made them rich

34:25.080 --> 34:27.080
a system has made them

34:27.080 --> 34:29.080
artificially rich

34:29.080 --> 34:31.080
has raised them

34:31.080 --> 34:33.080
artificially to be

34:33.080 --> 34:35.080
a player at this stage

34:35.080 --> 34:37.080
this android person over here

34:37.080 --> 34:39.080
is not a

34:39.080 --> 34:41.080
thought leader in any circle

34:41.080 --> 34:43.080
that he ever sits in

34:43.080 --> 34:45.080
at any dinner table he's ever at

34:45.080 --> 34:47.080
and that's not a wife

34:47.080 --> 34:49.080
who loves him

34:49.080 --> 34:51.080
this is not a married couple

34:51.080 --> 34:53.080
that sleeps together at night naked

34:53.080 --> 34:55.080
are you joking?

34:55.080 --> 34:57.080
watch them interact

34:57.080 --> 34:59.080
over the next ten minutes

34:59.080 --> 35:01.080
and tell me you see a couple in love

35:01.080 --> 35:03.080
it's pathetic

35:03.080 --> 35:05.080
we are being lied to

35:05.080 --> 35:07.080
by liars

35:11.080 --> 35:13.080
so our strategy at CZI

35:13.080 --> 35:15.080
is really to build tools

35:15.080 --> 35:17.080
fund science

35:17.080 --> 35:19.080
change the way basic scientists can

35:19.080 --> 35:21.080
see the world and how they can

35:21.080 --> 35:23.080
move quickly in their discoveries

35:23.080 --> 35:25.080
and so that's what we launched

35:25.080 --> 35:27.080
in 2015

35:27.080 --> 35:29.080
we do work in three ways

35:29.080 --> 35:31.080
we fund great scientists

35:31.080 --> 35:33.080
we build tools

35:33.080 --> 35:35.080
right now software tools

35:35.080 --> 35:37.080
to help move science along

35:37.080 --> 35:39.080
and make it easier for scientists

35:39.080 --> 35:41.080
software tools to sing

35:41.080 --> 35:43.080
you mentioned Stanford being an important pillar

35:43.080 --> 35:45.080
for our science work

35:45.080 --> 35:47.080
we've built what we call bio hubs

35:47.080 --> 35:49.080
and grant challenges

35:49.080 --> 35:51.080
to do work that wouldn't be possible

35:51.080 --> 35:53.080
in a single lab or within a single discipline

35:53.080 --> 35:55.080
and our first bio hub was launched

35:55.080 --> 35:57.080
in San Francisco

35:57.080 --> 35:59.080
a collaboration between Stanford

35:59.080 --> 36:01.080
UC Berkeley and UCSF

36:01.080 --> 36:03.080
why is that amazing?

36:03.080 --> 36:05.080
implies that

36:05.080 --> 36:07.080
there will either be a ton of knowledge

36:07.080 --> 36:09.080
gleaned from this effort

36:09.080 --> 36:11.080
which I'm certain there will be and there already has been

36:11.080 --> 36:13.080
we can talk about some of those early successes

36:13.080 --> 36:15.080
be very careful with the words

36:15.080 --> 36:17.080
there will be a lot of data extracted from this

36:17.080 --> 36:19.080
I don't know how much knowledge will be extracted

36:19.080 --> 36:21.080
from this Huberman moment

36:21.080 --> 36:23.080
but it also sort of implies that if we can understand

36:23.080 --> 36:25.080
some basic operations

36:25.080 --> 36:27.080
of diseases and cells

36:27.080 --> 36:29.080
that transcend autism

36:29.080 --> 36:31.080
Huntington's Parkinson's cancer

36:31.080 --> 36:33.080
and any other disease that

36:33.080 --> 36:35.080
perhaps there are some core principles

36:35.080 --> 36:37.080
that would make the big mission

36:37.080 --> 36:39.080
a real reality so to speak

36:39.080 --> 36:41.080
what I'm basically saying is

36:41.080 --> 36:43.080
how are you attacking this?

36:43.080 --> 36:45.080
because that the cell sits at the center

36:45.080 --> 36:47.080
of all discussion about disease

36:47.080 --> 36:49.080
given that our body is made up of cells

36:49.080 --> 36:51.080
and different types of cells

36:51.080 --> 36:53.080
so maybe you could just

36:53.080 --> 36:55.080
illuminate for us a little bit of what

36:55.080 --> 36:57.080
the cell is

36:57.080 --> 36:59.080
in your mind as it relates to disease

36:59.080 --> 37:01.080
and how one goes about understanding disease

37:01.080 --> 37:03.080
in the context of cells

37:03.080 --> 37:05.080
because ultimately that's what we're made up of

37:05.080 --> 37:07.080
yeah well let's get to the cell thing in a moment

37:07.080 --> 37:09.080
but just even take a step back from that

37:09.080 --> 37:11.080
we don't think that

37:11.080 --> 37:13.080
I want you to hear

37:13.080 --> 37:15.080
this is Huberman asking them

37:15.080 --> 37:17.080
a very hard question

37:17.080 --> 37:19.080
about the nuts and bolts

37:19.080 --> 37:21.080
of how the hell they're going to do this

37:21.080 --> 37:23.080
so the proposal is

37:23.080 --> 37:25.080
that they're going to study and catalog

37:25.080 --> 37:27.080
all the cells of the human body

37:27.080 --> 37:29.080
and catalog how all of the cells

37:29.080 --> 37:31.080
in the human body work

37:31.080 --> 37:33.080
that's one of the things he said

37:33.080 --> 37:35.080
in the beginning

37:35.080 --> 37:37.080
so of course he's formulated a question

37:37.080 --> 37:39.080
that has to do with that very description

37:39.080 --> 37:41.080
of how they plan to build this

37:41.080 --> 37:43.080
this ladder of observation

37:43.080 --> 37:45.080
which will ultimately lead to understanding

37:47.080 --> 37:49.080
and this mythology is very

37:49.080 --> 37:51.080
straightforward so

37:51.080 --> 37:53.080
as the smart guy that he is

37:53.080 --> 37:55.080
he just asks them a very straightforward question

37:55.080 --> 37:57.080
about it in terms of how are you

37:57.080 --> 37:59.080
going to go about doing it and

37:59.080 --> 38:01.080
Zuck says well let's

38:01.080 --> 38:03.080
in his very nice little android voice

38:03.080 --> 38:05.080
let's go back to that subject

38:05.080 --> 38:07.080
in just a minute but first let's address something

38:07.080 --> 38:09.080
else that I really like a lot

38:09.080 --> 38:11.080
hmm

38:11.080 --> 38:13.080
belief is that the cell

38:13.080 --> 38:15.080
sits at the center of all discussion about

38:15.080 --> 38:17.080
disease given that our body is made up of

38:17.080 --> 38:19.080
cells and different types of cells so maybe

38:19.080 --> 38:20.080
could just

38:20.080 --> 38:22.080
illuminate for us a little bit of what

38:22.080 --> 38:24.080
the cell is

38:24.080 --> 38:26.080
in your mind as it relates to disease

38:26.080 --> 38:28.080
and how one goes about understanding

38:28.080 --> 38:30.080
disease in the context of cells because ultimately

38:30.080 --> 38:31.080
that's what we're made up of.

38:31.080 --> 38:33.080
Yeah well let's get to the cell thing

38:33.080 --> 38:35.080
in a moment but just even taking a step back from that

38:35.080 --> 38:37.080
you know we don't think that it sees the eye

38:37.080 --> 38:39.080
that we're going to cure prevent or manage all diseases

38:39.080 --> 38:41.080
the goal is to basically give the scientific

38:41.080 --> 38:43.080
community and scientists around the world

38:43.080 --> 38:45.080
the tools to accelerate the pace of science

38:45.080 --> 38:47.080
and we spent a lot of time when we were

38:47.080 --> 38:49.080
getting started with this looking at the history

38:49.080 --> 38:51.080
of science and trying to understand the trends

38:51.080 --> 38:53.080
and how they've played out over time and if you look

38:53.080 --> 38:55.080
over the this very long-term arc

38:55.080 --> 38:57.080
most large-scale discoveries

38:57.080 --> 38:59.080
are preceded by the invention of a new tool

38:59.080 --> 39:01.080
or a new way to see something but it's not just in biology

39:01.080 --> 39:03.080
right it's like having a telescope

39:03.080 --> 39:05.080
before a lot of discoveries in astronomy

39:05.080 --> 39:08.080
and astrophysics but similarly you know the microscope

39:08.080 --> 39:10.080
and just different ways to observe things

39:10.080 --> 39:12.080
or different platforms like the ability to do vaccines

39:12.080 --> 39:15.080
preceded the ability to kind of cure a lot of different things

39:15.080 --> 39:17.080
so this is sort of the engineering part

39:17.080 --> 39:19.080
that you were talking about about building tools.

39:19.080 --> 39:21.080
We view our goal is to

39:21.080 --> 39:23.080
try to bring together some scientific

39:23.080 --> 39:25.080
and engineering knowledge to build tools that empower

39:25.080 --> 39:27.080
the whole field and that's sort of the big arc

39:27.080 --> 39:29.080
and a lot of the things that we're focused on

39:29.080 --> 39:31.080
including the work in single cell

39:31.080 --> 39:33.080
and cell understanding which

39:33.080 --> 39:35.080
I can't let it go I'm going to go back

39:35.080 --> 39:37.080
I can't let it go he's such a jack

39:37.080 --> 39:39.080
it just drives me bananas

39:39.080 --> 39:41.080
and the fact that these people I got to be careful

39:41.080 --> 39:43.080
because I'm going to start shouting

39:43.080 --> 39:45.080
but what he said was

39:45.080 --> 39:49.080
we needed to invent vaccines before we could cure things

39:53.080 --> 39:55.080
I mean think about how much of a jackass statement

39:55.080 --> 39:56.080
that is

39:56.080 --> 39:59.080
he's equating microscopes and the discovery of cells

39:59.080 --> 40:03.080
to vaccines and the curing of diseases

40:03.080 --> 40:07.080
I just want you to ponder that

40:07.080 --> 40:11.080
how ridiculous that analogy is or that parallel is

40:11.080 --> 40:15.080
and how dumb that makes

40:15.080 --> 40:19.080
it appear that he thinks we are

40:19.080 --> 40:23.080
is Huberman going to let such a stupid statement go by?

40:23.080 --> 40:27.080
probably because that's what he's being paid to do

40:27.080 --> 40:29.080
but this is really really sad

40:29.080 --> 40:33.080
because they're not even started trying to explain

40:33.080 --> 40:35.080
this nonsense claim

40:35.080 --> 40:37.080
and they're already talking

40:37.080 --> 40:39.080
absolute gibberish

40:39.080 --> 40:41.080
I don't expect anything different from this guy

40:41.080 --> 40:43.080
he's been a snake oil salesman

40:43.080 --> 40:45.080
liar his whole life

40:45.080 --> 40:47.080
but

40:47.080 --> 40:49.080
much more important and interesting

40:49.080 --> 40:51.080
is his wife no

40:53.080 --> 40:55.080
kind of a new tool

40:55.080 --> 40:56.080
to see something

40:56.080 --> 40:58.080
it's not just in biology right it's like having a telescope

40:58.080 --> 41:00.080
you know came before a lot of

41:00.080 --> 41:02.080
discoveries in astronomy and astrophysics

41:02.080 --> 41:04.080
but similarly you know the microscope

41:04.080 --> 41:06.080
and just different ways to observe things

41:06.080 --> 41:08.080
or different platforms like the ability to do vaccines

41:08.080 --> 41:11.080
preceded the ability to kind of cure a lot of different things

41:11.080 --> 41:13.080
you hear that right?

41:13.080 --> 41:16.080
I mean if my son

41:16.080 --> 41:18.080
said that to me at the dinner table

41:18.080 --> 41:20.080
I'd probably smack him on the side of his head

41:20.080 --> 41:24.080
that's how stupid that statement is

41:24.080 --> 41:26.080
have you not been listening to anything

41:26.080 --> 41:28.080
that you've ever learned in a biology class

41:28.080 --> 41:30.080
you jackass?

41:30.080 --> 41:32.080
of course he's not

41:32.080 --> 41:34.080
this is the message he's delivering

41:34.080 --> 41:38.080
this is a weaponized podcast

41:38.080 --> 41:44.080
on a podcast with lots and lots of viewers

41:44.080 --> 41:49.080
and it is weaponized to put these stupid thoughts in people's heads

41:49.080 --> 41:51.080
it's a subliminal thing

41:51.080 --> 41:53.080
that goes right through them

41:53.080 --> 41:55.080
nobody's pausing to talk or listen to me

41:55.080 --> 41:58.080
tell them this they're already going on

41:58.080 --> 42:00.080
so this is sort of the engineering part

42:00.080 --> 42:02.080
that you were talking about about building tools

42:02.080 --> 42:04.080
we view our goal is to

42:04.080 --> 42:07.080
try to bring together some scientific and engineering knowledge

42:07.080 --> 42:09.080
to build tools that empower the whole field

42:09.080 --> 42:11.080
and that's sort of the big arc

42:11.080 --> 42:13.080
and a lot of the things that we're focused on

42:13.080 --> 42:15.080
including the work in single cell and cell understanding

42:15.080 --> 42:19.080
which you can jump in and get into that if you want

42:19.080 --> 42:21.080
but yeah I think we generally agree

42:21.080 --> 42:23.080
with the premise that if you want to understand

42:23.080 --> 42:25.080
the stuff in those principles

42:25.080 --> 42:27.080
people study organs a lot

42:27.080 --> 42:29.080
study how things present across the body

42:29.080 --> 42:32.080
but there's not a very widespread understanding

42:32.080 --> 42:34.080
of how each cell operates

42:34.080 --> 42:36.080
and this is sort of a big part of

42:36.080 --> 42:38.080
some of the initial work that we try to do on the human cell atlas

42:38.080 --> 42:40.080
and understanding what are the different cells

42:40.080 --> 42:42.080
and there's a bunch more work that we want to do to carry that forward

42:42.080 --> 42:44.080
but overall I think

42:44.080 --> 42:46.080
when we think about the next ten years here

42:46.080 --> 42:48.080
of this long arc to try to empower the community

42:48.080 --> 42:51.080
to be able to cure, prevent or manage all diseases

42:51.080 --> 42:55.080
he's talking about running public health for ten years

42:55.080 --> 43:00.080
because he's got a lot of money

43:00.080 --> 43:02.080
we think that the next ten years

43:02.080 --> 43:04.080
should really be primarily about

43:04.080 --> 43:06.080
being able to measure and observe more things in human biology

43:06.080 --> 43:08.080
there are a lot of limits today

43:08.080 --> 43:10.080
it's like you want to look at something through a microscope

43:10.080 --> 43:12.080
you can't usually see living tissues

43:12.080 --> 43:14.080
because it's hard to see through skin or things like that

43:14.080 --> 43:16.080
so there are a lot of different techniques

43:16.080 --> 43:18.080
that will help us observe different things

43:18.080 --> 43:20.080
and this is sort of where the engineering background

43:20.080 --> 43:22.080
comes in a bit because

43:22.080 --> 43:24.080
when I think about this as from the perspective of

43:24.080 --> 43:26.080
how you write code or something

43:26.080 --> 43:28.080
the idea of trying to debug or fix a code base

43:28.080 --> 43:30.080
but not be able to step through the code line by line

43:30.080 --> 43:32.080
it's not going to happen

43:32.080 --> 43:34.080
at the beginning of any big project that we do

43:34.080 --> 43:36.080
that meta we like to spend a bunch of the time up front

43:36.080 --> 43:38.080
just trying to instrument things and understand

43:38.080 --> 43:40.080
what are we going to look at and how are we going to measure things

43:40.080 --> 43:42.080
so we know we're making progress and know what to optimize

43:42.080 --> 43:44.080
wait what you're building something for meta

43:44.080 --> 43:46.080
and you want to know how you're going to measure things

43:46.080 --> 43:48.080
interesting

43:48.080 --> 43:50.080
this is such a long-term journey

43:50.080 --> 43:52.080
and we think that it actually makes sense to take the next 10 years

43:52.080 --> 43:56.080
to build those kind of tools for biology and understanding

43:56.080 --> 43:58.080
just how the human body works in action

43:58.080 --> 44:00.080
and a big part of that is cells

44:00.080 --> 44:02.080
do you want to jump in and talk or talk about some of the efforts?

44:02.080 --> 44:04.080
Could I interrupt briefly and just ask about

44:04.080 --> 44:08.080
the different interventions so to speak

44:08.080 --> 44:10.080
that CZI is in a unique position

44:10.080 --> 44:13.080
to bring to the quest to cure all diseases

44:13.080 --> 44:15.080
I can think of

44:15.080 --> 44:17.080
I know as a scientist that money is necessary

44:17.080 --> 44:19.080
but not sufficient

44:19.080 --> 44:21.080
when you have money you can hire more people

44:21.080 --> 44:23.080
you can try different things so that's critical

44:23.080 --> 44:25.080
but a lot of philanthropy includes money

44:25.080 --> 44:27.080
the other component is

44:27.080 --> 44:29.080
you want to be able to see things as you pointed out

44:29.080 --> 44:31.080
so you want to know that normal disease process

44:31.080 --> 44:33.080
what is a healthy cell?

44:33.080 --> 44:35.080
what's a diseased cell?

44:35.080 --> 44:37.080
cells constantly being bombarded with challenges and then repairing those

44:37.080 --> 44:39.080
and then what we call cancer is just kind of run away

44:39.080 --> 44:41.080
train of those challenges not being met by the cell itself

44:41.080 --> 44:43.080
like that so better imaging tools

44:43.080 --> 44:45.080
and then it sounds like there's not just a hardware component

44:45.080 --> 44:47.080
but a software component

44:47.080 --> 44:49.080
this is where AI comes in

44:49.080 --> 44:51.080
so maybe we can break this up into three different avenues

44:51.080 --> 44:53.080
one is understanding disease processes

44:53.080 --> 44:55.080
and healthy processes will lump those together

44:55.080 --> 44:57.080
then there's hardware so microscopes, lenses

44:57.080 --> 44:59.080
digital deconvolution

44:59.080 --> 45:01.080
ways of seeing things in bolder relief

45:01.080 --> 45:03.080
and more precision

45:03.080 --> 45:05.080
and then there's how to manage all the data

45:05.080 --> 45:07.080
and then I love the idea that

45:07.080 --> 45:09.080
maybe AI could do what human brains can't do alone

45:09.080 --> 45:11.080
and manage understanding of the data

45:11.080 --> 45:13.080
because it's one thing to organize data

45:13.080 --> 45:15.080
it's another to say this as you point out on the analogy with code

45:15.080 --> 45:17.080
this particular gene and that particular gene

45:17.080 --> 45:19.080
are potentially interesting

45:19.080 --> 45:21.080
whereas a human being would never make that potential connection

45:21.080 --> 45:23.080
so you know the tools that's easy

45:23.080 --> 45:25.080
I can bring to the table

45:25.080 --> 45:27.080
we fund science like you're talking about

45:27.080 --> 45:29.080
and we try to

45:29.080 --> 45:31.080
there's lots of ways to fund science

45:31.080 --> 45:33.080
and just to be clear what we fund

45:33.080 --> 45:35.080
is a tiny fraction of what the NIH funds for instance

45:35.080 --> 45:37.080
you guys have been generous enough

45:37.080 --> 45:39.080
that it definitely holds weight

45:39.080 --> 45:41.080
to NIH's contribution

45:41.080 --> 45:43.080
but I think

45:43.080 --> 45:45.080
every funder has its own role in the ecosystem

45:45.080 --> 45:47.080
and for us it's really how do we incentivize

45:47.080 --> 45:49.080
new points of view, how do we incentivize collaboration

45:49.080 --> 45:51.080
how do we incentivize open science

45:51.080 --> 45:53.080
and so a lot of our grants

45:53.080 --> 45:55.080
include inviting people to look

45:55.080 --> 45:57.080
in look at different fields

45:57.080 --> 45:59.080
our first neuroscience RFA

45:59.080 --> 46:01.080
was aimed towards incentivizing

46:01.080 --> 46:03.080
people from different backgrounds

46:03.080 --> 46:05.080
immunologists, microbiologists

46:05.080 --> 46:07.080
and look at how our nervous system works

46:07.080 --> 46:09.080
and how to keep it healthy

46:09.080 --> 46:11.080
or we ask that our grantees

46:11.080 --> 46:13.080
participate in the pre-print movement

46:13.080 --> 46:15.080
to accelerate the rate of sharing knowledge

46:15.080 --> 46:17.080
and actually others being able to build upon science

46:17.080 --> 46:19.080
so that's the funding that we do

46:19.080 --> 46:21.080
in terms of building

46:21.080 --> 46:23.080
we build software

46:23.080 --> 46:25.080
so they said pre-print servers

46:25.080 --> 46:27.080
I would argue that pre-print servers came out

46:27.080 --> 46:31.080
very unfortunately right at the beginning of the pandemic

46:31.080 --> 46:33.080
or right before it

46:33.080 --> 46:35.080
and it enabled

46:35.080 --> 46:37.080
an opportunity

46:37.080 --> 46:39.080
for misinformation

46:39.080 --> 46:41.080
to occur at the systemic level

46:41.080 --> 46:45.080
so for fake papers to come out

46:45.080 --> 46:49.080
for papers to be

46:49.080 --> 46:51.080
publicized through censorship

46:51.080 --> 46:53.080
or cancellation

46:53.080 --> 46:55.080
and ideas to be perpetuated

46:55.080 --> 46:57.080
through these same mechanisms

46:57.080 --> 47:01.080
and that was unfortunately really bad

47:01.080 --> 47:03.080
during the first half of the pandemic

47:03.080 --> 47:05.080
so they're directly responsible

47:05.080 --> 47:07.080
for that

47:07.080 --> 47:09.080
and you could even argue that they may have been

47:09.080 --> 47:11.080
tasked with that

47:11.080 --> 47:13.080
as part of their role

47:13.080 --> 47:15.080
in the pandemic

47:15.080 --> 47:17.080
and hardware, like you mentioned

47:17.080 --> 47:19.080
we put together teams that

47:19.080 --> 47:21.080
can build tools that are more

47:21.080 --> 47:23.080
durable and scalable

47:23.080 --> 47:25.080
than someone in a single lab might be incentivized

47:25.080 --> 47:27.080
so we put together teams

47:27.080 --> 47:29.080
that can build tools that are

47:29.080 --> 47:31.080
more durable and what?

47:31.080 --> 47:33.080
I mean these are just all buzzwords

47:33.080 --> 47:35.080
they're words that don't mean anything

47:35.080 --> 47:37.080
in the context of

47:37.080 --> 47:39.080
how microscopes are

47:39.080 --> 47:41.080
too photon scanning technology

47:41.080 --> 47:45.080
or nano dies or anything like that

47:45.080 --> 47:47.080
she's just

47:47.080 --> 47:49.080
yappy yappin

47:49.080 --> 47:52.080
and again who is this person?

47:52.080 --> 47:53.080
just a doctor?

47:53.080 --> 47:55.080
married to a rich guy?

47:55.080 --> 47:57.080
I mean this might as well be

47:57.080 --> 48:00.080
the woman of Elon Musk

48:00.080 --> 48:03.080
or the wife of Bill Gates

48:03.080 --> 48:05.080
like really why do I need to listen

48:05.080 --> 48:07.080
to her about science?

48:07.080 --> 48:09.080
what makes her an authority

48:09.080 --> 48:11.080
other than the fact that she's

48:11.080 --> 48:13.080
married to a rich guy?

48:13.080 --> 48:15.080
there's lots of doctors in the world

48:15.080 --> 48:17.080
that have given their entire life

48:17.080 --> 48:19.080
to public health to

48:19.080 --> 48:21.080
solving a problem

48:21.080 --> 48:23.080
or curing a disease

48:23.080 --> 48:25.080
this is not that person

48:26.080 --> 48:28.080
and yet somehow we are being made

48:28.080 --> 48:29.080
to believe that this is a person

48:29.080 --> 48:30.080
we should look up to

48:30.080 --> 48:32.080
look for leadership

48:35.080 --> 48:37.080
we are under attack ladies and gentlemen

48:40.080 --> 48:42.080
there's a ton of great ideas

48:42.080 --> 48:44.080
and nowadays most scientists can

48:44.080 --> 48:45.080
tinker and build something

48:45.080 --> 48:46.080
useful for their lab

48:46.080 --> 48:48.080
but it's really hard for them

48:48.080 --> 48:49.080
to be able to share

48:49.080 --> 48:51.080
that tool sometimes beyond their own laptop

48:51.080 --> 48:53.080
or forget the next lab

48:53.080 --> 48:55.080
over or across the globe

48:55.080 --> 48:56.080
so we partner with scientists

48:56.080 --> 48:58.080
to see what is useful

48:58.080 --> 48:59.080
what kinds of tools

48:59.080 --> 49:01.080
in imaging Nepari

49:01.080 --> 49:03.080
it's a useful image annotation tool

49:03.080 --> 49:06.080
that is born from an open source community

49:06.080 --> 49:08.080
and how can we contribute to that?

49:08.080 --> 49:10.080
or a cell by gene

49:10.080 --> 49:12.080
which works on single cell data sets

49:12.080 --> 49:14.080
and how can we build a useful tool

49:14.080 --> 49:16.080
so that scientists can share data sets

49:16.080 --> 49:17.080
analyze their own

49:17.080 --> 49:18.080
and contribute to a larger

49:18.080 --> 49:20.080
corpus of information

49:20.080 --> 49:22.080
so we have software teams

49:22.080 --> 49:24.080
we are building, collaborating with scientists

49:24.080 --> 49:26.080
to make sure that we are building

49:26.080 --> 49:28.080
easy to use durable, translatable tools

49:28.080 --> 49:30.080
across the scientific community

49:30.080 --> 49:32.080
you got to refresh if you don't have sound

49:32.080 --> 49:33.080
we also have institutes

49:33.080 --> 49:35.080
this is where the imaging work comes in

49:35.080 --> 49:37.080
where we are proud owners

49:37.080 --> 49:39.080
of electron microscope right now

49:39.080 --> 49:41.080
it's going to be installed

49:41.080 --> 49:43.080
at our imaging institute

49:43.080 --> 49:44.080
and that will really contribute

49:44.080 --> 49:46.080
to a way where we can see work differently

49:46.080 --> 49:48.080
but the more hardware

49:48.080 --> 49:49.080
does need to be developed

49:49.080 --> 49:50.080
we are partnering

49:50.080 --> 49:52.080
with fantastic scientists

49:52.080 --> 49:54.080
in the biohub network

49:54.080 --> 49:56.080
to build a mini phase plate

49:56.080 --> 49:58.080
to increase, to align the electrons

49:58.080 --> 50:01.080
through these, through the electron microscope

50:01.080 --> 50:03.080
to be able to increase the resolution

50:03.080 --> 50:05.080
so we can see in sharper detail

50:05.080 --> 50:07.080
so there's a lot of innovative work

50:07.080 --> 50:09.080
within the network that's happening

50:09.080 --> 50:12.080
as if that kind of innovation

50:12.080 --> 50:14.080
is unique to their little pile of money

50:14.080 --> 50:17.080
she sounds like an ignorant little brat

50:17.080 --> 50:19.080
ooh we have an electron microscope

50:19.080 --> 50:21.080
oh and we're making a phase plate

50:21.080 --> 50:23.080
so it'll focus the electrons better

50:23.080 --> 50:24.080
we get higher results

50:24.080 --> 50:26.080
like she's some kind of polymath

50:26.080 --> 50:29.080
physicist, chemist, electron microscopist

50:31.080 --> 50:33.080
we are being bamboozled

50:33.080 --> 50:34.080
actively bamboozled

50:34.080 --> 50:37.080
by rich people who prepare big speeches

50:37.080 --> 50:39.080
to talk big tough things

50:41.080 --> 50:43.080
this is a scam

50:43.080 --> 50:45.080
we are being actively

50:45.080 --> 50:47.080
actively

50:47.080 --> 50:49.080
controlled, demolished

50:49.080 --> 50:51.080
our whole country is being destroyed

50:51.080 --> 50:53.080
by these people

50:53.080 --> 50:55.080
by his app, by her money

50:55.080 --> 50:58.080
and by this guy's complacency

50:58.080 --> 51:01.080
this is one of those academic biologists

51:01.080 --> 51:03.080
right here who should

51:03.080 --> 51:05.080
damn well known better

51:05.080 --> 51:07.080
that transfection is not immunization

51:07.080 --> 51:09.080
and although I respect him

51:09.080 --> 51:11.080
as a neurobiologist

51:11.080 --> 51:13.080
if we came to this discussion

51:13.080 --> 51:15.080
I would say that to his face in a heartbeat

51:15.080 --> 51:17.080
and I guarantee you

51:17.080 --> 51:19.080
that even with a whiteboard

51:19.080 --> 51:21.080
between us he would not win

51:21.080 --> 51:23.080
that conversation

51:23.080 --> 51:25.080
he probably knows more about

51:25.080 --> 51:27.080
the visual system than me

51:27.080 --> 51:28.080
I have no doubt

51:28.080 --> 51:29.080
he probably knows more about

51:29.080 --> 51:31.080
most of the brain than I do

51:31.080 --> 51:33.080
but immunology I got him in spades

51:33.080 --> 51:37.080
and these people are posers

51:37.080 --> 51:39.080
institutes have grand challenges

51:39.080 --> 51:41.080
that they're working on

51:41.080 --> 51:43.080
back to your question about cells

51:43.080 --> 51:45.080
cells are just the smallest

51:45.080 --> 51:47.080
unit that are alive

51:47.080 --> 51:49.080
and are your body

51:49.080 --> 51:51.080
all of our bodies have many many many cells

51:51.080 --> 51:53.080
there's some estimate of like

51:53.080 --> 51:55.080
37 trillion cells

51:55.080 --> 51:57.080
different cells in your body

51:57.080 --> 51:59.080
and what are they all doing

51:59.080 --> 52:01.080
and what do they look like when they're healthy

52:01.080 --> 52:03.080
what do they look like when you're sick

52:03.080 --> 52:05.080
and where we're at right now

52:05.080 --> 52:07.080
our understanding of cells

52:07.080 --> 52:09.080
and what happens when you get sick

52:09.080 --> 52:11.080
basically we have we've gotten pretty good

52:11.080 --> 52:13.080
at from the human genome project

52:13.080 --> 52:15.080
looking at how different mutations

52:15.080 --> 52:17.080
in your genetic code lead for you

52:17.080 --> 52:19.080
to be more susceptible to get sick

52:19.080 --> 52:21.080
or directly cause you to get sick

52:21.080 --> 52:23.080
so we go from a mutation in your DNA

52:23.080 --> 52:25.080
to wow you now have Huntington's disease

52:25.080 --> 52:27.080
for instance

52:27.080 --> 52:29.080
and there's a lot that happens in the middle

52:29.080 --> 52:31.080
and that's one of the questions

52:31.080 --> 52:33.080
that we're going after at CZI

52:33.080 --> 52:35.080
is what actually happens

52:35.080 --> 52:37.080
so an analogy that I like to use to share

52:37.080 --> 52:39.080
right now say we have a recipe for a cake

52:39.080 --> 52:41.080
we know there's a typo in the recipe

52:41.080 --> 52:43.080
and then the cake is awful

52:43.080 --> 52:45.080
that's all we know

52:45.080 --> 52:47.080
we don't know how the chef interprets the typo

52:47.080 --> 52:49.080
we don't know what happens in the oven

52:49.080 --> 52:51.080
and we don't actually know sort of how it's

52:51.080 --> 52:53.080
exactly connected to how the cake didn't turn out

52:53.080 --> 52:55.080
how you had expected

52:55.080 --> 52:57.080
a lot of that is unknown

52:57.080 --> 52:59.080
but we can actually systematically

52:59.080 --> 53:01.080
try to break this down

53:01.080 --> 53:03.080
so you want me to kick the shit out of that analogy

53:03.080 --> 53:05.080
I'm sorry I'm swearing

53:05.080 --> 53:07.080
but I'm getting really annoyed

53:07.080 --> 53:09.080
so she says that

53:09.080 --> 53:11.080
they have a recipe with a typo in it

53:11.080 --> 53:13.080
and the cake doesn't turn out right

53:13.080 --> 53:15.080
so they don't know really why that happens

53:15.080 --> 53:17.080
but they got to figure it out

53:17.080 --> 53:25.080
we don't know how the chef interprets the error

53:25.080 --> 53:33.080
it's very very frustrating

53:33.080 --> 53:35.080
ladies and gentlemen because again

53:35.080 --> 53:37.080
we have somebody making

53:37.080 --> 53:41.080
the pattern integrity of a human

53:41.080 --> 53:45.080
into something as dumb simple

53:45.080 --> 53:47.080
as baking a cake

53:47.080 --> 53:51.080
so we're not even talking about the production of a protein

53:51.080 --> 53:53.080
right

53:53.080 --> 53:57.080
we're talking about a diseased

53:57.080 --> 54:01.080
human and some genetic correlations

54:01.080 --> 54:05.080
silent mutations lead to disease

54:05.080 --> 54:09.080
we don't understand how they do that

54:09.080 --> 54:13.080
some mutations in proteins don't cause a detectable disease

54:13.080 --> 54:15.080
we don't know why

54:15.080 --> 54:19.080
and so we start with these dumb simple

54:19.080 --> 54:21.080
analogies because that's how you write a grant

54:21.080 --> 54:23.080
that's how

54:23.080 --> 54:25.080
how Huberman is used to thinking

54:25.080 --> 54:27.080
but that only gets you so far

54:27.080 --> 54:29.080
and it has only gotten us so far

54:29.080 --> 54:31.080
for a very very long time

54:31.080 --> 54:33.080
and in fact why we are hitting the wall

54:33.080 --> 54:37.080
is because those kinds of dumb simple

54:37.080 --> 54:41.080
analogies and

54:41.080 --> 54:43.080
extracting experimental questions

54:43.080 --> 54:47.080
based on those dumb simple analogies

54:47.080 --> 54:49.080
will never penetrate

54:49.080 --> 54:51.080
the actual complexity of life

54:51.080 --> 54:53.080
that you're trying to penetrate

54:53.080 --> 54:57.080
but that's not the point here

54:57.080 --> 54:59.080
the point is to bamboozle people

54:59.080 --> 55:01.080
into believing that this is an

55:01.080 --> 55:03.080
inevitable

55:03.080 --> 55:05.080
over the next hill all of these

55:05.080 --> 55:07.080
things are going to be known

55:07.080 --> 55:09.080
we barely know how any of these cells

55:09.080 --> 55:11.080
in the body work but you know we can do it

55:11.080 --> 55:15.080
we just have to keep driving over that hill

55:17.080 --> 55:19.080
and one segment of that journey

55:19.080 --> 55:21.080
that we're looking at is how that mutation

55:21.080 --> 55:23.080
gets translated and acted upon in

55:23.080 --> 55:25.080
your cells and all of your cells

55:25.080 --> 55:27.080
have what's called mRNA

55:27.080 --> 55:29.080
mRNA are the actual instructions

55:29.080 --> 55:31.080
that are taken from the DNA

55:31.080 --> 55:33.080
and what our work in single cell is

55:33.080 --> 55:35.080
looking at how

55:35.080 --> 55:37.080
every cell in your body is actually

55:37.080 --> 55:39.080
interpreting your DNA slightly differently

55:39.080 --> 55:41.080
and what happens

55:41.080 --> 55:43.080
when healthy cells are interpreting the DNA instructions

55:43.080 --> 55:45.080
and when sick cells are interpreting those directions

55:45.080 --> 55:47.080
basically what she's talking about

55:47.080 --> 55:49.080
is trying to kind of describe

55:49.080 --> 55:51.080
the complexity of life

55:51.080 --> 55:53.080
to the extent to which she can claim

55:53.080 --> 55:55.080
she wants to investigate

55:57.080 --> 55:59.080
it would be a bit like the guy that Mark covered

55:59.080 --> 56:01.080
a few weeks ago that did all the ribosome work

56:01.080 --> 56:03.080
for him to

56:03.080 --> 56:05.080
really beautifully explain how

56:05.080 --> 56:07.080
complicated ribosomes are and how

56:07.080 --> 56:09.080
little we understand them and then saying

56:09.080 --> 56:11.080
that's why he dedicated his life to that work

56:11.080 --> 56:13.080
did he solve it?

56:13.080 --> 56:15.080
no did he say he was going to solve it?

56:15.080 --> 56:17.080
no

56:17.080 --> 56:19.080
he picked little pieces of it and tried to push

56:19.080 --> 56:21.080
the ball forward for his entire life

56:21.080 --> 56:23.080
got a Nobel Prize and still doesn't know how

56:23.080 --> 56:25.080
the frickin hell a ribosome works

56:25.080 --> 56:27.080
or how proteins fold or anything like that

56:29.080 --> 56:31.080
and so she's doing an even dumber

56:31.080 --> 56:33.080
simple cartoon

56:33.080 --> 56:35.080
to try and show you that

56:35.080 --> 56:37.080
she can appreciate the complexity of life

56:37.080 --> 56:39.080
by

56:39.080 --> 56:41.080
inadequately articulating it

56:41.080 --> 56:43.080
and then just going to pivot to claiming

56:43.080 --> 56:45.080
that that

56:45.080 --> 56:47.080
inadequate articulation of what a pattern integrity

56:47.080 --> 56:49.080
is in a human being

56:49.080 --> 56:51.080
is licensed for her to say

56:51.080 --> 56:53.080
that she's going to give money to people

56:53.080 --> 56:55.080
so that they can figure out how it works

56:55.080 --> 56:57.080
that we're going to collect enough data

56:57.080 --> 56:59.080
so we can figure out how it works

57:01.080 --> 57:03.080
I'm just going to tell you right now

57:03.080 --> 57:05.080
these people are liars

57:05.080 --> 57:07.080
it's not even bullshitting

57:07.080 --> 57:09.080
it's just lying

57:09.080 --> 57:11.080
you know you're lying

57:11.080 --> 57:13.080
none of they don't believe anything they're saying

57:13.080 --> 57:15.080
and if they do they haven't really

57:15.080 --> 57:17.080
thought about it very long

57:17.080 --> 57:19.080
they're not very smart people

57:19.080 --> 57:21.080
and this

57:23.080 --> 57:25.080
this fake enthusiasm for the inevitability

57:25.080 --> 57:27.080
of transhumanism

57:27.080 --> 57:29.080
being able to collect enough data to understand

57:29.080 --> 57:31.080
and then remodel

57:31.080 --> 57:33.080
the human genome

57:33.080 --> 57:35.080
it's just a lack of reverence

57:35.080 --> 57:39.080
for the sacred that deserves punishment

57:41.080 --> 57:43.080
I really wish that these

57:43.080 --> 57:45.080
guys were on my basketball team or something

57:45.080 --> 57:47.080
and I could just make them go to the baseline

57:47.080 --> 57:49.080
and start running

57:49.080 --> 57:51.080
because it's just dumb

57:51.080 --> 57:53.080
and that is a ton of data

57:53.080 --> 57:55.080
I just told you there's 37 trillion cells

57:55.080 --> 57:57.080
there's different large

57:57.080 --> 57:59.080
sets of mRNA in each cell

57:59.080 --> 58:01.080
but the work that we've been funding is

58:01.080 --> 58:03.080
looking at how first of all

58:03.080 --> 58:05.080
gathering that information

58:05.080 --> 58:07.080
we've been incredibly lucky

58:07.080 --> 58:09.080
to be part of a very fast-moving

58:09.080 --> 58:11.080
field where

58:11.080 --> 58:13.080
we've gone from in 2017

58:13.080 --> 58:15.080
funding some methods work

58:15.080 --> 58:17.080
to now having really not complete

58:17.080 --> 58:19.080
but nearly complete atlases

58:19.080 --> 58:21.080
of how the human body works

58:21.080 --> 58:23.080
how flies work

58:23.080 --> 58:25.080
not complete but nearly complete atlases

58:25.080 --> 58:27.080
on how the fly works

58:27.080 --> 58:29.080
and the human body works

58:29.080 --> 58:31.080
what kind of stupid statement

58:31.080 --> 58:33.080
is that

58:33.080 --> 58:35.080
I mean seriously what kind of stupid

58:35.080 --> 58:37.080
statement is that

58:37.080 --> 58:39.080
this is the kind of arrogance

58:39.080 --> 58:41.080
that I'm talking about

58:41.080 --> 58:43.080
the complete lack of reverence

58:43.080 --> 58:45.080
for the biology they're studying

58:45.080 --> 58:47.080
it is absolutely disgusting

58:47.080 --> 58:49.080
it borders on blasphemy

58:49.080 --> 58:51.080
we've gone from in 2017

58:51.080 --> 58:53.080
funding some methods work

58:53.080 --> 58:55.080
to now having really

58:55.080 --> 58:57.080
not complete but nearly complete

58:57.080 --> 58:59.080
atlases of how the human body works

58:59.080 --> 59:01.080
how flies work

59:01.080 --> 59:03.080
how mice work

59:03.080 --> 59:05.080
at the single cell level

59:05.080 --> 59:07.080
and being able to then try to piece together

59:07.080 --> 59:09.080
and the neat thing about

59:09.080 --> 59:11.080
the sort of inflection point

59:11.080 --> 59:13.080
where we're at in AI

59:13.080 --> 59:15.080
is that I can't look at this data

59:15.080 --> 59:17.080
and make sense of it

59:17.080 --> 59:19.080
there's just too much of it

59:19.080 --> 59:21.080
and biology is complex

59:21.080 --> 59:23.080
human bodies are complex

59:23.080 --> 59:25.080
we need this much information

59:25.080 --> 59:27.080
but the use of large language models

59:27.080 --> 59:29.080
can help us actually

59:29.080 --> 59:31.080
look at that data and gain insights

59:31.080 --> 59:33.080
look at what trends are

59:33.080 --> 59:35.080
consistent with health

59:35.080 --> 59:37.080
understand patterns and genomes

59:37.080 --> 59:39.080
and what's consistent with disease

59:39.080 --> 59:41.080
and what's consistent with health

59:45.080 --> 59:47.080
I'm suspected

59:47.080 --> 59:49.080
and eventually our hope

59:49.080 --> 59:51.080
through the use of these data sets

59:51.080 --> 59:53.080
that we've helped curate in the application

59:53.080 --> 59:55.080
of large language models

59:55.080 --> 59:57.080
is to be able to formulate a virtual cell

59:57.080 --> 59:59.080
a cell that's completely

59:59.080 --> 01:00:01.080
built off of the data sets of what we know about the human body

01:00:01.080 --> 01:00:03.080
maybe you can use that virtual cell

01:00:03.080 --> 01:00:05.080
and then you can put on goggles

01:00:05.080 --> 01:00:07.080
and go do that shit forever

01:00:07.080 --> 01:00:09.080
just do your experiments all in your virtual cells

01:00:09.080 --> 01:00:11.080
and you can never talk to us again

01:00:11.080 --> 01:00:15.080
but allows us to manipulate and learn faster

01:00:15.080 --> 01:00:17.080
and try new things to help move science

01:00:17.080 --> 01:00:19.080
and then medicine alone

01:00:19.080 --> 01:00:21.080
do you think we've cataloged

01:00:21.080 --> 01:00:23.080
the total thing?

01:00:23.080 --> 01:00:25.080
think about how dumb that is

01:00:25.080 --> 01:00:27.080
we're going to make a model of a virtual cell

01:00:27.080 --> 01:00:29.080
that will allow us to do things

01:00:29.080 --> 01:00:31.080
that normal cells wouldn't

01:00:31.080 --> 01:00:33.080
you're going to make a MATLAB version of a cell

01:00:33.080 --> 01:00:37.080
that's going to give you some insight into how to manipulate cell function

01:00:37.080 --> 01:00:39.080
why don't you just make a model of the weather

01:00:39.080 --> 01:00:41.080
so that we can know what the weather is going to be tomorrow

01:00:41.080 --> 01:00:43.080
why don't you make a model of a pandemic

01:00:43.080 --> 01:00:45.080
so we know how many people are going to die

01:00:45.080 --> 01:00:49.080
make some models about what happens

01:00:49.080 --> 01:00:51.080
when people stop vaccinating

01:00:51.080 --> 01:00:53.080
maybe you can make some models

01:00:53.080 --> 01:00:55.080
about what happens when you

01:00:55.080 --> 01:00:57.080
put certain Facebook posts

01:00:57.080 --> 01:00:59.080
in front of certain people's faces

01:00:59.080 --> 01:01:03.080
and how many negative Trump Facebook posts

01:01:03.080 --> 01:01:05.080
would you have to put in front of Democrats

01:01:05.080 --> 01:01:07.080
registered Democrats' faces

01:01:07.080 --> 01:01:09.080
before there would be a civil war

01:01:09.080 --> 01:01:11.080
why don't you measure something useful

01:01:11.080 --> 01:01:13.080
with the data that you have

01:01:13.080 --> 01:01:17.080
these are charlatan liars

01:01:17.080 --> 01:01:19.080
charlatan liars at the highest

01:01:19.080 --> 01:01:21.080
absolute highest echelons

01:01:21.080 --> 01:01:25.080
of our visible social structure

01:01:25.080 --> 01:01:27.080
and I say that very

01:01:27.080 --> 01:01:29.080
specifically our visual

01:01:29.080 --> 01:01:31.080
visible social structure

01:01:31.080 --> 01:01:33.080
in America is how we are being covered

01:01:33.080 --> 01:01:35.080
these visible thought leaders

01:01:35.080 --> 01:01:37.080
that are put in front of us

01:01:37.080 --> 01:01:39.080
by the algorithms

01:01:39.080 --> 01:01:41.080
artificially elevated by the algorithms

01:01:41.080 --> 01:01:43.080
including these three people

01:01:47.080 --> 01:01:49.080
this guy, an academic biologist

01:01:49.080 --> 01:01:51.080
who almost certainly has used

01:01:51.080 --> 01:01:53.080
transfection in his lab

01:01:53.080 --> 01:01:55.080
multiple times on multiple projects

01:01:55.080 --> 01:01:57.080
but was too stupid to know that we shouldn't

01:01:57.080 --> 01:01:59.080
transfect healthy humans

01:02:03.080 --> 01:02:05.080
what else, how else should I characterize him?

01:02:05.080 --> 01:02:07.080
I mean if you were

01:02:07.080 --> 01:02:11.080
if you drove your car into somebody's house

01:02:11.080 --> 01:02:13.080
because you thought the garage door

01:02:13.080 --> 01:02:15.080
was going to open by itself

01:02:15.080 --> 01:02:17.080
you can't just say like well I mean

01:02:17.080 --> 01:02:19.080
you know

01:02:20.080 --> 01:02:22.080
and if you let a bunch of people

01:02:22.080 --> 01:02:24.080
transfect themselves and you should have done

01:02:24.080 --> 01:02:26.080
known better then you're dumb

01:02:26.080 --> 01:02:30.080
you should have spoke up

01:02:30.080 --> 01:02:32.080
maybe he doesn't even know yet

01:02:32.080 --> 01:02:34.080
but that's very unlikely

01:02:34.080 --> 01:02:38.080
it's very unlikely at this point

01:02:38.080 --> 01:02:40.080
that any of these people don't know

01:02:40.080 --> 01:02:42.080
and the ones that don't know

01:02:42.080 --> 01:02:44.080
choose not to know

01:02:44.080 --> 01:02:46.080
the lookaway doctrine

01:02:46.080 --> 01:02:48.080
in the military it's called

01:02:48.080 --> 01:02:50.080
just don't ask questions about what's happening behind that curtain

01:02:50.080 --> 01:02:52.080
what are those sounds?

01:02:52.080 --> 01:02:54.080
it doesn't matter it's none of your concern

01:02:56.080 --> 01:02:58.080
without a doubt

01:02:58.080 --> 01:03:00.080
all these people know how we're being governed

01:03:00.080 --> 01:03:02.080
without a doubt all these people know

01:03:02.080 --> 01:03:04.080
we're being governed by a mythology

01:03:04.080 --> 01:03:06.080
they are just participating

01:03:06.080 --> 01:03:08.080
because they get fame

01:03:08.080 --> 01:03:10.080
fortune and status from it

01:03:12.080 --> 01:03:14.080
number of different cell types

01:03:14.080 --> 01:03:16.080
every week I look at great journals like

01:03:16.080 --> 01:03:18.080
cell nature and science

01:03:18.080 --> 01:03:20.080
I saw recently that using single cell sequencing

01:03:20.080 --> 01:03:22.080
they've categorized

01:03:22.080 --> 01:03:24.080
18 plus different types of fat cells

01:03:24.080 --> 01:03:26.080
we always think like a fat cell versus a muscle cell

01:03:26.080 --> 01:03:28.080
so now you've got 18 types

01:03:28.080 --> 01:03:30.080
each one is going to express many many different

01:03:30.080 --> 01:03:32.080
genes and RNAs

01:03:32.080 --> 01:03:34.080
and perhaps the

01:03:34.080 --> 01:03:36.080
one of them is responsible for

01:03:36.080 --> 01:03:38.080
you know what we see in advanced type 2 diabetes

01:03:38.080 --> 01:03:40.080
or in other forms of obesity

01:03:40.080 --> 01:03:42.080
or where people can't lay down fat cells

01:03:42.080 --> 01:03:44.080
which turns out to be just as detrimental in those extreme cases

01:03:44.080 --> 01:03:46.080
so now you've got all these lists of genes

01:03:46.080 --> 01:03:49.080
but I always thought of single cell sequencing

01:03:49.080 --> 01:03:51.080
as necessary but not sufficient

01:03:51.080 --> 01:03:53.080
but you need the information but it doesn't

01:03:53.080 --> 01:03:55.080
resolve the problem and I think of it more of a

01:03:55.080 --> 01:03:57.080
as a hypothesis generating experiment

01:03:57.080 --> 01:03:59.080
okay so you have all these genes and you can say

01:03:59.080 --> 01:04:01.080
well this gene is particularly elevated in the

01:04:01.080 --> 01:04:03.080
diabetics cell type of

01:04:03.080 --> 01:04:05.080
let's say one of these fat cells or muscle cells

01:04:05.080 --> 01:04:06.080
for that matter

01:04:06.080 --> 01:04:08.080
whereas it's not in non-diabetics

01:04:08.080 --> 01:04:10.080
so then of the millions of different cells

01:04:10.080 --> 01:04:13.080
maybe only five of them differ dramatically

01:04:13.080 --> 01:04:15.080
so then you generate a hypothesis

01:04:15.080 --> 01:04:17.080
oh it's the ones that differ dramatically that are important

01:04:17.080 --> 01:04:19.080
but maybe one of those genes

01:04:19.080 --> 01:04:21.080
when it's only you know 50%

01:04:21.080 --> 01:04:23.080
changed has a huge effect because of some

01:04:23.080 --> 01:04:25.080
network biology effect and so I guess what I'm trying to

01:04:25.080 --> 01:04:27.080
get to here is you know how does one

01:04:27.080 --> 01:04:30.080
meet that challenge and can AI help resolve that challenge

01:04:30.080 --> 01:04:32.080
by essentially placing those lists of genes

01:04:32.080 --> 01:04:34.080
into a you know 10,000 hypotheses

01:04:34.080 --> 01:04:36.080
because I'll tell you that the graduate students and postdocs

01:04:36.080 --> 01:04:38.080
in my lab get a chance to test one hypothesis at a time

01:04:38.080 --> 01:04:40.080
and that's really the challenge let alone one lab

01:04:40.080 --> 01:04:42.080
and so for those that are listening to this

01:04:42.080 --> 01:04:44.080
and you know hopefully it's not getting outside the scope

01:04:44.080 --> 01:04:46.080
of kind of like standard understanding

01:04:46.080 --> 01:04:48.080
or the understanding we've generated here

01:04:48.080 --> 01:04:50.080
but what basically saying is you have to pick at some point

01:04:50.080 --> 01:04:53.080
more data always sounds great but then how do you decide what to test?

01:04:53.080 --> 01:04:55.080
so no we don't know all the cell types

01:04:55.080 --> 01:04:58.080
I think one of the one thing that was really exciting

01:04:58.080 --> 01:05:01.080
when we first launched this work was you know cystic fibrosis

01:05:01.080 --> 01:05:03.080
like cystic fibrosis is caused by mutation

01:05:03.080 --> 01:05:05.080
and CFTR that's pretty well known

01:05:05.080 --> 01:05:08.080
it affects a certain channel that makes it hard for mucus to be clear

01:05:08.080 --> 01:05:10.080
that's the basic cystic fibrosis when I went to medical school

01:05:10.080 --> 01:05:11.080
it was taught as fat

01:05:11.080 --> 01:05:13.080
so their lungs fill up with fluid

01:05:13.080 --> 01:05:15.080
carrying around sacks of fluid filling up

01:05:15.080 --> 01:05:18.080
I've known people I've worked with people and they have to literally dump the fluid out

01:05:18.080 --> 01:05:21.080
they can't run or do an intense exercise life is shorter

01:05:21.080 --> 01:05:25.080
life is shorter and when we applied single cell methodologies to the lungs

01:05:25.080 --> 01:05:30.080
they discovered an entirely new cell type that actually is affected by mutation

01:05:30.080 --> 01:05:33.080
and the CF mutation the cystic fibrosis mutation

01:05:33.080 --> 01:05:36.080
that actually changes the paradigm of how we think about cystic fibrosis

01:05:36.080 --> 01:05:39.080
just a note so I don't think we know all the cell types

01:05:39.080 --> 01:05:41.080
I think we'll continue to discover them

01:05:41.080 --> 01:05:44.080
we'll continue to discover new relationships between cell and disease

01:05:44.080 --> 01:05:47.080
which leads me to the second example I want to bring up is

01:05:47.080 --> 01:05:51.080
this large data set that the entire scientific community has built our own single cell

01:05:51.080 --> 01:05:54.080
is starting to allow us to say this mutation

01:05:54.080 --> 01:05:57.080
where is it expressed what types of cell types it's expressed in

01:05:57.080 --> 01:06:01.080
and we actually have built a tool at CZI called cell by gene

01:06:01.080 --> 01:06:04.080
where you can put in the mutation that you're interested in

01:06:04.080 --> 01:06:07.080
and it gives you a heat map of cross cell types

01:06:07.080 --> 01:06:11.080
of which cell types are expressing the gene that you're interested in

01:06:11.080 --> 01:06:16.080
and so then you can start looking at okay if I look at gene X

01:06:16.080 --> 01:06:20.080
and I know it's related to heart disease but if you look at the heat map

01:06:20.080 --> 01:06:24.080
it's also spiking in the pancreas that allows you to generate a hypothesis why

01:06:24.080 --> 01:06:28.080
and what happens when this gene is mutated and you're in the function of your pancreas

01:06:28.080 --> 01:06:31.080
really exciting way to look and ask questions differently

01:06:31.080 --> 01:06:36.080
and you can also imagine a world where if you're trying to develop a therapy

01:06:36.080 --> 01:06:40.080
a drug and the goal is to treat the function of the heart

01:06:40.080 --> 01:06:43.080
but you know that it's also really active in the pancreas again

01:06:43.080 --> 01:06:47.080
so what is there going to be an unexpected side effect that you should think about

01:06:47.080 --> 01:06:50.080
as you're bringing this drug to clinical trials

01:06:50.080 --> 01:06:54.080
so she's talking about bringing a drug to clinical trials

01:06:54.080 --> 01:06:58.080
she's talking about substances she's talking about products

01:06:58.080 --> 01:07:01.080
she's BSing

01:07:01.080 --> 01:07:04.080
and they're lying about this idea that they're going to cure all diseases

01:07:04.080 --> 01:07:06.080
they're going to collect all the data

01:07:06.080 --> 01:07:10.080
they're going to collect all the data as much data as they possibly can

01:07:10.080 --> 01:07:17.080
under the excuse of philanthropy and maybe public health

01:07:17.080 --> 01:07:22.080
but they're also talking about software tools that they're going to apply to this data

01:07:22.080 --> 01:07:27.080
so that it becomes proprietary information so that it becomes patentable

01:07:27.080 --> 01:07:31.080
make no mistake about it it's all the same game

01:07:31.080 --> 01:07:34.080
it's the same game as Absolera's playing

01:07:34.080 --> 01:07:37.080
they're playing the same game with the data

01:07:37.080 --> 01:07:39.080
they're trying to monetize it

01:07:39.080 --> 01:07:41.080
privatize it

01:07:41.080 --> 01:07:43.080
profitize it whatever

01:07:43.080 --> 01:07:45.080
make it into a product

01:07:45.080 --> 01:07:49.080
and these software stacks that they're using as informatics

01:07:49.080 --> 01:07:52.080
you know processing tools they're all patented

01:07:52.080 --> 01:07:54.080
they're all going to be rented out

01:07:56.080 --> 01:07:59.080
and even if they become public tools that's not the point

01:07:59.080 --> 01:08:02.080
the point is is that they're gathering the data

01:08:02.080 --> 01:08:05.080
and they're doing it under false pretenses

01:08:05.080 --> 01:08:08.080
they're doing it under the idea that they're going to help people

01:08:08.080 --> 01:08:10.080
what they want to do is control

01:08:13.080 --> 01:08:15.080
and I'm going to say it one more time

01:08:15.080 --> 01:08:17.080
because I think it's very important to point out

01:08:17.080 --> 01:08:19.080
if my wife and I were to do this interview

01:08:19.080 --> 01:08:21.080
you would see that we were married

01:08:21.080 --> 01:08:24.080
and it would be obvious that we sleep together

01:08:24.080 --> 01:08:29.080
the love between my wife and I would be obvious

01:08:29.080 --> 01:08:32.080
our affection would be obvious

01:08:32.080 --> 01:08:34.080
our mutual respect would be obvious

01:08:34.080 --> 01:08:38.080
and I don't see it here

01:08:38.080 --> 01:08:42.080
I don't see it here at all

01:08:42.080 --> 01:08:44.080
and that's because it's not there

01:08:44.080 --> 01:08:49.080
this is a fake couple that is being used to rule us

01:08:50.080 --> 01:08:53.080
I'm certain of it

01:08:53.080 --> 01:08:55.080
it's an incredibly exciting tool

01:08:55.080 --> 01:08:57.080
and one that's only going to get better

01:08:57.080 --> 01:09:00.080
as we get more and more sophisticated ways to analyze the data

01:09:00.080 --> 01:09:02.080
let's say I love that because if I look at the advances

01:09:02.080 --> 01:09:04.080
in neuroscience over the last 15 years

01:09:04.080 --> 01:09:07.080
most of them did necessarily come from looking at the nervous system

01:09:07.080 --> 01:09:10.080
came from the understanding that the immune system impacts the brain

01:09:10.080 --> 01:09:13.080
everyone prior to that talked about the brain as immune-privileged organ

01:09:13.080 --> 01:09:16.080
what you just said also bridges the divide between

01:09:16.080 --> 01:09:18.080
single cells organs and systems

01:09:18.080 --> 01:09:20.080
because ultimately cells make up organs

01:09:20.080 --> 01:09:22.080
and they're all talking to one another

01:09:22.080 --> 01:09:24.080
and everyone nowadays is familiar with gut brain access

01:09:24.080 --> 01:09:26.080
or the microbiome being so important

01:09:26.080 --> 01:09:29.080
but rarely is the discussion between organs

01:09:29.080 --> 01:09:32.080
discussed so to speak

01:09:32.080 --> 01:09:34.080
what's really frustrating about this

01:09:34.080 --> 01:09:37.080
is that this guy is actually a real physical fitness freak

01:09:37.080 --> 01:09:39.080
you can see he's kind of pumped up

01:09:39.080 --> 01:09:42.080
he talks about exercise a lot

01:09:42.080 --> 01:09:45.080
and about an exercise routine and about sleep

01:09:45.080 --> 01:09:49.080
and about house sleep

01:09:49.080 --> 01:09:53.080
thank you very much I know very well can ruin your physical mental health

01:09:53.080 --> 01:09:56.080
he knows all about food

01:09:56.080 --> 01:09:59.080
and about the gut brain interaction

01:09:59.080 --> 01:10:01.080
which he just casually glossed over there

01:10:01.080 --> 01:10:06.080
how does studying individual cells

01:10:06.080 --> 01:10:10.080
ever get you to the point to understand how they contribute to the pattern integrity

01:10:10.080 --> 01:10:15.080
and when is this guy going to say

01:10:15.080 --> 01:10:18.080
so what about this curing all diseases

01:10:18.080 --> 01:10:21.080
where's the health and wellness part about this

01:10:21.080 --> 01:10:23.080
where's the nutritional aspect of it

01:10:23.080 --> 01:10:27.080
are you funding anything that has to do with changing people's lifestyles

01:10:27.080 --> 01:10:31.080
as opposed to just fixing the problems that are caused by the crap

01:10:31.080 --> 01:10:34.080
habits that they have

01:10:34.080 --> 01:10:39.080
because that's the part of this narrative that gets very very frustrating

01:10:39.080 --> 01:10:43.080
because obese people are not people that need a cure

01:10:43.080 --> 01:10:46.080
obese people are not people that need medicine

01:10:46.080 --> 01:10:50.080
obese people are not people that need a special pill that hasn't been invented yet

01:10:50.080 --> 01:10:56.080
I'm going to go out and a limb here and say that a lot of obese people just need a kick in the ass

01:10:56.080 --> 01:10:59.080
they needed to be told they were fat

01:10:59.080 --> 01:11:02.080
a long frickin time ago by somebody that loved them

01:11:02.080 --> 01:11:07.080
so that they would have done something different about the choices that they made for the last 20 years

01:11:07.080 --> 01:11:11.080
but I hate to break it to you

01:11:11.080 --> 01:11:15.080
but if the math says that if you're 600 pounds there was a time when you were 300

01:11:15.080 --> 01:11:20.080
and when you were 300 pounds someone should have done told you that you're too stupid fat

01:11:20.080 --> 01:11:25.080
but none of that discussion is on this table here

01:11:25.080 --> 01:11:29.080
you talk about diabetes but you're not going to talk about the American diet

01:11:29.080 --> 01:11:33.080
and about how little we do to solve that problem in children

01:11:33.080 --> 01:11:42.080
you're not going to talk about the fact that zucker could frickin fund an entire public school

01:11:42.080 --> 01:11:48.080
lunch program for the money that he's doing and that would cure so many more diseases than anything else

01:11:48.080 --> 01:11:53.080
this is where this just gets absurd

01:11:53.080 --> 01:11:59.080
that these guys are all rubbing each other and getting all excited about all the big words they can use together

01:11:59.080 --> 01:12:01.080
and how they can agree on what they mean

01:12:01.080 --> 01:12:05.080
but they're not talking about the heart of the problem

01:12:05.080 --> 01:12:09.080
we got this 340 million people and most of them are unhealthy

01:12:09.080 --> 01:12:14.080
and it's not because we haven't solved the problem of the human genome yet

01:12:14.080 --> 01:12:19.080
I think that's a very clever way of putting it

01:12:19.080 --> 01:12:22.080
we've got 350 million people in the United States

01:12:22.080 --> 01:12:24.080
most of them are extremely unhealthy

01:12:24.080 --> 01:12:29.080
and it's not because we haven't solved the human genome yet

01:12:30.080 --> 01:12:32.080
you jackasses

01:12:32.080 --> 01:12:37.080
it's wonderful so that that tool was generated by CZI or CZI funded that tool

01:12:37.080 --> 01:12:38.080
we built that

01:12:38.080 --> 01:12:39.080
we built it

01:12:39.080 --> 01:12:41.080
so is it built by meta?

01:12:41.080 --> 01:12:42.080
it's its own engineers

01:12:42.080 --> 01:12:43.080
got it

01:12:43.080 --> 01:12:45.080
there are completely different organizations

01:12:45.080 --> 01:12:46.080
incredible

01:12:46.080 --> 01:12:49.080
and so a graduate student or postdoc who's interested in particular mutation

01:12:49.080 --> 01:12:51.080
can put this mutation into this database

01:12:51.080 --> 01:12:54.080
that graduate student or postdoc might be in a laboratory known for working on heart

01:12:54.080 --> 01:12:58.080
but suddenly find that they're collaborating with other scientists that work on the pancreas

01:12:58.080 --> 01:13:01.080
which also is wonderful because it bridges the divide between these fields

01:13:01.080 --> 01:13:03.080
fields are so siloed in science

01:13:03.080 --> 01:13:06.080
not just different buildings but people rarely talk unless things like this are happening

01:13:06.080 --> 01:13:10.080
I mean the graduate student is someone that we want to empower because one they're the future of science as you know

01:13:10.080 --> 01:13:14.080
and within cell by gene if you put in the gene you're interested in and it shows you the heat map

01:13:14.080 --> 01:13:17.080
we also will pull up like the most relevant papers to that gene

01:13:17.080 --> 01:13:19.080
and so like read these things

01:13:19.080 --> 01:13:20.080
fantastic

01:13:20.080 --> 01:13:23.080
as we all know quality nutrition influences of course our physical health

01:13:23.080 --> 01:13:25.080
but also our mental health and our cognitive functioning

01:13:25.080 --> 01:13:27.080
our memory our ability to learn new things and to focus

01:13:27.080 --> 01:13:30.080
and we know that one of the most important features of high quality nutrition

01:13:30.080 --> 01:13:35.080
is making sure that we get enough vitamins and minerals from high quality unprocessed or minimally processed sources

01:13:35.080 --> 01:13:40.080
as well as enough probiotics and prebiotics and fiber to support basically all the cellular functions in our body

01:13:40.080 --> 01:13:42.080
including the gut microbiome

01:13:42.080 --> 01:13:46.080
now I like most everybody try to get optimal nutrition from whole foods

01:13:46.080 --> 01:13:49.080
ideally mostly from minimally processed or non processed foods

01:13:49.080 --> 01:13:51.080
however one of the challenges that I and so many other people face

01:13:51.080 --> 01:13:54.080
is getting enough servings of high quality fruits and vegetables per day

01:13:54.080 --> 01:13:57.080
as well as fiber and probiotics that often accompany those fruits and vegetables

01:13:57.080 --> 01:14:00.080
that's why way back in 2012 long before I ever had a podcast

01:14:00.080 --> 01:14:02.080
I started drinking AG1

01:14:02.080 --> 01:14:05.080
and so I'm delighted that AG1 is sponsoring the Huberman lab podcast

01:14:05.080 --> 01:14:09.080
the reason I started taking AG1 and the reason I still drink AG1 once or twice a day

01:14:09.080 --> 01:14:11.080
is that it provides all of my foundational nutritional needs

01:14:11.080 --> 01:14:16.080
that is it provides insurance that I get the proper amounts of vitamins, minerals, probiotics and fiber

01:14:16.080 --> 01:14:19.080
to ensure optimal mental health, physical health and performance

01:14:19.080 --> 01:14:24.080
if you'd like to try AG1 you can go to drinkag1.com slash Huberman to claim a special offer

01:14:24.080 --> 01:14:28.080
they're giving away 5 free travel packs plus a year's supply of vitamin D3K2

01:14:28.080 --> 01:14:32.080
again that's drinkag1.com slash Huberman to claim that special offer

01:14:32.080 --> 01:14:34.080
I just think going back to your question

01:14:34.080 --> 01:14:37.080
that was too ironic more sometimes I could discover it I mean I assume so

01:14:37.080 --> 01:14:40.080
right I mean no catalog of the stuff is ever you know doesn't seem like we're ever done

01:14:40.080 --> 01:14:43.080
right we keep on finding more but I think that

01:14:43.080 --> 01:14:48.080
what is the problem with finding more do you understand that there's this thing

01:14:48.080 --> 01:14:51.080
right about lumpers and splitters in biology

01:14:51.080 --> 01:14:56.080
and what they're right now talking about is the splitting aspect of biology

01:14:56.080 --> 01:15:01.080
so biologists tend to be lumped into two groups or can be lumped into two groups

01:15:01.080 --> 01:15:04.080
they can be lumped into lumpers or splitters

01:15:04.080 --> 01:15:10.080
the splitters are people that like to split things into ever smaller categories to study ever more

01:15:10.080 --> 01:15:13.080
detailed categorization of things to try and understand things

01:15:13.080 --> 01:15:17.080
and lumpers are people that like to lump things together in order to understand them

01:15:17.080 --> 01:15:21.080
and generally speaking it is really true

01:15:21.080 --> 01:15:26.080
biologists are either one or the other and it seems like these guys have decided that the splitters are the right ones

01:15:26.080 --> 01:15:32.080
and so they're talking about classifying cells and every time they start to dissect a new cell population

01:15:32.080 --> 01:15:35.080
we find all kinds of new cell types

01:15:35.080 --> 01:15:38.080
that's the problem with being a splitter

01:15:38.080 --> 01:15:42.080
you know if you just say you like blondes that's a lot of girls

01:15:42.080 --> 01:15:45.080
if you start to say well I like blondes but I'm really picky about their

01:15:45.080 --> 01:15:50.080
whether how long the hair is and what color their eyes are and whether they like to wear jeans or not

01:15:50.080 --> 01:15:54.080
then you're a splitter

01:15:54.080 --> 01:16:00.080
and in biology trying to understand things exclusively by splitting things

01:16:00.080 --> 01:16:10.080
and ever smaller things is a bit like imagining that if you dissect the complexity of the pattern integrity enough

01:16:10.080 --> 01:16:14.080
that how its parts come together will make sense

01:16:14.080 --> 01:16:19.080
for example if you just studied all of the molecules in a wave of water

01:16:19.080 --> 01:16:24.080
eventually if you knew where all those molecules were you'd be able to simulate the wave

01:16:24.080 --> 01:16:28.080
and you understand how waves work

01:16:28.080 --> 01:16:32.080
and so you're studying these little molecules and the salt and everything

01:16:32.080 --> 01:16:36.080
but you don't have any concept of how they work together to form a wave

01:16:36.080 --> 01:16:39.080
to find how the server rides it

01:16:39.080 --> 01:16:44.080
and in this scenario they're talking about splitting cells into ever smaller groups with the idea

01:16:44.080 --> 01:16:49.080
that once we split them into the useful groups the right groups

01:16:49.080 --> 01:16:52.080
then the pattern that they make together will become obvious

01:16:52.080 --> 01:16:55.080
and I think this is really a wrong answer

01:16:55.080 --> 01:17:00.080
of modern LLMs is the ability to kind of imagine different states that things can be in

01:17:00.080 --> 01:17:04.080
so from all the work that we've done and funded on the human cell atlas

01:17:04.080 --> 01:17:09.080
there is a large deal you get the feeling that he looks at her sometimes to get a nod

01:17:09.080 --> 01:17:13.080
or like looks at her for approval

01:17:13.080 --> 01:17:18.080
I really feel like he's looking for her to say no or yes when he looks over to her

01:17:18.080 --> 01:17:24.080
and she's never looking at him she's always looking down really intensely listening in case she has to correct him

01:17:24.080 --> 01:17:29.080
purpose of data that you can now train a kind of large scale model on

01:17:29.080 --> 01:17:32.080
and one of the things that we're doing is see

01:17:32.080 --> 01:17:37.080
which I think is pretty exciting is building what we think is one of the largest non-profit life sciences

01:17:37.080 --> 01:17:41.080
AI clusters right it's like a you know the order of a thousand GPUs

01:17:41.080 --> 01:17:45.080
and you know it's larger than what most people have access to in academia

01:17:45.080 --> 01:17:47.080
you can do serious engineering work on

01:17:47.080 --> 01:17:52.080
and you know by basically training a model with all of the human cell atlas data

01:17:52.080 --> 01:17:54.080
and a bunch of other inputs as well

01:17:54.080 --> 01:17:59.080
we think you'll be able to basically imagine all of the different types of cells

01:17:59.080 --> 01:18:02.080
and all the different states that they can be in and when they're healthy and diseased

01:18:02.080 --> 01:18:08.080
and how they'll interact with different interact with each other interact with different potential drugs

01:18:08.080 --> 01:18:10.080
but I mean I think the state of LLMs

01:18:10.080 --> 01:18:13.080
I think this is where it's helpful to understand you know have a good understanding

01:18:13.080 --> 01:18:16.080
and be grounded in like the modern state of AI I mean these things are not full

01:18:16.080 --> 01:18:20.080
so he's talking about language models again LLMs

01:18:24.080 --> 01:18:28.080
so he's still talking about using a language model to crack the genome

01:18:30.080 --> 01:18:37.080
proof right I mean one of the flaws of modern LLMs is they hallucinate right so the question is how do you make it so that

01:18:37.080 --> 01:18:40.080
that can be an advantage rather than a disadvantage

01:18:40.080 --> 01:18:43.080
and I think the way that it ends up being an advantage is when they help you imagine

01:18:43.080 --> 01:18:47.080
a bunch of states that someone could be in but then you know as the scientist or engineer go and validate

01:18:47.080 --> 01:18:50.080
that those are true whether they're you know solutions to how a protein can be folded

01:18:50.080 --> 01:18:53.080
or possible states that a cell could be in when it's interacting with other things

01:18:53.080 --> 01:18:57.080
but you know we're not yet at the state with AI that you can just take the outputs of these things

01:18:57.080 --> 01:19:03.080
as like as gospel and run from there but they are very good I think as you said hypothesis generators

01:19:03.080 --> 01:19:08.080
or possible solution generators that then you can go validate so I know that's a very powerful

01:19:08.080 --> 01:19:12.080
thing that we can basically you know building on the first five years of science work

01:19:12.080 --> 01:19:15.080
around the human cell atlas and all the data that's been built out carry that forward

01:19:15.080 --> 01:19:18.080
into something that I think is going to be a very novel tool going forward

01:19:18.080 --> 01:19:23.080
and that's the type of thing that I think we're set up to do well I mean you all you had

01:19:23.080 --> 01:19:27.080
this exchange a little while back about you know funding levels and how CZI is

01:19:27.080 --> 01:19:32.080
you know just sort of a drop in the in the bucket compared to NIH but I think we have this

01:19:32.080 --> 01:19:37.080
the thing that I think we can do that's different is funding some of these longer term

01:19:37.080 --> 01:19:41.080
bigger projects that it is hard to galvanize the and pull together the energy to do that

01:19:41.080 --> 01:19:46.080
and it's a lot of what most science funding is is like relatively small projects that are exploring

01:19:46.080 --> 01:19:50.080
things over relatively short time horizons and one of the things that we try to do is

01:19:50.080 --> 01:19:54.080
like build these tools over you know five ten fifteen year periods they're often projects

01:19:54.080 --> 01:19:57.080
that require you know hundreds of millions of dollars of funding and world class engineering

01:19:57.080 --> 01:20:01.080
teams and infrastructure to do and that I think is a pretty cool contribution to the field

01:20:01.080 --> 01:20:06.080
that that I think is there aren't as many other folks who are doing that kind of thing

01:20:06.080 --> 01:20:09.080
but that's one of the reasons why I'm personally excited about the virtual cell stuff because

01:20:09.080 --> 01:20:12.080
it just it's like this perfect intersection of all the stuff that we've done in single cell

01:20:12.080 --> 01:20:19.080
to previous so virtual cells is dumb as a as a virtual cube of neurons that that

01:20:19.080 --> 01:20:24.080
Henry Markham said he was going to make in with the blue brain project way back in

01:20:24.080 --> 01:20:31.080
2005 or 2008 and he got a billion dollars from the EU to do a human brain project where

01:20:31.080 --> 01:20:36.080
he's going to simulate the whole human brain using neurons that had ten thousand compartments

01:20:37.080 --> 01:20:43.080
each he really got a billion dollars to do that because he talked that much smack his name is

01:20:43.080 --> 01:20:49.080
Henry Markham I've done some shows about him before years ago and this is no different

01:20:49.080 --> 01:20:56.080
this is the same dumb talk in the virtual cell I mean the virtual cell I mean come on

01:20:56.080 --> 01:21:04.080
the virtual cell the arrogance of this jack is just crazy but they all do it they all

01:21:04.080 --> 01:21:09.080
play the same game ladies and gentlemen all of them play the same game it's a bullshit

01:21:09.080 --> 01:21:16.080
game it's a lying game it's a confidence game and we are being governed by a confidence

01:21:16.080 --> 01:21:23.080
game by these kinds of people in a lot of ways these kinds of people have more influence

01:21:23.080 --> 01:21:28.080
behind the scenes and a lot of our politicians do our politicians are almost neutered at this

01:21:28.080 --> 01:21:36.080
stage they don't do anything they oversee a bankrupt government

01:21:36.080 --> 01:21:41.080
operations that we've done with the field and bring together the industry and AI expertise

01:21:41.080 --> 01:21:46.080
around this yeah I completely agree that the model of science that you're put together

01:21:46.080 --> 01:21:52.080
with CZI isn't just unique from NIH but it's extremely important the independent investigator

01:21:52.080 --> 01:21:55.080
model is what's driven the progression of science in this country and to some extent

01:21:55.080 --> 01:22:01.080
in northern Europe for the last hundred years and it's wonderful on the one hand because

01:22:01.080 --> 01:22:05.080
it allows for that image we have of a scientist kind of tinkering away or the people in their

01:22:05.080 --> 01:22:12.080
lab and then they reek us and that hopefully translates to better human health but I think

01:22:12.080 --> 01:22:16.080
in my opinion we've moved past that model as the most effective model or the only model

01:22:16.080 --> 01:22:19.080
that should be explored yeah I just think it's a balance you want that but you want to

01:22:19.080 --> 01:22:22.080
empower those people I think that that's these tools and show those and their mechanisms

01:22:22.080 --> 01:22:26.080
to do that like NIH but but it's hard to do collaborative sciences it's sort of interesting

01:22:26.080 --> 01:22:30.080
that we're sitting here not far because I grew up right near here as well I'm not far from

01:22:30.080 --> 01:22:35.080
the garage model of tech right he looked at the backward model not far from here at all

01:22:35.080 --> 01:22:40.080
and the idea was you know the tinkerer in the garage the inventor and then people often

01:22:40.080 --> 01:22:43.080
forget that to implement all the technologies they discovered took enormous factories and

01:22:43.080 --> 01:22:47.080
warehouse so you know there's there's a similarity there to Facebook meta etc but I think in

01:22:47.080 --> 01:22:50.080
science what we imagine that the scientists alone in their laboratory and those eureka

01:22:50.080 --> 01:22:54.080
moments but I think nowadays that the big questions really require extensive collaboration

01:22:54.080 --> 01:22:58.080
and certainly tool development and one of the tools that you keep coming back to is these

01:22:58.080 --> 01:23:01.080
LLM's these large language models and maybe you could just elaborate for for those that

01:23:01.080 --> 01:23:06.080
aren't familiar you know what are what is a large language model for that the uninformed

01:23:06.080 --> 01:23:11.080
what is it and what does it allow what does it allow us to do that you know different

01:23:11.080 --> 01:23:15.080
types of other types of AI don't allow or more importantly perhaps what does it allow us

01:23:15.080 --> 01:23:19.080
to do that a bunch of really smart people highly informed in a given area of science staring

01:23:19.080 --> 01:23:25.080
at the data what can I do that they can't do sure so I think a lot of the progression

01:23:25.080 --> 01:23:30.080
of machine learning has been about building systems neural networks or otherwise that

01:23:30.080 --> 01:23:35.080
can basically make sense and find patterns in larger and larger amounts of data and there's

01:23:35.080 --> 01:23:40.080
a breakthrough a number of years back that some folks at Google actually made called

01:23:40.080 --> 01:23:45.080
this transformer model architecture and it was this huge breakthrough because before then

01:23:45.080 --> 01:23:49.080
there was somewhat of a cap where you know if you fed more data into a neural network

01:23:49.080 --> 01:23:54.080
past some some point it didn't really glean more insights from it whereas transformers

01:23:54.080 --> 01:23:57.080
just you know we haven't seen the end of how big that can scale to yet I mean I think

01:23:57.080 --> 01:24:01.080
there's a chance that we run into some some ceiling but it's never asymptotes we haven't

01:24:01.080 --> 01:24:04.080
observed it yet but we just haven't built big enough systems yet so I would guess that

01:24:04.080 --> 01:24:08.080
I don't know I think this is actually one of the big questions and in the AI field today

01:24:08.080 --> 01:24:12.080
is basically our transformers and our current model architecture is sufficient and if you

01:24:12.080 --> 01:24:15.080
just build larger and larger clusters you eventually get something that's like human

01:24:15.080 --> 01:24:20.080
intelligence or super intelligence or is there some kind of fundamental limit to this architecture

01:24:20.080 --> 01:24:24.080
that we just haven't reached yet and once we we kind of get a little bit further

01:24:24.080 --> 01:24:27.080
in building them out then we'll reach that and we'll need a few more leaps before we get to

01:24:27.080 --> 01:24:32.080
you know the level of AI that I think will unlock you know a ton of really futuristic

01:24:32.080 --> 01:24:36.080
and amazing things but there's no doubt that even just being able to process the amount of data

01:24:36.080 --> 01:24:41.080
that we can now with this model architecture has unlocked a lot of new use cases and the

01:24:41.080 --> 01:24:46.080
other called large language models is because one of the first uses of them is people basically

01:24:46.080 --> 01:24:51.080
feed in all of the language from basically the world wide web and you can think about

01:24:51.080 --> 01:24:57.080
them as basically prediction machines so if you fit in and you put in prompt and it can

01:24:57.080 --> 01:25:01.080
basically you know predict a version of what should come next so you know you like type

01:25:01.080 --> 01:25:05.080
in a headline for a news story and it can kind of predict what it thinks the story should

01:25:05.080 --> 01:25:09.080
be or you could train it so that it can be a chatbot right where if you're prompted

01:25:09.080 --> 01:25:13.080
this question you can get this response but one of the interesting things is it turns

01:25:13.080 --> 01:25:17.080
out that there's actually nothing specific to using human language in it so if instead

01:25:17.080 --> 01:25:21.080
of feeding it human language if you use that model architecture for a network and instead

01:25:21.080 --> 01:25:27.080
you feed it all of the human cell outless data then if you prompt it with a state of a cell

01:25:27.080 --> 01:25:32.080
it can spit out different versions of like what you know how that cell can interact

01:25:32.080 --> 01:25:35.080
or different states that the cell could be a next when it interacts with different things so this is

01:25:35.080 --> 01:25:42.080
so stupid because what he's suggesting is that we have an equivalent understanding

01:25:42.080 --> 01:25:50.080
of biology that we do of human language that human biology has all these known grammatical

01:25:50.080 --> 01:25:57.080
rules that we already know and there are very limited set of grammatical rules and so it's

01:25:57.080 --> 01:26:02.080
very easy to predict what will come next but without the grammatical rules without any

01:26:02.080 --> 01:26:06.080
understanding of what the language means without understanding what the words mean after

01:26:06.080 --> 01:26:12.080
they're read without understanding what the words mean after they're read and then stored

01:26:12.080 --> 01:26:21.080
never mind what the stored information is then read out again in behavior

01:26:21.080 --> 01:26:27.080
again this is ridiculously dumb simple this is really bamboozling people and I'm so shocked

01:26:27.080 --> 01:26:33.080
but this dude right here is being so steamrolled it's like he's an idiot

01:26:33.080 --> 01:26:38.080
I think he's tried to ask a couple questions but I'm kind of ashamed of him actually

01:26:38.080 --> 01:26:46.080
I mean he just did he just did a an advertisement about nutrition and health right you know that

01:26:46.080 --> 01:26:50.080
right and he still isn't asked the question of wait let's get around this for a minute

01:26:50.080 --> 01:26:53.080
does it have to take a genetics class so for instance if you give it a bunch of genetics

01:26:53.080 --> 01:26:56.080
do you have to say hey by the way and then you give it a genetics class it would understand

01:26:56.080 --> 01:27:00.080
so you know you got DNA RNA mRNA and proteins I think that the basic nature of all these

01:27:00.080 --> 01:27:05.080
machine learning techniques is there they're basically pattern recognition systems so they're

01:27:05.080 --> 01:27:11.080
these like very deep statistical machines they still require somebody to teach them you still

01:27:11.080 --> 01:27:15.080
need someone to teach them the English language to give them the rules and to correct them

01:27:15.080 --> 01:27:20.080
when they're wrong for millions of iterations before they start to perform like he's talking

01:27:20.080 --> 01:27:27.080
about and so what are you gonna do sit Kevin McCurnan down at a table and say okay what's

01:27:27.080 --> 01:27:32.080
this gene mean Kevin type it in what do these two genes mean together type it in what if

01:27:32.080 --> 01:27:39.080
this gene's higher than that gene type it in Kevin what the hell is his plan to teach

01:27:39.080 --> 01:27:48.080
this model there's no plan they don't have anything to teach it they're gonna put a

01:27:48.080 --> 01:27:51.080
bunch of data in there and then what it's gonna mix it around like in a washing machine

01:27:51.080 --> 01:28:00.080
it's gonna come out all folded and sorted by color this is this is absolute enchantment

01:28:00.080 --> 01:28:08.080
it's just downright snake oil and the Zuckerberg for sure is not qualified enough as a as an

01:28:08.080 --> 01:28:15.080
armchair biologist even wax intellectual about these things and you can hear it looks like

01:28:15.080 --> 01:28:21.080
this woman is kind of ashamed of them they're very efficient at finding patterns so it's not

01:28:21.080 --> 01:28:25.080
actually you don't need to teach a language model that's trying to you know speak a language

01:28:25.080 --> 01:28:30.080
you know a lot of specific things about that language either you just feed it in a bunch

01:28:30.080 --> 01:28:35.080
of examples and then you know let's say you teach it about something in English but then you

01:28:35.080 --> 01:28:39.080
also give it a bunch of examples of people speaking Italian it'll actually be able to explain

01:28:39.080 --> 01:28:42.080
the thing that it learned in English and Italian right even though it is so the crossover

01:28:42.080 --> 01:28:47.080
and just the pattern recognition is the thing that is pretty profound and powerful about this

01:28:47.080 --> 01:28:52.080
but it really does apply to a lot of different things another example in the scientific community

01:28:52.080 --> 01:28:57.080
has been the work that alpha fold you know that basically the folks at DeepMind have done

01:28:57.080 --> 01:29:02.080
on protein fold it's you know just basically a lot of the same model architecture but instead

01:29:02.080 --> 01:29:06.080
of language there they fold it they kind of fed in all of these protein data and you can

01:29:06.080 --> 01:29:10.080
give it a state and it can spit out solutions to how those proteins get folded so it can

01:29:10.080 --> 01:29:14.080
fit out solutions and then those solutions still have to be checked in the real world against

01:29:14.080 --> 01:29:20.080
the real background the specific ribosomes you're using and the specific RNA transcript you're

01:29:20.080 --> 01:29:28.080
using in the specific cell type you're using you're lying oversimplifying jackass with

01:29:28.080 --> 01:29:34.080
no respect for the sacred biology of a human screw you it's very powerful I don't think

01:29:34.080 --> 01:29:40.080
we know yet as an industry what the what the natural limits of it are and that that's

01:29:40.080 --> 01:29:44.080
one of the things that's pretty exciting about the current state but it certainly allows you

01:29:44.080 --> 01:29:49.080
to solve problems that just weren't solved with the generation of machine learning that came

01:29:49.080 --> 01:29:54.080
before it sounds like CZI is moving a lot of work that was just done in vitro in dishes

01:29:54.080 --> 01:30:01.080
and in vivo in living organisms model organisms are humans to in silico as we say so do you

01:30:01.080 --> 01:30:07.080
foresee a future where a lot of biomedical research certainly that the work of CZI included

01:30:07.080 --> 01:30:12.080
is done by machines I mean obviously it's much lower cost and you can run millions of experiments

01:30:12.080 --> 01:30:16.080
which of course is not to say that humans are not going to be involved but I love the idea

01:30:16.080 --> 01:30:22.080
that we can run experiments in silico and mass I think the in silica experiments are going

01:30:22.080 --> 01:30:30.080
to be incredibly helpful to test things quickly to cheaply into just unleash a lot of creativity

01:30:30.080 --> 01:30:34.080
I do think you need to be very careful about making sure it still translates and unleash

01:30:34.080 --> 01:30:46.080
creativity is rich people talk for wasting people's time matches this humans you know one thing that's funny

01:30:46.080 --> 01:30:52.080
in basic sciences we've basically cured every single disease and mice like mice have we know what's

01:30:52.080 --> 01:30:57.080
going on when they have a number of diseases because they're used as a model organism but they are not

01:30:57.080 --> 01:31:05.080
used in a lot of times that research is relevant that is such a lie you want to know the truth

01:31:05.080 --> 01:31:10.080
about laboratory mice almost every laboratory mouse if you let them live long they die of cancer

01:31:10.080 --> 01:31:17.080
that's what they die of all of the inbred mice in the lab die of cancer all of them

01:31:17.080 --> 01:31:25.080
and I'm going to vomit a little bit in my throat when I say this but actually the person who

01:31:25.080 --> 01:31:31.080
is largely responsible for this story is Brett Weinstein the only scientific paper he ever really

01:31:31.080 --> 01:31:37.080
did looked at and made a prediction that it might be that because we're in breeding these mice and

01:31:37.080 --> 01:31:43.080
we're always killing them before they're very old that we might not really be selecting for mice

01:31:43.080 --> 01:31:49.080
that can fight cancer it turns out it seems like he was kind of right that almost all laboratory

01:31:49.080 --> 01:31:54.080
mice that are inbred die of cancer if you let them because of their telomeres and something else they

01:31:54.080 --> 01:32:00.080
don't seem to have any cancer fighting mechanisms really left over after all this inbreeding

01:32:00.080 --> 01:32:07.080
so we know how to cure all the diseases in mice we know how to cure all the diseases in mice

01:32:07.080 --> 01:32:19.080
as Dr Priscilla Chan I'm going to rewind it just so you can hear how just absolutely stupid she

01:32:19.080 --> 01:32:24.080
thinks we are in silica experiments are going to be incredibly helpful to test things quickly

01:32:24.080 --> 01:32:31.080
to cheaply into just unleash a lot of creativity I do think you need to be very careful about

01:32:31.080 --> 01:32:38.080
making sure it still translates and matches this humans you know one thing that's funny in basic

01:32:38.080 --> 01:32:43.080
sciences we've basically cured every single disease in mice like mice have we know what's going on

01:32:43.080 --> 01:32:48.080
when they have a number of diseases because they're used as a model organism but they are not humans

01:32:48.080 --> 01:32:54.080
and a lot of times that research is relevant but not directly one to one translatable to human

01:32:54.080 --> 01:33:00.080
not directly one to one translatable you mean mice lie monkeys mislead and the only way to know what happens

01:33:00.080 --> 01:33:08.080
in a human is a human or something like that now you're making it a little grayer we cured all the diseases in mice

01:33:08.080 --> 01:33:15.080
can somebody please take away her medical degree can someone please take away her keys to the car

01:33:15.080 --> 01:33:23.080
she does not belong distributing money to scientists and encouraging people to do work if this is the kind of

01:33:23.080 --> 01:33:31.080
understanding she has of the pattern integrity that is the human being this is just disgusting

01:33:35.080 --> 01:33:40.080
so you just have to be really careful about making sure that it actually works for humans

01:33:41.080 --> 01:33:46.080
sounds like what CZI is doing is actually creating a new field as I'm hearing all of this

01:33:46.080 --> 01:33:52.080
I'm thinking okay this transcends immunology department you know cardiothoracic surgery I mean neuroscience

01:33:52.080 --> 01:33:57.080
the idea of a new field where you certainly embrace the realities of universities and laboratories because that's where

01:33:57.080 --> 01:34:02.080
most of the work that you're funding is done is that right so maybe we need to think about what it means to do

01:34:02.080 --> 01:34:07.080
science differently and I think that's one of the things that's most exciting and along those lines it seems that

01:34:07.080 --> 01:34:13.080
being together a lot of different types of people different major institutions is going to be especially important

01:34:13.080 --> 01:34:20.080
so I know that the initial CZI bio hub gratefully included Stanford we'll put that first in the list

01:34:20.080 --> 01:34:27.080
but also UCSF forgive me many friends at UCSF and also Berkeley but there are now some additional institutions

01:34:27.080 --> 01:34:31.080
involved so maybe you talk about that and what motivated the decision to branch outside the bay area

01:34:31.080 --> 01:34:36.080
and and why you selected those particular additional institutions to be included

01:34:36.080 --> 01:34:41.080
well I'll just say part of a big part of why we wanted to create additional bio hubs is we were just so impressed

01:34:41.080 --> 01:34:47.080
by the work that the folks who are running the first bio hub did and I also think and you should walk through the work

01:34:47.080 --> 01:34:51.080
of the Chicago bio hub and the New York bio hub that we just announced but I think it's actually an interesting

01:34:51.080 --> 01:34:58.080
set of examples that balance the limits of what you want to do with like physical material engineering

01:34:58.080 --> 01:35:04.080
and and and where things are purely biological because the Chicago team is really building more sensors to build

01:35:04.080 --> 01:35:09.080
understand what's going on your body but that's more of like a physical oh the Chicago team is building sensors

01:35:09.080 --> 01:35:15.080
to collect data from your body no I see where we're going but you knew that already right

01:35:15.080 --> 01:35:25.080
I mean does it surprise you I shouldn't surprise you we've been talking about them chipping you for the last three years

01:35:26.080 --> 01:35:31.080
you don't think they're just going to chip you to put your money in your hand do you that would be a waste of time

01:35:31.080 --> 01:35:34.080
of course they're going to put a sensor in there

01:35:34.080 --> 01:35:40.080
engineering challenge whereas the the New York team we basically talk about this is like a cellular endoscope

01:35:40.080 --> 01:35:46.080
of being able to have like an immune cell or something that can go and understand you know what is like what's the thing

01:35:46.080 --> 01:35:50.080
that's going on in your body but it's not like a physical piece of hardware it's a cell that you can basically

01:35:50.080 --> 01:35:56.080
you know have have just go report out on on on different things that are happening inside the body so you should sell the microscope

01:35:56.080 --> 01:36:06.080
totally they're talking about making a cell that can report what's going on in the body to make a cell into a microscope

01:36:06.080 --> 01:36:16.080
and she says totally seriously your body but it's not like a physical piece of hardware it's a cell that you can basically

01:36:16.080 --> 01:36:22.080
you know have have just go report out on on different things that are happening inside the body so you should sell the microscope

01:36:22.080 --> 01:36:26.080
totally and then and then eventually actually being able to act on it but I mean but you should you should go into more detail

01:36:26.080 --> 01:36:33.080
on all this so a core principle of how we think about bio hubs it is that it has to be when we invited proposals it has to be at least

01:36:33.080 --> 01:36:40.080
three institutions so really breaking down the barrier of a single university oftentimes asking for the people designing the

01:36:40.080 --> 01:36:48.080
research aim to come from all different backgrounds and to explain why that the problem that they want to solve requires interdisciplinary

01:36:48.080 --> 01:36:56.080
inter-university institution collaboration to actually make happen we just put that request for proposal out there with our San Francisco

01:36:56.080 --> 01:37:05.080
bio hub as an example where they've done incredible work in single cell biology and infectious disease and we got I want to say like 57 proposals

01:37:05.080 --> 01:37:15.080
from over 150 institutions a lot of ideas came together and you know we are so so excited that we've been able to launch Chicago and New York

01:37:15.080 --> 01:37:22.080
Chicago is a collaboration between UIUC, University of Illinois, Urina Champaign and University of Chicago and Northwestern

01:37:22.080 --> 01:37:29.080
and if I obviously these universities are multifaceted but if I were to describe them by their like stereotypical strength

01:37:29.080 --> 01:37:39.080
Northwestern has an incredible medical system and hospital system University of Chicago brings to the table incredible basic science strengths

01:37:39.080 --> 01:37:47.080
University of Illinois is a computing powerhouse and so they came together and proposed that they were going to start thinking about cells in tissue

01:37:47.080 --> 01:37:55.080
so that one of the one of the layers that you just alluded to so how do the cells that we know behave and act differently when they come together as a

01:37:55.080 --> 01:38:03.080
tissue and the first one of the first tissues that they're starting with is skin so they've been already been able to as a collaboration under the leadership of Shana Kelly design

01:38:03.080 --> 01:38:18.080
art of engineered skin tissue the architecture looks the same as what's in you and I and what they've done is built these super super thin sensors and they embed these sensors throughout the layers of this engineered tissue

01:38:18.080 --> 01:38:26.080
and they read out the data they want to see how these cells what these cells are secreting how these cells talk to each other and what happens when these cells get inflamed

01:38:26.080 --> 01:38:35.080
inflammation is an incredibly important process that drives 50% of all deaths and so this is another sort of disease agnostic approach we want to understand inflammation

01:38:35.080 --> 01:38:46.080
and they're going to get a ton of information out from these sensors that tell you what happens when something goes awry because right now we can say like when you have an allergic reaction your skin gets red and puffy

01:38:46.080 --> 01:38:57.080
what is the earliest signal of that and these sensors can look at the behaviors of these cells over time and then you can apply a large language model to look at the earliest statistically significant changes

01:38:57.080 --> 01:39:12.080
always about technology implanted and then AI data collection it's always about that even if we already knew how cells work or how skin works

01:39:12.080 --> 01:39:26.080
it's not like we never studied it before they are not trying to understand how skin works they're trying to understand how to measure how skin works

01:39:26.080 --> 01:39:42.080
that they can then measure you these are products that they plan to put in every person they're not doing skin in a laboratory so they can understand skin in a laboratory so they never have to study you

01:39:42.080 --> 01:39:53.080
this is dual use stuff here this is big time this is for your children that's the plan

01:39:53.080 --> 01:40:09.080
and she's talking about inflammation curing all disease would really I would assume revolve around understanding the immune system yet for some reason in this talk this is the first time the immune system has ever even really come on the scene

01:40:09.080 --> 01:40:18.080
but they're gonna cure all diseases by looking at all the cells in the body well yeah the immune system does inflammation too

01:40:18.080 --> 01:40:28.080
that can allow you to intervene as early as possible so that that's what Chicago's doing they're starting in the skin cells they're also looking at the neuromuscular junction

01:40:28.080 --> 01:40:37.080
which is the connection between where a neuron attaches to a muscle and tells a muscle how to behave super important in things like ALS but also in aging

01:40:37.080 --> 01:40:44.080
the slowed transmission of information across that neuromuscular junction is what causes old people to fall their brain cannot trigger their muscles to react fast enough

01:40:44.080 --> 01:40:52.080
and so we want to be able to embed these sensors to understand how these different interconnected systems within our bodies work together

01:40:52.080 --> 01:40:59.080
in New York they're doing I think that's a little weird that he's not on that

01:40:59.080 --> 01:41:10.080
because the reason why people fall is because they can't activate their muscles so they want to look at neuromuscular junctions it has nothing to do with the neurons that feed down

01:41:10.080 --> 01:41:21.080
it's really the neuromuscular junction it's not neurons in the brain that are incapable of activating the neurofibers that lead to the muscle

01:41:22.080 --> 01:41:31.080
that's a pretty gigantic assumption that I'm almost certain is wrong and I'm almost certain that Huberman could correct her on it but he doesn't want to embarrass her

01:41:31.080 --> 01:41:41.080
he doesn't want to embarrass her by saying that your dumb simple model of why old people fall is really like ignoring all three trillion brain cells

01:41:41.080 --> 01:41:46.080
if you're looking at the neuromuscular junction

01:41:46.080 --> 01:41:53.080
is it really because acetylcholine isn't released on time? has nothing to do with the processing up here?

01:41:53.080 --> 01:42:01.080
I mean that kind of statement alone can show you exactly how compromised this whole podcast is

01:42:01.080 --> 01:42:09.080
he's done four commercials for products and he's basically asked one hard question that they didn't answer

01:42:10.080 --> 01:42:20.080
and since then he's kind of been capitulating and asking increasingly weaker questions that really are just compliments like I think you guys are inventing a whole new field

01:42:20.080 --> 01:42:25.080
why don't you just ask him out for dinner?

01:42:25.080 --> 01:42:37.080
a related but equally exciting project where they're engineering individual cells to be able to go in and identify changes in a human body

01:42:37.080 --> 01:42:44.080
so what they'll do is they're calling it a while I mean I love it I mean this is I don't want to go on attention

01:42:44.080 --> 01:42:50.080
but for those that want to look at adaptive optics you know there's a lot of distortion and interference when you try and look at something really small they're really far away

01:42:50.080 --> 01:43:00.080
and really smart physicists figured out well use the interference as part of the microscope make those actually lenses of the microscope

01:43:00.080 --> 01:43:04.080
it's extremely clever along those lines it's not intuitive but then when you hear it it's like it makes so much sense

01:43:04.080 --> 01:43:09.080
you know it's not immediately intuitive make the cells that are already can navigate to tissues or embed themselves in tissues

01:43:09.080 --> 01:43:11.080
be the microscope within that tissue I love it

01:43:11.080 --> 01:43:17.080
the way that I explain this to my friends and my family is this is fantastic voyage but real life

01:43:17.080 --> 01:43:23.080
like we are going into the human body and we're using the immune cells which you know are privileged and already working to keep your body healthy

01:43:23.080 --> 01:43:31.080
and being able to target them to examine certain things so like you can engineer an immune cell to go in your body and look inside your coronary arteries

01:43:31.080 --> 01:43:36.080
and say are these arteries healthy or are there plaques because plaques lead to it

01:43:36.080 --> 01:43:42.080
wow so that's a really interesting statement and the cell can then record that information and report it back up

01:43:42.080 --> 01:43:44.080
that's the first half of the New York biohub is going to do

01:43:44.080 --> 01:43:55.080
fantastic so that's an interesting thing she says that you can program immune cells to go places and then report back what they find

01:43:55.080 --> 01:44:01.080
now that's some pretty impressive biology I'm gonna have to investigate that claim

01:44:01.080 --> 01:44:08.080
because what she just described there sounds an awful lot like they know exactly how the immune system works and can make it bend to their will

01:44:08.080 --> 01:44:15.080
to examine certain things so like you can engineer an immune cell to go in your body and look inside your coronary arteries

01:44:15.080 --> 01:44:22.080
and say are these arteries healthy or are there plaques because plaques lead to blockage which lead to heart attacks

01:44:22.080 --> 01:44:28.080
and the cell can then record that information and report it back up that's the first half of the New York biohub is going to do

01:44:28.080 --> 01:44:33.080
the second half is can you then engineer the cells to go do something about it can I then tell a different cell

01:44:33.080 --> 01:44:41.080
I mean cell that is able to transport in your body to go in and clean that up in a targeted way and so it's incredibly exciting

01:44:41.080 --> 01:44:49.080
they're gonna study things that are sort of immune privilege that your immune system normally doesn't have access to things like ovarian and pancreatic cancer

01:44:49.080 --> 01:44:57.080
they'll also look at a number of neurodegenerative diseases since the immune system doesn't it's not really sounding like she's gonna cure all diseases

01:44:57.080 --> 01:45:05.080
it sounds like she's gonna work on some really hard ones someone with really obvious solutions or really obvious phenotypes

01:45:05.080 --> 01:45:16.080
it sounds like basically the title of this podcast and the claim that they make in the beginning is just elaborate forward thinking statements

01:45:16.080 --> 01:45:22.080
that may or may not be accurate

01:45:22.080 --> 01:45:35.080
they just seem like statements that are designed to bamboozle the average person the average carpenter plumber teacher into believing that these ultra smart people with their big words

01:45:35.080 --> 01:45:46.080
are so far ahead of us and we might as well just sit back and put more pop tarts in the toaster and log into Netflix

01:45:46.080 --> 01:45:57.080
because you know Robert Malone and Steve Kirsch got the mRNA all wrapped up and AI and everything is and the future of public health is already taken care of by Amazon Health

01:45:57.080 --> 01:46:06.080
and Zuckerberg and Free Speech is already controlled by Elon Musk so everything is fine what do I gotta do?

01:46:06.080 --> 01:46:21.080
Our children are under attack by these people our children are who they want slowly but surely they are going to separate us from our children by telling our children that our parents

01:46:21.080 --> 01:46:33.080
our skeptical parents are wrong our skeptical parents are selfish our skeptical parents should take our booster and shut up

01:46:33.080 --> 01:46:46.080
ladies and gentlemen strokes around the world among young people are on the rise and there is no explanation except for transfection

01:46:46.080 --> 01:46:49.080
and these people don't care

01:46:49.080 --> 01:47:11.080
SIDS deaths are on a rise and people don't care heart attacks are on the rise cancers on the rise auto immune diseases are on the rise and these people don't care because they're going to cure all disease

01:47:11.080 --> 01:47:27.080
and we have a ton of access into the nervous system but they it's it's both mind blowing and it feels like sci-fi but science is actually in a place where if you really pushed a group of incredibly qualified scientists say could you do this if given the chance the answer is like

01:47:27.080 --> 01:47:40.080
probably give us enough time the bright team and resources like still give us enough time and a bright team and enough resources in the computing power of the Borg and yeah we can figure out the genome sure no problem

01:47:40.080 --> 01:47:46.080
give us all the data from all the babies to be born in the next 20 years

01:47:47.080 --> 01:47:59.080
give us enough computing power to shove an AI shove it up an AI is backside and yeah we can probably do it but these are forward looking statements so they may not be accurate

01:47:59.080 --> 01:48:14.080
that's the basic way to summarize this is the basic way to summarize the WEF pitch the the food conferences pitch the TED talks that we see all the time it's all the same nonsense

01:48:14.080 --> 01:48:24.080
it's all these people saying that if they had enough data and they had enough computers and they had enough iPhones on enough people or enough eyewatches on enough people or enough skin

01:48:24.080 --> 01:48:31.080
skin detectors in enough people that we would gather enough data that eventually an AI would crack the puzzle

01:48:31.080 --> 01:48:44.080
it is absolutely no different than saying that if the AI plays enough go games it becomes a go master except this isn't go or chess

01:48:44.080 --> 01:48:49.080
it's a pattern integrity that lives between 80 and 100 years

01:48:49.080 --> 01:49:03.080
whose internal metabolic pathways and interactions are at best rudimentary understood

01:49:03.080 --> 01:49:11.080
the arrogance that's on display here and and quite frankly the submission that's on display here is really gross

01:49:11.080 --> 01:49:24.080
because Huberman I thought had a little more respect for the sacredness of the brain for the most unlockable secrets that are probably in the brain

01:49:24.080 --> 01:49:34.080
and the idea that the the emergent properties of a pattern integrity are the hardest things to understand and the emergent properties are life

01:49:34.080 --> 01:49:49.080
the emergent property is behavior the emergent property is consciousness and so to understand the emergent properties of a pattern integrity you can't be a splitter

01:49:49.080 --> 01:49:58.080
and take it into part all all its little parts and try to understand all the little details of all the little parts and then think at some point if you understand enough of those details

01:49:58.080 --> 01:50:11.080
that you're going to understand how they sum together because that's not how this works we may never be able to understand how it sums together

01:50:11.080 --> 01:50:24.080
and we're certainly not going to understand how it sums together if the observations that we're making are made on a population that's completely messed up and destroyed by toxins

01:50:24.080 --> 01:50:41.080
and by poisons and lack of proper physical behavior what are we going to really learn about the potential of the human genome if we study a bunch of people that are fat lazy and malnourished

01:50:42.080 --> 01:50:55.080
never mind if we study people that are 20 years fat lazy and malnourished what do we really understand what are we going to understand about the human genomes capabilities and possibilities when we study that

01:50:55.080 --> 01:51:04.080
what would we really learn about automobiles if we only went to use car lots

01:51:04.080 --> 01:51:18.080
what would we really know about dogs if the only thing we ever studied were I don't know I mean that's that's the best one what would we know about cars if we only went to use car lots

01:51:18.080 --> 01:51:26.080
would we really figure out a lot about how cars work we went to a junkyard

01:51:27.080 --> 01:51:32.080
no matter how much data we gathered from a junkyard would we really understand how a car works

01:51:35.080 --> 01:51:41.080
and so you have to also see that from that perspective that this population is kind of useless

01:51:42.080 --> 01:51:50.080
except for with regard to like understanding how RNA gets translated or how RNA might work maybe we need a lot of examples of that maybe that's what they're doing

01:51:51.080 --> 01:52:00.080
but I mean if they're not going to start with let's make the population as healthy as possible before we start measuring what the genome does that's pretty stupid

01:52:07.080 --> 01:52:13.080
but that shows you really what we're dealing with here shows you what kind of bamboozlement what kind of snake oil this is

01:52:14.080 --> 01:52:24.080
there's people waving their hand and talking big smack about large language models being able to solve the problem of the biological pattern integrity that is a human

01:52:26.080 --> 01:52:38.080
there is no reverence for the sacred here there's no married couple here these are not two people in love these are two people lying

01:52:39.080 --> 01:52:44.080
as part of a governance structure to control the world but more specifically the United States

01:52:45.080 --> 01:52:48.080
we are under the control of liars

01:52:50.080 --> 01:52:57.080
and we've been under control of liars since before the pandemic but now the pandemic has made it obvious

01:52:58.080 --> 01:53:13.080
yeah I mean it's a 10 to 15 year project yeah but it's it's awesome engineered cells yeah I love the optimism and and the moment you said make the cell the microscope so sweet I was like yes yes and yes it just makes so much sense

01:53:14.080 --> 01:53:20.080
how does it make sense Huberman explain to me how that works make the cell the microscope

01:53:20.080 --> 01:53:27.080
I mean that's just that's part of it it sounds like such a great idea you know but I mean he's smart enough to know that that doesn't make any sense he's smart enough to know to ask one question better than that like how does the cell report it back how do you read it out for example the the example that you gave you send it to the coronary artery to find if there's a plaque how does the how does the immune system

01:53:50.080 --> 01:54:19.080
report back to you and tell you that there is a plaque there is that a hard question to ask is that not the most obvious question after someone brags like that after someone lays out this ridiculously cool idea like I'm going to reprogram an immune cell to be a to be a little spy for me and to go into immune privilege places and come back and tell me what it found and you don't even at least have the biological background Dr. Huberman to ask her a follow up question exactly what it's going to do with it.

01:54:20.080 --> 01:54:31.080
Exactly how does that work is it a B cell it is a T cell a macrophage what are you sending up there in a scene of Phil.

01:54:31.080 --> 01:54:35.080
I really liked this guy until this interview.

01:54:35.080 --> 01:54:46.080
I really liked a lot of things he has to say about motivation and effort and stoicism and exercise and nutrition and sleep.

01:54:46.080 --> 01:55:00.080
It was all of that stuff that really fundamentally good biology on you to be just selling it all down the river because of these two rich head celebrities.

01:55:00.080 --> 01:55:05.080
It is absolutely pathetic I'm ashamed of him.

01:55:06.080 --> 01:55:13.080
It is a very interesting decision to do this in the context of an existing framework of graduate students that need to do a thesis and get a first author paper.

01:55:13.080 --> 01:55:28.080
There is a whole set of structures within academia that I think both facilitate but also limit the progression of science that independent investigator model that we talked about a little bit earlier.

01:55:28.080 --> 01:55:42.080
It is so core to the way science has been done this is very different and frankly sounds far more efficient if I'm to be completely honest and you know we'll see if I renew my NIH funding after saying that.

01:55:42.080 --> 01:55:46.080
But I think we all want the same thing we all want to as scientists and most ways science.

01:55:46.080 --> 01:55:57.080
That independent investigator model that we talked about a little bit earlier it is so core to the way science has been done this is very different and frankly sounds far more efficient if I'm to be completely honest and you know we'll see if I renew my NIH funding after saying that.

01:55:58.080 --> 01:56:11.080
How is it far more efficient to have to not have individuals thinking what does he think of himself if he says that far more efficient to do what to.

01:56:11.080 --> 01:56:17.080
To organize people around around money how is this any more efficient than what the NIH does.

01:56:18.080 --> 01:56:22.080
Just individuals versus groups all behaving the same way then.

01:56:22.080 --> 01:56:29.080
Let's get even more people together thinking the same with the same biases as they investigate a scientific problem.

01:56:29.080 --> 01:56:34.080
You see how stupid that sounds.

01:56:34.080 --> 01:56:41.080
Rather than having independent investigators working tirelessly against and competing with one another.

01:56:41.080 --> 01:56:49.080
Essentially checking each other's work to try and make that experiment happen first or that discovery first.

01:56:49.080 --> 01:56:58.080
Trying to undermine each other to make sure that that person is lying and not lying or not falsely misrepresenting his results.

01:56:58.080 --> 01:57:06.080
There's a lot to be gained by independent science and independent labs and independent principal investigators.

01:57:06.080 --> 01:57:18.080
That gets lost when you say let's all write a grant together under the pretense that we're all interested in the same goal and answering the same questions and getting the same answer.

01:57:18.080 --> 01:57:23.080
That's the kind of thing they're laughing about right now it's not a more efficient system.

01:57:23.080 --> 01:57:33.080
It's a more efficient system to get people to think the same way but it's a far less efficient system if you want to get people to explore novel hypotheses

01:57:33.080 --> 01:57:38.080
and steer away from a consensus that's created by rich people who don't know enough.

01:57:38.080 --> 01:57:40.080
Let's discuss.

01:57:40.080 --> 01:57:50.080
We all want the same thing we all want as scientists and as humans we want to understand the way we work and we want healthy people to persist to be healthy and we want sick people to get healthy.

01:57:50.080 --> 01:57:53.080
That's really ultimately the goal it's not super complicated it's just hard to do.

01:57:53.080 --> 01:58:03.080
So the teams at the Biohub are actually independent of the universities so each Biohub will probably have in total maybe 50 people working on sort of deep efforts.

01:58:03.080 --> 01:58:14.080
However it's an acknowledgement that not all the best scientists who can contribute to this area are actually going to one want to leave a university or want to take on the full-time scope of this project.

01:58:15.080 --> 01:58:26.080
So it's the ability to partner with universities and to have the faculty at all the universities be able to contribute to the overall project is how the Biohub is structured.

01:58:26.080 --> 01:58:27.080
Got it.

01:58:27.080 --> 01:58:39.080
But a lot of the way that we're approaching CZI is this long-term iterative project to try a bunch of different things, figure out which things produce the most interesting results and then double down on those in the next five-year push.

01:58:39.080 --> 01:58:44.080
So we just went through this period where we kind of wrapped up the first five years of the science program.

01:58:44.080 --> 01:58:48.080
We tried a lot of different models right all kinds of different things and it's not that the Biohub model.

01:58:48.080 --> 01:58:59.080
We don't think it's like the best or only model but we found that it was sort of a really interesting way to unlock a bunch of collaboration and bring some technical resources that allow for this longer-term development.

01:58:59.080 --> 01:59:04.080
And it's not something that is widely being pursued across the rest of the field.

01:59:04.080 --> 01:59:07.080
We figured, okay, this is like an interesting thing that we can help push on.

01:59:07.080 --> 01:59:10.080
But I mean, yeah, we do believe in the collaboration.

01:59:10.080 --> 01:59:17.080
But I also think that we come at this with, you know, we don't think that the way that we're pursuing this is like the only way to do this or the way that everyone should do it.

01:59:17.080 --> 01:59:23.080
We're pretty aware of, you know, that, you know, what is the rest of the ecosystem and how we can play a unique role in it.

01:59:23.080 --> 01:59:25.080
It feels very synergistic with the wayside.

01:59:25.080 --> 01:59:30.080
It's noticed that he has carnival hands, he has little tiny hands, little carny hands.

01:59:30.080 --> 01:59:34.080
That's why he keeps them on his lap because they're so tiny.

01:59:34.080 --> 01:59:43.080
Little tiny hands with no calluses and no fingernails, like one thin layer of skin, right?

01:59:43.080 --> 01:59:48.080
Because he's never actually had any friction, except for with keyboard keys.

01:59:48.080 --> 01:59:53.080
It's already done and also fills in incredibly important niche that frankly wasn't filled before.

01:59:53.080 --> 01:59:55.080
Along the lines of implementation.

01:59:55.080 --> 02:00:06.080
So let's say your large language models combined with imaging tools reveal that a particular set of genes acting in a cluster, I don't know, set up an organ crash.

02:00:06.080 --> 02:00:10.080
Let's say the pancreas crashes at a particular stage of pancreatic cancer.

02:00:10.080 --> 02:00:15.080
I mean, still one of the most deadlies of the cancers and there are others that you certainly wouldn't want to get.

02:00:15.080 --> 02:00:18.080
But that's among the ones you wouldn't want to get the most.

02:00:18.080 --> 02:00:26.080
And then the idea is that, okay, then AI reveals some potential drug targets, then bare out in vitro, in a dish and in a mouse model.

02:00:26.080 --> 02:00:29.080
How is the actual implementation to drug discovery?

02:00:29.080 --> 02:00:33.080
Or maybe this target is druggable, maybe it's not, maybe it requires some other approach.

02:00:33.080 --> 02:00:44.080
So here is Huberman again, playing into the product discovery idea of this model instead of the, how do we figure out how to make people optimally healthy?

02:00:44.080 --> 02:00:51.080
Not one single discussion about nutrition at baseline health.

02:00:51.080 --> 02:00:57.080
We are talking about curing diseases, not about making people healthy.

02:00:57.080 --> 02:01:00.080
We like people just the way they are.

02:01:00.080 --> 02:01:05.080
Because they've got plenty of problems and plenty of potential for profit.

02:01:05.080 --> 02:01:07.080
So we're not interested in improving people.

02:01:07.080 --> 02:01:14.080
We're interested in improving the way that we cure people, the way that we treat people, the way that we extract wealth from people.

02:01:14.080 --> 02:01:25.080
We are interested in increasing the amount of time that we need to interact with them medically so that we can extract as much data from their children as possible in the coming generations.

02:01:25.080 --> 02:01:31.080
There aren't going to be 10 billion on people on the planet forever, ladies and gentlemen, and these people know it.

02:01:31.080 --> 02:01:41.080
Because these people are part of a group of people that has a plan to make sure that in the coming generations there aren't 10 billion people anymore.

02:01:41.080 --> 02:01:48.080
That's why these same people aren't telling you that our population in pyramid is almost inverted now.

02:01:48.080 --> 02:01:59.080
That's why these same people aren't talking about Japan's population pyramid or China's population pyramid and what that means in 50 years for the population of the planet.

02:01:59.080 --> 02:02:05.080
None of them actually want to tell you that they know already the demographics of the planet are going to crash.

02:02:05.080 --> 02:02:09.080
And they know the consequences of that are going to be severe.

02:02:09.080 --> 02:02:18.080
And that they're going to be able to use all kinds of the problems that are caused by this demographic crash, this change in resources.

02:02:19.080 --> 02:02:29.080
They're going to be able to use that to coerce our children into accepting digital ID because they will already by then have accepted digital currency.

02:02:29.080 --> 02:02:35.080
And so by that time it will be a salesman pitch of convenience.

02:02:36.080 --> 02:02:49.080
I mean, if you've got to carry your digital money around in a little wallet like this, wouldn't it be better to have it on a ring?

02:02:49.080 --> 02:02:52.080
And then people can steal your ring when you're in the shower.

02:02:52.080 --> 02:02:57.080
So maybe you should just, you know, put it around your neck and it'll be waterproof.

02:02:57.080 --> 02:03:02.080
Or maybe we'll just put it under your skin right here by your watch.

02:03:02.080 --> 02:03:05.080
And then no one can ever steal it. No one can ever fake it. No one can ever.

02:03:05.080 --> 02:03:08.080
And we will let you use it on the Internet too.

02:03:08.080 --> 02:03:14.080
So you'll have a sovereign identity on the Internet that will interface with your bank account.

02:03:14.080 --> 02:03:17.080
And we'll have all your medical data on it. It'll be so convenient.

02:03:17.080 --> 02:03:21.080
And that way you won't have to have paperwork and passports and all this other stuff.

02:03:21.080 --> 02:03:27.080
And you can just travel wherever you want if you have the approved numbers.

02:03:27.080 --> 02:03:30.080
It's all convenient and safe.

02:03:30.080 --> 02:03:36.080
Because your children are going to be brainwashed into being afraid of anonymous people on the Internet.

02:03:36.080 --> 02:03:45.080
Your kids are going to be tortured and harassed by anonymous people on the Internet until you beg for ID on the Internet.

02:03:45.080 --> 02:03:52.080
People's money and banks are going to be contorted and destroyed until people beg for digital currency.

02:03:52.080 --> 02:03:58.080
They're already talking about a universal basic income in Canada.

02:03:58.080 --> 02:04:02.080
And they're already talking about a universal basic income in the Netherlands.

02:04:05.080 --> 02:04:11.080
The craziest part about that is that I have this cousin over in the Netherlands who has been totally on board

02:04:11.080 --> 02:04:15.080
with everything that I've been saying since the beginning of the pandemic.

02:04:15.080 --> 02:04:25.080
And suddenly, suddenly, that person is now actively campaigning for a universal basic income in the Netherlands.

02:04:25.080 --> 02:04:28.080
Like, I couldn't even believe it.

02:04:28.080 --> 02:04:44.080
After all this time, thinking that he understood exactly what we were up against, he is now on television as part of a political party advocating for universal basic income in the Netherlands.

02:04:45.080 --> 02:04:56.080
Where that entire society is being torn apart by immigrants and the concessions being made to immigrants and not being made to Dutch citizens, it's extraordinary.

02:04:56.080 --> 02:05:08.080
A country, a quarter of the size of Pennsylvania was 17 million people in it, accepting thousands of refugees a year.

02:05:09.080 --> 02:05:17.080
Is looking to start universal basic income. If one EU country starts universal basic income, where does that go?

02:05:17.080 --> 02:05:21.080
How does that work in a union like that?

02:05:21.080 --> 02:05:25.080
They are all lost.

02:05:25.080 --> 02:05:28.080
And we are almost lost.

02:05:28.080 --> 02:05:32.080
Because we are being governed by people who are not elected.

02:05:33.080 --> 02:05:39.080
We are being governed by weaponized piles of money like Black Rock State Street, etc.

02:05:39.080 --> 02:05:53.080
And the people who sit atop those weaponized piles of money like Elon Musk and Peter Teal, Mark Zuckerberg and Priscilla Chan, Bill and Melinda Gates.

02:05:53.080 --> 02:05:58.080
These weaponized piles of money also include weaponized piles of money in government.

02:05:58.080 --> 02:06:01.080
That's for sure.

02:06:01.080 --> 02:06:08.080
But we are pivoting, ladies and gentlemen, we are pivoting to the private weaponized piles of money.

02:06:08.080 --> 02:06:14.080
We are cau-towing to them. The US government cau-tows to those weaponized piles of money.

02:06:14.080 --> 02:06:26.080
The Saudis, the Israelis, the private banks, the central banks, the world bank, the international bank of settlements.

02:06:26.080 --> 02:06:35.080
These are all groups of people with more power than our government, because they control our government.

02:06:35.080 --> 02:06:43.080
And so we all need to wake up and apologize for having been asleep at the wheel and for losing control of our nation.

02:06:43.080 --> 02:06:49.080
And now we need to put our big boy pants on and take our nation back.

02:06:49.080 --> 02:06:55.080
From people that are liars, it should be very easy because they are just liars.

02:06:55.080 --> 02:07:00.080
That's all they are liars.

02:07:00.080 --> 02:07:05.080
Liars liars liars. And the liars have worked on one thing in particular.

02:07:05.080 --> 02:07:12.080
They've worked on an illusion of consensus about pandemic potential.

02:07:12.080 --> 02:07:16.080
And Zuckerberg and Chan, they agree fully with this pandemic potential.

02:07:16.080 --> 02:07:19.080
They agree that the millions were killed by the virus.

02:07:19.080 --> 02:07:25.080
They agree that the mRNA saved people. They won't question any of that, of course.

02:07:25.080 --> 02:07:28.080
And so they are a part of this illusion of consensus.

02:07:28.080 --> 02:07:32.080
Just the fact that they had that podcast about biology and public health

02:07:32.080 --> 02:07:38.080
and had no mention of a pandemic or the vaccines, except to say that as Mark said,

02:07:38.080 --> 02:07:44.080
we had to have vaccines before we could cure things.

02:07:44.080 --> 02:07:48.080
This illusion of consensus that this immunomythology makes any sense.

02:07:48.080 --> 02:07:52.080
This is all part of that.

02:07:52.080 --> 02:07:54.080
And so I've explained this many times.

02:07:54.080 --> 02:08:00.080
They've declared a dangerous novel virus detectable by a PCR test.

02:08:00.080 --> 02:08:04.080
But I think it's important that we think about it this way.

02:08:04.080 --> 02:08:10.080
If there was a mystery virus that is encoded by that red curve down here,

02:08:10.080 --> 02:08:13.080
we need to do some math on that.

02:08:13.080 --> 02:08:18.080
And I think that all of the liars in this theater do not do this math.

02:08:18.080 --> 02:08:26.080
So if you want to know what the real total excess deaths that the mystery virus caused,

02:08:26.080 --> 02:08:32.080
you need to take and figure out what the excess deaths are here as a total.

02:08:32.080 --> 02:08:35.080
And then from that total subtract the following.

02:08:35.080 --> 02:08:40.080
You need to subtract all the people that were killed in New York City, not by a virus,

02:08:40.080 --> 02:08:43.080
including do not resuscitate orders.

02:08:43.080 --> 02:08:50.080
And everywhere around the United States in the world where that was done because of a deadly virus that they didn't want to spread.

02:08:50.080 --> 02:08:55.080
You need to subtract from that all the people that were ventilated that shouldn't have been ventilated.

02:08:55.080 --> 02:09:01.080
You need to subtract to them that all of the people that didn't get antibiotics that should have gotten antibiotics.

02:09:01.080 --> 02:09:10.080
You need to subtract from that all of the people who were either given steroids inappropriately or not given steroids when they were appropriate.

02:09:10.080 --> 02:09:19.080
You need to subtract all of the extra opioid deaths from this excess because everybody that dies of an opioid death is not an expected death.

02:09:19.080 --> 02:09:21.080
Those are excess deaths.

02:09:21.080 --> 02:09:30.080
We don't expect 150,000 people to die of opioid deaths and that's not figured into this blue line over here.

02:09:31.080 --> 02:09:47.080
The change from this pattern that you see year on year on year, this change right here cannot be understood as a pandemic until you remove all of these things and everything else included there.

02:09:49.080 --> 02:09:53.080
In order to understand where this excess deaths came from.

02:09:54.080 --> 02:10:06.080
And the curious thing about Denny Rancor's data which is most powerful with regard to this list is that the deaths from COVID in the United States correlate with poverty.

02:10:06.080 --> 02:10:09.080
You know what else correlates with poverty?

02:10:09.080 --> 02:10:11.080
Bad treatment in the hospitals.

02:10:11.080 --> 02:10:15.080
You know what else correlates with poverty? Opioid deaths.

02:10:15.080 --> 02:10:26.080
And yet they want you to believe that a million people were killed by viruses. Everything else is just normal.

02:10:26.080 --> 02:10:29.080
And that's the lie.

02:10:29.080 --> 02:10:32.080
That's the lie that everybody's telling.

02:10:32.080 --> 02:10:41.080
And it's quite frankly the lie that everybody tells when they say that there's DNA in the shots and so the adulteration is there and therefore we can sue them.

02:10:41.080 --> 02:10:44.080
Why is that the case?

02:10:44.080 --> 02:10:51.080
It's a lie because we should never have had the shots in the first place because of this list.

02:10:51.080 --> 02:10:55.080
This list is the reason why we have the shot.

02:10:55.080 --> 02:10:58.080
So the shots contaminated big deal.

02:10:58.080 --> 02:11:05.080
We should have never had the shot if any of you idiots would have spoke up when you should have instead of now about the DNA.

02:11:05.080 --> 02:11:08.080
But you had spoke up in the beginning.

02:11:08.080 --> 02:11:11.080
When we knew they were lying we just didn't know how badly.

02:11:11.080 --> 02:11:13.080
None of this would have happened.

02:11:13.080 --> 02:11:18.080
If Robert Malone would have pulled his head out in 2020 none of this would have happened.

02:11:18.080 --> 02:11:26.080
If Steve Kirsch would have pulled his head out in 2020 none of this would have happened.

02:11:26.080 --> 02:11:37.080
If Brett Weinstein would have pulled his head out maybe none of this would have happened but none of those people that are now leaders of this movement will speak about this list and do this math correctly.

02:11:37.080 --> 02:11:45.080
Every one of those people says that a million people were killed by a virus.

02:11:45.080 --> 02:11:50.080
None of those people will do this math and that's why they are liars.

02:11:50.080 --> 02:12:01.080
And if you're going to focus on the DNA and the shot at least admit that the shot should have never come out in the first place and wouldn't have come out in the first place had they not done these things.

02:12:01.080 --> 02:12:09.080
Had they not allowed the opioid deaths to go skyrocketing in the United States there wouldn't have been enough excess deaths to make a stink over.

02:12:09.080 --> 02:12:24.080
If they wouldn't have put enough people on ventilators or stop the use of antibiotics or stop the use of steroids correctly or implemented the use of remdesivir everywhere there wouldn't have been enough excess deaths to blame on anything.

02:12:25.080 --> 02:12:32.080
There wouldn't be any of that there wouldn't be this part of the graph here.

02:12:32.080 --> 02:12:43.080
And the reason why is because if you subtract all of these things it goes right back to this seasonal pattern of between 50 and 60,000 deaths per week.

02:12:43.080 --> 02:12:49.080
The only reason why it's shot up for four weeks in America is because of New York City and a few other places.

02:12:49.080 --> 02:12:56.080
And the only reason why it's shot up in the next winter is because of these things.

02:12:56.080 --> 02:13:12.080
Because of the use of a PCR test to convert what would have been just pneumonia and a respiratory disease to something that they knew how to treat incorrectly and had a million different ways of doing it.

02:13:12.080 --> 02:13:18.080
And the fact of the matter is is that most people can't tell the whole story.

02:13:18.080 --> 02:13:21.080
They never bothered to.

02:13:21.080 --> 02:13:26.080
And now that's what you see in spades with this DNA adulteration.

02:13:26.080 --> 02:13:35.080
They're going to try and pretend the transfection wasn't bad it was the DNA and we know this story already because we did it with spike.

02:13:35.080 --> 02:13:49.080
We did it with RNA impurities and then we did it with the LNP.

02:13:49.080 --> 02:13:52.080
This is the way it should have been.

02:13:52.080 --> 02:13:58.080
And nobody's doing this math because they don't want to admit that there is no excuse.

02:13:58.080 --> 02:14:01.080
They only excuses were manufactured.

02:14:01.080 --> 02:14:09.080
The only excuses were fear uncertainty and doubt that drove people to do really stupid things for three years.

02:14:09.080 --> 02:14:19.080
And I went to a doctor's office today and half the people were wearing masks so this has driven people to do stupid things for three and a half years.

02:14:19.080 --> 02:14:29.080
People are still confused, still in fear, still frustrated, still having doubts because of the effectiveness of these lies.

02:14:29.080 --> 02:14:37.080
Because of the effectiveness of this illusion of consensus because of the effectiveness of these liars.

02:14:37.080 --> 02:14:46.080
Because of the lack of integrity of these liars.

02:14:46.080 --> 02:14:56.080
They created an illusion of consensus about a mystery that didn't need to be solved and that illusion of consensus created confusion that led us to believe we didn't understand what was going on.

02:14:56.080 --> 02:15:09.080
And because we didn't understand what was going on doctors followed all kinds of orders they never would have and behaved in ways they never would have had they not been thoroughly lied to.

02:15:09.080 --> 02:15:19.080
And now I'm not giving doctors on out I'm telling you right now I think every doctor that's going along with this is an idiot.

02:15:19.080 --> 02:15:28.080
But I also have faith that those people when they figure it out they'll actually realize well I wasn't an idiot just like I was.

02:15:28.080 --> 02:15:35.080
Just like you got to tell somebody when they're overweight you got to tell people when they're idiots.

02:15:35.080 --> 02:15:42.080
And some of these these people need to be told that your assumption is that the TV doesn't lie.

02:15:42.080 --> 02:15:46.080
Your assumption is that trillions of dollars won't make people behave badly.

02:15:46.080 --> 02:15:56.080
Your assumption is is that power doesn't corrupt.

02:15:56.080 --> 02:16:02.080
That's the assumption that every skilled TV watcher has now been bamboozled into making.

02:16:02.080 --> 02:16:05.080
Money doesn't corrupt power doesn't corrupt.

02:16:05.080 --> 02:16:13.080
There's no incentive to lie and that people would not use lies to govern a world.

02:16:13.080 --> 02:16:17.080
A planet.

02:16:17.080 --> 02:16:22.080
But these same people sit on their couch and they know damn well they're being governed.

02:16:22.080 --> 02:16:26.080
They know damn well they're being governed.

02:16:26.080 --> 02:16:35.080
They think that Trump lies they think the Democrats lie they think the Republicans lie.

02:16:35.080 --> 02:16:43.080
It's all a great big show it's an illusion of consensus that makes you believe there's nothing to solve.

02:16:43.080 --> 02:16:45.080
The way it worked was really simple.

02:16:45.080 --> 02:16:52.080
They told you there was a lab leak and then they told you it spread around and at the same time they argued about it.

02:16:52.080 --> 02:16:53.080
They lied about it.

02:16:53.080 --> 02:16:56.080
They sent emails about it and then redacted the emails.

02:16:56.080 --> 02:17:01.080
They kept telling you that new variants were circulating as almicron now.

02:17:01.080 --> 02:17:06.080
Maybe it was released by a good guy to vaccinate the world.

02:17:06.080 --> 02:17:10.080
And now we hear our whatever variant we are now.

02:17:10.080 --> 02:17:19.080
In this scenario of course we color the yellow red because the lockdowns hurt some people but it's a gain of function virus.

02:17:19.080 --> 02:17:25.080
In this scenario we've defeated epidemics in the past using vaccination and there's an illusion of consensus about that.

02:17:25.080 --> 02:17:33.080
There's an illusion of consensus about pandemic potential and an illusion of consensus about rare false positives with PCR.

02:17:33.080 --> 02:17:43.080
An illusion of consensus about variants and the fact that the virus might be mutating in response to the RNA because it works so well.

02:17:43.080 --> 02:17:48.080
And gain of function research is real.

02:17:49.080 --> 02:17:52.080
The faith is a lie.

02:17:52.080 --> 02:18:01.080
They've used a conflated background signal to trick people into killing each other and ignoring it.

02:18:01.080 --> 02:18:07.080
It's a mythology that we are being governed by and the protocols were murder and transfection is not medicine.

02:18:07.080 --> 02:18:11.080
I believe the best description is probably an infectious clone release.

02:18:11.080 --> 02:18:14.080
It's the simplest one in my mind.

02:18:15.080 --> 02:18:19.080
But it could have been a transfection with a different agent in different places.

02:18:19.080 --> 02:18:23.080
I don't know with that background.

02:18:23.080 --> 02:18:30.080
I do know for sure that this is not the right answer.

02:18:30.080 --> 02:18:36.080
And I do know for sure that the players are not addressing these things.

02:18:36.080 --> 02:18:43.080
I don't know I feel like I'm kind of ending on a weak note but I'm tired and I'm annoyed.

02:18:43.080 --> 02:18:46.080
And I didn't think I would get so annoyed by Zuckerberg and Chan.

02:18:46.080 --> 02:18:49.080
I thought it was going to be funnier.

02:18:49.080 --> 02:18:56.080
But I guess I'm getting more and more insulted by the lack of reverence, the lack of humbleness.

02:18:56.080 --> 02:19:10.080
And it's a bit like, I don't know, it's hard for me to explain really how sad that kind of thought process makes me.

02:19:10.080 --> 02:19:14.080
It's a little bit of arrogance and a little bit of naivety and it's a lot of lying.

02:19:14.080 --> 02:19:17.080
Because I know that they know that they're also lying.

02:19:17.080 --> 02:19:19.080
They're deceiving children.

02:19:19.080 --> 02:19:23.080
They're deceiving people who aren't sophisticated enough to know they're being deceived.

02:19:23.080 --> 02:19:30.080
And they're doing it happily as part of the group of people that governs us.

02:19:30.080 --> 02:19:39.080
That's why there's a picture of Zuckerberg and all these other people having a dinner right before Obama is elected.

02:19:39.080 --> 02:19:45.080
Toasting glasses.

02:19:45.080 --> 02:19:47.080
We are being governed by these people.

02:19:47.080 --> 02:19:51.080
They are working together behind the scenes to govern us with lies.

02:19:51.080 --> 02:19:55.080
And they may even be controlling our government with these lies.

02:19:55.080 --> 02:19:59.080
Because the people in our government for the most part aren't that bright.

02:19:59.080 --> 02:20:04.080
I think that was one thing that you can take to the bank now.

02:20:04.080 --> 02:20:08.080
For every Thomas Massey there's about 500 people that are idiots.

02:20:08.080 --> 02:20:15.080
So these are the easiest kinds of people to control.

02:20:15.080 --> 02:20:20.080
The people in our government now are the most ridiculous jokes.

02:20:20.080 --> 02:20:23.080
It's a big clown show and that's what they wanted.

02:20:23.080 --> 02:20:28.080
Because this is a controlled demolition of America.

02:20:28.080 --> 02:20:32.080
It's going to get worse.

02:20:32.080 --> 02:20:34.080
There are going to be more clowns in government.

02:20:34.080 --> 02:20:36.080
There's going to be more clowning in government.

02:20:36.080 --> 02:20:40.080
No matter what happens in 2024 it's going to be clowning.

02:20:40.080 --> 02:20:48.080
There's a few different scenarios but they're all clowning.

02:20:48.080 --> 02:20:50.080
This is where we're at.

02:20:50.080 --> 02:20:53.080
This is where they're going.

02:20:53.080 --> 02:20:57.080
And if you want to escape you just got to learn the biology that underpins this.

02:20:57.080 --> 02:21:03.080
Understand what transfection is and why it's not immunization and understand why actually

02:21:03.080 --> 02:21:06.080
the intramuscular injection of pretty much anything is dumb.

02:21:06.080 --> 02:21:11.080
If you're trying to augment the immune system of a healthy human.

02:21:11.080 --> 02:21:17.080
Please stop all transfections in humans and they are trying to eliminate the control group by any means necessary.

02:21:17.080 --> 02:21:22.080
So Mike Vandenberg mixed date.

02:21:22.080 --> 02:21:28.080
Didn't I put that one up there? I thought I put this one up there.

02:21:28.080 --> 02:21:30.080
That's what I thought I put up there. Sorry about that.

02:21:30.080 --> 02:21:33.080
I did that last night too.

02:21:33.080 --> 02:21:37.080
I don't know. I feel like I kind of blew it on this one because I got so negative.

02:21:37.080 --> 02:21:40.080
So I apologize for that.

02:21:40.080 --> 02:21:44.080
It's been a long day.

02:21:44.080 --> 02:21:49.080
And I'm very happy to say that I've had more energy today than I've had in a long, long time.

02:21:49.080 --> 02:21:54.080
Two nights in a row of sleeping has been miracle.

02:21:54.080 --> 02:21:59.080
And so I have pretty high hopes that this energy is going to keep building.

02:21:59.080 --> 02:22:05.080
So let me give the cords a rest and I'll see you guys again tomorrow.

02:22:05.080 --> 02:22:34.080
And there it is, right? That's the alpha fold nonsense that they made and told everybody that now we know how old proteins

02:22:34.080 --> 02:22:41.080
fold.

02:22:41.080 --> 02:22:46.080
Don't forget to follow that Jeff from Earth link that he puts in there every night.

02:22:46.080 --> 02:22:52.080
Jeff from Earth is one of my best supporters, my longest supporters.

02:22:52.080 --> 02:22:58.080
And he makes Instagram cuts of my streams and they're always really good.

02:22:58.080 --> 02:23:06.080
And he's been doing it for a long time. For goodness sakes, please share that dude's work and spread it around.

02:23:06.080 --> 02:23:11.080
Because those shorts are probably a lot of times better than three hour shares.

02:23:11.080 --> 02:23:17.080
So anyway, thanks guys. I'll see you guys again tomorrow.

