WEBVTT

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That brings in a house!

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I'm so excited I can freak.

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Not just this muscle car that gets me excited, it's my guest today.

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Sooth spider is in the in the chat already.

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Look at that, garden variety human.

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That should be good.

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Just give me a sound check if you're in the chat already, please, for this one.

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Just to make sure everybody's lined up, it should be okay, I just started it.

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Thank you there, sir.

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And yes, we are speaking to William M. Briggs today.

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He is a member of this broken science initiative, it's my first BSI guest.

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If you didn't follow my directive yesterday and go and overload the website of brokenscience.org

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with your signups, please do it today so that Emily and Greg get a little blip in their

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website traffic and they see when we mention us, it would be really great to show them

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the power of gigo-on-biological.

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Matt, you may recognize Rodney Mullen here in this little spin.

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Oh, yeah.

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Guys, it's sweetheart.

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Oh, my goodness.

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Did you know that he got surgery on his spine and now he can stand up straight like skating

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again like differently and he's got to relearn everything now because his body moves again.

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It's crazy.

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I'll be thought, no, I have no idea.

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Apparently he's got like three more inches on him though.

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I'm afraid that the latest data tells us that we're dealing with essentially...

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I want you to go meet my friend Mark on this show that's now on the screen after this.

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I think he's a really good guy and you guys would be good acquaintances, I think.

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Sure.

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And he's got readers that watch him, you know, so you're a book guy.

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I mean, he's got readers.

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Not that my viewers don't read, I'm not saying that, but Mark has got some really studious

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followers.

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I really like Mark.

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I think truth is good for kids.

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We're so busy lying, we don't even recognize the truth no more in society.

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We want everybody to feel good.

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That's not the way life is.

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But you can tell if someone's lying, you know, you can sort of feel it in people.

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And I have lied.

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I'm sure I'll lie again.

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I don't want to lie, you know, and I don't think I'm a liar.

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I try not to be a liar.

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I don't want to be a liar.

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I think it's like really important not to be a liar.

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Ah, ladies and gentlemen, I'm so excited to be here today and have this show running

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in the afternoon working in sync with my family and I just feels really good to start

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the stream in the afternoon and so I'm really happy that we've got Matt on the show.

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Just a few more seconds.

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I got to get this, the recording in the background running, so I'm going to keep this intro going

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as usual.

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It's like one more minute or so, I think I'm in it 20 seconds.

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If you are joining us for the first time, this is Giga Ohm Biological.

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It's a high-resistance low-noise information brief brought to you by a biologist.

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We are based in Pittsburgh, Pennsylvania in the back of a rented garage and yeah, that's

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the state of knowledge and information in our world.

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We're basically working to dispel an enchantment.

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It is an enchantment that has been laid down a long time ago and laid down deep and thick.

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If you have been following me for a while, you're here at the top of the wave where we

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are staying focused on the biology.

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We don't take the bait on TV and social media and we love our neighbors.

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The way it works is that people share this work and some people even financially contribute

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and slowly biology finds new people and more people wake up.

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That's my plan anyway.

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You can find me at GigaOhm Biological.com and you should be watching at stream.gigohm.vial

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if you're not already, which is the new place that I'm trying to store all these videos.

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I think it's working out really well thanks to our supporter and follower Ted on the other

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side of the planet.

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Mike Vandenberg, mixed date.

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These are the people that make Giga Ohm Biological possible including all of the subscribers

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that are giving some small or large amount monthly or even per year.

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There are also sub-stack subscribers that have recently joined the fight and it's been

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really a privilege to see how far we can get with this.

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I hope that we can continue to bring this biology to the masses.

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This is the independent bright web.

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It's the opposite of the intellectual dark web which is this loosely organized group

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of influencers that was put in place before the pandemic to control the narrative, if

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you will, to set this limited spectrum of debate that we are all trapped within.

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But it's actually an illusion that is sustained only through your active participation.

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You can drop your own hand from your own eyes and escape.

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It is just a question of non-compliance.

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You've got to actually accept that you've been lied to.

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And I think one of the most exciting things about broken science is that it really, although

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to steal a phrase from Greg, it may not be for everybody, but it is for anybody to understand

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how science has been distorted, has been essentially co-opted into something that no longer is

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a real pursuit of knowledge, but it's instead a pursuit of noise or the creation of noise

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and I hope that somebody has sophisticated, unlike me, William M. Briggs is going to

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be able to help us get through this because the distortion of science and the pursuit

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of knowledge has been an integral part of the conscious and intelligent manipulation

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of the organized habits and opinions of the masses, and it is an important element of

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how we are governed.

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In fact, I would argue it is a primary way by which we are governed, and as a recovering

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academic biologist, I really feel as though I have a unique, sad insight into how distorted

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one's mind can become if you pursue questions based on this reiterative, preparean, questioning

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mechanism, and don't realize how easily that can spin out of control and stop producing

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knowledge and instead produce noise.

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I believe that's how the intellectual dark web works, and I believe that's how they've

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essentially removed our ability to exercise informed consent with almost anything with

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regard to socioeconomic status and also with regard to public health, with regard to the

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education of our children.

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We're just so clueless that we just don't have the ability to exercise informed consent

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anymore.

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And one of the primary bats with with barbed wire wrapped around it is this use of academic

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science to create the illusion of knowledge that can then be used against us in a technocratic

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sort of way.

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Oh my gosh, I'm still talking.

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Let me see.

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I got him over here.

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Yes.

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Good.

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I'm going to put this down and I'm going to shut this music off and I'm going to say hello

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to my good friend, William M. Briggs, actually Matt and I met on a beach and it was very

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romantic.

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Matt was overdressed for the beach and I was flying my kite and I don't know.

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We later met at another meeting and so it was wonderful.

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We've been friends since.

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I don't know.

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Go ahead and introduce yourself.

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Tell us what you're up to and then we'll just, I don't know, randomly discuss our clean

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eyes.

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You know, Greg Glassman and Emily Capin, like you said, the broken science thing.

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They found me and I've been delighted to be able to have found them in return and people

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like yourself.

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There's a core group of people who understand what's going on and what's going wrong with

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science, I should say.

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It's not everybody like you say, like Greg says, but it is, it is some people anyway

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and we, we are trying to gather together and at least speak to those willing to listen

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about what we think is wrong with science and how we could go about fixing it.

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And my background is like yourself, recovering academic, you know, I started off in academia.

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I was in the, I was in the Department of Medicine at Cornell University, you know, the med school.

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I was a statistician there, bio statistician there.

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I'm sorry, but my favorite part is the fact that you were a meteorologist because when

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I was a child, when I was a child, I thought that the weather was also very fascinating.

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And I sat outside in, in thunderstorms and stuff.

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I thought that tornado warnings were kind of cool and my parents never understood it.

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But I just, I was never afraid of these things.

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I just thought they were interesting.

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The reason I got involved, I was in the Air Force a long time ago doing cryptography.

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And I was wondering, should I go get out of the Air Force and do something more useful

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with my life?

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And at that time, this was in the late 80s, early 90s, global warming as it was called

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then was the thing.

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And I thought, hey, this is, this is interesting.

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And I sort of, and I wrote some of the people and the original IPCC, the Intergovernmental

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Panel on Climate Change, some of the scientists involved in that.

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And I was just this, nobody sergeant at a cadina Air Force base in Okinawa.

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I said, hey, can I read this, this in that paper?

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And this one gentleman sent it to me and to my surprise, I could read it.

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So I thought, you know what, maybe I better get out and do something about this.

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It seems so important.

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And I, I bought all this stuff.

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I mean, I thought it was just as dire and, and calamitous as was being predicted and

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so forth because I was young and naive.

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But I, I progressed through my studies and my specialty actually, I got my Ph.D.

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and not, not in atmospheric physics.

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I got, that was my masters, but I got, I did mathematical statistics.

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And the reason is, I was interested in models.

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What makes a good model?

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What makes a good climate model, medical model, biological model, any of these kind of models?

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And how do you tell if it's good or bad?

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And that involves probability and uncertainty and all this kind of a thing.

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So that's how I slowly became a skeptic, the more I learned about how uncertainty really

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works and how, about how over certain everything that they pushing at us is, and not everything

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I should say, you know, you know, we have a pretty good understanding of the way capacitors

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work, for instance, and so forth.

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But these are not political things.

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It's all the stuff that turned political that the over certainty is vast as we've learned

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to our, you know, sadness this last four years.

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Right, right.

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I think over certain is a, is a wonderful way to really overlaps with how I've been saying

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it is that you can almost, you should immediately be skeptical of anyone who's certain and then

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even more if they're certain about a very simple explanation for a complex thing.

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And oftentimes I've started with that basis of interpreting the pandemic, for example,

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is it just can't be this simple that somebody spilled something in Wuhan and now it's everywhere.

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And so anyway, what do you think from the perspective of the broad picture, how can we,

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in your experience, you know, you're selling, you're selling a couple books, which I just

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put up before it, I don't mean it to say it in a bad way.

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I mean it to say it in a good way that, that he is capable of putting down his thoughts

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in a way that I can't and has done it several times.

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I actually, I don't, I, somewhere I can't find everything you believe is wrong book.

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I know I have it, but for some reason I couldn't find it to put on the screen, but I do have

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the book on certainty.

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And I think because that's the one I have, that's the only one I've read.

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That's this one here.

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How do we get people interested in this philosophical level of science in a way that, you know,

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most people won't be, you know, I mean, they'll be interested in the more practical matters

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of here's what these people are trying to sell you and here's why it's wrong.

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I mean, we could clearly see that they're trying to manipulate you into taking certain

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medications and so forth or they're trying to, they're trying to tell you a certain

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thing.

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Tomorrow I have a thing on gas stoves, now they're going after gas stoves even as they

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tell you they're not going to.

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They want to tell you that they're bad for the, the environment of their bad for your

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health and that kind of stuff.

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So we could show that those kinds of claims are wrong and that's enough for most people

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and that's fine.

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But if you want to know why those things are wrong, how it is that the scientists themselves

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beyond simple matters like fraud and lying and so forth, which we see a lot from top experts

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and rulers and the like, but most scientists are sincere in their beliefs.

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They're just vastly over certain or they're wrong, but they don't know how they're being

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wrong.

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And that's the level at which we need to fix science.

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Now a lot of people can just, you know, follow along, they can gain entry to, you know, podcasts

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like yours and so forth.

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The woman found it.

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Others that show what things are doing wrong.

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That's the other one.

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The woman found it.

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Thank you.

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And then we can get people to sort of grasp these more fundamental things, but they are

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more difficult.

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You know, I mean, it's, it's, you were there, we were all there last week or a week and

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a half ago.

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I guess we were out in back in Arizona for another epistemological boot camp or I guess

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we called it and they were difficult subjects being broached.

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You know, what, what is the nature of probability if we're speaking of uncertainty, we must

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use probability.

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How do we, how do we then understand what probability is and so forth and how does that

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relate across a broad range of sciences, everything from, you know, medicine biology all the way

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to astrophysics, fundamental quantum probability and all those kind of a thing.

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So it's difficult.

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We're not going to get everybody along and that kind of a thing, but we can get people

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to understand that because people are using these procedures that they think is guaranteeing

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them certainty or not, we could just show that they're wrong on that kind of a thing.

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Whether they get delved deeper into the mechanics of why exactly it's wrong, that's, that's,

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that's going to be only for a fraction of people, I think.

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Do you think that, um, one of the things that struck me years ago already was that, um,

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in neuroscience, there was this time, I, I, I've been in neuroscience, I was in neuroscience

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for around 18 years.

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And when I started in neuroscience, I noticed that there was this momentum in the acquisition

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of physicists.

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And the reason why was because physicists could make simple models and models were what

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every, every, uh, every biology's journal wanted you to have a model with a little neuronal

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network that showed how your data was relevant to brain function.

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And because these physicists had this toolbox, you could just bring them in to a, to a department

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and they could be on every paper that needed a model and every paper needs a model in this,

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in this, in this kind of scheme.

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And I was struck by how, what are they doing like transport models and the like that kind

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of thing.

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So just simple network models that show that, you know, something, some connection could

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be relevant.

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Um, it, to me, it's really just a question of again, how are we using them and are we

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using them to advance our understanding or are we using them to, to, I don't know,

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obfuscate obfuscate or, or, or, or to add complexity for the sake of, for the sake of

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complexity, which turns it into science.

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It's funny.

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You mentioned it's just a week ago or two weeks ago that somebody had a paper out that

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looked at literature papers of all things.

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And I guess these are spoof papers.

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They had regular papers and then they had the same papers, but they had equations in them.

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And the ones that had equations in them just for no reason were the ones that were viewed

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as a much more scientific.

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Oh, yeah.

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Wow.

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That, that's the level of stuff.

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I mean, it's easy to create a lot.

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I mean, not, not everybody can do it, but once you, once you learn how it's, it's trivial

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to create models.

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That's one of the problems.

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It's, it's too easy.

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Science is too easy.

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It's, it's difficult to understand that maybe, but because science is so easy to do now in

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a lot.

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Hi, what happened there?

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Is garage is like my garage?

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Come on back now.

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You hear?

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I don't know.

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Maybe you should snap to that might help on he'll call back.

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I want very much to see if there's too many people doing it and numbers of people just

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because there's going to be a lot more error, but it's something, something.

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Here you go.

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There we go.

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Yeah.

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It's probably me, the, the camera, it doesn't, it does a visual acuity check on me and sort

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of bocks.

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Yeah, it's all good.

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So, we were talking about models and it just took for a couple of seconds.

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So it's okay.

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What is different?

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I mean, for the, for example, you were in meteorology, they could only get away with,

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they can't get away with crappy models, right?

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If they use a crappy model, then they're, they're prediction for even tomorrow would

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be off, right?

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But to a certain, this is, this is, this is a, you bring up a good point, an excellent

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point, as a matter of fact, because it's one of the few fields.

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In fact, one of the very, very few fields that model goodness, the idea of what we call

19:56.680 --> 20:00.880
it, the technical term is verification, model verification.

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That's where it was really born.

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That's where all the statistics and the probability and the understanding of the philosophy of

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model goodness arose from meteorology, because it's for obvious reasons.

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They make weather forecasts, they come out at least twice a day, and they want to verify

20:16.220 --> 20:18.000
how good they're doing.

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And because they did, they developed all kinds of ideas, all kinds of measures, like numerical

20:26.280 --> 20:30.840
measures that show you how good the predictions are behaving and the like.

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And they use those to sort of feedback and improve the models.

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And they have meteorological models over the last 50 years or so, they've increased

20:38.800 --> 20:40.040
dramatically.

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They're now very accurate out to a short, short period of time, out to like four or five

20:46.120 --> 20:51.560
days, they're great, out to beyond that, they diminish in quality fairly rapidly.

20:51.560 --> 20:56.360
And you know, they, for technical reasons, you know, the atmosphere is just chaotic and

20:56.360 --> 21:00.280
it's, it's not cooperative at, at times past that.

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And let me ask you this, just in the context, if they had more measuring devices with these

21:05.240 --> 21:09.320
models become better or the models are already at their peak sort of in that sense.

21:09.320 --> 21:10.320
No.

21:10.320 --> 21:11.960
So not really.

21:11.960 --> 21:12.960
Okay.

21:12.960 --> 21:18.520
Part of the problem, they have, we have satellite data and they can sort of make satellite data

21:18.520 --> 21:22.360
for a lot of a lot more points and so forth.

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But their computational power becomes one of the, one of the bottlenecks.

21:27.120 --> 21:29.360
The other is the atmospheric chaotic.

21:29.360 --> 21:30.360
Good Lorenz.

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He was one of the, a meteorologist at the time using early versions of computers.

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He was running a program, very simple dynamical model, very simple dynamical model.

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I think he coded it in Fortran.

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And he was running the thing and then he had to step away or something like this and he

21:46.720 --> 21:51.680
came back and somebody else had used the time on the computer.

21:51.680 --> 21:55.400
Back then you had to get time on a computer, you didn't have personal use of it.

21:55.400 --> 21:58.520
You had to sort of subscribe for time.

21:58.520 --> 22:01.680
And he wanted to restart the computer exactly where it left off.

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So he reprogrammed in the, you know, using punch cards, the constants that the model

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spit out at a certain point, but he didn't get them to the exact decimal level all the

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way out to however many digits the computer did.

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And he found to his surprise that diverged wildly.

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And chaos as a science was born from his paper.

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And it's amazing.

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And it turns out to be sensitivity to initial conditions is sort of defining characteristic

22:33.200 --> 22:34.200
of chaos.

22:34.200 --> 22:41.640
In other words, you could have a perfectly defined rigorously analytic dynamical system

22:41.640 --> 22:46.440
that because it starts off in a slightly, just the tiniest little, you wouldn't make

22:46.440 --> 22:54.320
any difference initial point, but can diverge wildly from a model that starts off slightly

22:54.320 --> 22:55.720
different from it.

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So that's the way the atmosphere sort of behaves, people are discovering that you can

22:59.240 --> 23:03.840
kind of get away with that by making your predictions coarser.

23:03.840 --> 23:10.280
And only speaking of averages and the like and do a little bit better in that respect.

23:10.280 --> 23:14.840
But it's still meteorology that led the way.

23:14.840 --> 23:18.880
And that's how I learned of all this kind of stuff, because that's how I started doing

23:18.880 --> 23:22.600
and doing model goodness, how do you check?

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So you could apply this, these same tools to all kinds of models, like we were being

23:26.520 --> 23:28.520
foisted upon us all the time.

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And it turns out a lot of the models just stink.

23:31.080 --> 23:32.080
They're terrible.

23:32.080 --> 23:33.080
They're never verified.

23:33.080 --> 23:39.000
In fact, the leading practice of most science, the leading practice of most science and most

23:39.000 --> 23:50.160
science papers is to fit a model to data, announce the quality of the fit and stop.

23:50.160 --> 23:51.160
That's it.

23:51.160 --> 23:53.560
And then they act as if they've found something.

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But as we already discussed, anybody could fit a model to data, anybody could fit any

23:59.840 --> 24:01.400
kind of model to any kind of data.

24:01.400 --> 24:04.280
It just doesn't take that much ingenuity anymore.

24:04.280 --> 24:10.920
And therefore, we're flooded with, you know, just acres and acres of this stuff of bad

24:10.920 --> 24:14.040
science of just good model fits.

24:14.040 --> 24:15.120
Well, so what?

24:15.120 --> 24:17.760
These models are never checked against reality after that.

24:17.760 --> 24:20.920
But everybody takes it as if it was.

24:20.920 --> 24:21.920
It's a huge problem.

24:21.920 --> 24:22.920
Right.

24:22.920 --> 24:26.800
And I think you could formulate that in the reverse and say that if your scientific paper

24:26.800 --> 24:29.800
doesn't start out with, my model predicts the following.

24:29.800 --> 24:36.400
So we checked or measured the following, then you're really not, you're really not verifying

24:36.400 --> 24:37.400
a model.

24:37.400 --> 24:42.600
You're actually, again, as you said, making observations and then fitting a model to it,

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which is big deal.

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That's a big difference.

24:46.520 --> 24:49.680
You can almost say anything you want if you use that strategy.

24:49.680 --> 24:56.280
But the vast majority papers, biology and medicine, and all of the soft sciences, almost

24:56.280 --> 24:58.120
all their models are like this.

24:58.120 --> 25:04.720
They just fit a model and they're all ad hoc models, for the most part, in the soft sciences.

25:04.720 --> 25:07.480
They fit the model to the data.

25:07.480 --> 25:13.600
They announced after they manipulate the data and the model that the model fits well.

25:13.600 --> 25:14.600
And that's it.

25:14.600 --> 25:18.520
And now we're supposed to take their word that they have found some causative agent in

25:18.520 --> 25:19.520
nature.

25:19.520 --> 25:21.960
And it's just not the case.

25:21.960 --> 25:24.240
They may have happened upon something.

25:24.240 --> 25:26.320
They may have gotten lucky.

25:26.320 --> 25:30.160
But it's not because of their procedures that they're correct.

25:30.160 --> 25:35.560
It's just by chance, by luck, that they're off and right.

25:35.560 --> 25:41.720
Or by some intuition too, a lot of scientists are smart guys, but it doesn't mean that

25:41.720 --> 25:47.840
the procedures they're using are guaranteeing them the kind of veracity that they tell us

25:47.840 --> 25:49.240
that they have.

25:49.240 --> 25:53.280
That's why when people say, follow the science, no, it isn't like that.

25:53.280 --> 26:00.440
We should not follow the science and tell that science has been independently, by disinterested

26:00.440 --> 26:08.640
parties, checked and its accuracy, its predictive accuracy, otherwise we've never known.

26:08.640 --> 26:13.800
One of the things that I've been thinking about is how can, because broken science

26:13.800 --> 26:19.000
brings together this really weird group of people where there can be lots of owners

26:19.000 --> 26:24.200
of CrossFit gyms or athletes or martial artists or people interested in nutrition

26:24.200 --> 26:31.080
or training, and then also, you know, physicists and people like yourself with a million different

26:31.080 --> 26:33.040
things in their background.

26:33.040 --> 26:39.040
And what I'm wondering is a lot of times we get so deep into that discussion that we can't,

26:39.040 --> 26:45.240
we need, I love the list that Greg has made of all of the pseudo sciences.

26:45.240 --> 26:53.040
But I felt like the last meeting was missing a little session about concrete examples,

26:53.040 --> 26:54.800
which is what you said earlier.

26:54.800 --> 26:59.760
And I almost want to suggest that somebody should build a website of concrete examples

26:59.760 --> 27:03.800
of what the model is and how it's not ever tested.

27:03.800 --> 27:09.000
I mean, the ones that I've been plugging for a long time are the model of antibodies

27:09.000 --> 27:11.880
equals immunity, but they never really test that.

27:12.880 --> 27:17.160
And when they do, they don't get the answer that their model predicts they should get,

27:17.160 --> 27:19.080
but they don't care.

27:19.080 --> 27:24.640
And so I wonder if it wouldn't be useful for us to make a list like that.

27:24.640 --> 27:25.640
That was my first thing.

27:25.640 --> 27:28.120
And I also have a thing about funding, but what do you think about that?

27:28.120 --> 27:29.120
Is there a...

27:29.120 --> 27:33.000
I think it's a fantastic idea, of course, absolutely.

27:33.000 --> 27:39.880
I do these things all the time, you know, maybe once or twice a week, I publish a piece daily,

27:39.880 --> 27:45.680
but maybe once or twice a week is looking at bad papers and from all range of science

27:45.680 --> 27:47.440
that use these techniques.

27:47.440 --> 27:49.760
So yeah, it would be very worthwhile.

27:49.760 --> 27:50.760
That's a good project.

27:50.760 --> 27:57.880
I'm going to consider that and how to bring it about putting it on a more systematic basis.

27:57.880 --> 27:59.520
I think that's a fantastic idea.

27:59.520 --> 28:04.120
I mean, neuroscience is just littered with models of how the brain works that have like

28:04.120 --> 28:06.120
two variables.

28:06.120 --> 28:07.920
And it's extraordinary.

28:07.920 --> 28:11.760
I mean, and then they would take an fMRI measurement and look, there it is.

28:11.760 --> 28:17.840
We got some difference between these two relative signals and it looks a lot like our model.

28:17.840 --> 28:20.920
That's a perfect example of the type of thing where models...

28:20.920 --> 28:22.840
It's model upon model.

28:22.840 --> 28:23.840
The fMRI...

28:23.840 --> 28:27.320
You'd know this better than I do, but for your listeners probably know it better than

28:27.320 --> 28:28.320
I do too.

28:28.320 --> 28:29.320
But the fMRI...

28:29.320 --> 28:32.120
The output from that is already a model.

28:32.120 --> 28:33.200
It's already a model.

28:33.200 --> 28:36.640
It's not like they've gone into the brain and dissected, taking a look at this mirror

28:36.640 --> 28:39.560
and saying, oh, yeah, this is a bigger fashion over here.

28:39.560 --> 28:43.160
No, this thing is an inverse problem and it's already a model.

28:43.160 --> 28:46.720
So there's uncertainty already in the data that you have out of it.

28:46.720 --> 28:53.920
And then they use this uncertain data as if it were certain downstream and further models.

28:53.920 --> 28:58.560
And to some of the most preposterous things, I mean, Sam Harris was...

28:58.560 --> 29:01.600
He has one I picked apart a long time ago.

29:01.600 --> 29:03.240
Ridiculous things.

29:03.240 --> 29:07.520
People are more likely to believe in Santa Claus if the sublocutist area of the brain

29:07.520 --> 29:09.240
lights up in their effort.

29:09.240 --> 29:15.960
All kind of statistical nonsense that they try to pull off because it shows how well their

29:15.960 --> 29:19.720
model could fit, the model of the model.

29:19.720 --> 29:25.840
So yes, all he's there is they all suffer from the same problems over modeling.

29:25.840 --> 29:29.640
What do you have to have models in science, but they're not verified.

29:29.640 --> 29:35.440
Another place that models probably play a more reasonable role in describing reality

29:35.440 --> 29:40.520
is in business where uncertainty equals risk.

29:40.520 --> 29:47.400
Is there any way we could use the idea that businesses don't use models that don't work?

29:47.400 --> 29:49.720
Insurance companies don't use models that don't work.

29:49.720 --> 29:55.160
So there have to be people that understand that model verification is at the heart of

29:55.160 --> 29:56.160
knowledge generation.

29:57.160 --> 29:58.160
Oh, yeah, sorry.

29:58.160 --> 29:59.160
Did you hear what I just said?

29:59.160 --> 30:01.640
I'm going to let that come back.

30:01.640 --> 30:09.320
Yeah, business models of verification and business models use uncertainty as risk.

30:09.320 --> 30:16.040
And so there's a lot of sort of feedback in business for their models in actuarial science

30:16.040 --> 30:17.040
and whatever.

30:17.040 --> 30:23.680
So there have to be people that from a professional perspective understand how if they do it like

30:23.680 --> 30:28.920
this at work, but academic science doesn't like that, then there ought to be lots of

30:28.920 --> 30:33.960
people that could be woken up very easily who just because of their professional background

30:33.960 --> 30:38.320
are, this will just snap into their head, don't you think or?

30:38.320 --> 30:42.720
I think you're absolutely right, actuaries are an excellent point because if the insurance

30:42.720 --> 30:49.160
companies didn't model accurately, they would stand to lose a lot of money.

30:49.160 --> 30:55.520
Of course, the other problem after that is not scientific, but sociological, the government

30:55.520 --> 31:00.240
comes in and begins to regulate them because their models are sometimes too accurate and

31:00.240 --> 31:05.760
they're not politically desirable accuracies, that's so that's a whole other problem.

31:05.760 --> 31:06.760
Oh, wow.

31:06.760 --> 31:08.360
I didn't know that that happened.

31:08.360 --> 31:09.360
Okay.

31:09.360 --> 31:13.600
Well, you know, you ever you know, we don't want to talk about all this idea, but you

31:13.600 --> 31:21.440
know, there's official, you know, there's some groups need more need more help from

31:21.440 --> 31:23.000
the government dollars.

31:23.000 --> 31:26.120
And so the government steps in and and and modifies it.

31:26.120 --> 31:30.440
Well, you know, you're not you're not allowed to have noticing of certain things.

31:30.440 --> 31:35.120
And so now I see what you mean, yeah, there is that, but but actuaries do do a good job

31:35.120 --> 31:38.520
of looking at how well their models portray.

31:38.520 --> 31:44.880
And because, you know, they're not the most complex models in the world, they're more

31:44.880 --> 31:49.040
or less just, you know, counting.

31:49.040 --> 31:50.040
Here's what happened.

31:50.040 --> 31:53.320
And here's what we can expect to happen again in the future, which is a very good way to

31:53.320 --> 31:54.320
do things.

31:54.320 --> 31:55.320
Right.

31:55.320 --> 31:56.320
Not that the math is simple.

31:56.320 --> 31:57.320
I don't mean that.

31:57.320 --> 32:03.640
But I mean, to say that as far as model complexity goes, it's not that not that difficult.

32:03.640 --> 32:10.080
Would you would you want to, um, for my viewers, just take a crack at a brief explanation

32:10.080 --> 32:18.920
of how the the no hypothesis falsification thing isn't actually verifying models and,

32:18.920 --> 32:22.400
you know, pull an example out of your out of your brain at random?

32:22.400 --> 32:23.400
Is that a?

32:23.400 --> 32:26.280
Yeah, it's a it's a very good question.

32:26.280 --> 32:31.360
Not the null hypothesis and statistics is kind of the straw man of statistics.

32:31.360 --> 32:34.640
And everybody knows the straw man is a sort of informal fallacy.

32:34.640 --> 32:38.640
It's it's set up to be able to knock down and knock down easily.

32:38.640 --> 32:45.280
Normally, what happens is you set up an ad hoc model, something like a regression, very common

32:45.280 --> 32:52.560
thing, and you relate some X to some Y, some, some measure to some observable thing that

32:52.560 --> 32:54.760
you're interested in.

32:55.760 --> 33:01.480
The null hypothesis is that whatever your X is has nothing to do with this Y.

33:01.480 --> 33:07.920
And you encapsulate that into a probability model, a parameterized probability model,

33:07.920 --> 33:12.960
and you set the value of that parameter at some level, typically zero, you say, if this

33:12.960 --> 33:19.440
parameter exactly equals zero precisely exactly equals zero, why that's my null hypothesis.

33:19.440 --> 33:26.240
And then you calculate all kinds of numbers, namely, like a P value, a bit of arcane magic

33:26.240 --> 33:30.560
that if it's small enough, you can go waving your we P value in front of people saying,

33:30.560 --> 33:37.640
look how small my P value is, I have disproved my null hypothesis and therefore the opposite

33:37.640 --> 33:40.880
of the contrary of the null hypothesis must be true.

33:40.880 --> 33:47.920
If we've said this parameter is exactly precisely equal to zero, and the parameter is a very

33:47.920 --> 33:55.960
sort of measure of strength, therefore it's not zero, the null hypothesis has been shown

33:55.960 --> 34:02.000
to be false, and so not zero, therefore it must be important, this X must be important

34:02.000 --> 34:09.160
in describing this Y. Of course, it's not true. For one thing, you've never disproved

34:09.160 --> 34:13.280
the null hypothesis, you've just made an active will, you said, you know what, I'm going

34:13.280 --> 34:19.560
to decide that this null hypothesis is not significant, and what does significant mean?

34:19.560 --> 34:23.640
Significant means the P value is small. And what does the P value being small mean? Well,

34:23.640 --> 34:30.040
it means significant. It's a circular definition. It's just an active will to say, I think this

34:30.040 --> 34:36.760
correlation is causation. And that's it. I think this correlation that I have found

34:36.760 --> 34:42.480
is causation. And you have some mathematical apparatus that helps you in this ritual. One

34:42.480 --> 34:50.920
of the guys who was at the thing last week, Greg Gigarenzer, it's his phrase, the ritual

34:50.920 --> 34:57.320
of statistics or ritualized statistics or something. Just following these things to sort of bless

34:57.320 --> 35:02.800
your results, you run these typical procedures over them, and if things work out, well then

35:02.800 --> 35:10.160
you claim that you have found something. So null hypotheses, we can eliminate them entirely.

35:10.160 --> 35:17.320
We don't need them in science at all. We can instead form our models in this predictive

35:17.320 --> 35:23.280
sense. If you say that X, knowing what X is, tells you something about Y. So one of my

35:23.280 --> 35:31.040
favorite examples is, I've used this example many times, attendance at a Fourth of July

35:31.040 --> 35:37.000
parade when you're young. That's our X. The attendance at a Fourth of July parade when

35:37.040 --> 35:41.960
you're young, predicts whether or not you're going to turn out to be a Republican. That's

35:41.960 --> 35:52.120
our why. You're frozen again, just hold on one second. Darn it. He's got to fix that

35:52.120 --> 35:58.520
camera. At least he stopped at a place where I can accurately characterize where he needs

35:58.520 --> 36:04.320
hypothesis is catching up again, your camera parade attendance has. Yeah, sorry, your camera

36:04.320 --> 36:08.280
just caught you off. So I don't know what the deal is. It's a it's okay. You're at

36:08.280 --> 36:13.640
perfect. It's right at the part where you said, okay, parade attendance predicts whether

36:13.640 --> 36:17.800
or not you're going to be a Republican and then it stopped. So all right. So the null

36:17.800 --> 36:21.680
hypothesis is parade attendance has nothing to do with it. Whether you're going to be

36:21.680 --> 36:27.080
a Republican or Democrat or something like this. So you form this model, you put it

36:27.080 --> 36:31.520
in a regression, you get a VP value, which is what these researchers from Harvard did.

36:31.520 --> 36:40.000
And they said, yes, attendance at a Fourth of July parade, when you're young, predicts

36:40.000 --> 36:44.360
whether or not you're going to be a Republican. And that's the that was what the headlines

36:44.360 --> 36:49.360
read. So, you know, these young patriotic little kids you're imagining are being radical

36:49.360 --> 36:55.640
on ice into into being far right extremist leg waivers by attending Fourth of July parade,

36:55.640 --> 37:01.560
but they cheated. They didn't use for the July parade attendance. What they did was

37:01.560 --> 37:07.600
they looked at people's residents when they were young, the address that they thought

37:07.600 --> 37:12.360
they lived at when they were young. And then they went back into the meteorological records

37:12.360 --> 37:18.920
and looked whether or not it rained on the Fourth of July. And if there was any precipitation

37:18.920 --> 37:26.120
on the Fourth of July, they said there was no parade. And the child could not have gone.

37:26.120 --> 37:31.680
And if it did not rain, if it was any kind of cloudy or clear day, they said, well, there

37:31.680 --> 37:36.680
must have been a Fourth of July parade and there must have been the kid attending it.

37:36.680 --> 37:42.920
That's the level of science. It's absolutely absurd because I always use this joke, but

37:42.920 --> 37:47.840
in San Francisco, of course, it never rains on the Fourth of July, almost never. And so

37:47.840 --> 37:54.520
the city should be teeming with Republicans because they would all go to these parades

37:54.520 --> 37:59.720
and so forth. But because we have this null hypothesis that's so easy to swap down. I

37:59.720 --> 38:04.040
mean, if you're not getting, if you're not rejecting your null hypothesis, you're not

38:04.040 --> 38:09.880
trying hard enough. It's always possible to do just just requires a little bit of ingenuity

38:09.880 --> 38:13.360
and you could do it. That's why we have so much bad science.

38:13.720 --> 38:21.600
Yeah, wow, that's a really good example because there you're really actually testing for

38:21.600 --> 38:28.200
even raining on Fourth of July. It's not even the parade, right? So that's one thing. But

38:28.200 --> 38:33.760
I really feel like that's a very good analogy for how far we are from actual understanding

38:33.760 --> 38:37.640
of what happened during the pandemic. They want you to believe it's as simple as some

38:37.640 --> 38:43.000
RNA molecules filled in a puddle and Wuhan. And now it's everywhere. But there's so much

38:43.000 --> 38:53.640
involved in that it's a massive pile of assumptions in the end. And I think it's useful to think

38:53.640 --> 39:00.040
of that as a model of reality that makes predictions. If you say that that's the way the pandemic

39:00.040 --> 39:04.720
worked, then that makes all kinds of predictions about how future RNA should transmit around

39:04.720 --> 39:10.120
the world and how these future signals could be found. And I think those that's where this

39:10.160 --> 39:15.360
whole thing would really fall apart like a like it should. I mean, because again, if

39:15.360 --> 39:22.600
you challenge them to make predictions, they can't. And that's I feel another side of the

39:22.600 --> 39:27.720
coin with regard to models being fitted to data versus, you know, making a model and

39:27.720 --> 39:32.440
then going out and seeking validation of it. I don't know. Absolutely. I don't know where

39:32.440 --> 39:36.120
I was going with that question, but no, no, it's absolutely right. You're absolutely right.

39:36.120 --> 39:41.080
If you can't make observe predictions of observables, it's the whole point is these

39:41.080 --> 39:45.880
observables are the things that are supposed to matter to us. And if you can't make, you

39:45.880 --> 39:52.080
can't make skillful predictions, it's a technical term, skillful predictions of these things,

39:52.080 --> 39:56.520
then you have no business feisting it off onto the public and making us follow the science

39:56.520 --> 39:59.200
is just, it's absurd, it's absurd.

39:59.200 --> 40:05.480
Well, that's the reason why I think it's important for people to understand then that if if done

40:05.480 --> 40:12.000
well, and even with malevolence, for example, if they want to create the illusion of pandemic

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potential using academic biology, they can do it. And they can even put p values behind

40:17.360 --> 40:23.000
it. And they can get peer review behind it. And so this whole system, right, has been

40:23.000 --> 40:29.600
distorted into, they call it knowledge, but it's not knowledge, it's not generating knowledge.

40:29.600 --> 40:35.440
And I really feel that big picture is something that a lot of people can get a grasp on. And

40:35.440 --> 40:41.680
if they do, it might put them in a place of healthy skepticism that gives them also a

40:41.680 --> 40:48.000
short, rather a small toolbox. You know, if you approach these problems as okay, what

40:48.000 --> 40:53.240
is the model that they are presenting to me and try to understand the cartoon version

40:53.240 --> 40:58.160
of reality that they are foisting upon you with the story that they tell and then evaluate

40:58.160 --> 41:02.200
whether or not that model makes any predictions that have value. I don't think that's hard

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for well, it could be hard for some people, but I think there are a lot of people who

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are capable of doing it.

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Couldn't be. It shouldn't be difficult. I mean, the best science we have, all science

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uses models. It's not using models. That's the problem that we should emphasize. You

41:18.440 --> 41:24.000
have to have a model, because you can't encompass all of reality all at once. You have to have

41:24.000 --> 41:30.680
a slice of it. You have to have some abstraction and some kind of model that that's not wrong.

41:30.680 --> 41:38.080
But the model should at least have proved itself. And fitting past data is nothing.

41:38.080 --> 41:44.800
It's absolutely zero. It's necessary, but it's far from sufficient, because any set

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of past data that you give me and any field, I can fit not just one, but any number of

41:51.600 --> 41:59.720
models to it and to any arbitrary precision. I can fit them perfectly. I can always find

41:59.760 --> 42:06.320
at least one model that will fit that past data perfectly. And this is a trivial, absolutely

42:06.320 --> 42:12.880
trivial mathematical exercise. So that we have sophisticated models done on a computer

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and they're done by experts and the model has been peer reviewed by people who also

42:17.160 --> 42:22.760
create their own models means nothing. It's absolutely nothing. It adds nothing to it.

42:22.760 --> 42:27.360
Like you say, it's not generating knowledge. It's generating noise. It's only when these

42:27.400 --> 42:34.080
models are verified and we're found. And again, it has to be by disinterested people.

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It can't be the same people who create the model and then make predictions to say, yeah,

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trust us. Everything's fine. No, no, we should know enough about now how the world works

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that we cannot have people who have an extreme interest in their own project, be that their

42:51.200 --> 42:56.360
own judge. There's legal maximums that guide this kind of a stuff. We need those same

42:56.360 --> 43:00.680
maximums to guide science. That was one of the useful discussions. I think we had two over

43:00.680 --> 43:06.600
the last week. Right. Right. I think a concrete example for people that are listening or watching

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for what Matt just said would be a mechanical model of the solar system. And you could crank

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it backwards and you can see that, Oh, wow, last month, it's okay. And the month before that,

43:16.840 --> 43:21.800
it's okay. And wow, it works for that eclipse from two months ago. But then you crank it

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forward and you go to tomorrow and the moon is backwards. And then you go a week a farther than

43:28.040 --> 43:32.680
that. And it's like the earth turns upside down. Then something inside of that box is not working

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correctly. Because even though when you crank it backwards, it seems to recapitulate the past

43:38.200 --> 43:42.840
astronomical data, when you crank it forward, if it doesn't make a prediction that happens in

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the future, then those gears are not working. And I think if you if you apply that to some of

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these biological models, or these nutritional models, or these, you know, a lot of this pandemic

43:53.320 --> 43:57.880
models, you get to the point where when you crank that thing forward, it's useless. It's

43:57.880 --> 44:05.400
absolutely useless. That's precisely it. And it's even worse than the picture than you're painting.

44:06.280 --> 44:10.120
Because think about this, think about that model we have now, we do have a model of the solar

44:10.120 --> 44:15.560
system, like a Copernican type model, we do crank it forward. And we do see that it does work rather

44:15.560 --> 44:22.440
well. However, there was an older model, a tolemaic model that had all these epicycles of things

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spinning this way and that way. That model also predicted very well. It predicted beautifully.

44:29.000 --> 44:36.760
So even if this is why it's a bare minimum to get the model to make good predictions,

44:36.760 --> 44:42.120
that's the first entry. After that, it still doesn't mean that you have proved you found a real

44:42.120 --> 44:47.400
cause of something. I love that. Oh, I love that you have a lot more to do.

44:47.960 --> 44:52.360
I love that you said that. Wow, that's really spectacular. I love that you said that. And that's

44:52.360 --> 44:57.960
really where where the grinding of science happens when there are two models that work pretty well.

44:57.960 --> 45:03.400
And now you need to come up with experiments that's able to dissect their their predictive

45:03.960 --> 45:10.120
value like that. That I imagined as a kid was what what all scientists were doing was at the

45:10.120 --> 45:14.680
me too, the cutting edge of knowledge deciding between the two best models

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with a clever experiment that got them a lab like obviously, isn't that what they're all doing?

45:21.160 --> 45:26.040
I don't know. Sometimes we can't always do experiments, you know, like in cosmology and so

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forth, or they're very limited. But yeah, that's it. That's it. We have to find, like I'm always

45:33.000 --> 45:38.440
saying, you we have to, this brings us back to broken science. One of the things that's broken

45:38.520 --> 45:46.200
about science is is the metaphysics. In order to do physics, you have to first have a metaphysics.

45:46.200 --> 45:54.280
So we need to understand the limits of the metaphysics that we currently use and of

45:54.280 --> 45:58.520
possible replacements and the superiority of replacements and so forth. But that's a whole

45:58.520 --> 46:05.640
other discussion. That's that's why it's very difficult to explain these kinds of things because

46:06.520 --> 46:12.440
we have to first get past the point where we recognize we've had a broken model and then we

46:12.440 --> 46:17.160
have to get to the point where we realize that models are not being used in a predictive sense.

46:17.160 --> 46:22.840
And then we have to realize that we have to have a firm understanding of what it means to cause

46:22.840 --> 46:29.000
and what what does cause something mean and all that kind of thing. So we really have to we really

46:29.000 --> 46:33.480
have to fix the foundations of science. And that's that's kind of what this broken science

46:33.480 --> 46:39.640
things about. I heard you leading a disc or having a discussion randomly between sessions.

46:40.920 --> 46:46.680
And it had to do a little bit with this, the idea that the table doesn't exist because actually

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it's mostly space. And then if we go farther and farther and farther, all of a sudden there's

46:50.040 --> 46:56.200
no causes at all or something like this. And that's really kind of where in a sort of,

46:56.920 --> 47:02.040
I'm terrible at these kinds of discussions, but that's sort of where where Sam Harris is with

47:02.040 --> 47:06.200
regard to, you know, free will and everything else is just we're all particles moving, right?

47:06.200 --> 47:10.600
And if we just had enough data, we would be able to predict the future and everything.

47:12.040 --> 47:16.520
Of course, I think that's false. And that he does. He's the kind of guy that there's not the

47:16.520 --> 47:21.720
table's not really there because it's mostly mostly empty space. And my metaphysics says the

47:21.720 --> 47:27.160
table's here. It's real. And in fact, the particles don't exist in it anymore. They only exist virtually

47:27.160 --> 47:32.920
in potentia. They're now part of the thing that is the table. There's no separation of

47:32.920 --> 47:38.040
particles and atoms anymore that makes the table. It just is a table, just as common sense

47:38.600 --> 47:43.320
shows it to you. And of course, about free will, I always make the joke. It's a standard joke,

47:43.320 --> 47:48.760
but it's true that you read these guys papers who try to prove to you that free will doesn't

47:48.760 --> 47:54.360
exist. And every one of these papers, they always say, if only people understood they couldn't make

47:54.360 --> 48:01.080
choices, they would make better choices. They always want to save the world by telling us that

48:01.080 --> 48:05.800
we really can't make choices. That's absurd. It's absurd. Here, what are they doing? They're

48:05.800 --> 48:11.960
using their free will to write it. That contradiction, when you call them on it, they'll just say,

48:11.960 --> 48:16.840
Oh, no, no, it doesn't work. It's an illusion. Okay. Fine. It's an illusion. Who is having the

48:16.840 --> 48:23.960
illusion? Right. Wow. Yeah. Who is having the illusion? It's a bluff. It's always a fluff. There's

48:23.960 --> 48:29.000
a lot of bluffs in science. And that's one of them. Yeah, that's wonderful way to put it. That's

48:29.000 --> 48:37.160
great. I'm I'm just sort of humbled by the fact that the first couple of these meetings were so

48:37.160 --> 48:44.360
overwhelming to me that the big picture of how to use this is a way to bring people to the light

48:44.360 --> 48:50.440
of things. It really felt like this was the first time where Greg nailed it, where other people nailed

48:50.520 --> 48:57.400
it and where I was comfortable enough in my seat to absorb it. And so I'm very, very excited about

48:58.120 --> 49:05.400
being able to to apply these ideas as you do now to biological papers and to like think about

49:06.040 --> 49:11.320
what I'm really calling out as far as what's broken with how they're doing it. And the other

49:11.320 --> 49:15.960
thing that was missing from my descriptions was understanding that there were lots of people

49:16.040 --> 49:20.360
who've thought about this, right? It's not some kind of secret. I mean, people have written books

49:20.360 --> 49:27.000
like you, but before you as well that that that part of the reason why we're here is because people

49:27.000 --> 49:33.800
like Popper wrote stuff. And yeah, and so it is important. I was pretty naive to all of that,

49:33.800 --> 49:38.920
that literature. And so it's been a big eye opening experience for me to realize that in the

49:38.920 --> 49:43.400
best. Well, that's just it. You could go through as a scientist. I did. I went through the whole

49:43.400 --> 49:50.280
thing. Cornell, I got my master's and atmospheric, you know, PhD, whatever. Many, many people,

49:50.280 --> 49:56.680
you never once have to be confronted by any of these philosophical matters. Never.

49:57.400 --> 50:02.520
Never. You can go through and finish. You don't, at the end, you're going to ask, what is science?

50:02.520 --> 50:08.040
Well, science is what I do. Yeah, right. You know, that's an answer, but it's not, it's not a very

50:08.040 --> 50:12.360
good answer. But that's what most people's answer is. And if they've absorbed anything,

50:12.360 --> 50:18.280
they've absorbed this idea of the Popperian, you know, falsifiability, and you now see the limits of

50:18.280 --> 50:25.000
that. But most scientists don't because they sort of dismiss a lot of philosophers.

50:25.560 --> 50:30.040
Well, I think they should dismiss a lot of them. There's a lot of these academic philosophers,

50:30.040 --> 50:35.400
you know, kind of postmodernistic kind of nonsense that they preach. It doesn't seem to have any

50:35.400 --> 50:41.800
bearing on what a scientist does. And they're right, it doesn't. But there's, there's, you still

50:41.800 --> 50:47.800
can't operate and be a scientist without some philosophy. You might not recognize that it's a

50:47.800 --> 50:53.640
philosophy, but you have one. You absolutely must have a philosophy in order to apply

50:55.320 --> 51:01.240
the skills that you've learned to, to build your models, to understand your models,

51:01.240 --> 51:04.120
to know what a model is, make predictions, all these kinds of things.

51:04.120 --> 51:10.840
I mean, I'll give you an example of how, I mean, how bad I'm going to burn myself here, but I

51:10.840 --> 51:18.840
think it's an important example. I think you'll be able to see this. So if you, if you had a

51:18.840 --> 51:24.920
neuron and a slice of brain where I used to work, and it would be surrounded by other neurons,

51:24.920 --> 51:30.920
of course. And in that slice, there would also be axons, which were coming in from other brain

51:30.920 --> 51:36.440
regions that were cut off, but were still transmitting neurotransmitter to the neuron

51:36.440 --> 51:41.560
that you're going to record from. And so one of the measurements that you would get from this

51:41.560 --> 51:48.840
recording would be these spontaneous events, which would look like this. And they're basically

51:49.960 --> 51:55.480
signals that are coming from these synapses, which have been cut, but are still active.

51:55.480 --> 52:01.720
And you can see the synaptic events in real time as this neuron receives them. Now, interestingly,

52:01.720 --> 52:07.640
one of the main, not one of the main, one of the ways that you create this, this scenario where

52:07.640 --> 52:15.480
you can write a paper about it would be to apply a drug here. And if the baseline amount of this

52:15.480 --> 52:24.920
signal were to change, then you would say that somehow or another, that drug is interacting with

52:25.800 --> 52:30.440
with these, the receptors or the proteins on these and changing the release probability

52:30.440 --> 52:35.640
of the glutamate there. And so then you start to understand how the drug works or doesn't work.

52:35.640 --> 52:41.160
Now, of course, this assumption would be backed up by pharmacological blocking here and all kinds

52:41.160 --> 52:48.280
of other things like that. But my point is more abstract in the sense of if you imagine how complex

52:48.280 --> 52:55.960
the brain is, it's pretty easy to get here and have a significant result when you pharmacologically

52:55.960 --> 53:05.320
alter the circuit. Now, the reason why I'm saying this is because I feel as though we're at the

53:05.320 --> 53:11.640
verge of being able to explain to people how it is. And with where I wasn't, I'm going to stop

53:11.640 --> 53:19.560
talking very soon. When I was in grad school, no one ever used the word straw man to describe

53:19.560 --> 53:27.080
the null hypothesis. And that was partly because it was kind of a sacred ritual. You would never

53:27.080 --> 53:33.880
be so, be so, how would you say coy or, or, or sorry, it's almost like sarcastic and not,

53:33.880 --> 53:39.560
it's not the right word. But you're being so honest, if you said that, that it was a straw man, and

53:39.640 --> 53:45.160
but it would be so useful from the perspective of teaching a graduate student exactly what this

53:45.160 --> 53:50.360
game is. Quite frankly, I feel as though knowing what I know now in broken science, I could go

53:50.360 --> 53:56.280
back to academia and be much more successful, because I understand what the formula is asking

53:56.280 --> 54:03.400
for. And it's not clouded by this ideal of what I imagine I'm doing. Can you can you relate to that

54:03.400 --> 54:09.160
illusion that I created for myself? That's absolutely it. That's that's that's how people

54:09.880 --> 54:15.160
that you described how a lot of people wake up from the rituals that they're practicing and

54:15.160 --> 54:22.200
understanding that this this method just doesn't give what they they wanted to give. I mean,

54:22.200 --> 54:25.880
you're trying to what you're trying to do there, you're trying to fundamentally fundamentally

54:25.880 --> 54:31.400
understand the causes at play. And that's exactly the right way to do it. That's absolutely the

54:31.400 --> 54:37.960
right way to do it. But it's brutal hard work, right? I mean, there's all kind of you realize

54:37.960 --> 54:42.440
after you set up a little simple experiment that you just drew right there. And and you saw the

54:42.440 --> 54:46.680
signals and suddenly you realize or you have other evidence that comes in later, you know,

54:46.680 --> 54:50.920
there's other things that could account for that change in signal to. And you have to keep

54:50.920 --> 54:57.000
eliminating them one by one by one. And it takes a very long time. It's very difficult to do the

54:57.080 --> 55:02.440
very best. Brutal brutal brutal hard work. Absolutely. That's what science is really like.

55:02.440 --> 55:08.760
But we've tried to make it too easy and it is now far too easy just apply this formula. And if it

55:08.760 --> 55:13.880
pops out this number pops out, well, then you you you prove what you want to do it. It's very sweet

55:13.880 --> 55:19.560
the way you say it because actually I I felt when I was stepping out of neuroscience or when I was

55:19.560 --> 55:26.600
my career was waning before I got let go. It was because I was still having to put in

55:27.320 --> 55:32.600
hundreds of hours to get my data set. I couldn't just roll someone into an fMRI machine and then

55:32.600 --> 55:38.760
go back to my computer for a couple weeks and then publish a paper. I had to find something that

55:38.760 --> 55:43.720
was in effect and then do some other experiments that showed that the effect wasn't from something

55:43.720 --> 55:48.840
else. And that was the pharmacological blocking and this kind of thing. And so I did feel

55:49.640 --> 55:55.480
a little bit like I was part of a small group of people that was still and I'm not trying to

55:55.480 --> 56:00.120
to my own horn. I'm trying to say how I felt it didn't fit in. I didn't have this

56:00.760 --> 56:05.240
five papers a year machine going. I couldn't figure I couldn't see it. I didn't I didn't

56:05.240 --> 56:09.640
have an answer to that problem, which was, you know, you're going to need eight papers in the next

56:09.640 --> 56:14.520
three years or you're not going to make it. And it's like what if you tell me that I could say

56:14.520 --> 56:18.440
immediately in that meeting, well, then I'm not going to make it because what I do doesn't work

56:18.440 --> 56:24.280
like that. And so do I need more students? Do I don't know what the answer was? But I was already

56:25.240 --> 56:29.240
finding that wheel unable to grind. I just couldn't do it anymore.

56:29.240 --> 56:34.200
Oh, yeah. For everybody who hasn't been exposed to academia, it's toxic in that way.

56:34.200 --> 56:38.920
It really is publisher parish. It's terrible. It's terrible. That accounts for the flood of bad

56:38.920 --> 56:43.320
papers. I mean, they're pushing out a lot of nonsense just because they have to keep their

56:43.320 --> 56:53.240
positions or to bring in grants to pay for themselves and their lab or and most importantly to to provide

56:53.560 --> 57:00.280
kickback to the deans in the form of overhead. That's you could write one paper a year or even

57:00.280 --> 57:05.480
none if you're bringing in good overhead. If you're bringing in that, which is, you know,

57:05.480 --> 57:11.720
it was at Cornell at one point, I think it was up to like 60%, 59 and a half percent overhead.

57:12.440 --> 57:17.640
I mean, so you get the grant grants a million dollars and then they tack on another $590,000

57:18.600 --> 57:23.480
overhead, and that just goes to the college, which becomes part of the slush funds for the

57:23.480 --> 57:29.640
deans. Yeah, absolutely. When my grant application or my grant, R01, for example, if I had

57:31.400 --> 57:40.200
when my supervisor got an R01, he paid 155% of my salary to the university. So 55% of my salary

57:40.200 --> 57:45.800
was rent. And he did that for every student and himself. So that's quite a, that's quite a load of

57:45.800 --> 57:50.200
money. If you think there are 130 faculty members just in that department that are all

57:50.200 --> 57:54.200
working under the same model and all those grad students walking around are all paying rent

57:54.760 --> 58:01.080
to the tune of thousands of dollars a year. I mean, it's extraordinary amount of money. That's why

58:01.080 --> 58:05.240
they can pay deans millions of dollars a year. They pay deans as much as they pay basketball

58:05.240 --> 58:12.760
coaches these days. Yep. It's extraordinary. So how about this? How about this? What have we got

58:12.760 --> 58:21.000
a bunch of weaponized money and started funding science that was, you know, we do have that in

58:21.000 --> 58:28.360
the way though, private companies that are not tied up with the government that are not tied up

58:28.360 --> 58:34.200
with the government that that proviso is the strongest one I could possibly make. You can say

58:34.200 --> 58:38.200
it's what I have an interest with the government and it becomes political. Well, that's just the

58:38.200 --> 58:44.680
same as being the government. That's where things can be done. So it's exactly that. It's private

58:44.680 --> 58:52.040
money in a patronage-like system trying to discover things for the sake of discovering them.

58:52.840 --> 58:58.920
They could be towards the desire of having a useful product or something. There's nothing

58:58.920 --> 59:07.640
per se wrong with that. It's only when it becomes, you know, as we saw with COVID, when it becomes,

59:07.640 --> 59:13.960
you know, a very political thing that the whole thing becomes somewhat difficult. And it becomes

59:13.960 --> 59:18.760
no different than regular academia. And you know as well as I do that same people go in and out of

59:18.760 --> 59:23.320
these corporations into the government that judges the granting agencies and all this kind of stuff.

59:23.320 --> 59:32.120
It's the same set of people. Yeah. It's a very difficult problem to get your head around. I

59:32.120 --> 59:37.240
think one of the things that I liked that I heard somebody say at the meeting, which I also think

59:37.240 --> 59:41.160
is something that can get people's wheels turning if they haven't thought about this before, is that

59:41.160 --> 59:48.440
all the real good sciences top secret, all the, I don't know to what extent that's really true

59:48.440 --> 59:55.000
or if it's just funny, but I do think that they're that identifying places like, you know, meteorology,

59:55.000 --> 01:00:04.040
like actuarial science, where models are required to verify, we can find examples where, you know,

01:00:04.920 --> 01:00:10.680
we can we can push science in that direction over time. We can teach, you know, if we can break

01:00:10.680 --> 01:00:15.640
into a university and teach and teach this to undergraduates, that would be great. We probably

01:00:15.640 --> 01:00:23.960
ruin their graduate experience, but I don't know. I'm still trying to do it. We can do it. The tools

01:00:23.960 --> 01:00:29.960
are there. Like I say, the tools, the tools we have, it's not it's not unknown. I mean, there's all

01:00:29.960 --> 01:00:35.560
kinds of investigations that can go on to improve these tools and so forth, but the basic set

01:00:36.440 --> 01:00:43.080
are there waiting for us to be used. Can I ask you a personal question now? Hey, can I ask you

01:00:43.080 --> 01:00:48.920
a personal question? What what do you do for, for, you know, to make ends meet? Are you teaching

01:00:48.920 --> 01:00:54.440
anywhere? Do you do you have online course or I mean, why not do it online? I should do an online

01:00:54.520 --> 01:01:01.240
course. No, I I basically have private clients. Okay. Would you want would you want enough?

01:01:01.800 --> 01:01:06.840
Would you want any help with an online course? Like I could be your technical guy or or I could

01:01:06.840 --> 01:01:11.800
I do need to figure out. I do need to do it. I keep swearing. I'm going to, but video is not

01:01:11.800 --> 01:01:18.040
I'm very naive about video. Oh, but you could, for example, you could record it in your own home.

01:01:18.040 --> 01:01:23.160
I have I have a chalkboard. Right. But then you then you send me the video and I edit it for you and

01:01:23.160 --> 01:01:28.680
add subtitles and all that stuff. I can do all that. I would be happy to do it. And we'll chat

01:01:28.680 --> 01:01:33.960
about that. Okay. I would think this would be really a good idea. And also, I would obviously

01:01:33.960 --> 01:01:39.160
learn from it a lot too. I think that's where we need to go. I think we need people like you

01:01:40.360 --> 01:01:45.320
taking over this thing. You know, I mean, if you could it, well, how awesome would it be if you

01:01:45.320 --> 01:01:52.200
meet somebody randomly? Oh, yeah, I took Matt Briggs's course last year. Bang. It should be a it should

01:01:52.200 --> 01:01:57.080
be something that people put on their website or people put on their social media. It I think

01:01:57.080 --> 01:02:02.600
you're a guy who, you know, you have the kind of charisma that can do it. You're you're a good

01:02:02.600 --> 01:02:07.640
speaker. And you and you've got all of the work done already in your books. You've already thought

01:02:07.640 --> 01:02:12.440
about this and develop these ideas. And so there's nothing I would have colored chalk. Well, there

01:02:12.440 --> 01:02:17.960
you go. I mean, is it the fancy stuff from Japan or is it just good? No, that's the one I got that

01:02:17.960 --> 01:02:24.600
got the one to this Korean. Yeah, yeah, that's good too. Yeah, no dust.

01:02:27.480 --> 01:02:31.080
That's fabulous. Oh my gosh, that's great. I love writing. That's awesome.

01:02:32.520 --> 01:02:36.920
If we are at like 59 minutes and I don't know what we said for time, but it feels like a very

01:02:36.920 --> 01:02:41.560
good time to wrap up and you just promised to come back because I'll have you on with more material

01:02:41.560 --> 01:02:46.440
and more more questions next time we can go into something specific that I get out of James as I

01:02:46.440 --> 01:02:52.600
read it. Oh, absolutely. Okay, cool. So I'm going to put this link up one more time.

01:02:52.600 --> 01:02:57.000
This is where you can find, unfortunately, it's an Amazon link, but that's the easiest for me.

01:02:57.000 --> 01:03:01.800
You can see his full name William M Briggs. You can see the books that he's got available.

01:03:01.800 --> 01:03:06.440
I would recommend the yellow one and everything you believe is not wrong, although he has contributed

01:03:06.440 --> 01:03:13.480
to this price of panic, which is specifically about the COVID COVID thing. And you can also find

01:03:13.480 --> 01:03:18.120
him at I think William M Briggs dot sub stack, something like that is at William Briggs.

01:03:18.120 --> 01:03:26.440
W M W M Briggs at sub stack. So thank you very much, Matt, for joining me.

01:03:26.440 --> 01:03:32.280
Can you remind me again? Well, maybe I'll do that offline. But I wanted to say hello to your wife

01:03:32.280 --> 01:03:37.560
as well. And for whatever reason, my mind is blanking on a name. So please give her a hug and say hello

01:03:37.560 --> 01:03:43.000
for me. And we will we will have you on within a month. I guarantee it unless you really object

01:03:43.640 --> 01:03:47.720
anytime. Cool. Very good. Very good. Thank you, Matt, for joining me. And I will talk to you again soon.

01:03:51.720 --> 01:03:57.080
Bao, Zawi. Okay, that went well. Thank you very much, guys, for joining me. I'm going to put this

01:03:57.080 --> 01:04:02.600
little slide back up again. This is, of course, the broken science meeting that happened a couple

01:04:02.600 --> 01:04:07.400
weeks ago. I hope Matt wasn't surprised that I let him go there so quick. I'll call him back on

01:04:07.400 --> 01:04:16.360
the phone. We are a group of people loosely organized around two people named Emily Kaplan

01:04:16.360 --> 01:04:22.520
and Greg Glassman. Greg Glassman is the originator, the creator of CrossFit, a very smart guy who

01:04:22.520 --> 01:04:28.440
actually had a very smart Papa named Jeff Glassman who worked for Hughes Aviation. And it was his

01:04:28.440 --> 01:04:33.720
upbringing with his very intelligent dad that has brought him all the way to understanding this

01:04:33.720 --> 01:04:40.520
stuff because CrossFit ran into legal problems with fake science. Basically, science, fake science

01:04:40.520 --> 01:04:45.960
was published to try and say that CrossFit enters people. And over the course of investigating this,

01:04:45.960 --> 01:04:50.600
he found out that his dad's been right about science his whole life. And it has put him on this

01:04:50.600 --> 01:04:57.960
crusade to try and, you know, leave a legacy for his children and for our children of his dad and

01:04:58.920 --> 01:05:06.760
these philosophers like Stove, these thinkers like Stove, and also our contemporaries like Matt

01:05:06.760 --> 01:05:13.560
Briggs. And so I really think this is one of the most worthwhile sort of initiatives and I'm so

01:05:14.120 --> 01:05:19.560
humbled to be considered a small part of it. So thank you, Emily. Thank you, Greg. And thank

01:05:19.560 --> 01:05:25.720
you, Matt, for coming on the show. And I think we're going to have a lot of interesting conversations

01:05:25.800 --> 01:05:30.120
as we go forward. At some point in time, of course, we're going to have Greg on the show. But I don't

01:05:30.120 --> 01:05:34.920
want to, I don't want to play that card too early because I don't, number one, I'm no, he's a busy

01:05:34.920 --> 01:05:39.560
guy. And so that'll be hard to organize. But number two, I want to have all of my ducks in a row and

01:05:39.560 --> 01:05:44.360
already have been paid back a little bit to him, the stuff that I've learned through a series of

01:05:44.360 --> 01:05:49.240
streams that this is the first one of. So thank you very much, guys, for joining me. This has been

01:05:49.240 --> 01:05:55.320
Giga Ohm Biological, a high-resistance, low-noise information brief brought to you by a biologist.

01:05:55.320 --> 01:06:02.840
19th of March, 2024. The afternoon shows are going to be a street because I really like this time frame.

01:06:05.080 --> 01:06:11.400
Thanks for joining me, guys. 73 people in the chat. Very understandable conversation. I hope

01:06:13.000 --> 01:06:17.240
good to see you, Pamela. Thanks for the link in the chat. She's always good for that.

01:06:17.240 --> 01:06:22.680
Can I see Janet Reno in the audience? Awesome. I didn't see you there earlier. I'm sorry, Janet.

01:06:22.680 --> 01:06:30.360
Janet's one of my favorite anonymous viewers because obviously it's not Janet Reno. It might be,

01:06:30.360 --> 01:06:39.000
it might be Ford. Some reason I think it might be Ford or something like that anyway. Thanks very

01:06:39.000 --> 01:06:47.080
much for joining me, guys.

01:07:17.240 --> 01:07:23.240
Thanks for joining me, and I'll see you in the next video.

