That brings in a house!
I'm so excited I can freak.
Not just this muscle car that gets me excited, it's my guest today.
Sooth spider is in the in the chat already.
Look at that, garden variety human.
That should be good.
Just give me a sound check if you're in the chat already, please, for this one.
Just to make sure everybody's lined up, it should be okay, I just started it.
Thank you there, sir.
And yes, we are speaking to William M. Briggs today.
He is a member of this broken science initiative, it's my first BSI guest.
If you didn't follow my directive yesterday and go and overload the website of brokenscience.org
with your signups, please do it today so that Emily and Greg get a little blip in their
website traffic and they see when we mention us, it would be really great to show them
the power of gigo-on-biological.
Matt, you may recognize Rodney Mullen here in this little spin.
Oh, yeah.
Guys, it's sweetheart.
Oh, my goodness.
Did you know that he got surgery on his spine and now he can stand up straight like skating
again like differently and he's got to relearn everything now because his body moves again.
It's crazy.
I'll be thought, no, I have no idea.
Apparently he's got like three more inches on him though.
I'm afraid that the latest data tells us that we're dealing with essentially...
I want you to go meet my friend Mark on this show that's now on the screen after this.
I think he's a really good guy and you guys would be good acquaintances, I think.
Sure.
And he's got readers that watch him, you know, so you're a book guy.
I mean, he's got readers.
Not that my viewers don't read, I'm not saying that, but Mark has got some really studious
followers.
I really like Mark.
I think truth is good for kids.
We're so busy lying, we don't even recognize the truth no more in society.
We want everybody to feel good.
That's not the way life is.
But you can tell if someone's lying, you know, you can sort of feel it in people.
And I have lied.
I'm sure I'll lie again.
I don't want to lie, you know, and I don't think I'm a liar.
I try not to be a liar.
I don't want to be a liar.
I think it's like really important not to be a liar.
Ah, ladies and gentlemen, I'm so excited to be here today and have this show running
in the afternoon working in sync with my family and I just feels really good to start
the stream in the afternoon and so I'm really happy that we've got Matt on the show.
Just a few more seconds.
I got to get this, the recording in the background running, so I'm going to keep this intro going
as usual.
It's like one more minute or so, I think I'm in it 20 seconds.
If you are joining us for the first time, this is Giga Ohm Biological.
It's a high-resistance low-noise information brief brought to you by a biologist.
We are based in Pittsburgh, Pennsylvania in the back of a rented garage and yeah, that's
the state of knowledge and information in our world.
We're basically working to dispel an enchantment.
It is an enchantment that has been laid down a long time ago and laid down deep and thick.
If you have been following me for a while, you're here at the top of the wave where we
are staying focused on the biology.
We don't take the bait on TV and social media and we love our neighbors.
The way it works is that people share this work and some people even financially contribute
and slowly biology finds new people and more people wake up.
That's my plan anyway.
You can find me at GigaOhm Biological.com and you should be watching at stream.gigohm.vial
if you're not already, which is the new place that I'm trying to store all these videos.
I think it's working out really well thanks to our supporter and follower Ted on the other
side of the planet.
Mike Vandenberg, mixed date.
These are the people that make Giga Ohm Biological possible including all of the subscribers
that are giving some small or large amount monthly or even per year.
There are also sub-stack subscribers that have recently joined the fight and it's been
really a privilege to see how far we can get with this.
I hope that we can continue to bring this biology to the masses.
This is the independent bright web.
It's the opposite of the intellectual dark web which is this loosely organized group
of influencers that was put in place before the pandemic to control the narrative, if
you will, to set this limited spectrum of debate that we are all trapped within.
But it's actually an illusion that is sustained only through your active participation.
You can drop your own hand from your own eyes and escape.
It is just a question of non-compliance.
You've got to actually accept that you've been lied to.
And I think one of the most exciting things about broken science is that it really, although
to steal a phrase from Greg, it may not be for everybody, but it is for anybody to understand
how science has been distorted, has been essentially co-opted into something that no longer is
a real pursuit of knowledge, but it's instead a pursuit of noise or the creation of noise
and I hope that somebody has sophisticated, unlike me, William M. Briggs is going to
be able to help us get through this because the distortion of science and the pursuit
of knowledge has been an integral part of the conscious and intelligent manipulation
of the organized habits and opinions of the masses, and it is an important element of
how we are governed.
In fact, I would argue it is a primary way by which we are governed, and as a recovering
academic biologist, I really feel as though I have a unique, sad insight into how distorted
one's mind can become if you pursue questions based on this reiterative, preparean, questioning
mechanism, and don't realize how easily that can spin out of control and stop producing
knowledge and instead produce noise.
I believe that's how the intellectual dark web works, and I believe that's how they've
essentially removed our ability to exercise informed consent with almost anything with
regard to socioeconomic status and also with regard to public health, with regard to the
education of our children.
We're just so clueless that we just don't have the ability to exercise informed consent
anymore.
And one of the primary bats with with barbed wire wrapped around it is this use of academic
science to create the illusion of knowledge that can then be used against us in a technocratic
sort of way.
Oh my gosh, I'm still talking.
Let me see.
I got him over here.
Yes.
Good.
I'm going to put this down and I'm going to shut this music off and I'm going to say hello
to my good friend, William M. Briggs, actually Matt and I met on a beach and it was very
romantic.
Matt was overdressed for the beach and I was flying my kite and I don't know.
We later met at another meeting and so it was wonderful.
We've been friends since.
I don't know.
Go ahead and introduce yourself.
Tell us what you're up to and then we'll just, I don't know, randomly discuss our clean
eyes.
You know, Greg Glassman and Emily Capin, like you said, the broken science thing.
They found me and I've been delighted to be able to have found them in return and people
like yourself.
There's a core group of people who understand what's going on and what's going wrong with
science, I should say.
It's not everybody like you say, like Greg says, but it is, it is some people anyway
and we, we are trying to gather together and at least speak to those willing to listen
about what we think is wrong with science and how we could go about fixing it.
And my background is like yourself, recovering academic, you know, I started off in academia.
I was in the, I was in the Department of Medicine at Cornell University, you know, the med school.
I was a statistician there, bio statistician there.
I'm sorry, but my favorite part is the fact that you were a meteorologist because when
I was a child, when I was a child, I thought that the weather was also very fascinating.
And I sat outside in, in thunderstorms and stuff.
I thought that tornado warnings were kind of cool and my parents never understood it.
But I just, I was never afraid of these things.
I just thought they were interesting.
The reason I got involved, I was in the Air Force a long time ago doing cryptography.
And I was wondering, should I go get out of the Air Force and do something more useful
with my life?
And at that time, this was in the late 80s, early 90s, global warming as it was called
then was the thing.
And I thought, hey, this is, this is interesting.
And I sort of, and I wrote some of the people and the original IPCC, the Intergovernmental
Panel on Climate Change, some of the scientists involved in that.
And I was just this, nobody sergeant at a cadina Air Force base in Okinawa.
I said, hey, can I read this, this in that paper?
And this one gentleman sent it to me and to my surprise, I could read it.
So I thought, you know what, maybe I better get out and do something about this.
It seems so important.
And I, I bought all this stuff.
I mean, I thought it was just as dire and, and calamitous as was being predicted and
so forth because I was young and naive.
But I, I progressed through my studies and my specialty actually, I got my Ph.D.
and not, not in atmospheric physics.
I got, that was my masters, but I got, I did mathematical statistics.
And the reason is, I was interested in models.
What makes a good model?
What makes a good climate model, medical model, biological model, any of these kind of models?
And how do you tell if it's good or bad?
And that involves probability and uncertainty and all this kind of a thing.
So that's how I slowly became a skeptic, the more I learned about how uncertainty really
works and how, about how over certain everything that they pushing at us is, and not everything
I should say, you know, you know, we have a pretty good understanding of the way capacitors
work, for instance, and so forth.
But these are not political things.
It's all the stuff that turned political that the over certainty is vast as we've learned
to our, you know, sadness this last four years.
Right, right.
I think over certain is a, is a wonderful way to really overlaps with how I've been saying
it is that you can almost, you should immediately be skeptical of anyone who's certain and then
even more if they're certain about a very simple explanation for a complex thing.
And oftentimes I've started with that basis of interpreting the pandemic, for example,
is it just can't be this simple that somebody spilled something in Wuhan and now it's everywhere.
And so anyway, what do you think from the perspective of the broad picture, how can we,
in your experience, you know, you're selling, you're selling a couple books, which I just
put up before it, I don't mean it to say it in a bad way.
I mean it to say it in a good way that, that he is capable of putting down his thoughts
in a way that I can't and has done it several times.
I actually, I don't, I, somewhere I can't find everything you believe is wrong book.
I know I have it, but for some reason I couldn't find it to put on the screen, but I do have
the book on certainty.
And I think because that's the one I have, that's the only one I've read.
That's this one here.
How do we get people interested in this philosophical level of science in a way that, you know,
most people won't be, you know, I mean, they'll be interested in the more practical matters
of here's what these people are trying to sell you and here's why it's wrong.
I mean, we could clearly see that they're trying to manipulate you into taking certain
medications and so forth or they're trying to, they're trying to tell you a certain
thing.
Tomorrow I have a thing on gas stoves, now they're going after gas stoves even as they
tell you they're not going to.
They want to tell you that they're bad for the, the environment of their bad for your
health and that kind of stuff.
So we could show that those kinds of claims are wrong and that's enough for most people
and that's fine.
But if you want to know why those things are wrong, how it is that the scientists themselves
beyond simple matters like fraud and lying and so forth, which we see a lot from top experts
and rulers and the like, but most scientists are sincere in their beliefs.
They're just vastly over certain or they're wrong, but they don't know how they're being
wrong.
And that's the level at which we need to fix science.
Now a lot of people can just, you know, follow along, they can gain entry to, you know, podcasts
like yours and so forth.
The woman found it.
Others that show what things are doing wrong.
That's the other one.
The woman found it.
Thank you.
And then we can get people to sort of grasp these more fundamental things, but they are
more difficult.
You know, I mean, it's, it's, you were there, we were all there last week or a week and
a half ago.
I guess we were out in back in Arizona for another epistemological boot camp or I guess
we called it and they were difficult subjects being broached.
You know, what, what is the nature of probability if we're speaking of uncertainty, we must
use probability.
How do we, how do we then understand what probability is and so forth and how does that
relate across a broad range of sciences, everything from, you know, medicine biology all the way
to astrophysics, fundamental quantum probability and all those kind of a thing.
So it's difficult.
We're not going to get everybody along and that kind of a thing, but we can get people
to understand that because people are using these procedures that they think is guaranteeing
them certainty or not, we could just show that they're wrong on that kind of a thing.
Whether they get delved deeper into the mechanics of why exactly it's wrong, that's, that's,
that's going to be only for a fraction of people, I think.
Do you think that, um, one of the things that struck me years ago already was that, um,
in neuroscience, there was this time, I, I, I've been in neuroscience, I was in neuroscience
for around 18 years.
And when I started in neuroscience, I noticed that there was this momentum in the acquisition
of physicists.
And the reason why was because physicists could make simple models and models were what
every, every, uh, every biology's journal wanted you to have a model with a little neuronal
network that showed how your data was relevant to brain function.
And because these physicists had this toolbox, you could just bring them in to a, to a department
and they could be on every paper that needed a model and every paper needs a model in this,
in this, in this kind of scheme.
And I was struck by how, what are they doing like transport models and the like that kind
of thing.
So just simple network models that show that, you know, something, some connection could
be relevant.
Um, it, to me, it's really just a question of again, how are we using them and are we
using them to advance our understanding or are we using them to, to, I don't know,
obfuscate obfuscate or, or, or, or to add complexity for the sake of, for the sake of
complexity, which turns it into science.
It's funny.
You mentioned it's just a week ago or two weeks ago that somebody had a paper out that
looked at literature papers of all things.
And I guess these are spoof papers.
They had regular papers and then they had the same papers, but they had equations in them.
And the ones that had equations in them just for no reason were the ones that were viewed
as a much more scientific.
Oh, yeah.
Wow.
That, that's the level of stuff.
I mean, it's easy to create a lot.
I mean, not, not everybody can do it, but once you, once you learn how it's, it's trivial
to create models.
That's one of the problems.
It's, it's too easy.
Science is too easy.
It's, it's difficult to understand that maybe, but because science is so easy to do now in
a lot.
Hi, what happened there?
Is garage is like my garage?
Come on back now.
You hear?
I don't know.
Maybe you should snap to that might help on he'll call back.
I want very much to see if there's too many people doing it and numbers of people just
because there's going to be a lot more error, but it's something, something.
Here you go.
There we go.
Yeah.
It's probably me, the, the camera, it doesn't, it does a visual acuity check on me and sort
of bocks.
Yeah, it's all good.
So, we were talking about models and it just took for a couple of seconds.
So it's okay.
What is different?
I mean, for the, for example, you were in meteorology, they could only get away with,
they can't get away with crappy models, right?
If they use a crappy model, then they're, they're prediction for even tomorrow would
be off, right?
But to a certain, this is, this is, this is a, you bring up a good point, an excellent
point, as a matter of fact, because it's one of the few fields.
In fact, one of the very, very few fields that model goodness, the idea of what we call
it, the technical term is verification, model verification.
That's where it was really born.
That's where all the statistics and the probability and the understanding of the philosophy of
model goodness arose from meteorology, because it's for obvious reasons.
They make weather forecasts, they come out at least twice a day, and they want to verify
how good they're doing.
And because they did, they developed all kinds of ideas, all kinds of measures, like numerical
measures that show you how good the predictions are behaving and the like.
And they use those to sort of feedback and improve the models.
And they have meteorological models over the last 50 years or so, they've increased
dramatically.
They're now very accurate out to a short, short period of time, out to like four or five
days, they're great, out to beyond that, they diminish in quality fairly rapidly.
And you know, they, for technical reasons, you know, the atmosphere is just chaotic and
it's, it's not cooperative at, at times past that.
And let me ask you this, just in the context, if they had more measuring devices with these
models become better or the models are already at their peak sort of in that sense.
No.
So not really.
Okay.
Part of the problem, they have, we have satellite data and they can sort of make satellite data
for a lot of a lot more points and so forth.
But their computational power becomes one of the, one of the bottlenecks.
The other is the atmospheric chaotic.
Good Lorenz.
He was one of the, a meteorologist at the time using early versions of computers.
He was running a program, very simple dynamical model, very simple dynamical model.
I think he coded it in Fortran.
And he was running the thing and then he had to step away or something like this and he
came back and somebody else had used the time on the computer.
Back then you had to get time on a computer, you didn't have personal use of it.
You had to sort of subscribe for time.
And he wanted to restart the computer exactly where it left off.
So he reprogrammed in the, you know, using punch cards, the constants that the model
spit out at a certain point, but he didn't get them to the exact decimal level all the
way out to however many digits the computer did.
And he found to his surprise that diverged wildly.
And chaos as a science was born from his paper.
And it's amazing.
And it turns out to be sensitivity to initial conditions is sort of defining characteristic
of chaos.
In other words, you could have a perfectly defined rigorously analytic dynamical system
that because it starts off in a slightly, just the tiniest little, you wouldn't make
any difference initial point, but can diverge wildly from a model that starts off slightly
different from it.
So that's the way the atmosphere sort of behaves, people are discovering that you can
kind of get away with that by making your predictions coarser.
And only speaking of averages and the like and do a little bit better in that respect.
But it's still meteorology that led the way.
And that's how I learned of all this kind of stuff, because that's how I started doing
and doing model goodness, how do you check?
So you could apply this, these same tools to all kinds of models, like we were being
foisted upon us all the time.
And it turns out a lot of the models just stink.
They're terrible.
They're never verified.
In fact, the leading practice of most science, the leading practice of most science and most
science papers is to fit a model to data, announce the quality of the fit and stop.
That's it.
And then they act as if they've found something.
But as we already discussed, anybody could fit a model to data, anybody could fit any
kind of model to any kind of data.
It just doesn't take that much ingenuity anymore.
And therefore, we're flooded with, you know, just acres and acres of this stuff of bad
science of just good model fits.
Well, so what?
These models are never checked against reality after that.
But everybody takes it as if it was.
It's a huge problem.
Right.
And I think you could formulate that in the reverse and say that if your scientific paper
doesn't start out with, my model predicts the following.
So we checked or measured the following, then you're really not, you're really not verifying
a model.
You're actually, again, as you said, making observations and then fitting a model to it,
which is big deal.
That's a big difference.
You can almost say anything you want if you use that strategy.
But the vast majority papers, biology and medicine, and all of the soft sciences, almost
all their models are like this.
They just fit a model and they're all ad hoc models, for the most part, in the soft sciences.
They fit the model to the data.
They announced after they manipulate the data and the model that the model fits well.
And that's it.
And now we're supposed to take their word that they have found some causative agent in
nature.
And it's just not the case.
They may have happened upon something.
They may have gotten lucky.
But it's not because of their procedures that they're correct.
It's just by chance, by luck, that they're off and right.
Or by some intuition too, a lot of scientists are smart guys, but it doesn't mean that
the procedures they're using are guaranteeing them the kind of veracity that they tell us
that they have.
That's why when people say, follow the science, no, it isn't like that.
We should not follow the science and tell that science has been independently, by disinterested
parties, checked and its accuracy, its predictive accuracy, otherwise we've never known.
One of the things that I've been thinking about is how can, because broken science
brings together this really weird group of people where there can be lots of owners
of CrossFit gyms or athletes or martial artists or people interested in nutrition
or training, and then also, you know, physicists and people like yourself with a million different
things in their background.
And what I'm wondering is a lot of times we get so deep into that discussion that we can't,
we need, I love the list that Greg has made of all of the pseudo sciences.
But I felt like the last meeting was missing a little session about concrete examples,
which is what you said earlier.
And I almost want to suggest that somebody should build a website of concrete examples
of what the model is and how it's not ever tested.
I mean, the ones that I've been plugging for a long time are the model of antibodies
equals immunity, but they never really test that.
And when they do, they don't get the answer that their model predicts they should get,
but they don't care.
And so I wonder if it wouldn't be useful for us to make a list like that.
That was my first thing.
And I also have a thing about funding, but what do you think about that?
Is there a...
I think it's a fantastic idea, of course, absolutely.
I do these things all the time, you know, maybe once or twice a week, I publish a piece daily,
but maybe once or twice a week is looking at bad papers and from all range of science
that use these techniques.
So yeah, it would be very worthwhile.
That's a good project.
I'm going to consider that and how to bring it about putting it on a more systematic basis.
I think that's a fantastic idea.
I mean, neuroscience is just littered with models of how the brain works that have like
two variables.
And it's extraordinary.
I mean, and then they would take an fMRI measurement and look, there it is.
We got some difference between these two relative signals and it looks a lot like our model.
That's a perfect example of the type of thing where models...
It's model upon model.
The fMRI...
You'd know this better than I do, but for your listeners probably know it better than
I do too.
But the fMRI...
The output from that is already a model.
It's already a model.
It's not like they've gone into the brain and dissected, taking a look at this mirror
and saying, oh, yeah, this is a bigger fashion over here.
No, this thing is an inverse problem and it's already a model.
So there's uncertainty already in the data that you have out of it.
And then they use this uncertain data as if it were certain downstream and further models.
And to some of the most preposterous things, I mean, Sam Harris was...
He has one I picked apart a long time ago.
Ridiculous things.
People are more likely to believe in Santa Claus if the sublocutist area of the brain
lights up in their effort.
All kind of statistical nonsense that they try to pull off because it shows how well their
model could fit, the model of the model.
So yes, all he's there is they all suffer from the same problems over modeling.
What do you have to have models in science, but they're not verified.
Another place that models probably play a more reasonable role in describing reality
is in business where uncertainty equals risk.
Is there any way we could use the idea that businesses don't use models that don't work?
Insurance companies don't use models that don't work.
So there have to be people that understand that model verification is at the heart of
knowledge generation.
Oh, yeah, sorry.
Did you hear what I just said?
I'm going to let that come back.
Yeah, business models of verification and business models use uncertainty as risk.
And so there's a lot of sort of feedback in business for their models in actuarial science
and whatever.
So there have to be people that from a professional perspective understand how if they do it like
this at work, but academic science doesn't like that, then there ought to be lots of
people that could be woken up very easily who just because of their professional background
are, this will just snap into their head, don't you think or?
I think you're absolutely right, actuaries are an excellent point because if the insurance
companies didn't model accurately, they would stand to lose a lot of money.
Of course, the other problem after that is not scientific, but sociological, the government
comes in and begins to regulate them because their models are sometimes too accurate and
they're not politically desirable accuracies, that's so that's a whole other problem.
Oh, wow.
I didn't know that that happened.
Okay.
Well, you know, you ever you know, we don't want to talk about all this idea, but you
know, there's official, you know, there's some groups need more need more help from
the government dollars.
And so the government steps in and and and modifies it.
Well, you know, you're not you're not allowed to have noticing of certain things.
And so now I see what you mean, yeah, there is that, but but actuaries do do a good job
of looking at how well their models portray.
And because, you know, they're not the most complex models in the world, they're more
or less just, you know, counting.
Here's what happened.
And here's what we can expect to happen again in the future, which is a very good way to
do things.
Right.
Not that the math is simple.
I don't mean that.
But I mean, to say that as far as model complexity goes, it's not that not that difficult.
Would you would you want to, um, for my viewers, just take a crack at a brief explanation
of how the the no hypothesis falsification thing isn't actually verifying models and,
you know, pull an example out of your out of your brain at random?
Is that a?
Yeah, it's a it's a very good question.
Not the null hypothesis and statistics is kind of the straw man of statistics.
And everybody knows the straw man is a sort of informal fallacy.
It's it's set up to be able to knock down and knock down easily.
Normally, what happens is you set up an ad hoc model, something like a regression, very common
thing, and you relate some X to some Y, some, some measure to some observable thing that
you're interested in.
The null hypothesis is that whatever your X is has nothing to do with this Y.
And you encapsulate that into a probability model, a parameterized probability model,
and you set the value of that parameter at some level, typically zero, you say, if this
parameter exactly equals zero precisely exactly equals zero, why that's my null hypothesis.
And then you calculate all kinds of numbers, namely, like a P value, a bit of arcane magic
that if it's small enough, you can go waving your we P value in front of people saying,
look how small my P value is, I have disproved my null hypothesis and therefore the opposite
of the contrary of the null hypothesis must be true.
If we've said this parameter is exactly precisely equal to zero, and the parameter is a very
sort of measure of strength, therefore it's not zero, the null hypothesis has been shown
to be false, and so not zero, therefore it must be important, this X must be important
in describing this Y. Of course, it's not true. For one thing, you've never disproved
the null hypothesis, you've just made an active will, you said, you know what, I'm going
to decide that this null hypothesis is not significant, and what does significant mean?
Significant means the P value is small. And what does the P value being small mean? Well,
it means significant. It's a circular definition. It's just an active will to say, I think this
correlation is causation. And that's it. I think this correlation that I have found
is causation. And you have some mathematical apparatus that helps you in this ritual. One
of the guys who was at the thing last week, Greg Gigarenzer, it's his phrase, the ritual
of statistics or ritualized statistics or something. Just following these things to sort of bless
your results, you run these typical procedures over them, and if things work out, well then
you claim that you have found something. So null hypotheses, we can eliminate them entirely.
We don't need them in science at all. We can instead form our models in this predictive
sense. If you say that X, knowing what X is, tells you something about Y. So one of my
favorite examples is, I've used this example many times, attendance at a Fourth of July
parade when you're young. That's our X. The attendance at a Fourth of July parade when
you're young, predicts whether or not you're going to turn out to be a Republican. That's
our why. You're frozen again, just hold on one second. Darn it. He's got to fix that
camera. At least he stopped at a place where I can accurately characterize where he needs
hypothesis is catching up again, your camera parade attendance has. Yeah, sorry, your camera
just caught you off. So I don't know what the deal is. It's a it's okay. You're at
perfect. It's right at the part where you said, okay, parade attendance predicts whether
or not you're going to be a Republican and then it stopped. So all right. So the null
hypothesis is parade attendance has nothing to do with it. Whether you're going to be
a Republican or Democrat or something like this. So you form this model, you put it
in a regression, you get a VP value, which is what these researchers from Harvard did.
And they said, yes, attendance at a Fourth of July parade, when you're young, predicts
whether or not you're going to be a Republican. And that's the that was what the headlines
read. So, you know, these young patriotic little kids you're imagining are being radical
on ice into into being far right extremist leg waivers by attending Fourth of July parade,
but they cheated. They didn't use for the July parade attendance. What they did was
they looked at people's residents when they were young, the address that they thought
they lived at when they were young. And then they went back into the meteorological records
and looked whether or not it rained on the Fourth of July. And if there was any precipitation
on the Fourth of July, they said there was no parade. And the child could not have gone.
And if it did not rain, if it was any kind of cloudy or clear day, they said, well, there
must have been a Fourth of July parade and there must have been the kid attending it.
That's the level of science. It's absolutely absurd because I always use this joke, but
in San Francisco, of course, it never rains on the Fourth of July, almost never. And so
the city should be teeming with Republicans because they would all go to these parades
and so forth. But because we have this null hypothesis that's so easy to swap down. I
mean, if you're not getting, if you're not rejecting your null hypothesis, you're not
trying hard enough. It's always possible to do just just requires a little bit of ingenuity
and you could do it. That's why we have so much bad science.
Yeah, wow, that's a really good example because there you're really actually testing for
even raining on Fourth of July. It's not even the parade, right? So that's one thing. But
I really feel like that's a very good analogy for how far we are from actual understanding
of what happened during the pandemic. They want you to believe it's as simple as some
RNA molecules filled in a puddle and Wuhan. And now it's everywhere. But there's so much
involved in that it's a massive pile of assumptions in the end. And I think it's useful to think
of that as a model of reality that makes predictions. If you say that that's the way the pandemic
worked, then that makes all kinds of predictions about how future RNA should transmit around
the world and how these future signals could be found. And I think those that's where this
whole thing would really fall apart like a like it should. I mean, because again, if
you challenge them to make predictions, they can't. And that's I feel another side of the
coin with regard to models being fitted to data versus, you know, making a model and
then going out and seeking validation of it. I don't know. Absolutely. I don't know where
I was going with that question, but no, no, it's absolutely right. You're absolutely right.
If you can't make observe predictions of observables, it's the whole point is these
observables are the things that are supposed to matter to us. And if you can't make, you
can't make skillful predictions, it's a technical term, skillful predictions of these things,
then you have no business feisting it off onto the public and making us follow the science
is just, it's absurd, it's absurd.
Well, that's the reason why I think it's important for people to understand then that if if done
well, and even with malevolence, for example, if they want to create the illusion of pandemic
potential using academic biology, they can do it. And they can even put p values behind
it. And they can get peer review behind it. And so this whole system, right, has been
distorted into, they call it knowledge, but it's not knowledge, it's not generating knowledge.
And I really feel that big picture is something that a lot of people can get a grasp on. And
if they do, it might put them in a place of healthy skepticism that gives them also a
short, rather a small toolbox. You know, if you approach these problems as okay, what
is the model that they are presenting to me and try to understand the cartoon version
of reality that they are foisting upon you with the story that they tell and then evaluate
whether or not that model makes any predictions that have value. I don't think that's hard
for well, it could be hard for some people, but I think there are a lot of people who
are capable of doing it.
Couldn't be. It shouldn't be difficult. I mean, the best science we have, all science
uses models. It's not using models. That's the problem that we should emphasize. You
have to have a model, because you can't encompass all of reality all at once. You have to have
a slice of it. You have to have some abstraction and some kind of model that that's not wrong.
But the model should at least have proved itself. And fitting past data is nothing.
It's absolutely zero. It's necessary, but it's far from sufficient, because any set
of past data that you give me and any field, I can fit not just one, but any number of
models to it and to any arbitrary precision. I can fit them perfectly. I can always find
at least one model that will fit that past data perfectly. And this is a trivial, absolutely
trivial mathematical exercise. So that we have sophisticated models done on a computer
and they're done by experts and the model has been peer reviewed by people who also
create their own models means nothing. It's absolutely nothing. It adds nothing to it.
Like you say, it's not generating knowledge. It's generating noise. It's only when these
models are verified and we're found. And again, it has to be by disinterested people.
It can't be the same people who create the model and then make predictions to say, yeah,
trust us. Everything's fine. No, no, we should know enough about now how the world works
that we cannot have people who have an extreme interest in their own project, be that their
own judge. There's legal maximums that guide this kind of a stuff. We need those same
maximums to guide science. That was one of the useful discussions. I think we had two over
the last week. Right. Right. I think a concrete example for people that are listening or watching
for what Matt just said would be a mechanical model of the solar system. And you could crank
it backwards and you can see that, Oh, wow, last month, it's okay. And the month before that,
it's okay. And wow, it works for that eclipse from two months ago. But then you crank it
forward and you go to tomorrow and the moon is backwards. And then you go a week a farther than
that. And it's like the earth turns upside down. Then something inside of that box is not working
correctly. Because even though when you crank it backwards, it seems to recapitulate the past
astronomical data, when you crank it forward, if it doesn't make a prediction that happens in
the future, then those gears are not working. And I think if you if you apply that to some of
these biological models, or these nutritional models, or these, you know, a lot of this pandemic
models, you get to the point where when you crank that thing forward, it's useless. It's
absolutely useless. That's precisely it. And it's even worse than the picture than you're painting.
Because think about this, think about that model we have now, we do have a model of the solar
system, like a Copernican type model, we do crank it forward. And we do see that it does work rather
well. However, there was an older model, a tolemaic model that had all these epicycles of things
spinning this way and that way. That model also predicted very well. It predicted beautifully.
So even if this is why it's a bare minimum to get the model to make good predictions,
that's the first entry. After that, it still doesn't mean that you have proved you found a real
cause of something. I love that. Oh, I love that you have a lot more to do.
I love that you said that. Wow, that's really spectacular. I love that you said that. And that's
really where where the grinding of science happens when there are two models that work pretty well.
And now you need to come up with experiments that's able to dissect their their predictive
value like that. That I imagined as a kid was what what all scientists were doing was at the
me too, the cutting edge of knowledge deciding between the two best models
with a clever experiment that got them a lab like obviously, isn't that what they're all doing?
I don't know. Sometimes we can't always do experiments, you know, like in cosmology and so
forth, or they're very limited. But yeah, that's it. That's it. We have to find, like I'm always
saying, you we have to, this brings us back to broken science. One of the things that's broken
about science is is the metaphysics. In order to do physics, you have to first have a metaphysics.
So we need to understand the limits of the metaphysics that we currently use and of
possible replacements and the superiority of replacements and so forth. But that's a whole
other discussion. That's that's why it's very difficult to explain these kinds of things because
we have to first get past the point where we recognize we've had a broken model and then we
have to get to the point where we realize that models are not being used in a predictive sense.
And then we have to realize that we have to have a firm understanding of what it means to cause
and what what does cause something mean and all that kind of thing. So we really have to we really
have to fix the foundations of science. And that's that's kind of what this broken science
things about. I heard you leading a disc or having a discussion randomly between sessions.
And it had to do a little bit with this, the idea that the table doesn't exist because actually
it's mostly space. And then if we go farther and farther and farther, all of a sudden there's
no causes at all or something like this. And that's really kind of where in a sort of,
I'm terrible at these kinds of discussions, but that's sort of where where Sam Harris is with
regard to, you know, free will and everything else is just we're all particles moving, right?
And if we just had enough data, we would be able to predict the future and everything.
Of course, I think that's false. And that he does. He's the kind of guy that there's not the
table's not really there because it's mostly mostly empty space. And my metaphysics says the
table's here. It's real. And in fact, the particles don't exist in it anymore. They only exist virtually
in potentia. They're now part of the thing that is the table. There's no separation of
particles and atoms anymore that makes the table. It just is a table, just as common sense
shows it to you. And of course, about free will, I always make the joke. It's a standard joke,
but it's true that you read these guys papers who try to prove to you that free will doesn't
exist. And every one of these papers, they always say, if only people understood they couldn't make
choices, they would make better choices. They always want to save the world by telling us that
we really can't make choices. That's absurd. It's absurd. Here, what are they doing? They're
using their free will to write it. That contradiction, when you call them on it, they'll just say,
Oh, no, no, it doesn't work. It's an illusion. Okay. Fine. It's an illusion. Who is having the
illusion? Right. Wow. Yeah. Who is having the illusion? It's a bluff. It's always a fluff. There's
a lot of bluffs in science. And that's one of them. Yeah, that's wonderful way to put it. That's
great. I'm I'm just sort of humbled by the fact that the first couple of these meetings were so
overwhelming to me that the big picture of how to use this is a way to bring people to the light
of things. It really felt like this was the first time where Greg nailed it, where other people nailed
it and where I was comfortable enough in my seat to absorb it. And so I'm very, very excited about
being able to to apply these ideas as you do now to biological papers and to like think about
what I'm really calling out as far as what's broken with how they're doing it. And the other
thing that was missing from my descriptions was understanding that there were lots of people
who've thought about this, right? It's not some kind of secret. I mean, people have written books
like you, but before you as well that that that part of the reason why we're here is because people
like Popper wrote stuff. And yeah, and so it is important. I was pretty naive to all of that,
that literature. And so it's been a big eye opening experience for me to realize that in the
best. Well, that's just it. You could go through as a scientist. I did. I went through the whole
thing. Cornell, I got my master's and atmospheric, you know, PhD, whatever. Many, many people,
you never once have to be confronted by any of these philosophical matters. Never.
Never. You can go through and finish. You don't, at the end, you're going to ask, what is science?
Well, science is what I do. Yeah, right. You know, that's an answer, but it's not, it's not a very
good answer. But that's what most people's answer is. And if they've absorbed anything,
they've absorbed this idea of the Popperian, you know, falsifiability, and you now see the limits of
that. But most scientists don't because they sort of dismiss a lot of philosophers.
Well, I think they should dismiss a lot of them. There's a lot of these academic philosophers,
you know, kind of postmodernistic kind of nonsense that they preach. It doesn't seem to have any
bearing on what a scientist does. And they're right, it doesn't. But there's, there's, you still
can't operate and be a scientist without some philosophy. You might not recognize that it's a
philosophy, but you have one. You absolutely must have a philosophy in order to apply
the skills that you've learned to, to build your models, to understand your models,
to know what a model is, make predictions, all these kinds of things.
I mean, I'll give you an example of how, I mean, how bad I'm going to burn myself here, but I
think it's an important example. I think you'll be able to see this. So if you, if you had a
neuron and a slice of brain where I used to work, and it would be surrounded by other neurons,
of course. And in that slice, there would also be axons, which were coming in from other brain
regions that were cut off, but were still transmitting neurotransmitter to the neuron
that you're going to record from. And so one of the measurements that you would get from this
recording would be these spontaneous events, which would look like this. And they're basically
signals that are coming from these synapses, which have been cut, but are still active.
And you can see the synaptic events in real time as this neuron receives them. Now, interestingly,
one of the main, not one of the main, one of the ways that you create this, this scenario where
you can write a paper about it would be to apply a drug here. And if the baseline amount of this
signal were to change, then you would say that somehow or another, that drug is interacting with
with these, the receptors or the proteins on these and changing the release probability
of the glutamate there. And so then you start to understand how the drug works or doesn't work.
Now, of course, this assumption would be backed up by pharmacological blocking here and all kinds
of other things like that. But my point is more abstract in the sense of if you imagine how complex
the brain is, it's pretty easy to get here and have a significant result when you pharmacologically
alter the circuit. Now, the reason why I'm saying this is because I feel as though we're at the
verge of being able to explain to people how it is. And with where I wasn't, I'm going to stop
talking very soon. When I was in grad school, no one ever used the word straw man to describe
the null hypothesis. And that was partly because it was kind of a sacred ritual. You would never
be so, be so, how would you say coy or, or, or sorry, it's almost like sarcastic and not,
it's not the right word. But you're being so honest, if you said that, that it was a straw man, and
but it would be so useful from the perspective of teaching a graduate student exactly what this
game is. Quite frankly, I feel as though knowing what I know now in broken science, I could go
back to academia and be much more successful, because I understand what the formula is asking
for. And it's not clouded by this ideal of what I imagine I'm doing. Can you can you relate to that
illusion that I created for myself? That's absolutely it. That's that's that's how people
that you described how a lot of people wake up from the rituals that they're practicing and
understanding that this this method just doesn't give what they they wanted to give. I mean,
you're trying to what you're trying to do there, you're trying to fundamentally fundamentally
understand the causes at play. And that's exactly the right way to do it. That's absolutely the
right way to do it. But it's brutal hard work, right? I mean, there's all kind of you realize
after you set up a little simple experiment that you just drew right there. And and you saw the
signals and suddenly you realize or you have other evidence that comes in later, you know,
there's other things that could account for that change in signal to. And you have to keep
eliminating them one by one by one. And it takes a very long time. It's very difficult to do the
very best. Brutal brutal brutal hard work. Absolutely. That's what science is really like.
But we've tried to make it too easy and it is now far too easy just apply this formula. And if it
pops out this number pops out, well, then you you you prove what you want to do it. It's very sweet
the way you say it because actually I I felt when I was stepping out of neuroscience or when I was
my career was waning before I got let go. It was because I was still having to put in
hundreds of hours to get my data set. I couldn't just roll someone into an fMRI machine and then
go back to my computer for a couple weeks and then publish a paper. I had to find something that
was in effect and then do some other experiments that showed that the effect wasn't from something
else. And that was the pharmacological blocking and this kind of thing. And so I did feel
a little bit like I was part of a small group of people that was still and I'm not trying to
to my own horn. I'm trying to say how I felt it didn't fit in. I didn't have this
five papers a year machine going. I couldn't figure I couldn't see it. I didn't I didn't
have an answer to that problem, which was, you know, you're going to need eight papers in the next
three years or you're not going to make it. And it's like what if you tell me that I could say
immediately in that meeting, well, then I'm not going to make it because what I do doesn't work
like that. And so do I need more students? Do I don't know what the answer was? But I was already
finding that wheel unable to grind. I just couldn't do it anymore.
Oh, yeah. For everybody who hasn't been exposed to academia, it's toxic in that way.
It really is publisher parish. It's terrible. It's terrible. That accounts for the flood of bad
papers. I mean, they're pushing out a lot of nonsense just because they have to keep their
positions or to bring in grants to pay for themselves and their lab or and most importantly to to provide
kickback to the deans in the form of overhead. That's you could write one paper a year or even
none if you're bringing in good overhead. If you're bringing in that, which is, you know,
it was at Cornell at one point, I think it was up to like 60%, 59 and a half percent overhead.
I mean, so you get the grant grants a million dollars and then they tack on another $590,000
overhead, and that just goes to the college, which becomes part of the slush funds for the
deans. Yeah, absolutely. When my grant application or my grant, R01, for example, if I had
when my supervisor got an R01, he paid 155% of my salary to the university. So 55% of my salary
was rent. And he did that for every student and himself. So that's quite a, that's quite a load of
money. If you think there are 130 faculty members just in that department that are all
working under the same model and all those grad students walking around are all paying rent
to the tune of thousands of dollars a year. I mean, it's extraordinary amount of money. That's why
they can pay deans millions of dollars a year. They pay deans as much as they pay basketball
coaches these days. Yep. It's extraordinary. So how about this? How about this? What have we got
a bunch of weaponized money and started funding science that was, you know, we do have that in
the way though, private companies that are not tied up with the government that are not tied up
with the government that that proviso is the strongest one I could possibly make. You can say
it's what I have an interest with the government and it becomes political. Well, that's just the
same as being the government. That's where things can be done. So it's exactly that. It's private
money in a patronage-like system trying to discover things for the sake of discovering them.
They could be towards the desire of having a useful product or something. There's nothing
per se wrong with that. It's only when it becomes, you know, as we saw with COVID, when it becomes,
you know, a very political thing that the whole thing becomes somewhat difficult. And it becomes
no different than regular academia. And you know as well as I do that same people go in and out of
these corporations into the government that judges the granting agencies and all this kind of stuff.
It's the same set of people. Yeah. It's a very difficult problem to get your head around. I
think one of the things that I liked that I heard somebody say at the meeting, which I also think
is something that can get people's wheels turning if they haven't thought about this before, is that
all the real good sciences top secret, all the, I don't know to what extent that's really true
or if it's just funny, but I do think that they're that identifying places like, you know, meteorology,
like actuarial science, where models are required to verify, we can find examples where, you know,
we can we can push science in that direction over time. We can teach, you know, if we can break
into a university and teach and teach this to undergraduates, that would be great. We probably
ruin their graduate experience, but I don't know. I'm still trying to do it. We can do it. The tools
are there. Like I say, the tools, the tools we have, it's not it's not unknown. I mean, there's all
kinds of investigations that can go on to improve these tools and so forth, but the basic set
are there waiting for us to be used. Can I ask you a personal question now? Hey, can I ask you
a personal question? What what do you do for, for, you know, to make ends meet? Are you teaching
anywhere? Do you do you have online course or I mean, why not do it online? I should do an online
course. No, I I basically have private clients. Okay. Would you want would you want enough?
Would you want any help with an online course? Like I could be your technical guy or or I could
I do need to figure out. I do need to do it. I keep swearing. I'm going to, but video is not
I'm very naive about video. Oh, but you could, for example, you could record it in your own home.
I have I have a chalkboard. Right. But then you then you send me the video and I edit it for you and
add subtitles and all that stuff. I can do all that. I would be happy to do it. And we'll chat
about that. Okay. I would think this would be really a good idea. And also, I would obviously
learn from it a lot too. I think that's where we need to go. I think we need people like you
taking over this thing. You know, I mean, if you could it, well, how awesome would it be if you
meet somebody randomly? Oh, yeah, I took Matt Briggs's course last year. Bang. It should be a it should
be something that people put on their website or people put on their social media. It I think
you're a guy who, you know, you have the kind of charisma that can do it. You're you're a good
speaker. And you and you've got all of the work done already in your books. You've already thought
about this and develop these ideas. And so there's nothing I would have colored chalk. Well, there
you go. I mean, is it the fancy stuff from Japan or is it just good? No, that's the one I got that
got the one to this Korean. Yeah, yeah, that's good too. Yeah, no dust.
That's fabulous. Oh my gosh, that's great. I love writing. That's awesome.
If we are at like 59 minutes and I don't know what we said for time, but it feels like a very
good time to wrap up and you just promised to come back because I'll have you on with more material
and more more questions next time we can go into something specific that I get out of James as I
read it. Oh, absolutely. Okay, cool. So I'm going to put this link up one more time.
This is where you can find, unfortunately, it's an Amazon link, but that's the easiest for me.
You can see his full name William M Briggs. You can see the books that he's got available.
I would recommend the yellow one and everything you believe is not wrong, although he has contributed
to this price of panic, which is specifically about the COVID COVID thing. And you can also find
him at I think William M Briggs dot sub stack, something like that is at William Briggs.
W M W M Briggs at sub stack. So thank you very much, Matt, for joining me.
Can you remind me again? Well, maybe I'll do that offline. But I wanted to say hello to your wife
as well. And for whatever reason, my mind is blanking on a name. So please give her a hug and say hello
for me. And we will we will have you on within a month. I guarantee it unless you really object
anytime. Cool. Very good. Very good. Thank you, Matt, for joining me. And I will talk to you again soon.
Bao, Zawi. Okay, that went well. Thank you very much, guys, for joining me. I'm going to put this
little slide back up again. This is, of course, the broken science meeting that happened a couple
weeks ago. I hope Matt wasn't surprised that I let him go there so quick. I'll call him back on
the phone. We are a group of people loosely organized around two people named Emily Kaplan
and Greg Glassman. Greg Glassman is the originator, the creator of CrossFit, a very smart guy who
actually had a very smart Papa named Jeff Glassman who worked for Hughes Aviation. And it was his
upbringing with his very intelligent dad that has brought him all the way to understanding this
stuff because CrossFit ran into legal problems with fake science. Basically, science, fake science
was published to try and say that CrossFit enters people. And over the course of investigating this,
he found out that his dad's been right about science his whole life. And it has put him on this
crusade to try and, you know, leave a legacy for his children and for our children of his dad and
these philosophers like Stove, these thinkers like Stove, and also our contemporaries like Matt
Briggs. And so I really think this is one of the most worthwhile sort of initiatives and I'm so
humbled to be considered a small part of it. So thank you, Emily. Thank you, Greg. And thank
you, Matt, for coming on the show. And I think we're going to have a lot of interesting conversations
as we go forward. At some point in time, of course, we're going to have Greg on the show. But I don't
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
guy. And so that'll be hard to organize. But number two, I want to have all of my ducks in a row and
already have been paid back a little bit to him, the stuff that I've learned through a series of
streams that this is the first one of. So thank you very much, guys, for joining me. This has been
Giga Ohm Biological, a high-resistance, low-noise information brief brought to you by a biologist.
19th of March, 2024. The afternoon shows are going to be a street because I really like this time frame.
Thanks for joining me, guys. 73 people in the chat. Very understandable conversation. I hope
good to see you, Pamela. Thanks for the link in the chat. She's always good for that.
Can I see Janet Reno in the audience? Awesome. I didn't see you there earlier. I'm sorry, Janet.
Janet's one of my favorite anonymous viewers because obviously it's not Janet Reno. It might be,
it might be Ford. Some reason I think it might be Ford or something like that anyway. Thanks very
much for joining me, guys.
Thanks for joining me, and I'll see you in the next video.