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Speaker 1: Welcome to Tech Stuff, a production from iHeartRadio. Hey there,

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Speaker 1: and welcome to tech Stuff. I'm your host, Jonathan Strickland.

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Speaker 1: I'm an executive producer with iHeartRadio and how the tech

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Speaker 1: are you listeners? We have a treat today. We've got

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Speaker 1: a guest on the show. He's a neuroscientist, he's a technologist.

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Speaker 1: Most importantly, he's a fellow podcaster, David Eagleman, host of

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Speaker 1: Inner Cosmos with David Eagleman.

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Speaker 2: Welcome to the.

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Speaker 3: Show, Thanks Jonathan. Great to be here.

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Speaker 2: David.

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Speaker 1: It's a pleasure to have you on. I think you

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Speaker 1: may only be the first or second person I've ever

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Speaker 1: had on the show who has done a ted talk.

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Speaker 1: I've had film directors on the show. I've had production

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Speaker 1: designers for things like Stranger Things on the show. But

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Speaker 1: it's rare that I get a chance to sit down

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Speaker 1: with a neuroscientist. So this is going to be a

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Speaker 1: real pleasure, great pleasure for me too. Well, let's start

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Speaker 1: off by talking a bit about Inner Cosmos, because you know,

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Speaker 1: this show has launched, it's got thirty episodes out as

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Speaker 1: of the time we're recording this episode, thirty I think

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Speaker 1: published the day before we're recording this. That's right, And

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Speaker 1: first I want to applaud you for your restraint that

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Speaker 1: it took seventeen episodes before you gave us the definitive

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Speaker 1: answer on what is consciousness?

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Speaker 3: Yeah, well, you know the show as you well though,

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Speaker 3: it's about you know, the intersection of the brain with

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Speaker 3: our lives, our daily lives, and right, so it took

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Speaker 3: a little while to work up consciousness. Unfortunately, we still

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Speaker 3: do not have a definitive answer to that, and it's

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Speaker 3: you know, this is really at the center of the

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Speaker 3: mystery of neuroscience is how do you put together eighty

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Speaker 3: six billion neurons and have all this you know, three

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Speaker 3: pound glob of wet biological stuff that is you? But

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Speaker 3: why does it feel like anything to be you? Why

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Speaker 3: aren't you just you know, my Mac laptop here has

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Speaker 3: a whole bunch of transistors, but presumably it doesn't find

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Speaker 3: things funny or interesting or painful or curious. Yeah.

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Speaker 2: Yeah.

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Speaker 1: I often talk about in the sense of the ability

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Speaker 1: to have an experience versus the ability of just being

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Speaker 1: able to process information, and a lot of what you

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Speaker 1: talk about has obvious crossover into the world of technology,

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Speaker 1: but beyond that, like when we start talking about things

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Speaker 1: like artificial intelligence or artificial neuron networks. Like that's where

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Speaker 1: we start really having this interesting cross talk, because I

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Speaker 1: get the sense that often the technologist side has maybe

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Speaker 1: a superficial understanding or sometimes a complete misunderstanding of the neuroscience.

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Speaker 1: But they're trying to use neuroscience to describe their technologies,

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Speaker 1: and meanwhile you have neuroscientists who are often trying to

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Speaker 1: straighten out the concepts, and it can become a complicated

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Speaker 1: conversation where people are talking on two different levels. So

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Speaker 1: one of the things I wanted to ask you actually

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Speaker 1: relates to this, which is, as a neuroscientist and as

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Speaker 1: a technologist, how do you actually define artificial intelligence? Because

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Speaker 1: it's such an enormous discipline, and I feel like a

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Speaker 1: lot of the reporting around it gets very reductive.

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Speaker 3: Yeah, all great questions. Here's the thing, you know, we

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Speaker 3: don't even know exactly how to define natural intelligence. What

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Speaker 3: I mean is people have known, for example, since the

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Speaker 3: nineteen twenties that the IQ test measures something useful and

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Speaker 3: it correlates with one's success in life along various metrics

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Speaker 3: career and so on. But there's a big debate about

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Speaker 3: what intelligence is in humans. Is it the ability to

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Speaker 3: better simulate possible futures and evaluate them. Is it the

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Speaker 3: ability to squelch distractors and so on? There's lots of

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Speaker 3: there's lots of different diferent ideas about what intelligence might be,

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Speaker 3: but we don't know. It's probably a word that has

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Speaker 3: too much semantic weight on it, meaning that it's not

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Speaker 3: just one thing anyway, So when you ask the question

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Speaker 3: of what is artificial intelligence, it can mean a lot

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Speaker 3: of things, and people mean different things by it. And

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Speaker 3: so the way we're building AI is the way we

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Speaker 3: build any sort of machine where you're trying to get

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Speaker 3: the machine to do something for you that you don't

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Speaker 3: want to do. So, for example, you know, Google Maps

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Speaker 3: knows every street, every place I should go, how a

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Speaker 3: best way to get there? That is artificial intelligence in

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Speaker 3: the sense that it's way better than I would be

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Speaker 3: at memorizing an entire atlas, And so that's a very

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Speaker 3: useful machine for me. You know, Chat, GPT compiling the

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Speaker 3: world's data and being able to tell it to you

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Speaker 3: in the structure of a human speaking. It's sort of

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Speaker 3: like what we appreciated about Google, which is this compilation

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Speaker 3: of all the world's data. But now it can just

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Speaker 3: spit it out to you in various ways. Lots of

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Speaker 3: ways to think about what artificial intelligence is. But I

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Speaker 3: will I do want to say something, which is that

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Speaker 3: you know, AI butted off from neuroscience because neuroscientists figured

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Speaker 3: out a while ago, Look, you know, you've got neurons.

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Speaker 3: These neurals are connected to one another. Now, as it

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Speaker 3: turns out, neurons are really complicated. Each neuron is about

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Speaker 3: as complicated as a city. It's got the entire human

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Speaker 3: genome inute. It's trafficking millions of proteins around very complicated cascades,

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Speaker 3: and it's constantly plugging and unplugging and seeking and plugging

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Speaker 3: into other neurons and so on, and so people a

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Speaker 3: while ago came up with this idea of an artificial

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Speaker 3: neural network where they said, look, what if we just

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Speaker 3: have units that are connected to each other and those

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Speaker 3: those connections have particular strengths that can change. And so,

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Speaker 3: as it turns out, that has been the trick that

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Speaker 3: has led to this renaissance in AI that's been incredible,

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Speaker 3: but it also it's departed from the biology. I would

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Speaker 3: say they're now sort of oft in different directions. It's

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Speaker 3: very cartoonish version of the biology. In some ways it

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Speaker 3: is much better than the biology, and in other ways

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Speaker 3: it's much worse. Just as an example, AI, even given

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Speaker 3: the absolutely mind blowing things it's doing right now, it's

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Speaker 3: not as good as let's say, a five year old

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Speaker 3: is at navigating a complex room and socially manipulating adults

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Speaker 3: to get what she wants, and you know, all these

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Speaker 3: things that you know, getting food to her mouth when

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Speaker 3: she's hungry and so on. There's a lot of things

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Speaker 3: that humans can. You know, I should say that ais

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Speaker 3: to there's still gonna be work ahead of it to

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Speaker 3: get there.

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Speaker 2: Yeah, yeah, I remember.

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Speaker 1: One of my favorite stories was from a few years

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Speaker 1: back when I was at south By Southwest and a

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Speaker 1: roboticist was talking about a project in which they had

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Speaker 1: a robot that was capable of moving through human environments

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Speaker 1: and they were attempting to have the robot figure out

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Speaker 1: how to open a door, and they said the process

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Speaker 1: of that had the robots standing in a corridor for

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Speaker 1: like three days just analyzing door and getting in the

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Speaker 1: way of people who needed to go place.

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Speaker 2: Like everyone hated the robot.

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Speaker 1: By the end of this, but they were explaining like,

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Speaker 1: this is something where as a human, once you have

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Speaker 1: the experience with a door, you can start to reason

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Speaker 1: out how to interact with other doors, even if the

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Speaker 1: mechanism is different. Right, you can start to interpret how

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Speaker 1: to interact with this barrier, and it may mean that

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Speaker 1: you have to try a couple of different things before

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Speaker 1: you successfully open the door, but you have an idea.

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Speaker 1: But in artificial intelligence, that's something that we're only starting

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Speaker 1: to approach right now. Another great example was when DHARPA

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Speaker 1: did the General Challenge where they had the humanoid robots

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Speaker 1: that needed to complete a series of tasks like driving

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Speaker 1: a vehicle, getting out of it, walking through a door,

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Speaker 1: picking up a tool, using it, and they found that

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Speaker 1: one of the toughest challenges was walking through the door.

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Speaker 1: There's so many videos. Don't look it up, people, it

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Speaker 1: will break your heart, but so many videos are very expensive.

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Speaker 1: Robots face plan think while trying to walk through a doorway,

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Speaker 1: and it just shows that these these things that can

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Speaker 1: come very easily to humans do not necessarily come easily

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Speaker 1: to machines. Whether we're talking about an AI software running

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Speaker 1: in the background or a robot.

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Speaker 3: That's true, but let me jump in with one question

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Speaker 3: about what we mean by come easily to humans, because

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Speaker 3: what's interesting is we've had millions of years of evolution

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Speaker 3: building this incredibly complex structure, the most complex thing we've

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Speaker 3: ever found on the planet, the human brain, And so

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Speaker 3: what seems easy to us is actually not easy at all,

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Speaker 3: of course from other nature to have built. But yes,

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Speaker 3: I totally agree with you. Anything physical is something that

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Speaker 3: AI still has a long way to go to get.

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Speaker 1: Yeah, and to your point, yes, the human brain is

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Speaker 1: a phenomenon of evolution where it's what makes this comparatively

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Speaker 1: easy for us when you combine, you know, compare that

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Speaker 1: against a machine. I had mentioned earlier about your TED talk.

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Speaker 1: I loved it because it was all about using technology

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Speaker 1: to expand human senses. And also you very well, you

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Speaker 1: put it extremely well about how our understanding of the

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Speaker 1: world around us, our understanding of the universes, obviously funneled

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Speaker 1: through our senses, which means when you look at what

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Speaker 1: our senses are capable of, you realize we're experiencing the

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Speaker 1: tiniest fraction of what's actually going on out there. That

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Speaker 1: there are entire frequencies of the electromagnetic spectrum that we

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Speaker 1: cannot observe directly, and that's just part of the limitations

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Speaker 1: we have on ourselves. And that when we think about that,

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Speaker 1: and we think about technology having the capacity to expand that,

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Speaker 1: that's really a fascinating thing. And I thought it was

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Speaker 1: great because I had also done stories about biohacking and grinders,

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Speaker 1: people who are kind of taking the underground auto mechanic

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Speaker 1: garage approach to this sort of thing where you might

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Speaker 1: be talking about implanting magnets on your fingertips so you

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Speaker 1: could sense electromagnetic fields. Usually it means directing stimuli in

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Speaker 1: a way that you would pick up with one of

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Speaker 1: your traditional senses, not creating a new one necessarily, sort

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Speaker 1: of the same way that you would say, like infrared

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Speaker 1: goggles translates information into the visual spectrum so we can

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Speaker 1: see it. But I'm curious, since that talk, since you've

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Speaker 1: given that talk where you were looking into different ways

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Speaker 1: to bring stimuli in and have the brain interpret it

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Speaker 1: and perhaps even augment or create new senses, have you

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Speaker 1: done any more work along those lines since you get

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Speaker 1: that talk, and I would love to hear about it.

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Speaker 3: Yeah, great, great, So maybe I should back up just

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Speaker 3: for one second, if for those listeners who haven't heard

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Speaker 3: the toss, it was just about this issue of how

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Speaker 3: the brain is locked in silence and darkness inside your skull.

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Speaker 3: It doesn't see any of the world, doesn't hear any

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Speaker 3: of the world. All it ever has or these little

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Speaker 3: electrical spikes zipping around among neurons and then release of neurotransmitters.

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Speaker 3: But that say, it's just this electrochemical world buzzing around

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Speaker 3: in the dark, and yet it constructs this whole cosmos

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Speaker 3: for you, you know, the colors and the lights and

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Speaker 3: the joy and the smells and the tastes and the

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Speaker 3: touch and all that. So this is you know, we

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Speaker 3: actually mentioned this at the beginning about the mystery of consciousness.

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Speaker 3: But this is what brains are good at doing, is

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Speaker 3: extracting patterns and assigning meaning to those and figuring out

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Speaker 3: how to navigate itself in the world. So what I

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Speaker 3: got interested in is, you know, if I were to

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Speaker 3: show you a little piece of you know, of the brain.

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Speaker 3: Let's say I could do this in real life with

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Speaker 3: you know, like drilling a little hole in there and

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Speaker 3: you get to see all the action the activity inside

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Speaker 3: that little piece of brain. And I said to you, hey,

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Speaker 3: what part of the brain is this Is this visual

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Speaker 3: information or auditory or touch or smell? You wouldn't be

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Speaker 3: able to tell me. I wouldn't be able to tell

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Speaker 3: you because it looks the same everywhere the cortex. The

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Speaker 3: wrinklely bent on the outside of the brain is actually

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Speaker 3: the same structure all across the brain, even though we

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Speaker 3: tend to call this part, oh, that's your visual center,

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Speaker 3: and here's your auditory system, and here's your amount of

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Speaker 3: sensory system for touch and so on. In fact, it's

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Speaker 3: all exactly the same structure. It's just a matter of

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Speaker 3: which data cables are getting plugged into there. So that's

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Speaker 3: the background. Whatever data you send in there, it figures

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Speaker 3: out what to do with it and how to make

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Speaker 3: it relevant. So I got interested in this question of

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Speaker 3: sensory substitution, which is can we feed in completely new

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Speaker 3: kinds of senses into the brain. And so what I

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Speaker 3: presented at that talk, as you know, of course, Jonathan,

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Speaker 3: is a vest with vibratory motors on it, and we

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Speaker 3: were able to show, for example, that we could that

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Speaker 3: deaf people, people who could not hear, who are profoundly

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Speaker 3: or severely deaf, they could come to understand the auditory

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Speaker 3: world just by patterns vibration on their skin. This is

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Speaker 3: what your inner ear does. You know, when you capture

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Speaker 3: air compression waves on your ear drum, that goes through

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Speaker 3: a few stages, then it gets broken up from high

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Speaker 3: to low frequency, then shipped off to your brain as

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Speaker 3: patterns of spikes. All we're doing is capturing sound on

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Speaker 3: microphones and breaking it up from high low frequency and

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Speaker 3: putting that into different parts of the skin as vibrations.

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Speaker 3: And it turns out that people who are deaf could

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Speaker 3: come to hear this way. And so what we have done,

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Speaker 3: and answer your question, what we've done since then, is

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Speaker 3: we have shrunk the VEST down to a wristband and

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Speaker 3: I spun this company off from my labs called Neosensory,

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Speaker 3: and the wrist band does exactly the same thing as

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Speaker 3: the VEST. It's capturing sound in here. It's got full

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Speaker 3: computational capacity, and it has these vibratory motors along the

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Speaker 3: inside of the wrist, multiple motors, and it's turning that

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Speaker 3: sound into patterns of vibration on the skin of your wrist,

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Speaker 3: and people who are deaf can come to hear this way.

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Speaker 3: The really wild part is that after about six months

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Speaker 3: when I ask them, hey, when you hear a sound,

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Speaker 3: what is it? You know? Do you hear a dog

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Speaker 3: mark and you feel a buzz on your wrist and

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Speaker 3: you think, oh, what was that? Must be a dog

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Speaker 3: where And they say, no, I just hear the dog.

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Speaker 3: And I say, well, what do you mean by that?

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Speaker 3: They say, I just I hear the dog out there,

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Speaker 3: which is wild, right, except that's all that your ears

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Speaker 3: are doing too. I mean, we've long stopped appreciating that

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Speaker 3: because you've been using your ear since you were a baby.

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Speaker 3: But for goodness sakes, all you have are spikes running around.

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Speaker 3: And yet you think, oh, I'm hearing Eagleman's voice here

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Speaker 3: from out there, whereas in fact, all of the sound

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Speaker 3: is just happening in your head. It's the interpretation of

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Speaker 3: an auditory signal that's happening in your consciousness. So turns

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Speaker 3: out it doesn't matter how you push the information in there,

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Speaker 3: you get the same thing happening there. And we also,

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Speaker 3: we've done a whole We have several products on the

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Speaker 3: market now and we're on risks all over the world.

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Speaker 3: One of them is the you know, to replace hearing aids.

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Speaker 3: So most people, as they get older they lose their

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Speaker 3: high frequencies because yes, high frequencies. Yeah, you know, that

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Speaker 3: has the most energy, and it kills the little cells

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Speaker 3: inside your in your ear eventually, so they stop hearing

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Speaker 3: high frequencies. And that's why often as people get older,

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Speaker 3: they have a harder time understanding women and children because

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Speaker 3: their voices tend to be a slight higher register. And

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Speaker 3: so what we're doing is using exactly the same risk band.

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Speaker 3: It's using cutting at ji to listen in real time

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Speaker 3: for different high frequency parts of speech called phonemes. And

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Speaker 3: when it detects, oh, there was an S, there was

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Speaker 3: a K, there was a V, there was a T,

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Speaker 3: and so on, it buzzes in different ways to tell you.

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Speaker 3: And so what's happening is that somebody's ears doing fine

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Speaker 3: at the medium and low frequency, and the risk band

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Speaker 3: is simply clarifying what is happening at the high frequency.

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Speaker 3: And so that turns out to be a really great solution.

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Speaker 3: It It takes about a week or two for people

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Speaker 3: to get used to it, to understand how to use

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Speaker 3: it. It's all unconscious learning, but they just get better and

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Speaker 3: better at understanding what's being said, and they'll just mention

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Speaker 3: one other thing which was sort of as it's fascinating

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Speaker 3: that it worked, which is that for which is ringing

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Speaker 3: in the ears. What we do is we have people

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Speaker 3: wear the wristband and we play just for ten minutes

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Speaker 3: to day. We play a series of tones boom boo,

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Speaker 3: boo boo, boom boom, boom boom, and as you're hearing

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Speaker 3: the tones, you're feeling the buzz on the wristband. And

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Speaker 3: it turns out this drives down tenatus as well as

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Speaker 3: any solution that exists. And the reason is there are

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Speaker 3: complicated arguments for us. But I think the simple reason

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Speaker 3: this works is because you're teaching the brain what is

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Speaker 3: a real sound boom boom boom boom, because you're getting

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Speaker 3: verification on the wristband, but the internal ring in the ears,

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Speaker 3: the bea doesn't get any confirmation and so the brain says, Okay,

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Speaker 3: that's fake news and drives it down.

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Speaker 1: I'm going to need to get one of these wristbands.

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Speaker 1: I spent a lot of my youth going to the

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Speaker 1: University of Georgia and all the little rock clubs in

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Speaker 1: the surrounding area, and I have paid for my decisions.

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Speaker 1: Kids use earbuds or not earbuds, ear plugs. Use earplugs

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Speaker 1: when you go to the shows, because trust me, yes,

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Speaker 1: trust me, we're gonna have to take a quick break,

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Speaker 1: but when we come back we have more. With my

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Speaker 1: conversation with David Eagleman, the other thing I wanted to

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Speaker 1: point out for folks who may not have picked up

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Speaker 1: on it, is that this solution where you're using this

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Speaker 1: tactile feedback, where it's taking in sound and creating this

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Speaker 1: not only is it unconscious learning, it obviously does not

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Speaker 1: require invasive surgery. It can be orders of magnitude less

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Speaker 1: expensive and thus far more accessible, which means that it's

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Speaker 1: a technology that can help the heart of hearing, or

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Speaker 1: beyond heart of hearing, to experience the world on a

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Speaker 1: greater scale than they otherwise could, which is always a

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Speaker 1: wonderful story to tell when you're talking about tech, because

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Speaker 1: often we're talking about tech solutions that are not really solutions.

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Speaker 1: They are convenient is for a select.

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Speaker 3: Few, Yeah, thank you. I mean this is this is

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Speaker 3: why we're on risks all over the world now, and

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Speaker 3: lots of deaf schools in really impoverished countries as well,

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Speaker 3: precisely because you know, the only other option for somebody

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Speaker 3: who is deaf is a cochlear implant, which is one

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Speaker 3: hundred thousand dollars and an invasive surgery. So yeah, this

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Speaker 3: makes a big difference, and it works so well just

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Speaker 3: to push the information into the brain via an unusual channel.

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Speaker 2: It's fascinating to me.

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Speaker 1: The more you think about it, the more the more

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Speaker 1: you start to come to the conclusion of well, of

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Speaker 1: course it works when you start to break it down,

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Speaker 1: as the brain is creating everything we're experiencing. It's just

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Speaker 1: it's bringing in information from various sensors, and the nature

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Speaker 1: of the sensor doesn't matter as much as how the

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Speaker 1: brain can synthesize that data and turn it into something

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Speaker 1: that we can experience as something meaningful exactly.

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Speaker 3: I mean, those two spheres on the front of your head,

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Speaker 3: those are just capturing photons and turning them into spikes.

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Speaker 3: In your ear, your very sophisticated ear, is just capturing

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Speaker 3: air compression waves and turning into spikes. And your nose

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Speaker 3: in your mouth are capturing mixtures of molecules turning those

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Speaker 3: into spikes. But it's all it's all ending up in

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Speaker 3: the same common currency.

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Speaker 1: Well, David, one of the other things I wanted to

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Speaker 1: mention to you. I recently was doing some episodes about

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Speaker 1: the Ignobel Prizes, which you know, obviously a series of

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Speaker 1: joke prizes that came out of a satirical science magazine.

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Speaker 1: It's meant to either celebrate or sometimes raz people who

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Speaker 1: have done various things in science. One of the things

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Speaker 1: I thought was really interesting was they gave an Ignobel

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Speaker 1: Prize to a group of people Craig Bennett, Abigail Baird,

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Speaker 1: Michael Miller, and George Wolford who wrote a fascinating paper.

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Speaker 1: The paper is titled Neural Correlates of Interspecies Perspective taking

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Speaker 1: in the post mortem Atlantic Salmon An argument for proper

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Speaker 1: multiple comparisons correction. So this study was all about how

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Speaker 1: using very very sensitive equipment, mostly fMRI I actually and

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Speaker 1: through misuse of statistics, it could be very easy to

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Speaker 1: get false positives with things of this nature. And thus

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Speaker 1: what it means is that we need to be very

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Speaker 1: very careful when designing controls when we're using things like

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Speaker 1: fMRI I to draw conclusions. And I'm curious as your

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Speaker 1: work as a neuroscientist, as your work as a technologist,

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Speaker 1: have you encountered this sort of thing where there's always

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Speaker 1: this emphasis on moving fast, you know, move fast and

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Speaker 1: break things and to get to the end product as

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Speaker 1: quickly as possible. But in the process you have to

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Speaker 1: make sure that the science is right or else the

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Speaker 1: conclusions you draw, the solutions you make aren't really addressing anything.

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Speaker 1: Have you encountered things of that nature?

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Speaker 3: Sure, I mean spending a career in science when deals

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Speaker 3: with this all the time, which is to say, you know,

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Speaker 3: science constantly tries to drive forward, but it's always two

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Speaker 3: steps forward, one step back in the sense I mean

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Speaker 3: this fMRI paper with the salmon for anyone who does know,

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Speaker 3: it's you know, they put a dead salmon in the scanner,

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Speaker 3: and we're able to demonstrate that if you do this

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Speaker 3: the math in a certain way, that it looks like

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Speaker 3: there's activity in the salmon's brain even though there can't

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Speaker 3: possibly be so. So this is actually not an indictment

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Speaker 3: of functional metic residence imaging, the brain imaging technology that

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Speaker 3: we use. Instead, it's just an indictment of how you

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Speaker 3: do the statistics and so on. This is an incredibly

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Speaker 3: complicated device fMRI, and the you know, the way you

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Speaker 3: analyze the data requires, you know, a long time to

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Speaker 3: get to get up to speed on it. But what

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Speaker 3: it allows us to do is see what's happening in

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Speaker 3: the brain, in the live brain of a human when

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Speaker 3: they're doing a task or thinking about something. It's been

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Speaker 3: absolutely incredible the amount of data that's come out of this.

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Speaker 3: But what their paper shows is that, hey, you just

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Speaker 3: have to make sure you're doing the statistics right and

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Speaker 3: so on. And you know, like every field and sence,

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Speaker 3: you find lots and lots of papers that are publishing

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Speaker 3: right at the P equals point zero five, you know,

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Speaker 3: statistical significant value, meaning that there you know, people are saying, oh,

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Speaker 3: we've just spent a year during the study, and maybe

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Speaker 3: it's not quite statistically significant, so there's a there's some

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Speaker 3: little amount of fraud that happens. Look, let's do it

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Speaker 3: this way. My mentors was Francis Crick, the guy who

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Speaker 3: discovered the structure of DNA with Watson, and he, you know,

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Speaker 3: he would read the journals every single week when they

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Speaker 3: came out, and he said to me that he just

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Speaker 3: assumes that twenty five percent of what he reads is wrong,

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Speaker 3: whether by fraud or accident or whatever, doesn't matter. But

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Speaker 3: this is the thing science is the only human endeavor

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Speaker 3: we have that is constantly knocking down its own walls

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Speaker 3: and being self correcting, So you know, think, of course

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Speaker 3: there's mistakes and fraud and terrible stuff happens all the time,

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Speaker 3: size but at least that gets ironed out and straightened out,

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Speaker 3: whereas in every other field of human endeavor it doesn't.

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Speaker 1: Yeah, I thought the paper was incredibly invaluable. It was

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Speaker 1: one of those where, like I get the idea of

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Speaker 1: the Ignobel Prize going to it because the whole idea

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Speaker 1: of it makes you laugh and then it makes you think.

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Speaker 1: But my argument was, now, this is, this is a

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Speaker 1: statement to other researchers saying, just make sure that you're

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Speaker 1: absolutely positive you're doing everything you know correctly with scientific rigor,

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Speaker 1: that it's replicable, that it's replicable by other scientists who

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Speaker 1: are not directly working on whatever it is you're working on,

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Speaker 1: so that you ultimately are getting to the right conclusion,

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Speaker 1: because otherwise you're going to feel that you're you're sort

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Speaker 1: of wandering off the path, and perhaps worst case scenario,

433
00:23:41,280 --> 00:23:43,920
Speaker 1: you could say you're wasting time, or truly, worst case scenario,

434
00:23:43,920 --> 00:23:45,159
Speaker 1: you could be misleading people.

435
00:23:45,600 --> 00:23:46,080
Speaker 2: You know you.

436
00:23:46,000 --> 00:23:49,040
Speaker 1: Combine that with the world of like startups and Silicon

437
00:23:49,160 --> 00:23:52,639
Speaker 1: Valley and the general philosophy that they have, and you

438
00:23:52,640 --> 00:23:56,159
Speaker 1: can see that there's not parody between the two. And

439
00:23:56,200 --> 00:23:59,720
Speaker 1: so I imagine it requires a lot of balancing to make

440
00:23:59,760 --> 00:24:04,080
Speaker 1: certain that the science is there before the real big

441
00:24:04,119 --> 00:24:07,200
Speaker 1: moves in technology. Otherwise you would run the danger of

442
00:24:07,240 --> 00:24:10,879
Speaker 1: running into something like a Therah noose, which obviously a

443
00:24:11,000 --> 00:24:12,600
Speaker 1: poster child for bad science.

444
00:24:13,480 --> 00:24:16,639
Speaker 3: Yeah, exactly. I mean, again, the good news about science

445
00:24:16,720 --> 00:24:18,959
Speaker 3: or startups is that if somebody is doing something that

446
00:24:19,040 --> 00:24:24,040
Speaker 3: doesn't work, the truth outs. Yes, And you know, unfortunately

447
00:24:24,040 --> 00:24:26,520
Speaker 3: in science all the in science and startups all the time,

448
00:24:26,880 --> 00:24:30,480
Speaker 3: this stuff happens again, you know, sometimes by fraud and

449
00:24:30,560 --> 00:24:34,560
Speaker 3: often by mistake or accident. But you can't keep that

450
00:24:34,640 --> 00:24:36,760
Speaker 3: ship running for very long. I mean, look, you know,

451
00:24:36,800 --> 00:24:39,200
Speaker 3: the big thing that happened more recently is this issue

452
00:24:39,200 --> 00:24:43,200
Speaker 3: about Alzheimer's disease. And you've got these plaques and tangles

453
00:24:43,240 --> 00:24:45,040
Speaker 3: and so on. It turns out that it was discovered

454
00:24:45,040 --> 00:24:48,159
Speaker 3: that about thirteen years ago, this guy who published the

455
00:24:48,200 --> 00:24:52,280
Speaker 3: papers that actually fraudulently doctored the photographs to make it

456
00:24:52,320 --> 00:24:55,720
Speaker 3: look like something was going on there. Cut to thirteen

457
00:24:55,840 --> 00:25:01,080
Speaker 3: years of research and countless millions of dollars going into

458
00:25:01,080 --> 00:25:03,720
Speaker 3: researching this thing that was all predicated on a fraud,

459
00:25:04,240 --> 00:25:07,320
Speaker 3: and that sucks. But you know, in thirteen years is

460
00:25:07,359 --> 00:25:10,000
Speaker 3: a long time, but yeah, it gets figured out. At

461
00:25:10,000 --> 00:25:11,800
Speaker 3: some point, nothing's gonna last.

462
00:25:11,880 --> 00:25:14,720
Speaker 1: Yeah, yeah, it's which is that is something of a

463
00:25:14,760 --> 00:25:16,679
Speaker 1: comfort to know that in the long run it all

464
00:25:16,720 --> 00:25:19,040
Speaker 1: gets hashed out. I mean, obviously, if you are someone

465
00:25:19,080 --> 00:25:20,840
Speaker 1: who's caught up in it, in the middle of it,

466
00:25:20,840 --> 00:25:23,119
Speaker 1: it can be it can be life changing in a

467
00:25:23,520 --> 00:25:27,160
Speaker 1: really negative way. But but there is there is comfort

468
00:25:27,200 --> 00:25:30,679
Speaker 1: to know that the very nature of the process of

469
00:25:30,720 --> 00:25:35,040
Speaker 1: science is one that will weed this stuff out. And

470
00:25:35,119 --> 00:25:37,000
Speaker 1: I also argue all the time and like, you know

471
00:25:37,040 --> 00:25:40,000
Speaker 1: that science works because if science didn't work, none of

472
00:25:40,040 --> 00:25:43,240
Speaker 1: your technology would work. Your technology is proof science works.

473
00:25:43,280 --> 00:25:48,800
Speaker 1: It is the actual manifestation, physical manifestation of science. And

474
00:25:49,400 --> 00:25:52,480
Speaker 1: it's also why I get very let's say, passionate when

475
00:25:52,480 --> 00:25:56,800
Speaker 1: I encounter people who are in some ways science deniers.

476
00:25:56,359 --> 00:25:59,520
Speaker 1: It's it's it's unthinkable to me because I think how

477
00:25:59,600 --> 00:26:04,280
Speaker 1: much of your life revolves around your interaction with things

478
00:26:04,320 --> 00:26:08,520
Speaker 1: that literally would not work if science, that the scientific

479
00:26:08,560 --> 00:26:09,520
Speaker 1: process didn't work.

480
00:26:10,000 --> 00:26:12,720
Speaker 3: I agree with you, But let me play devil's advocate

481
00:26:12,760 --> 00:26:16,119
Speaker 3: here for just a second, which is, you know, given

482
00:26:16,200 --> 00:26:19,159
Speaker 3: that science is wrong some of the time, and you know,

483
00:26:19,200 --> 00:26:21,680
Speaker 3: thank goodness, it's self correcting and we'll get it straightened out.

484
00:26:22,520 --> 00:26:25,480
Speaker 3: I always have a little bit of I give a

485
00:26:25,480 --> 00:26:28,679
Speaker 3: little wiggle room to people who say, well, how do

486
00:26:28,720 --> 00:26:32,360
Speaker 3: we know that's true? You know, maybe these scientists are

487
00:26:32,359 --> 00:26:35,000
Speaker 3: influenced by politics or something. And that's true too, that

488
00:26:35,040 --> 00:26:37,240
Speaker 3: happens all the time. And in fact, the grant money

489
00:26:37,280 --> 00:26:41,119
Speaker 3: goes towards things that any society and any moment in

490
00:26:41,119 --> 00:26:45,280
Speaker 3: time finds politically interesting or expedient to do. And you know,

491
00:26:45,320 --> 00:26:47,920
Speaker 3: science money doesn't go to those things that society doesn't

492
00:26:47,960 --> 00:26:48,320
Speaker 3: care about.

493
00:26:48,359 --> 00:26:48,679
Speaker 2: And so on.

494
00:26:48,960 --> 00:26:53,160
Speaker 3: Anyway, I whenever I hear somebody saying, hey, I don't

495
00:26:53,200 --> 00:26:54,719
Speaker 3: think this is true, or that, how do we know

496
00:26:54,840 --> 00:26:57,119
Speaker 3: climate change? How do we know the vaccines, whatever, I

497
00:26:57,160 --> 00:27:00,600
Speaker 3: always feel like, good man, keep asking the questions, and

498
00:27:00,760 --> 00:27:02,840
Speaker 3: you know, and the truth will out as a result

499
00:27:02,960 --> 00:27:05,760
Speaker 3: of that, and one side or the other will find

500
00:27:05,760 --> 00:27:06,840
Speaker 3: out who's wrong. Right.

501
00:27:07,240 --> 00:27:11,000
Speaker 1: Yeah, I'm certainly in favor of asking questions. I think

502
00:27:11,000 --> 00:27:14,040
Speaker 1: I get frustrated with people who when faced with an

503
00:27:14,080 --> 00:27:19,520
Speaker 1: answer that they don't like. Are They're not interested in

504
00:27:19,560 --> 00:27:22,040
Speaker 1: the process that arrived to that answer. They're interested in

505
00:27:22,080 --> 00:27:24,719
Speaker 1: the answer changing. Right, Yeah, yeah, fair enough, fair enough.

506
00:27:24,720 --> 00:27:27,399
Speaker 3: There's lots of there's lots of wrong reasons to criticize science.

507
00:27:28,040 --> 00:27:31,359
Speaker 1: But I mean, but to your point, the whole, the

508
00:27:31,440 --> 00:27:34,960
Speaker 1: whole of science is all about, like, I discovered this thing.

509
00:27:35,119 --> 00:27:37,080
Speaker 1: I think it's a thing. Can you look at this

510
00:27:37,160 --> 00:27:39,280
Speaker 1: thing and tell me if you also think it's a thing?

511
00:27:39,640 --> 00:27:41,399
Speaker 1: And if enough people think it's a thing, then we

512
00:27:41,440 --> 00:27:44,320
Speaker 1: have a scientific consensus about it being a thing. And

513
00:27:44,359 --> 00:27:48,440
Speaker 1: then that we can we can say we're fairly comfortable

514
00:27:48,480 --> 00:27:52,480
Speaker 1: this is a thing. Maybe sometime in the future we'll discover, oh,

515
00:27:52,560 --> 00:27:54,400
Speaker 1: we were totally wrong, that wasn't a thing at all,

516
00:27:54,480 --> 00:27:56,880
Speaker 1: But for now we're comfortable in calling it a thing,

517
00:27:57,880 --> 00:28:01,760
Speaker 1: which is a crazy genderic way of describing it.

518
00:28:02,200 --> 00:28:03,760
Speaker 3: No, I like the way you describe it, but it

519
00:28:03,800 --> 00:28:06,360
Speaker 3: is it is important to look. I'm totally on your

520
00:28:06,400 --> 00:28:08,679
Speaker 3: side on this, but just just to fill out the

521
00:28:08,720 --> 00:28:11,439
Speaker 3: other side for a moment, all we ever have in

522
00:28:11,480 --> 00:28:14,120
Speaker 3: science is the weight of evidence, So we never can

523
00:28:14,160 --> 00:28:17,080
Speaker 3: conclude this is the truth or this is the thing,

524
00:28:17,720 --> 00:28:20,520
Speaker 3: and so all we say is, look, all the data

525
00:28:20,560 --> 00:28:22,840
Speaker 3: seems to indicate, seems to point in this. For example,

526
00:28:23,119 --> 00:28:27,080
Speaker 3: we all believe in quantum mechanics because it's the best

527
00:28:27,119 --> 00:28:30,440
Speaker 3: theory we have. It predicts experiments correctly to fourteen decimal places.

528
00:28:30,840 --> 00:28:32,840
Speaker 3: We don't know it's true. We don't know it's true.

529
00:28:32,840 --> 00:28:34,199
Speaker 3: I mean, you know, there may be something in one

530
00:28:34,240 --> 00:28:37,000
Speaker 3: hundred years from now. We just cover. Oh that's way better.

531
00:28:37,080 --> 00:28:39,200
Speaker 3: By the way, it predicts experiments to thirty decimal places,

532
00:28:39,200 --> 00:28:41,480
Speaker 3: and it's it's a totally different thing. And I mean

533
00:28:41,480 --> 00:28:44,080
Speaker 3: in the same way that let's take Newtonian physics. You know,

534
00:28:44,120 --> 00:28:45,680
Speaker 3: you drop a ball or what do you shoot a

535
00:28:45,720 --> 00:28:48,440
Speaker 3: cannon ball or something, and it works pretty well, but

536
00:28:48,480 --> 00:28:52,200
Speaker 3: it doesn't it doesn't expand to deal with things that

537
00:28:52,800 --> 00:28:55,440
Speaker 3: at the very big or very small. Sure, and so

538
00:28:55,920 --> 00:28:58,800
Speaker 3: we say, okay, well it's good enough for here. It's fine.

539
00:28:59,400 --> 00:29:01,880
Speaker 3: But yeah, yeah, I don't know. I always there's a

540
00:29:01,920 --> 00:29:05,440
Speaker 3: little piece in my heart that always is fine with

541
00:29:05,520 --> 00:29:09,280
Speaker 3: people questioning anything, because really, the whole history of science

542
00:29:09,640 --> 00:29:12,800
Speaker 3: is that in every generation people have thought, okay, we've

543
00:29:12,800 --> 00:29:14,600
Speaker 3: got all the puzzle pieces here, we sort of know

544
00:29:14,720 --> 00:29:18,080
Speaker 3: the answer, and it's never been true once ever, And

545
00:29:18,280 --> 00:29:23,600
Speaker 3: just imagine trying to explain, you know, how the heart

546
00:29:23,640 --> 00:29:27,520
Speaker 3: works before the discovery of electricity, or how the northern

547
00:29:27,600 --> 00:29:31,040
Speaker 3: lights work before the discovery of the discovery of you know,

548
00:29:31,240 --> 00:29:34,080
Speaker 3: photons and the magnetosphere around the planet and stuff like

549
00:29:34,080 --> 00:29:36,200
Speaker 3: like you could you'd be making up theories, but you

550
00:29:36,200 --> 00:29:40,080
Speaker 3: would be doomed to be incorrect. And so whoever was

551
00:29:40,160 --> 00:29:43,560
Speaker 3: the naysayers in those days, good good on them for saying,

552
00:29:43,560 --> 00:29:45,520
Speaker 3: I don't know, you know, just because all the sciences

553
00:29:45,600 --> 00:29:47,959
Speaker 3: agree on this doesn't necessitate it's correct.

554
00:29:48,000 --> 00:29:51,320
Speaker 1: Well, and I do think that curiosity is probably one

555
00:29:51,320 --> 00:29:54,040
Speaker 1: of the most important human qualities to have, right. You

556
00:29:54,160 --> 00:29:57,160
Speaker 1: need to have curiosity, You need to have that desire

557
00:29:57,200 --> 00:30:00,840
Speaker 1: to learn. And like I remember, I was chatting with

558
00:30:00,920 --> 00:30:04,320
Speaker 1: my niece who was asking very deep questions for an

559
00:30:04,320 --> 00:30:07,000
Speaker 1: eight year old, and I was explaining things like I

560
00:30:07,120 --> 00:30:10,440
Speaker 1: was explaining in big terms things like Newtonian physics and

561
00:30:10,440 --> 00:30:12,920
Speaker 1: then quantum physics. And then she said, at what stage

562
00:30:13,520 --> 00:30:16,960
Speaker 1: do you swap from describing something in Newtonian physics to

563
00:30:17,040 --> 00:30:19,520
Speaker 1: quantum physics. I said, you have hit upon a question

564
00:30:20,240 --> 00:30:23,520
Speaker 1: that no one has an answer to, and I know

565
00:30:23,600 --> 00:30:26,240
Speaker 1: that's not satisfying to you. But this is where you

566
00:30:26,320 --> 00:30:30,240
Speaker 1: have to know that adults don't know everything God exactly.

567
00:30:30,400 --> 00:30:32,040
Speaker 3: But you know what the funny part is, it's not

568
00:30:32,080 --> 00:30:34,880
Speaker 3: that your niece was asking deep questions, that she's just

569
00:30:34,960 --> 00:30:37,480
Speaker 3: asking good questions. Yes, as someone looking at through the

570
00:30:37,520 --> 00:30:39,320
Speaker 3: whole thing with fresh eyes, like wait, why do you

571
00:30:39,360 --> 00:30:41,480
Speaker 3: guys believe in this and that? Yeah?

572
00:30:41,800 --> 00:30:43,960
Speaker 2: Yeah, it's like the simpler way.

573
00:30:44,040 --> 00:30:45,560
Speaker 1: I mean when I was a kid, I was asking, well,

574
00:30:45,600 --> 00:30:47,600
Speaker 1: what makes that a hill and that a mountain if

575
00:30:47,640 --> 00:30:51,400
Speaker 1: they're both the same height, which just gets into semantics

576
00:30:52,280 --> 00:30:55,920
Speaker 1: right right, But it's great, like I'm glad that she's

577
00:30:56,000 --> 00:30:59,320
Speaker 1: thinking of things in her way and that she's gonna

578
00:30:59,600 --> 00:31:02,680
Speaker 1: totally leave me in the dust, which is fantastic, that's

579
00:31:02,720 --> 00:31:05,640
Speaker 1: what I want to say. But exactly, you know, it's

580
00:31:05,640 --> 00:31:08,000
Speaker 1: that the question seems deep to us only because we

581
00:31:08,120 --> 00:31:10,280
Speaker 1: have forgotten to ask it anymore.

582
00:31:10,760 --> 00:31:12,840
Speaker 3: That's the frustrating part. Is we get older because look,

583
00:31:12,960 --> 00:31:14,920
Speaker 3: the job in the brain is to make an internal

584
00:31:15,000 --> 00:31:17,040
Speaker 3: model of the outside world to say, okay, this is

585
00:31:17,040 --> 00:31:19,120
Speaker 3: how this works, is how that works. And as we

586
00:31:19,160 --> 00:31:21,480
Speaker 3: get older and older, we say, okay, I've kind of

587
00:31:21,480 --> 00:31:23,040
Speaker 3: got this figured out. I know how the world works.

588
00:31:23,040 --> 00:31:25,000
Speaker 3: I know how people respondated this. I know how to

589
00:31:25,080 --> 00:31:27,479
Speaker 3: ride a bicycle, I know where my door is. But

590
00:31:27,640 --> 00:31:31,200
Speaker 3: as a result of this, we, you know, we stop

591
00:31:31,280 --> 00:31:34,960
Speaker 3: asking questions. We lose the curiosity that a child has

592
00:31:35,000 --> 00:31:37,520
Speaker 3: because we kind of think we know the answers to things.

593
00:31:37,960 --> 00:31:40,520
Speaker 3: And what makes good science or I say, you know,

594
00:31:40,560 --> 00:31:45,280
Speaker 3: like a good scientist is anyone who just retains the natiety,

595
00:31:45,400 --> 00:31:49,360
Speaker 3: the youthful, you know, acting like a child their entire

596
00:31:49,520 --> 00:31:51,600
Speaker 3: lives and saying, wait a minute, how do we know that?

597
00:31:52,160 --> 00:31:52,480
Speaker 2: Yeah?

598
00:31:52,560 --> 00:31:55,080
Speaker 1: So I like that. I like that a lot. And

599
00:31:55,600 --> 00:31:59,760
Speaker 1: those moments where it hits me, which are well, I'll

600
00:31:59,800 --> 00:32:02,640
Speaker 1: say they're rare, like it does hit me occasionally, but

601
00:32:02,680 --> 00:32:05,360
Speaker 1: it's always wonderful when it does. And I ask a

602
00:32:05,440 --> 00:32:08,560
Speaker 1: question that I just I didn't even think to ask before,

603
00:32:09,120 --> 00:32:12,040
Speaker 1: and then going down that rabbit hole can be really

604
00:32:13,120 --> 00:32:15,560
Speaker 1: informative and entertaining. Even if you don't arrive at an

605
00:32:15,560 --> 00:32:18,360
Speaker 1: answer that you can fully apply to whatever the question was,

606
00:32:19,200 --> 00:32:22,160
Speaker 1: it's the journey along the way that can be really fascinating.

607
00:32:23,040 --> 00:32:27,080
Speaker 1: It's great because, like, I have discussions with different kinds

608
00:32:27,120 --> 00:32:30,080
Speaker 1: of people. So I've had discussions with scientists and I've

609
00:32:30,080 --> 00:32:33,520
Speaker 1: had discussions with engineers, and it can be fun to

610
00:32:33,520 --> 00:32:36,480
Speaker 1: see the different approaches people take, because engineers tend to

611
00:32:36,480 --> 00:32:38,480
Speaker 1: see the world as a series of problems and they're

612
00:32:38,480 --> 00:32:42,000
Speaker 1: always thinking about solutions, and scientists tend to look at

613
00:32:42,040 --> 00:32:44,960
Speaker 1: the world and just ask questions about the very nature

614
00:32:45,000 --> 00:32:47,400
Speaker 1: of it and how they can have a better understanding

615
00:32:47,440 --> 00:32:50,400
Speaker 1: of it, and how does that fit within our current understanding?

616
00:32:51,000 --> 00:32:52,960
Speaker 1: Does it fit or does it mean that we have

617
00:32:53,040 --> 00:32:57,200
Speaker 1: to actually adjust our current understanding. And I learned so

618
00:32:57,400 --> 00:33:00,440
Speaker 1: much by talking to these two different groups of people,

619
00:33:00,680 --> 00:33:02,600
Speaker 1: and it's always a joy.

620
00:33:02,960 --> 00:33:05,440
Speaker 3: Yeah, And you know, one of the things with neuroscience

621
00:33:05,480 --> 00:33:08,960
Speaker 3: in particular is it's fundamentally the question of how we

622
00:33:09,600 --> 00:33:12,400
Speaker 3: perceive reality at all. So, just as an example, you

623
00:33:12,440 --> 00:33:13,920
Speaker 3: may know this, but I've spent a big chunk of

624
00:33:13,960 --> 00:33:17,760
Speaker 3: my career studying the perception of time. And you know,

625
00:33:17,800 --> 00:33:19,840
Speaker 3: this is one of those things that just seems obvious, like, oh, yeah,

626
00:33:19,880 --> 00:33:21,880
Speaker 3: five minutes past, no problem, you know, or this happened

627
00:33:21,920 --> 00:33:24,040
Speaker 3: before that, or you know, this is blinking at a

628
00:33:24,080 --> 00:33:26,840
Speaker 3: particular rate or whatever, like you don't even think to

629
00:33:26,920 --> 00:33:30,280
Speaker 3: question it. But you know, I've spent whatever twenty five

630
00:33:30,440 --> 00:33:35,440
Speaker 3: years studying this really hard, and just the deeper you

631
00:33:35,480 --> 00:33:38,200
Speaker 3: reach down to this system, the more it sometimes feels like,

632
00:33:38,560 --> 00:33:43,720
Speaker 3: oh my gosh, it's so weird, you know, or you know,

633
00:33:43,800 --> 00:33:47,120
Speaker 3: we mentioned conscious before, like the colors. Colors don't exist

634
00:33:47,120 --> 00:33:50,000
Speaker 3: in the outside world, you know, there's just electromagnetic radiation

635
00:33:50,040 --> 00:33:53,120
Speaker 3: of different wavelengths, and your brain assigns what we call

636
00:33:53,160 --> 00:33:56,520
Speaker 3: equaliya to it, so we say, oh, that's red, that's blue,

637
00:33:56,560 --> 00:33:59,040
Speaker 3: that's green. But those the colors don't exist. It's all

638
00:33:59,280 --> 00:34:02,320
Speaker 3: in your head, you know. And sound doesn't exist as such.

639
00:34:02,360 --> 00:34:06,560
Speaker 3: It's just air compression waves. So the world outside your

640
00:34:06,600 --> 00:34:08,920
Speaker 3: head you can't even really picture it. I mean, it's

641
00:34:08,960 --> 00:34:12,680
Speaker 3: just electromaic magnetic radiation and air compression waves and whatever.

642
00:34:12,760 --> 00:34:17,360
Speaker 3: But but we experienced it as this colorful cacophonousts, you

643
00:34:17,400 --> 00:34:22,279
Speaker 3: know place. So anyway, the more you reach down into

644
00:34:22,320 --> 00:34:26,120
Speaker 3: this stuff, sometimes it feels like, oh my gosh, it's

645
00:34:26,360 --> 00:34:28,799
Speaker 3: it's not even clear what's true or not true. And then,

646
00:34:28,840 --> 00:34:32,920
Speaker 3: of course, with the AI having had this explosion the

647
00:34:32,960 --> 00:34:35,919
Speaker 3: way it has recently, you know, in Silicon Valley, there's

648
00:34:35,960 --> 00:34:39,719
Speaker 3: been this real shift from people saying they're atheists to

649
00:34:39,840 --> 00:34:42,600
Speaker 3: being creationists. Creationists meaning like you know, we're an AI

650
00:34:42,680 --> 00:34:48,400
Speaker 3: simulation by who the heck knows, And you know suddenly

651
00:34:48,520 --> 00:34:50,440
Speaker 3: that that idea, you know, we've all talked about as

652
00:34:50,440 --> 00:34:52,000
Speaker 3: our whole career is about, Hey, how do we know

653
00:34:52,000 --> 00:34:54,000
Speaker 3: if we're in a simulation? And so on? But all

654
00:34:54,040 --> 00:34:55,759
Speaker 3: of a sudden, it seems like, whoa, that's not as

655
00:34:55,800 --> 00:34:57,160
Speaker 3: crazy as it once seemed.

656
00:34:57,440 --> 00:35:00,719
Speaker 1: Yeah, David and I got a little more to talk about.

657
00:35:00,800 --> 00:35:03,280
Speaker 1: But before we can get to that, let's take another

658
00:35:03,400 --> 00:35:16,520
Speaker 1: quick break. If it is possible to build a system

659
00:35:17,200 --> 00:35:22,160
Speaker 1: that is capable of creating a synthetic reality where the

660
00:35:22,200 --> 00:35:25,520
Speaker 1: inhabitants of that synthetic reality would assume it is real.

661
00:35:25,640 --> 00:35:28,839
Speaker 1: If that is in fact possible, then the chances are

662
00:35:28,920 --> 00:35:32,839
Speaker 1: that some civilization at some point has done that, and

663
00:35:32,880 --> 00:35:35,600
Speaker 1: that the chances that we are the civilization that will

664
00:35:35,640 --> 00:35:39,920
Speaker 1: eventually get to that point. Is incredibly egotistical to suggest

665
00:35:39,960 --> 00:35:42,440
Speaker 1: that if there are all these things, the more you

666
00:35:42,480 --> 00:35:45,040
Speaker 1: look at it, as you're taking all these things to consideration,

667
00:35:45,120 --> 00:35:48,200
Speaker 1: the odds are diminishingly likely that we're in a real

668
00:35:48,239 --> 00:35:49,719
Speaker 1: reality in a simulated one.

669
00:35:50,040 --> 00:35:52,319
Speaker 3: Exactly, the odds are that we're in a simulated reality. Yeah,

670
00:35:52,320 --> 00:35:54,960
Speaker 3: this is Nick Bostrom's argument. So the whole notion of

671
00:35:54,960 --> 00:35:56,880
Speaker 3: what is reality and why do you experience? Why do

672
00:35:56,880 --> 00:35:58,880
Speaker 3: you experience things the way we are? I feel like

673
00:35:58,960 --> 00:36:01,400
Speaker 3: it's that that has complexified even from the time I

674
00:36:01,520 --> 00:36:04,040
Speaker 3: was whatever, from the time I was a student till now,

675
00:36:04,680 --> 00:36:08,600
Speaker 3: just trying to understand what reality even is basic question.

676
00:36:08,800 --> 00:36:11,560
Speaker 3: So it's interesting because the engineers have a simpler job

677
00:36:11,560 --> 00:36:13,040
Speaker 3: in a sense, which is, okay, if I launch this

678
00:36:13,080 --> 00:36:16,200
Speaker 3: cannonball doesn't land there, great, I can you know, refine

679
00:36:16,320 --> 00:36:20,160
Speaker 3: and change the angle and terrific. But the scientists are

680
00:36:20,160 --> 00:36:23,040
Speaker 3: facing really, really tough questions.

681
00:36:23,120 --> 00:36:26,520
Speaker 1: Yeah, I would urge my listeners not to think about

682
00:36:26,560 --> 00:36:29,319
Speaker 1: it for too long or too hard, or you'll find

683
00:36:29,320 --> 00:36:30,360
Speaker 1: yourself in a spiral.

684
00:36:30,640 --> 00:36:32,640
Speaker 3: I've always founded interesting by the way the bounce back,

685
00:36:32,680 --> 00:36:35,840
Speaker 3: which is to say, you know, no matter how deeply

686
00:36:35,920 --> 00:36:40,480
Speaker 3: you study something about reality or colors or whatever, you know,

687
00:36:40,680 --> 00:36:42,960
Speaker 3: you stand up from your desk where you're reading this

688
00:36:43,000 --> 00:36:45,960
Speaker 3: book or whatever, and you go out and then suddenly think, oh,

689
00:36:46,040 --> 00:36:48,920
Speaker 3: that honeysuckle smells awesome. It's been such a good mood.

690
00:36:48,960 --> 00:36:51,919
Speaker 3: And oh, this guy is so blue it's beautiful. Oh,

691
00:36:51,960 --> 00:36:55,200
Speaker 3: and here's my beautiful wife and I feel so happy

692
00:36:55,239 --> 00:36:57,760
Speaker 3: to see her, and here's my children. I'm so happy

693
00:36:57,760 --> 00:36:59,839
Speaker 3: to you know, all the stuff that you just can

694
00:37:00,000 --> 00:37:03,560
Speaker 3: pcluded was all illusory. You know, you can't help but

695
00:37:04,080 --> 00:37:04,960
Speaker 3: experience that.

696
00:37:05,200 --> 00:37:05,480
Speaker 2: Yeah.

697
00:37:05,680 --> 00:37:08,200
Speaker 3: In other words, if I explained to you, if I

698
00:37:08,280 --> 00:37:10,560
Speaker 3: had a way of diagramming and showing with equations so

699
00:37:10,640 --> 00:37:13,600
Speaker 3: on the whole reason why you like, you know, chocolate

700
00:37:13,640 --> 00:37:16,719
Speaker 3: ice cream, it would actually have no difference in your

701
00:37:16,760 --> 00:37:18,920
Speaker 3: life to your enjoyment of chocolate ice cream. You would

702
00:37:19,040 --> 00:37:21,200
Speaker 3: you would say, wow, I can't believe it's all mechanical

703
00:37:21,239 --> 00:37:23,719
Speaker 3: and here's dopamine serotonin in this. But then you eat,

704
00:37:23,760 --> 00:37:25,240
Speaker 3: You're like, wow, that sure is delicious.

705
00:37:25,280 --> 00:37:25,600
Speaker 2: Yeah.

706
00:37:25,680 --> 00:37:28,880
Speaker 1: Yeah, Yeah, the knowing does not diminish the experience.

707
00:37:29,160 --> 00:37:29,759
Speaker 2: Yeah.

708
00:37:30,000 --> 00:37:32,600
Speaker 1: I get very pragmatic about it. I think if it's

709
00:37:32,600 --> 00:37:36,080
Speaker 1: a synthetic reality, if what we're in is a simulation,

710
00:37:36,719 --> 00:37:39,960
Speaker 1: it makes no difference to my day to day existence.

711
00:37:40,080 --> 00:37:43,799
Speaker 1: Like I still want to make sure, like my motto is,

712
00:37:44,280 --> 00:37:46,799
Speaker 1: I want to leave things better than they were when

713
00:37:46,840 --> 00:37:48,640
Speaker 1: I got here. If I can do that, then that

714
00:37:48,960 --> 00:37:54,040
Speaker 1: at any level I can, whether the people are simulated

715
00:37:54,160 --> 00:37:57,239
Speaker 1: or perhaps there's some outer reality where people are looking

716
00:37:57,280 --> 00:37:57,520
Speaker 1: at it and.

717
00:37:57,520 --> 00:37:59,880
Speaker 2: Saying like I did his best. He didn't know his

718
00:38:00,080 --> 00:38:02,560
Speaker 2: get it right, but he tried really hard. Well, I'm

719
00:38:02,560 --> 00:38:04,359
Speaker 2: going to consider that a win in the long run.

720
00:38:05,120 --> 00:38:06,320
Speaker 3: Excellent. Excellent.

721
00:38:06,640 --> 00:38:09,440
Speaker 1: Yeah, and I wish I could keep you here to

722
00:38:09,480 --> 00:38:12,319
Speaker 1: talk about all of the different topics you've covered on

723
00:38:12,360 --> 00:38:15,280
Speaker 1: Inner Cosmos, but obviously there's a whole podcast that exists

724
00:38:15,320 --> 00:38:19,480
Speaker 1: for that very purpose, and would be rather ridiculous of

725
00:38:19,480 --> 00:38:21,520
Speaker 1: me to have you repeat it all. I mean, there

726
00:38:21,560 --> 00:38:23,799
Speaker 1: are topics that you talk about that I used to

727
00:38:23,800 --> 00:38:26,800
Speaker 1: discuss on a show I did called Forward Thinking, about

728
00:38:26,920 --> 00:38:30,440
Speaker 1: things like the concept that some people have bandied about,

729
00:38:30,480 --> 00:38:34,480
Speaker 1: about the idea of uploading yourself into some sort of

730
00:38:34,480 --> 00:38:38,319
Speaker 1: of computer system in order to maintain some semblance of

731
00:38:38,320 --> 00:38:41,799
Speaker 1: digital immortality. I don't know how you specifically feel about that.

732
00:38:41,880 --> 00:38:44,520
Speaker 1: I've always felt that this kind of is in the

733
00:38:44,560 --> 00:38:49,320
Speaker 1: realm of techno billionaires who are absolutely terrified at the

734
00:38:49,400 --> 00:38:52,719
Speaker 1: thought of ceasing to exist. But I'm curious about your

735
00:38:52,760 --> 00:38:53,359
Speaker 1: own take.

736
00:38:54,080 --> 00:38:56,239
Speaker 3: It's not just techno billionaires. They can do something about it,

737
00:38:56,239 --> 00:38:59,320
Speaker 3: but some fractions of the population really cares about dying.

738
00:39:00,480 --> 00:39:02,880
Speaker 3: They're really horrified by that. I just happened to be

739
00:39:02,920 --> 00:39:04,640
Speaker 3: at the other end of the spectrum where I really

740
00:39:04,680 --> 00:39:08,960
Speaker 3: don't care. It's just I mean, I just understand mortality,

741
00:39:09,040 --> 00:39:10,520
Speaker 3: and you know, I know that I'll die at some

742
00:39:10,560 --> 00:39:13,520
Speaker 3: point and that's that. But some people really care about

743
00:39:13,560 --> 00:39:17,160
Speaker 3: figuring out. Look, could I, for example, freeze my whole

744
00:39:17,200 --> 00:39:20,359
Speaker 3: body or just freeze my head? And I'm actually doing

745
00:39:20,440 --> 00:39:23,479
Speaker 3: another episode on this coming up. Okay, here's a question

746
00:39:23,520 --> 00:39:26,839
Speaker 3: for you. The first guy to freeze himself for the

747
00:39:26,880 --> 00:39:30,000
Speaker 3: hope of getting rebooted in the future, you know, cut

748
00:39:30,000 --> 00:39:32,040
Speaker 3: to five hundred years from now when people know, ah,

749
00:39:32,080 --> 00:39:36,000
Speaker 3: here's how we can revivify a frozen body. What year

750
00:39:36,160 --> 00:39:39,240
Speaker 3: was that guy born in the first person who's frozen

751
00:39:39,280 --> 00:39:39,560
Speaker 3: like that?

752
00:39:39,640 --> 00:39:44,720
Speaker 1: Gosh, oh, that's a great question. Okay, so I'm gonna

753
00:39:45,040 --> 00:39:47,560
Speaker 1: just give a shot in the dark. I'm gonna say

754
00:39:47,600 --> 00:39:48,600
Speaker 1: nineteen twenty.

755
00:39:48,880 --> 00:39:52,080
Speaker 3: Okay, eighteen ninety eight is the first guy, and he

756
00:39:52,120 --> 00:39:55,439
Speaker 3: froze himself in the late sixties. Yeah, and I didn't

757
00:39:55,440 --> 00:39:57,160
Speaker 3: remember any people have been thinking about this guy thing

758
00:39:57,200 --> 00:39:59,400
Speaker 3: for a long time. Or Hey, could I freeze myself

759
00:39:59,440 --> 00:40:01,920
Speaker 3: and do a Hail Mary pass into the future and

760
00:40:02,080 --> 00:40:04,359
Speaker 3: just hope that people then will know a lot more

761
00:40:04,360 --> 00:40:06,200
Speaker 3: and we know now and they'll fig because you know,

762
00:40:06,280 --> 00:40:08,759
Speaker 3: you can revivify a body of someone who's let's say,

763
00:40:08,760 --> 00:40:12,080
Speaker 3: frozen in a lake. You know, they've fallen through the ice,

764
00:40:12,160 --> 00:40:14,960
Speaker 3: they freeze, their heart stopped, their vitals have all stopped,

765
00:40:14,960 --> 00:40:17,320
Speaker 3: and you know you can warm them up slowly and

766
00:40:17,360 --> 00:40:20,600
Speaker 3: get them going again. And so we know this is possible,

767
00:40:21,719 --> 00:40:24,240
Speaker 3: and yeah, so a lot of people are really interested

768
00:40:24,239 --> 00:40:25,480
Speaker 3: in this. There's two ways to do it. One is

769
00:40:25,480 --> 00:40:27,400
Speaker 3: you freeze the body. The other one is this issue

770
00:40:27,440 --> 00:40:32,439
Speaker 3: of could you somehow extract all the interesting structure of

771
00:40:32,560 --> 00:40:34,720
Speaker 3: what's going on in somebody's brain, you know, the whole

772
00:40:34,800 --> 00:40:39,080
Speaker 3: reconfiguration of their network that represents their life. Could you

773
00:40:39,120 --> 00:40:42,440
Speaker 3: reconstruct that digitally? It would save a lot of space

774
00:40:42,480 --> 00:40:44,400
Speaker 3: and so on, And then you're and then you know,

775
00:40:44,480 --> 00:40:47,360
Speaker 3: essentially run the person in a simulation. So that's that's

776
00:40:47,480 --> 00:40:50,399
Speaker 3: where we might already be. But in any case, we're

777
00:40:50,440 --> 00:40:53,600
Speaker 3: trying to do a simulation inside a simulation of saying,

778
00:40:54,640 --> 00:40:57,120
Speaker 3: you know, could we could we reboot you in some

779
00:40:57,200 --> 00:41:00,480
Speaker 3: other world? And and the thing is about the computationalacity

780
00:41:01,040 --> 00:41:04,480
Speaker 3: of the planet. It's still the estimate is that to

781
00:41:04,560 --> 00:41:07,120
Speaker 3: capture all the information and a brain, all the connections

782
00:41:07,160 --> 00:41:09,680
Speaker 3: and exactly the three D structure would be about a

783
00:41:09,760 --> 00:41:14,680
Speaker 3: zetabyte of information, which is about four times more than

784
00:41:14,680 --> 00:41:17,160
Speaker 3: the computational capacity of the entire planet right now. So

785
00:41:17,520 --> 00:41:20,480
Speaker 3: it's an enormous thing. But boy, and you know, in

786
00:41:20,640 --> 00:41:22,880
Speaker 3: fifty years we won't even care. You'll be wearing a

787
00:41:23,080 --> 00:41:24,560
Speaker 3: zetabyte on your wrist or something.

788
00:41:24,640 --> 00:41:28,279
Speaker 1: So well, we know, we know in the short term

789
00:41:28,280 --> 00:41:31,399
Speaker 1: who the people will be who would go for that,

790
00:41:31,440 --> 00:41:33,279
Speaker 1: because it would be the people who could afford to

791
00:41:33,320 --> 00:41:33,880
Speaker 1: set aside.

792
00:41:33,960 --> 00:41:34,640
Speaker 2: Is that a bite of.

793
00:41:35,239 --> 00:41:38,920
Speaker 1: Of processing power and computer data storage.

794
00:41:39,080 --> 00:41:41,919
Speaker 3: But what's interesting is if you're the tech billionaire who

795
00:41:41,920 --> 00:41:44,560
Speaker 3: gets yourself uploaded some stimulation, you are now at the

796
00:41:45,320 --> 00:41:49,120
Speaker 3: be you know, you're now slave to the programmers. Yes, right,

797
00:41:49,160 --> 00:41:52,080
Speaker 3: And so the programmers might say, look, we're gonna do

798
00:41:52,200 --> 00:41:54,279
Speaker 3: something terrible to you here or whatever we're gonna you know,

799
00:41:54,280 --> 00:41:56,040
Speaker 3: you need to send some money to us, so that

800
00:41:56,040 --> 00:41:59,000
Speaker 3: because we're now controlling your entire world. Yeah, I don't know,

801
00:41:59,000 --> 00:41:59,959
Speaker 3: it would be such a great pause.

802
00:42:00,719 --> 00:42:03,200
Speaker 1: No, I think it ends up being myopic, which is

803
00:42:03,280 --> 00:42:06,319
Speaker 1: sort of how I felt at the very start of

804
00:42:06,320 --> 00:42:09,879
Speaker 1: this conversation too about that whole concept. Like, I do

805
00:42:09,920 --> 00:42:14,080
Speaker 1: think it's an intense fear of mortality that ends up

806
00:42:14,160 --> 00:42:16,640
Speaker 1: driving this. I think, actually, if I'm being honest, I

807
00:42:16,640 --> 00:42:18,920
Speaker 1: think a lot of futurists kind of fall into that

808
00:42:19,000 --> 00:42:23,239
Speaker 1: category two, specifically the ones who have talked about the

809
00:42:23,320 --> 00:42:27,000
Speaker 1: concepts like the singularity and transhumanism and things, elements of

810
00:42:27,040 --> 00:42:30,800
Speaker 1: which we're seeing play out to some extent, but certainly

811
00:42:30,840 --> 00:42:35,759
Speaker 1: not in the form or scope that has been talked

812
00:42:35,760 --> 00:42:38,279
Speaker 1: about for a couple of decades. And it's not to

813
00:42:38,320 --> 00:42:40,360
Speaker 1: say that we're not going to get to a point

814
00:42:40,400 --> 00:42:44,799
Speaker 1: where remarkable change isn't happening at a scale where we

815
00:42:44,840 --> 00:42:47,080
Speaker 1: can't even talk about the present. Maybe that does happen,

816
00:42:47,560 --> 00:42:51,160
Speaker 1: but it just seems to me that there's this underlying

817
00:42:51,239 --> 00:42:54,680
Speaker 1: theme under a lot of those writings of that seems

818
00:42:54,719 --> 00:42:57,040
Speaker 1: to suggest a fear of mortality. This could just be

819
00:42:57,160 --> 00:43:00,719
Speaker 1: armchair psychology bypart, and maybe it's just a theory I

820
00:43:00,760 --> 00:43:04,520
Speaker 1: have which I totally will own, but just it keeps

821
00:43:04,520 --> 00:43:07,120
Speaker 1: coming back to me because I spent five years doing

822
00:43:07,320 --> 00:43:12,560
Speaker 1: a show kind of exploring futurists and futurism thinking and

823
00:43:12,640 --> 00:43:15,439
Speaker 1: that sort of things, and those were the the things

824
00:43:15,440 --> 00:43:19,080
Speaker 1: that's sort of they weren't they weren't even subtext, but

825
00:43:19,120 --> 00:43:22,840
Speaker 1: it seemed to be kind of an underlying motivating factor.

826
00:43:23,239 --> 00:43:26,000
Speaker 3: You know, I'm not sure I see that only because

827
00:43:26,080 --> 00:43:28,480
Speaker 3: I happen to not care about the mortality issue. Yeah,

828
00:43:28,520 --> 00:43:30,640
Speaker 3: and yet I'm very interested in the future. I know,

829
00:43:30,680 --> 00:43:32,680
Speaker 3: I spent a lot of my time thinking about futurism

830
00:43:32,719 --> 00:43:36,160
Speaker 3: and so on. So they don't I don't think they

831
00:43:36,239 --> 00:43:41,200
Speaker 3: necessarily go together. Being thoughtful and creative about what's happening

832
00:43:41,239 --> 00:43:44,120
Speaker 3: next and where things are going feels to me like,

833
00:43:44,200 --> 00:43:46,320
Speaker 3: you know, this is the best part about human brains,

834
00:43:46,400 --> 00:43:49,840
Speaker 3: is our capacity to do this, to say, Wow, look,

835
00:43:50,239 --> 00:43:53,640
Speaker 3: I'm gonna I'm going to simulate forward. Given all the

836
00:43:53,680 --> 00:43:55,560
Speaker 3: stuff that I know, the future is probably not going

837
00:43:55,640 --> 00:43:58,560
Speaker 3: to be just like the past. Instead, you're gonna have

838
00:43:58,600 --> 00:44:01,200
Speaker 3: these things come together and then this other technology merge

839
00:44:01,239 --> 00:44:03,040
Speaker 3: with that, and then wow, look at all these things.

840
00:44:03,040 --> 00:44:06,000
Speaker 3: It leads to I mean, we're always wrong, of course, right, sure,

841
00:44:06,239 --> 00:44:08,880
Speaker 3: terribly wrong. But if you look at the world's fares

842
00:44:09,840 --> 00:44:14,719
Speaker 3: through the decades, like the nineteen sixty Worldfare had displays

843
00:44:14,760 --> 00:44:18,680
Speaker 3: on like underwater hotels or using lasers to cut down

844
00:44:18,719 --> 00:44:21,200
Speaker 3: trees or things like that, they had all kinds of

845
00:44:21,239 --> 00:44:25,680
Speaker 3: amazing stuff, but nobody even thought of the Internet. I

846
00:44:25,680 --> 00:44:28,440
Speaker 3: mean really one of the most is the biggest change

847
00:44:28,440 --> 00:44:30,400
Speaker 3: is no one thought of that, And you get the

848
00:44:30,400 --> 00:44:33,799
Speaker 3: most creative thinkers together, and yet they just they miss

849
00:44:33,920 --> 00:44:37,120
Speaker 3: what are the really big changes, like cell phones or something.

850
00:44:37,800 --> 00:44:38,800
Speaker 2: Yeah, yeah, I mean.

851
00:44:38,680 --> 00:44:41,879
Speaker 1: Well, it's like before the transistor, no one could think

852
00:44:41,920 --> 00:44:45,480
Speaker 1: about an era of maniaturization. Right, Like, if you were

853
00:44:45,480 --> 00:44:48,480
Speaker 1: before the transistor, you would never have thought that a

854
00:44:48,520 --> 00:44:50,879
Speaker 1: computer would be something that someone could own in their home,

855
00:44:50,960 --> 00:44:54,480
Speaker 1: let alone carry in their pocket. And there'd be no way, like,

856
00:44:54,520 --> 00:44:58,120
Speaker 1: you would have no frame of reference to say, because

857
00:44:58,160 --> 00:45:01,200
Speaker 1: in your experience, the way coulduter's got more powerful was

858
00:45:01,239 --> 00:45:04,800
Speaker 1: that they were larger, Right, So like if your computer

859
00:45:04,920 --> 00:45:09,400
Speaker 1: held up, held up an entire floor over underneath Stanford,

860
00:45:09,840 --> 00:45:12,080
Speaker 1: that was a really good computer. There's no way you

861
00:45:12,080 --> 00:45:14,720
Speaker 1: could have one of those in your home. And I'm

862
00:45:14,880 --> 00:45:16,759
Speaker 1: very much the same way. I'll think back occasionally and

863
00:45:16,840 --> 00:45:19,160
Speaker 1: I'll go, wow, those knuckleheads get it all wrong, and

864
00:45:19,160 --> 00:45:20,560
Speaker 1: I thought, yeah, but if I were back then, I

865
00:45:20,560 --> 00:45:22,879
Speaker 1: would have gotten it wrong too, because I didn't have

866
00:45:22,960 --> 00:45:26,840
Speaker 1: the knowledge that the transistor was right around the corner exactly.

867
00:45:26,920 --> 00:45:28,160
Speaker 3: By the way, you know, when you said that that

868
00:45:28,239 --> 00:45:31,000
Speaker 3: just suddenly a thought crossed my mind, which is it's

869
00:45:31,040 --> 00:45:33,600
Speaker 3: funny that we still call this a PC a personal

870
00:45:33,680 --> 00:45:37,120
Speaker 3: computer to distinguish it from you know, like I can wow,

871
00:45:37,120 --> 00:45:38,520
Speaker 3: I can take it one home myself.

872
00:45:39,320 --> 00:45:41,360
Speaker 1: Well, I also grew up in the seventies and eighties,

873
00:45:41,400 --> 00:45:44,040
Speaker 1: so for me, like it's cemented in my head because

874
00:45:44,040 --> 00:45:47,279
Speaker 1: that's how I was exposed to it. And and as

875
00:45:47,280 --> 00:45:49,640
Speaker 1: I'm sure you're aware, sometimes we have a tendency to

876
00:45:49,719 --> 00:45:53,680
Speaker 1: kind of arrest definitions in our head based upon when

877
00:45:53,680 --> 00:45:58,200
Speaker 1: we encountered the thing for the first time. Absolutely, well, David,

878
00:45:58,239 --> 00:45:59,960
Speaker 1: before we go, can you tell us a little bit

879
00:46:00,040 --> 00:46:02,400
Speaker 1: more about inner cosmos for those who I mean, obviously

880
00:46:02,440 --> 00:46:06,560
Speaker 1: you cover tons of different topics related to the brain,

881
00:46:06,719 --> 00:46:09,719
Speaker 1: but give our listeners a little insight into what the

882
00:46:09,719 --> 00:46:12,560
Speaker 1: show's all about, because I want text of listeners to

883
00:46:12,600 --> 00:46:13,680
Speaker 1: definitely go and check it out.

884
00:46:14,400 --> 00:46:15,200
Speaker 3: Thank you.

885
00:46:15,200 --> 00:46:15,400
Speaker 2: You know.

886
00:46:15,600 --> 00:46:18,279
Speaker 3: Part of what's really hard about doing the podcast but

887
00:46:18,360 --> 00:46:21,719
Speaker 3: so rewarding is each week is a monologue. I just

888
00:46:21,760 --> 00:46:23,799
Speaker 3: you know, for forty five minutes or an hour, I

889
00:46:23,840 --> 00:46:27,239
Speaker 3: talk about some topic. But it's like I'm doing storytelling.

890
00:46:27,280 --> 00:46:28,920
Speaker 3: What I'm doing is I'm plenty of other threads of

891
00:46:29,239 --> 00:46:33,480
Speaker 3: history and culture and science and extrapolation in the future

892
00:46:33,880 --> 00:46:37,400
Speaker 3: to address questions of various sorts, like this question of

893
00:46:37,440 --> 00:46:39,960
Speaker 3: you know, could we ever upload our brains? Or will

894
00:46:40,000 --> 00:46:42,479
Speaker 3: we ever do mind reading? There's all kinds of stuff

895
00:46:42,480 --> 00:46:45,600
Speaker 3: in the media about you know, reading minds. Or sometimes

896
00:46:45,600 --> 00:46:48,200
Speaker 3: I address mysteries like you know, why do some people

897
00:46:48,239 --> 00:46:52,759
Speaker 3: think the northern lights make noise? And quick hint on

898
00:46:52,800 --> 00:46:55,719
Speaker 3: that is it has to do with synesthesia, which is

899
00:46:55,760 --> 00:46:58,400
Speaker 3: where someone has a blending of the senses. Or I

900
00:46:58,440 --> 00:47:02,719
Speaker 3: talk about various issues about our perception of time, like

901
00:47:02,840 --> 00:47:05,600
Speaker 3: does time actually run in slow motion when you're in

902
00:47:05,640 --> 00:47:08,120
Speaker 3: fear for your life? This is something that happened to

903
00:47:08,120 --> 00:47:09,759
Speaker 3: me when I was a kid and almost died. I

904
00:47:09,760 --> 00:47:12,359
Speaker 3: fell off of a a house under construction, and then

905
00:47:12,400 --> 00:47:14,520
Speaker 3: I ended up studying this as an neuroscientist known and

906
00:47:14,600 --> 00:47:18,120
Speaker 3: ever studied this question before about this time slow down?

907
00:47:18,640 --> 00:47:20,840
Speaker 3: And so it's all about things like this. And actually

908
00:47:20,880 --> 00:47:23,200
Speaker 3: in the episode that's coming out next week, I'm talking

909
00:47:23,239 --> 00:47:26,040
Speaker 3: about the dress. You remember the dress? Was it you

910
00:47:26,120 --> 00:47:28,840
Speaker 3: know blue or black or white or gold? Yeah, Like

911
00:47:28,880 --> 00:47:32,920
Speaker 3: I'm really unpacking that and other other memes that you

912
00:47:32,960 --> 00:47:35,719
Speaker 3: may remember, Like you know, this thing, does it sound

913
00:47:35,760 --> 00:47:38,520
Speaker 3: like brainstorm or green Needle. It's the same audio file

914
00:47:38,600 --> 00:47:41,200
Speaker 3: but can sound like either one. Anyway, I'm putting a

915
00:47:41,239 --> 00:47:43,439
Speaker 3: whole bunch of things together. What I realized, I think,

916
00:47:43,600 --> 00:47:46,200
Speaker 3: is that to my knowledge, no one's ever sort of

917
00:47:46,239 --> 00:47:49,560
Speaker 3: put all these together and served it up in a

918
00:47:49,600 --> 00:47:54,319
Speaker 3: way where it's where you really get something deeper out

919
00:47:54,360 --> 00:47:55,920
Speaker 3: of it, like, oh, yeah, I saw that meme, I

920
00:47:55,960 --> 00:47:57,719
Speaker 3: saw that. Mean But what does this all mean to us?

921
00:47:57,719 --> 00:47:59,640
Speaker 3: What does this mean for our brains the way we

922
00:47:59,680 --> 00:48:02,200
Speaker 3: see the world. What does it mean for who we

923
00:48:02,239 --> 00:48:06,359
Speaker 3: are in terms of our neural networks. One theme that

924
00:48:06,719 --> 00:48:08,680
Speaker 3: if you've listened to several of the episodes, you might have

925
00:48:08,719 --> 00:48:11,240
Speaker 3: already been picking up on this theme. I'm very interested

926
00:48:11,239 --> 00:48:13,280
Speaker 3: in the fact that we are essentially like a team

927
00:48:13,360 --> 00:48:17,200
Speaker 3: of rivals underneath the hood, meaning you're not just one thing.

928
00:48:17,239 --> 00:48:21,760
Speaker 3: You've got all these different networks running and they're often

929
00:48:21,840 --> 00:48:24,840
Speaker 3: in conflict. And so this is why humans are nuanced

930
00:48:24,880 --> 00:48:27,399
Speaker 3: and subtle and interesting, Because you know, if I put

931
00:48:27,440 --> 00:48:30,160
Speaker 3: some chocolate chip cookies in front of you, part of

932
00:48:30,160 --> 00:48:32,600
Speaker 3: your brain says, hey, that's a good energy source, let's

933
00:48:32,600 --> 00:48:34,120
Speaker 3: eat it, and party brain says, no, don't eat it,

934
00:48:34,120 --> 00:48:35,640
Speaker 3: you'll get fat, and Party of Brains says, Okay, I'll

935
00:48:35,640 --> 00:48:37,160
Speaker 3: eat it, but I'll go to the gym tonight. And

936
00:48:37,800 --> 00:48:40,160
Speaker 3: you know, you can start contracting yourself and cussing with

937
00:48:40,200 --> 00:48:43,520
Speaker 3: yourself and like who's talking to whom? Here? It's all you,

938
00:48:43,600 --> 00:48:46,759
Speaker 3: but it's different parts of you and anyway, So all

939
00:48:46,800 --> 00:48:49,799
Speaker 3: this stuff is of great interest to me, and I

940
00:48:49,840 --> 00:48:55,920
Speaker 3: think is the material for storytelling and really diving into

941
00:48:55,960 --> 00:48:58,560
Speaker 3: this stuff. So I just love that people care about

942
00:48:58,600 --> 00:48:59,120
Speaker 3: this stuff.

943
00:48:59,320 --> 00:48:59,560
Speaker 2: Yeah.

944
00:49:00,080 --> 00:49:02,719
Speaker 1: To me, David, it's like, like it reminds me of

945
00:49:02,800 --> 00:49:06,320
Speaker 1: conversations for some reason, I associate when I was a teenager,

946
00:49:06,840 --> 00:49:10,480
Speaker 1: but because I was the nerdy teenager, I didn't you know,

947
00:49:10,640 --> 00:49:12,680
Speaker 1: drink or anything. I would hang out with my friends

948
00:49:13,040 --> 00:49:16,560
Speaker 1: and we just start asking questions we did not know

949
00:49:16,600 --> 00:49:19,799
Speaker 1: the answers to, and like you get into all the

950
00:49:20,000 --> 00:49:25,080
Speaker 1: like the stereotypical ones, like when I see red and

951
00:49:25,120 --> 00:49:27,600
Speaker 1: I experienced the color red, how do I know that

952
00:49:27,640 --> 00:49:30,279
Speaker 1: when you're looking at something that's red it looks the

953
00:49:30,320 --> 00:49:32,920
Speaker 1: same to you as it does to me, Or maybe

954
00:49:32,960 --> 00:49:36,239
Speaker 1: your red looks like blue to me, but we've both

955
00:49:36,280 --> 00:49:39,479
Speaker 1: agreed that this thing is red. Therefore that's what red

956
00:49:39,520 --> 00:49:41,239
Speaker 1: is to me. It's what red is to you, but

957
00:49:41,320 --> 00:49:45,080
Speaker 1: our experiences are and you just gone into this deep conversation.

958
00:49:45,880 --> 00:49:47,920
Speaker 3: Yeah, exactly. And by the way, this is just like

959
00:49:48,000 --> 00:49:50,560
Speaker 3: with your niece who when you said she's eight years old.

960
00:49:51,000 --> 00:49:53,839
Speaker 3: I mean, this is the same thing. It's that as

961
00:49:53,880 --> 00:49:57,120
Speaker 3: we age, we forget to ask those questions. We just

962
00:49:57,280 --> 00:50:00,000
Speaker 3: somehow stopped asking. Think, oh, I don't know the answer,

963
00:50:00,120 --> 00:50:03,040
Speaker 3: and that's the end of it. And so you know,

964
00:50:03,080 --> 00:50:05,359
Speaker 3: it's funny that you had mentioned that because I often think,

965
00:50:05,520 --> 00:50:08,560
Speaker 3: for example, you know a few of my episodes have

966
00:50:08,560 --> 00:50:11,120
Speaker 3: been about visual illusions, and this one about the dress

967
00:50:11,160 --> 00:50:14,120
Speaker 3: and so includes some issues about that. But I often

968
00:50:14,120 --> 00:50:16,960
Speaker 3: think that visual illusions are really interesting to ten year

969
00:50:16,960 --> 00:50:19,960
Speaker 3: olds and then to neuroscientists when they grow up. But

970
00:50:20,000 --> 00:50:22,239
Speaker 3: that's it, Like everyone else sort of forgets about visual

971
00:50:22,280 --> 00:50:25,480
Speaker 3: illusions unless they're you know, taking three seconds to appreciate

972
00:50:25,520 --> 00:50:28,760
Speaker 3: a meme on the internet. But like, why does that happen?

973
00:50:29,560 --> 00:50:32,960
Speaker 3: It's an incredibly powerful inroad into what is happening in

974
00:50:32,960 --> 00:50:36,840
Speaker 3: the brain to understand what's happening with an illusion anyway,

975
00:50:36,920 --> 00:50:39,080
Speaker 3: So what I'm trying to do with Inner Cosmos is

976
00:50:39,120 --> 00:50:44,440
Speaker 3: bring all these threads together so we can really understand ourselves,

977
00:50:44,520 --> 00:50:46,640
Speaker 3: understand who we are and what our reality is.

978
00:50:46,719 --> 00:50:47,120
Speaker 2: I love it.

979
00:50:47,160 --> 00:50:49,880
Speaker 1: I mean it's a tall ask for a podcast, but

980
00:50:49,920 --> 00:50:51,640
Speaker 1: I think you're up to the challenge.

981
00:50:51,880 --> 00:50:52,279
Speaker 3: Thank you.

982
00:50:52,560 --> 00:50:52,799
Speaker 2: Yeah.

983
00:50:53,080 --> 00:50:55,919
Speaker 1: I highly recommend that y'all check it out. That's Inner

984
00:50:55,960 --> 00:50:58,160
Speaker 1: Cosmos with David Eagleman. It's one of those things that

985
00:50:58,200 --> 00:51:03,360
Speaker 1: I listened to just as a fan. Thank you terrific, well, David,

986
00:51:03,400 --> 00:51:04,560
Speaker 1: Thank you for coming on the show.

987
00:51:04,560 --> 00:51:05,560
Speaker 2: I really appreciate it.

988
00:51:06,239 --> 00:51:07,880
Speaker 3: Thank you for having me. It's such a pleasure to

989
00:51:07,920 --> 00:51:08,560
Speaker 3: be here and talk with you.

990
00:51:08,640 --> 00:51:11,200
Speaker 2: Jonathan fantastic guys. That's it.

991
00:51:11,760 --> 00:51:13,840
Speaker 1: Go check out the show and I'll talk to you

992
00:51:13,880 --> 00:51:23,920
Speaker 1: again really soon. Tech Stuff is an iHeartRadio production. For

993
00:51:24,000 --> 00:51:28,839
Speaker 1: more podcasts from iHeartRadio, visit the iHeartRadio app, Apple Podcasts,

994
00:51:28,960 --> 00:51:31,000
Speaker 1: or wherever you listen to your favorite shows.