Speaker 1: A few years ago, around the time of Twitter's I
Speaker 1: p O, I noticed what I thought was an odd coincidence.
Speaker 1: It's chief engineer had studied birds. Specifically, he'd studied the
Speaker 1: auditory cortex of zebra finches. I thought that was pretty
Speaker 1: funny for an engineer, especially because Twitter's mascot is that
Speaker 1: little blue bird. I forgot about it until a couple
Speaker 1: of years ago when I noticed another bird brain scholar
Speaker 1: in the top echelons of tech. This person had been
Speaker 1: hired by Elon Musk, the entrepreneur behind Tesla and SpaceX,
Speaker 1: to join his new company, Neuralink. Neuralink is a very
Speaker 1: secretive futuristic company which is trying to supercharge the human brain.
Speaker 1: This all sounds pretty obscure. You'd think studying bird brains
Speaker 1: wouldn't be too relevant to studying human brains or figuring
Speaker 1: out social media, right, That's why I remembered it. It
Speaker 1: just felt so random, But now here were two. So
Speaker 1: one day, when I was a little bored, I just
Speaker 1: tried type being zebra finch and the names of several
Speaker 1: big tech companies into Google. And what did you find?
Speaker 1: I found quite a few employees who knew a lot
Speaker 1: about zebra finches. That was that companies like Intelent, Apple
Speaker 1: and Google too. I was surprised. Okay, hang on, Sarah,
Speaker 1: I'm googling this. The zebra finch is the most common
Speaker 1: astrill did finch of central Australia and ranges over most
Speaker 1: of the continent, avoiding only the cool, moist south and
Speaker 1: tropical far North. Zebrafinches are loud and boisterous singers. So
Speaker 1: what's the connection here, Well, that's where I jumped into
Speaker 1: the reporting process. Our goal is to figure out why
Speaker 1: tech companies are hiring bird brain experts, and this little
Speaker 1: quest has taken us to college campuses around the country.
Speaker 1: We visited several university labs. Some smelled better than others.
Speaker 1: We heard a lot of birds on So this is
Speaker 1: our guy, and so they're they're very light, um, but
Speaker 1: super active. How big would you say that thing is?
Speaker 1: It's like, so they're about fifty in Graham's total weight.
Speaker 1: They're mostly feathers. That's Tim Achi, a neuroscientist and assistant
Speaker 1: professor at Boston University, showing us czebra finch in his
Speaker 1: bird lab and telling us about his research on bird brains.
Speaker 1: He's running some pretty extraordinary experiments. Oh no, but the
Speaker 1: the implant. So this is actually just a lens. And
Speaker 1: so if you look at the top, so you see
Speaker 1: that little black thing that's sticking up, So that's a lens.
Speaker 1: If you look down and you couldn't see the brain,
Speaker 1: but there's not probably not enough light getting down there
Speaker 1: that you could actually see it. And it turns out
Speaker 1: what Tim's learning about the brains of these tiny birds
Speaker 1: is of great interest to the world's largest tech companies.
Speaker 1: I'm brad Stone and Sarah mcbrad that, I'm Ashley Vance,
Speaker 1: and you're listening to decrypt it. So, guys, tell me
Speaker 1: a little bit more about Tim Acchi and his I'm
Speaker 1: sure wonderfully smelling lab out in Boston. Yeah, well, Tim's
Speaker 1: lab was one of the key st ups on our trip.
Speaker 1: Tim took over a Boston University lab from Tim Gardner,
Speaker 1: the guy who left to work for Elon Musk and Neuralink.
Speaker 1: Tim Acchi is a forty year old guy with black
Speaker 1: frame glasses who throws in plenty of references to Portlandia
Speaker 1: and other TV shows when he check chats on his
Speaker 1: office wall. He's got a print of voltage chases called
Speaker 1: five Seconds of Donkey Kong. Now we're talking about something
Speaker 1: I'm familiar with, So what's the connection here. It turns
Speaker 1: out that that old eighties video game became the subject
Speaker 1: of a famous neuroscience research paper. There's one other thing
Speaker 1: you should know about Tim. He has a giant zebra
Speaker 1: finch tattoo on his right forearm. Brad, how familiar are
Speaker 1: you with zebra finch? Well, actually, other than my very
Speaker 1: brief Wikipedia search just now, other than small birds that
Speaker 1: sing a lot, I would say not very familiar. They
Speaker 1: are very cute little birds. Indeed, they're about four inches long.
Speaker 1: The males have orange cheeks and black and white striped
Speaker 1: feathers across their chests, hence the name. They're super easy
Speaker 1: to breed, and they chirp a lot. Here's Tim's interpretation.
Speaker 1: So how did Tim get into studying bird brains? He
Speaker 1: took a circuitous route into neuroscience. One thing I found
Speaker 1: interesting is he started as an engineer working at a
Speaker 1: company that helped factories to automate. His job was teaching
Speaker 1: robots how to sort stuff, everything from car parts to
Speaker 1: gives moos for circuit boards. It was just astounding to
Speaker 1: me how difficult it was to get things to do this,
Speaker 1: and these were tasks that you know, children do. It
Speaker 1: really put in my mind the idea that, you know,
Speaker 1: a lot of the things that that children can do
Speaker 1: almost effortlessly and without almost any training, are incredibly impossible
Speaker 1: to get artificial systems to do or take an enormous
Speaker 1: amount of thought. Okay, so I think I understand Tim
Speaker 1: was curious about why a certain task is so easy
Speaker 1: for a child but so difficult for a robot. Yeah,
Speaker 1: that's exactly it. So after a detour to Tim ended
Speaker 1: up studying neuroscience at Harvard, and that's where he discovered
Speaker 1: zebra finches. Now he's teaching it be you and doing
Speaker 1: his own research. One thing Tim focuses on is how
Speaker 1: zebra finches learned to sing. Tim described his study of
Speaker 1: zebra finches is more of a means to an end.
Speaker 1: I don't, you know, really think of myself or or
Speaker 1: care too much about, you know, songbird neuroscience specifically, I
Speaker 1: take it as a as a way to investigate general
Speaker 1: principles and mechanisms in neuroscience and how brains function generally.
Speaker 1: So Tim is saying that he studies these teeny tiny
Speaker 1: zebra finch brains because he thinks it will give us
Speaker 1: insights into the way human brains function. That's right. Researchers
Speaker 1: study all different kinds of animals for all different kinds
Speaker 1: of purposes, but in this case they're trying to learn
Speaker 1: more about the human brain. Seeing how birds learn to sing,
Speaker 1: for example, can provide insights into how we learn things.
Speaker 1: So I think that you know, the songbird is one
Speaker 1: of those systems that we probably understand the best in
Speaker 1: terms of different brain regions that are involved, in terms
Speaker 1: of the roles of those different brain regions, and so
Speaker 1: I think that we can ask very very precise questions
Speaker 1: in the songbird about the interaction between brain activity and behavior. Sarah,
Speaker 1: you started out by saying that studying the brains of
Speaker 1: zebra finches are somehow interesting for tech companies. So if
Speaker 1: Tim studies the birds because there are connections between bird
Speaker 1: brains and human brains, are there also connections between bird
Speaker 1: brains and computers. Absolutely, But to understand it, I need
Speaker 1: to explain a little bit more about Tim's research. When
Speaker 1: we met him in Boston, before we went into the
Speaker 1: room with the real life zebra finches, he played us
Speaker 1: one of his best songbird clips, and so that's what
Speaker 1: the song sounds like. Okay, so that's one. Yeah, so
Speaker 1: that's what the that's what this this particular zebra finch sings.
Speaker 1: All of them have slightly different songs. That sounded like
Speaker 1: a cartoon. Yeah, it sounded a little like woody woodpecker. Right,
Speaker 1: but that but that is actually, uh, that is actually
Speaker 1: what it sounds. Yeah. Yeah, we had listened to these
Speaker 1: all day long. Tim told us while male and female
Speaker 1: zebra finches can chirp, only male zebra finches sing, even
Speaker 1: if their song is so short it doesn't sound like
Speaker 1: much of a song to us. Tim studies the brains
Speaker 1: of the baby birds as they learn. Here's the baby
Speaker 1: zebra finch trying to imitate his dad's song. He makes
Speaker 1: an early effort, and then a better one a month later. Okay,
Speaker 1: so this is the father this one, well, okay, and
Speaker 1: then we can do the middle one where he's not
Speaker 1: quite good. A right, Okay, this is the one. Yeah
Speaker 1: that's pretty good. So Ashley, I'm kind of praying that
Speaker 1: to these experiments, he's not hurting these beautiful, teeny tiny birds. Basically,
Speaker 1: what he does is they take these birds that have
Speaker 1: been injected with a benign virus. The virus makes their
Speaker 1: brains produce a type of protein that causes individual neurons
Speaker 1: to light up when they fire, they glow green and red.
Speaker 1: To see them in action, Tim and the grad students
Speaker 1: in his lab performed very delicate surgery on the zebra
Speaker 1: finches to implant tiny, tiny microscopes in their brain. Tim
Speaker 1: showed us one bird who had gone through this procedure,
Speaker 1: male zebra finish. And so haven't we on his hiding
Speaker 1: He's got one of these windows that I was telling
Speaker 1: you about. And so this guy has already been through
Speaker 1: the procedure in which we inject the viruses into the
Speaker 1: brain and then we've put a window or a lens
Speaker 1: on top so that we can actually image through it.
Speaker 1: And it's going to get a microscope. He will eventually
Speaker 1: get a microscope. Auch Sarah, that sounds completely crazy. Give
Speaker 1: us a better sense for what this looks like. So
Speaker 1: there are all these little birds hopping around with a
Speaker 1: tiny bit of their skull missing, and instead they have
Speaker 1: a microscope there and a little hole where you can
Speaker 1: look in and see what's going on in their brain.
Speaker 1: And the microscope sits there for days, weeks, months, at
Speaker 1: a time, looking at their neurons fire in in real time,
Speaker 1: and the birds are in their little cages, and then
Speaker 1: when you go into their lab, it's actually it's pretty cool.
Speaker 1: There's all these wires going off these cages straight into
Speaker 1: like a data center where they store all of this information.
Speaker 1: And so basically you just get to just get to
Speaker 1: watch this bird's brain behave in real time and then
Speaker 1: go back and look through all the information. So, Sarah,
Speaker 1: should we feel sorry for these birds? I didn't. They
Speaker 1: seemed really happy. They were hopping around, they were chirping,
Speaker 1: they were acting normal as far as they could tell
Speaker 1: based on the ones they saw without microscopes in their head.
Speaker 1: They really didn't seem to be too much difference. Okay,
Speaker 1: so how is all this useful for the neuroscientists. They
Speaker 1: look deep inside each bird's brain and they look to
Speaker 1: see which neurons are firing and for how long, when
Speaker 1: say the bird is learning to sing, and they can
Speaker 1: make hypothesis on the relationship between different neurons. So that's
Speaker 1: actually a technique used pretty widely in science now and
Speaker 1: all kinds of animals. While we were visiting Tim in Boston,
Speaker 1: we also stopped by a mouse lab that does something similar,
Speaker 1: and a fish lab. In each of the labs, the
Speaker 1: neuroscientists there are studying other things as well, like how
Speaker 1: animals move, what happens in their brains when they make decisions.
Speaker 1: For example, at the Roland Institute in Cambridge, we saw
Speaker 1: a couple of video games that mice play using tiny
Speaker 1: joysticks size for a mouse paw Wow. So how do
Speaker 1: they how do they mice get the quarters in the
Speaker 1: video game machine. They're highly trained and and the video
Speaker 1: games tells us what it tells us how they're making
Speaker 1: decisions exactly. So the mice are a lot like the birds.
Speaker 1: They have bits of their their skull have been removed,
Speaker 1: and we're watching their brains and real time again and
Speaker 1: and so you watch them play these video games, and
Speaker 1: you see how they adapt. Sometimes they get different rules,
Speaker 1: and you see how the mouse learns the rules of
Speaker 1: the game, and they move this choice stick around and
Speaker 1: one of them to find the edges of a box.
Speaker 1: And if the mouse is successful, it gets a little
Speaker 1: bit of sugar water. And and all this time the
Speaker 1: scientists are sitting in there seeing which parts of the
Speaker 1: brain light up and how the mouse reacts to these
Speaker 1: different situations. So what is it about this research that
Speaker 1: tech companies find so interesting? One reason is that these
Speaker 1: scientists are working with tons and tons of data, and
Speaker 1: that's something that every tech company needs to do. And
Speaker 1: also it's to do with artificial intelligence, the field that
Speaker 1: has computer systems mastering tasks that require human traits like
Speaker 1: visual perception or decision making. Maybe how to identify a cat.
Speaker 1: That sounds simple, but it's actually way more nuanced than
Speaker 1: a traditional computer task and very hard for a computer
Speaker 1: to masse term. So there's this one school of thought
Speaker 1: that AI moving forward should loosely be modeled after the
Speaker 1: human brain. The AI systems we have today are still
Speaker 1: basically number crunching systems. They're doing tons of statistical calculations.
Speaker 1: And if we want to get to this this future
Speaker 1: that Sarah's talking about where you actually have decision making
Speaker 1: and much more sophisticated thought, the ideas that we could
Speaker 1: borrow from the human brain and and maybe I have
Speaker 1: something that's way more flexible than what a computer could do.
Speaker 1: And so presumably, since we can't cut open human skulls
Speaker 1: and make people play video games against their will, the
Speaker 1: zebra finch brains are first Yeah, exactly, human brains are
Speaker 1: a little too big and complicated to study in the
Speaker 1: kind of detail we can get from animals right now,
Speaker 1: they're smaller, easier to study, and obviously the ethics of
Speaker 1: studying a living human brain very tricky. So I'm so
Speaker 1: curious about why zebra finches have become the bird to
Speaker 1: study and what exactly tech companies are doing with all this.
Speaker 1: Let's get to that after the break, Okay, Sarah n Ashley,
Speaker 1: So before the break, you explain how neuroscience is starting
Speaker 1: to inform the way tech companies design AI systems. So
Speaker 1: I guess this means it's a very good time to
Speaker 1: be a neuroscientist. Yeah. So these people that used to
Speaker 1: be in academia or working at universities for their whole
Speaker 1: career are now finding tons of job opportunities in Silicon Valley.
Speaker 1: Companies like Google and Apple and Amazon are all snatching
Speaker 1: them up, including a lot of these zebra finch experts
Speaker 1: that we've been talking about. It's how the company that
Speaker 1: probably makes the chips in your laptop won't say exactly
Speaker 1: how many people it has working in AI, but it's
Speaker 1: a lot, and many of them have this expertise. Intel
Speaker 1: helps its customers soup up machines to get them to
Speaker 1: behave smarter and in more uman lake ways. That could
Speaker 1: mean maybe working with a self driving carmaker, and that
Speaker 1: carmaker needs its vehicles to make lightning fast decisions on
Speaker 1: the road, and they have to be good decisions. A
Speaker 1: promising way to do that is to build computer systems
Speaker 1: that imitate how the human brain works. In a human brain,
Speaker 1: the systems are called signapses and pathways. In a machine,
Speaker 1: they're called neural networks. We talked to a mirror because Shahi,
Speaker 1: the chief technology officer for Intel's AI products divisions of
Speaker 1: architectures having wildly successful and uh sends around two thousand
Speaker 1: eleven UM, and while wider and excreted vision, m navigation,
Speaker 1: reforcement learning things that are car related to neuroscience becauses
Speaker 1: are also tacit humans doing pretty well. A mirror is
Speaker 1: a computational neuroscientists by training. He got his PhD at
Speaker 1: UC Berkeley, and he ended up hiring another Berkeley PhD grad,
Speaker 1: Tyler Lee, to work it into and helping virtual assistants
Speaker 1: understand human speech. Tyler spends a lot of time thinking
Speaker 1: about how speech works in different environments like cars. Knowing
Speaker 1: context simple things like if the speakers the driver, or
Speaker 1: the passenger makes it a lot easier to understand what
Speaker 1: they're saying. For example, a driver is more likely to
Speaker 1: ask about directions. Some of the work on context he
Speaker 1: does actually reminds him a lot of his PhD work
Speaker 1: studying you guessed it zebra fitches. The bird has to
Speaker 1: recognize what type of call is being being omitted by
Speaker 1: by the one it's hearing, uh, and then then it
Speaker 1: can go and recognize who that bird is. Is that
Speaker 1: is that a family member of mate? You know? Is
Speaker 1: it another zuber fringe or a different type of bird?
Speaker 1: Should I be concerned? The vocal identification is one thing
Speaker 1: where it's it's context specific, and recognizing the context would
Speaker 1: let you better understand the vocal signature. Mostly, Tyler says
Speaker 1: his studies help him big picture stuff. Neuroscience teaches you
Speaker 1: how to think about complex problems of signal processing. Where
Speaker 1: I take some something from the world that comes in.
Speaker 1: It's an image, it's uh, it's sound, and it's it's noisy,
Speaker 1: and it's high very very high dimensional, and I have
Speaker 1: to like break it down into into features that I
Speaker 1: can then use to to like do something with, solve
Speaker 1: a task with. That's what the brain does all the time,
Speaker 1: and that's sort of the abstract level. Tyler's boss a
Speaker 1: Mirror says he's no zebra finch chauvinist. He's got people
Speaker 1: on his team who studied flies, rats, locus, even worms.
Speaker 1: These very simple organisms exhibit really complex behaviors that are
Speaker 1: still a challenge for us to to simulate and silicon
Speaker 1: using our neual networks and machine and learning. So even
Speaker 1: a simple worm inchworm is a really complicated robotic machine
Speaker 1: that's really miraculous. So we look for inspiration from simple
Speaker 1: to humans. So I guess Intel and all these other
Speaker 1: companies must be developing products which incorporate AI developed with
Speaker 1: the help of these zebra finch experts. Besides commands to
Speaker 1: self driving cars, is there any area where knowing a
Speaker 1: lot about sound itself is helpful? And I guess when
Speaker 1: when do all these years of studying zebra finches finally
Speaker 1: pay off? You can think about features on your phone
Speaker 1: or computer that let you unlock the device with your voice,
Speaker 1: or stuff like noise reduction and phone calls and on
Speaker 1: video calls. I can't wait till I can sing a
Speaker 1: little Zebra Finch song and my phone unlocks. But anything
Speaker 1: beyond gadgets, yeah, absolutely. Animal neuroscience connects to a lot
Speaker 1: of fields, especially health. Somewhere could be relevant for Parkinson's
Speaker 1: research because animals help researchers figure out how to stop tremors.
Speaker 1: They also help in areas like how to handle prosthetic limbs.
Speaker 1: Outside of medicine, the work might grow even more futuristic.
Speaker 1: This is where we get into dystopian scenarios. I suspect
Speaker 1: what what else exactly like Neuralink. I mentioned that company
Speaker 1: earlier because Ellen Musk get hired a Zebra Finch scholar,
Speaker 1: and it's one of the companies that we believe is
Speaker 1: working on very futuristic technology. Ellen Musk keeps dropping hints
Speaker 1: on Twitter that the company is about to announce a
Speaker 1: big breakthrough. Nobody knows exactly what, but it's going to
Speaker 1: have something to do with brain machine interfaces. So I'm
Speaker 1: trying hard not to think about some Star Trek episodes
Speaker 1: on this topic, which all ended quite badly. But help
Speaker 1: me understand actually what a brain machine interfaces about. I mean,
Speaker 1: at its most basic level, is this idea that you
Speaker 1: have a two way interplay between humans and computers where
Speaker 1: you could actually funnel information back and forth. We already
Speaker 1: have examples of stuff like this with the implants that
Speaker 1: help people here or stop Parkinson's tremors in this case,
Speaker 1: I think people are looking at much more futuristic applications
Speaker 1: where you might even have like a mesh that's attached
Speaker 1: to your brain and you could full on download your
Speaker 1: brain to a machine or learn Japanese in five seconds.
Speaker 1: There's another company called Colonel that's in this same field,
Speaker 1: and like Neuralalink, it's also very mysterious, and in some
Speaker 1: ways that's kind of the best part. When we don't
Speaker 1: know exactly what they're doing, we can imagine all kinds
Speaker 1: of crazy stuff. Going back to Elon Musk, he's been
Speaker 1: talking about where neurally could go, maybe allowing people to
Speaker 1: have this kind of superhuman cognition where you could you
Speaker 1: could think on par with a machine, are certainly much
Speaker 1: better than we do today. That means basically stuff like
Speaker 1: you could download an entire FOURGN language directly into your brain,
Speaker 1: or maybe instantly grabbing encyclopedia. People like Timachi have been
Speaker 1: thinking about exactly these scenarios for years and can really
Speaker 1: nerd out on the possibilities. Trivia would be over, Jeopardy
Speaker 1: would not be a thing anymore. Um, Alex Trebek would
Speaker 1: be out of a job. He has some more serious
Speaker 1: thoughts on the topic too. I find the idea that
Speaker 1: we could, you know, pretend one day in the maybe
Speaker 1: just in future, you know, really right information directly into
Speaker 1: the brain, that we could actually have a high bandwidth way,
Speaker 1: uh to get really sci fi about it, kind of
Speaker 1: a matrix E like, Um, I think that would be amazing.
Speaker 1: We are nowhere near knowing anything about how to get there.
Speaker 1: We can barely even scratch the surface of what that
Speaker 1: would be like. But you know, in terms of fantasy,
Speaker 1: what would I like to do one day? I would
Speaker 1: love to be able to contribute even a small way
Speaker 1: to figuring out how we can have this sort of
Speaker 1: bi directional interface with the brain. Oh my god. So yeah,
Speaker 1: this is While the evocation of the matrix does not
Speaker 1: make me feel more comfortable about this, what do the
Speaker 1: skeptics say about about it all? Well, there are plenty
Speaker 1: of skeptics out there. I checked in with one scholar
Speaker 1: at the University of Chicago, Dan Margolias. The idea that
Speaker 1: we're going to reverse engineers, not reverse engineer, uh, forward
Speaker 1: engineer or the human brain, so we can download tons
Speaker 1: of material into it very rapidly. And I don't know
Speaker 1: what pick up a language overnight or something. I it's
Speaker 1: it's the way people make progresses to dream and so
Speaker 1: I'm I'm a scientist. I'm for that. But that really
Speaker 1: sounds more fantastical than realistic. It ignores it ignores the
Speaker 1: remarkable ways we learn, and it ignores our evolutionary history. Um,
Speaker 1: so I would, I would. Uh, it'll be interesting to
Speaker 1: see what progress they make for sure. So guys, the
Speaker 1: possibility of super human cognition sounds appealing. Um, you know,
Speaker 1: if we're up to you two and and somebody was
Speaker 1: offering to put a chip or an apparatus and in
Speaker 1: the year brains like they're doing to the to the
Speaker 1: poor little zebra finch, would you do it? I mean,
Speaker 1: you know, in some sence, we're already doing this stuff today.
Speaker 1: If you have an implant to help you here, or
Speaker 1: things to stop Parkinson's tremors, yeah, if I had one
Speaker 1: of those conditions, I would absolutely get one of these
Speaker 1: implants and supercharge myself. When you start going into this
Speaker 1: this next wave of stuff. It gets I think, far
Speaker 1: more philosophical and complicated, because you're talking about changing humans
Speaker 1: from what they are, some sort of weird next step
Speaker 1: of evolution where we're kind of half man half machine. Um.
Speaker 1: You know, in some ways there's people I talked to,
Speaker 1: like the guys at Colonel, who argue that this is
Speaker 1: the only way humans will be able to keep up
Speaker 1: with machines. And we always hear about losing jobs artificial
Speaker 1: intelligence and and seeing what humans can do going away.
Speaker 1: And so you know, if you're half and half, you
Speaker 1: can you can keep up, but maybe keep some of
Speaker 1: your humanness as well. Sarah, what about cyborg? Sarah McBride,
Speaker 1: will we ever see? Will we ever see that? I
Speaker 1: already hate myself, so I doubt it. But um, what
Speaker 1: about this idea of keeping up with robots? And and yeah,
Speaker 1: there are people who think that AI could help us
Speaker 1: solve problems like climate change. I think that's too optimistic.
Speaker 1: The funny part about doing the story for me was
Speaker 1: that that in the AI camp you tend to have
Speaker 1: I feel like people who think the technology is really
Speaker 1: far along. You know, if you're talking about specifically computer
Speaker 1: scientists and the Silicon Valley kind of crew. Um, they're
Speaker 1: very impressed what they've come up with over the last
Speaker 1: few years. When we went to talk to all these
Speaker 1: brain researchers, the ones who are down in the box,
Speaker 1: down at the neurons, they seemed, on the whole to
Speaker 1: me much more skeptical about when we would see huge breakthroughs.
Speaker 1: They seem to think that a lot of this stuff
Speaker 1: was years and years away, and and I felt like
Speaker 1: they had this sense of how complicated the brain really
Speaker 1: is and that unlocking its secrets is going to take along.
Speaker 1: But given the fact that they are taking the first
Speaker 1: steps to what will ultimately be very transformative and challenging,
Speaker 1: controversial technology. Sarah, did you get the sense that they
Speaker 1: were wrestling with the ethical complications of their work? You know,
Speaker 1: a lot of them actually turned out to have studied
Speaker 1: philosophy at some point in their careers, which I thought
Speaker 1: was pretty interesting. And yeah, they talk about the decisions
Speaker 1: that a carmaker might have to make. Is it more
Speaker 1: important to preserve the life of a passenger or a pedestrian,
Speaker 1: stuff like that. So, yeah, they're thinking about these big problems.
Speaker 1: That doesn't mean they know how to answer them though,
Speaker 1: any more than we would. But they say that it
Speaker 1: means these systems will be very human one day. Do
Speaker 1: they understand that they have now found themselves right at
Speaker 1: the center of this next wave in in computing. I
Speaker 1: mean they do to a degree. They're definitely excited. I mean,
Speaker 1: just in very crass terms. A lot of these people
Speaker 1: get much better job offers than they would have in
Speaker 1: the past. You know, there used to be far less
Speaker 1: neuroscience graduates. It wasn't that appealing of a field. We
Speaker 1: didn't know much about the brain. All these promised breakthroughs
Speaker 1: weren't happening at all. In Now you can go to
Speaker 1: a university do this amazing work, or if you kind
Speaker 1: of get tired of that, or you want to poke
Speaker 1: around somewhere else, you can go work for one of
Speaker 1: these tech companies and get paid I don't know, like
Speaker 1: ten times what you need to make at one of
Speaker 1: these labs. Well, to bring this all the way back
Speaker 1: to the beginning, Sarah, is it too early? When I
Speaker 1: talked to Alexa or Sirie or Google Voice to thank
Speaker 1: the little zebra finch? Can we can we see any
Speaker 1: of the zebra finch and that research in today's AI.
Speaker 1: We see some of it already and things like voice
Speaker 1: recognition and making better audio quality, but I think some
Speaker 1: of the biggest stuff is uh yet to come, and
Speaker 1: we can always check in with Timachi. And that's it
Speaker 1: for this week's episode of Decrypted. Thanks for listening. We
Speaker 1: always like to know what you think of the show.
Speaker 1: You can write to us at Decrypted at Bloomberg dot
Speaker 1: net or I'm on Twitter at McBride s G, I'm
Speaker 1: at Bradstone, and I'm at Valley Hack. And please help
Speaker 1: us spread the word about our show by leaving us
Speaker 1: a rating or review in your favorite podcast app. This
Speaker 1: episode was produced by Pierre Gadkari and Lindsay Cradowell. Our
Speaker 1: story editor was Aki Ito. Thank you also to Ann
Speaker 1: vander May and Emily Buso. Francesca Levi is head of
Speaker 1: Bloomberg Podcasts. We'll see you next week.