Speaker 1: Brought to you by Toyota. Let's go places. Welcome to
Speaker 1: Forward Thinking Heater, and welcome to Forward Thinking, the podcast
Speaker 1: that looks at the future and says you better think.
Speaker 1: I'm job and Strickland, I'm Lauren Vocaban, and I'm Joe McCormick.
Speaker 1: So hey everybody. Hey, last time we were in the
Speaker 1: podcast studio, which in reality, was a few minutes ago, right,
Speaker 1: we never left Peek behind the curtain, we talked about
Speaker 1: the differences between brains and computers a lot of times,
Speaker 1: a lot of them. Yeah, a lot of times people
Speaker 1: talk about one sort of using the other as a
Speaker 1: metaphor for it, like the brain is like a big computer,
Speaker 1: or computers are thinking. You might say that, and it
Speaker 1: seems like a really good metaphor on the surface, but
Speaker 1: when you get down into the nitty gritty of how
Speaker 1: each one works, they are incredibly different. Yeah. So they
Speaker 1: have some similarities. They both process information, they both store information,
Speaker 1: have memory, they both can perform logic, they both uh,
Speaker 1: they both use electricity. Let's say that there are a
Speaker 1: lot of they have input and output, but they're fundamentally
Speaker 1: different in a lot of other ways. Fundamentally different, and
Speaker 1: especially in the fact that we really understand how computers
Speaker 1: and code information, and we don't fully understand all the
Speaker 1: ins and outs of how information functions in the brain.
Speaker 1: But we do know a lot about things brains are
Speaker 1: good at that computers aren't good at, and we talked
Speaker 1: about some of those in the last podcast. For example,
Speaker 1: brains are more energy efficient than computers for the amount
Speaker 1: of processing they do. Brains are much more versatile than computers.
Speaker 1: So computer might very be able to very quickly execute
Speaker 1: a very specific, pre programmed set of instructions. But brains
Speaker 1: can learn how to do new things, they can adapt
Speaker 1: to new scenarios, they can reprogram them themselves to do
Speaker 1: a task better. Computers can't do any of this without specific,
Speaker 1: you know, augmentation to allow them to do it. So
Speaker 1: we want to talk today about how we can make
Speaker 1: computers more like brains. Yeah, this is not a new idea.
Speaker 1: Um people have been I mean we've we've had discussions
Speaker 1: about this. Even even Touring had some thoughts about how
Speaker 1: a computer could at least simulate intelligence enough for it
Speaker 1: to seem like it was thinking like a person. Now
Speaker 1: in that case, he was really talking about, uh kind
Speaker 1: of a simulation the idea that if it's simulated so
Speaker 1: close to human behavior that we might as well assume
Speaker 1: it has intelligence. We're not necessarily talking intelligence here in
Speaker 1: the sense of a a self aware of thinking machine.
Speaker 1: We're talking about giving a computer some capabilities that are uh,
Speaker 1: that come very easily to brains, but not so easily
Speaker 1: to computers. Yeah. I think the difference between this and
Speaker 1: the kind of artificial intelligence we talked about in the
Speaker 1: Turing test is that's more like the appearance of intelligence.
Speaker 1: This is working like a brain works. So if you were, example, uh,
Speaker 1: teaching a computer how to recognize certain phrases spoken out loud,
Speaker 1: and then you had someone else come in who was
Speaker 1: a different age, gender, and perhaps even a different dialect
Speaker 1: say that same phrase, the computer would still be able
Speaker 1: to pick up on it, even though the phonetics might
Speaker 1: be remarkably different. Yeah. So people were pretty good at this.
Speaker 1: Not depending on where you are, you might if you're
Speaker 1: in Yorkshire and you happen to be from the South,
Speaker 1: you might not understand a word anyone is saying. I
Speaker 1: maintained that no one in Yorkshire understands what anyone else
Speaker 1: is saying. But but if you're a computer, I mean,
Speaker 1: these issues can become difficult even with subtle changes. So
Speaker 1: it's well, it's not just sounds like that. One of
Speaker 1: the other things I should have mentioned at the beginning
Speaker 1: that human brains are very good at that computers are
Speaker 1: not very good at is dealing with real world data.
Speaker 1: So computers do great jobs with data that's like a formatted,
Speaker 1: structured set of number values. They do not do a
Speaker 1: great job with say a visual image or a sound
Speaker 1: or you know, the feeling of a of a touch.
Speaker 1: All that has to be translated into digital language for
Speaker 1: the computer. It just doesn't work intuitively like it does
Speaker 1: for us. It's also really bad at making associations between
Speaker 1: things without you having built in entire ontology to teach
Speaker 1: it what things are in their relationships to each other.
Speaker 1: So making a computer think more or perhaps work more
Speaker 1: like a brain has a lot of a lot of
Speaker 1: of appeal to it. And like I said this, this
Speaker 1: appeal goes back quite a way. It is in fact
Speaker 1: John von Neumann. In our last podcast, we talked about
Speaker 1: von Neuman architecture and the fact that the their your
Speaker 1: basic computer today is based on von Neumann architecture. This
Speaker 1: idea of essentially a CPU and RAM. That's that's your
Speaker 1: basic when you ole it all down, that's what it
Speaker 1: comes down to. Um. He actually was working on a
Speaker 1: book and the book was titled The Computer and the Brain.
Speaker 1: He passed away before finishing it, but even then he
Speaker 1: was sort of looking at the differences between computers and
Speaker 1: brains and how could you perhaps start to close the
Speaker 1: gap between the two, Uh in which way? Well, and
Speaker 1: in the way of making computers more like brains, not brains,
Speaker 1: more like computers. He wasn't suggested, He wasn't suggesting that
Speaker 1: we all have positronic brains installed in our heads. Um,
Speaker 1: some people would like that world. I just thought we
Speaker 1: should be clear. Yeah, that's fair, it's fair. The clarification
Speaker 1: has been made. Then. We also have a professor Carver
Speaker 1: Mead of the California Institute of Technology who began to
Speaker 1: experiment with designing computer chips that were inspired by brains,
Speaker 1: and he called them neuromorphic chips. Will be talking a
Speaker 1: lot about those today. And one of his collection of
Speaker 1: transistors mimicked how our brains processed visual data, and it
Speaker 1: actually allowed them to create a computer that could recognize
Speaker 1: the edges of objects in an image. So being able
Speaker 1: to tell where one object ends and something else begins,
Speaker 1: whether that's just space or another object or whatever, which
Speaker 1: again very easy for humans to do, not necessarily easy
Speaker 1: for a machine to do. You know, you have to
Speaker 1: figure out how to how how do you have the
Speaker 1: machine identify that actually very difficult for machines to do.
Speaker 1: Uh So, his work emulated brains but didn't simulate them,
Speaker 1: and he also admitted his work was much harder than
Speaker 1: he anticipated and that achieving a brain like computer system
Speaker 1: was a non trivial challenge that was going to require
Speaker 1: a lot of innovation because you could brute force it
Speaker 1: and as in other words, you could you could use
Speaker 1: a lot of classic processors to carry that heavy load,
Speaker 1: but that defeats the purpose. The whole purpose of making
Speaker 1: a brain like computer is so that you can take
Speaker 1: advantage of the brain's properties, including that I'm amazing. Energy
Speaker 1: efficiency doesn't be any good if you are able to
Speaker 1: brute force your way into behaving kind of like a brain.
Speaker 1: But you're, yeah, yeah, you're you're. Every time you turn
Speaker 1: it on, all the lights in the neighboring houses go
Speaker 1: womb where they fade out for a moment in the cup.
Speaker 1: Some crunching numbers. Again, that's not useful, So that would
Speaker 1: be uh, you know, that was in the nineteen eighties
Speaker 1: and there were some like I said, some success skipping
Speaker 1: way ahead. Uh, there are a lot a lot more
Speaker 1: work has been done in the fairly recent past. Back
Speaker 1: in two thousand eight, DARPA announced the Synapse program with
Speaker 1: the goal to break free from von Neuman architecture and
Speaker 1: create a new model of computer designs. So this is
Speaker 1: about as dramatic a departure as you can imagine for
Speaker 1: a computer science You're talking about not just getting away
Speaker 1: from that CPU and RAM model, but possibly getting away
Speaker 1: from binary data entirely. So that's I mean, that's that's
Speaker 1: so different from what we've been doinging that it's uh,
Speaker 1: it's it's completely revolutionary. I mean there's really hard to
Speaker 1: it's hard to imagine it just because we've been relying
Speaker 1: on computer so long that are stuck on this binary
Speaker 1: system binary just zeros and ones or off and on switches,
Speaker 1: if you want to think of it that way. Um,
Speaker 1: So in a broader category, you could just think about
Speaker 1: it not in terms of binary but just digital data
Speaker 1: of any kind, right, Well, I mean with binary specifically
Speaker 1: you're talking about your basic unit has two states, right,
Speaker 1: That's that's what you're limited to. You can't have any
Speaker 1: more than two states. And that's one thing that Darka
Speaker 1: was saying. Let's try and get away from that. Let's
Speaker 1: look at a way of doing computation where we're not
Speaker 1: limited by this too state of your basic unit, which
Speaker 1: is incredible. I mean, it requires a total different approach. Now,
Speaker 1: two thousand eleven, IBM unveiled at the first synapse chips
Speaker 1: that could play Pong. Well, come on, now, they couldn't
Speaker 1: just play, actually learn how to play. Right, So you
Speaker 1: sit your little brother a little little sister down at
Speaker 1: a video game, and because you're mean, you don't tell
Speaker 1: them how to play, and you just keep beating them
Speaker 1: over and over at Mortal Kombat or whatever. Eventually they
Speaker 1: figure out how to play. They might not be real good,
Speaker 1: but they figure out what the buttons do and how
Speaker 1: to win, and because they're younger than you are, they've
Speaker 1: got amazing twitch skills and eventually they dominate you in
Speaker 1: every game. And then you just don't want to play.
Speaker 1: That's the killer instinct, right, Yeah, that's what this computer
Speaker 1: did at Pong. They sat it down they were mean
Speaker 1: to it. They didn't tell it how to play. They
Speaker 1: just let it know when it was doing good or
Speaker 1: doing bad. Right if it if it blocked the ball,
Speaker 1: then it got essentially a little bit of a reward
Speaker 1: like good job. And if it let the ball go
Speaker 1: by and uh and fly past and the opponent got
Speaker 1: a point, essentially got a message saying, you're not doing
Speaker 1: so well. And so it began to learn that what
Speaker 1: it was supposed to do was block the ball from
Speaker 1: going in, and so it got better and better and
Speaker 1: better at doing that. So in in human terms that
Speaker 1: sounds kind of trivial because we can all do that,
Speaker 1: but in a computer that's a big deal. The idea
Speaker 1: of learning computers could very well revolutionize the kinds of
Speaker 1: jobs computers can do. And another example they had, which
Speaker 1: you know is less trivial than learning to play pong,
Speaker 1: is they had this these kind of chips as a
Speaker 1: guidance system, part of a guidance system for a flying
Speaker 1: drone that could just follow a yellow line, specifically yellow
Speaker 1: lines on a road, and so it could actually follow
Speaker 1: along a road and stay on track because it new
Speaker 1: to recognize this particular yellow line and that that was
Speaker 1: the course it was supposed to follow. So it was
Speaker 1: able to, you know, observe an environment and identify the
Speaker 1: important feature of that environment and follow that feature, which
Speaker 1: again something very easy for humans to do. You tell
Speaker 1: a human as long as they have their senses, and
Speaker 1: you tell human, hey, you need to do when when
Speaker 1: you see or experience this, you need to do this.
Speaker 1: It's very easy for a machine, not necessarily that easy. Well,
Speaker 1: like us in navigating the real world. Computers like these
Speaker 1: are learning, not just being told what to do, but
Speaker 1: figuring out what it is they need to do. Yeah.
Speaker 1: So two thousand thirteen, so very recent past, HRL built
Speaker 1: a chip that can alter the connections between neurons synthetic
Speaker 1: neurons in this uh in this chip, and it's similar
Speaker 1: to the way the brain can change synapses. So it
Speaker 1: learns quote unquote like a brain. It's able to make
Speaker 1: these connections that are emulating what happens within a human brain. Now,
Speaker 1: keep in mind, as we said before, we still don't
Speaker 1: fully understand all the complexity that is the human brain
Speaker 1: and the human nervous system and and how we think.
Speaker 1: We're still learning quite a bit about that. But by
Speaker 1: emulating some of the physical processes. We're starting to see
Speaker 1: some improvements and things like energy efficiency for specific types
Speaker 1: of computer tasks. Yeah. Well, there are also very large
Speaker 1: scale adaptations that are being made in computers to make
Speaker 1: them more like brains. For example, you've got neural networks.
Speaker 1: You're familiar with this concept. Yeah, yeah, So in your
Speaker 1: brain you have a neural network. You have a network
Speaker 1: of neurons, and they're these nerve cells that are connected
Speaker 1: in tons of different ways all throughout your brain, sending
Speaker 1: signals back and forth to each other. And somehow this process, uh,
Speaker 1: that's a very simple representation of it. There there are
Speaker 1: other kinds of cells and things going on to somehow
Speaker 1: this whole process creates thinking, It manifests itself and thought right.
Speaker 1: And artificial neural network tries to use some of the
Speaker 1: same basic concepts to make a computer or network of
Speaker 1: computers work more like a brain. So, neural networks are
Speaker 1: systems of interconnected nodes that simulate Each node simulates the
Speaker 1: behavior of a neuron. In basic terms, neurons in the
Speaker 1: brain receive a signal, and if the signal is strong enough,
Speaker 1: they pass that signal along to other neurons. Artificial neuron
Speaker 1: nodes receive quote weighted signals, and they received them from
Speaker 1: lots of other nodes, and then the waiting system determines
Speaker 1: whether or not that signal gets passed along to the
Speaker 1: next layer of nodes or to a final output node.
Speaker 1: And so these networks are very good at things like
Speaker 1: pattern recognition and learning how to deal with new types
Speaker 1: of data sets, and when spread out across lots of
Speaker 1: different processors, artificial neural networks are very good at processing
Speaker 1: huge amounts of data very quickly. Yeah. So an example
Speaker 1: of this is Google Brain. You may have heard of that.
Speaker 1: That was a hush hush Google x project, right, yeah,
Speaker 1: one of many, uh neural network approach just like you
Speaker 1: were saying. So it uses thousands of computers to mimic
Speaker 1: the synaptic connections in our brains and rely is on
Speaker 1: simple sets of rules also called algorithms, right, So it's
Speaker 1: it's relying on that to learn on its own. So
Speaker 1: the example everyone loves to mention, you'll see it all
Speaker 1: over the internet because it's the Internet, is that it
Speaker 1: learned what a cat was by scanning thousands of pictures
Speaker 1: of cats. Now it had been told the term cat,
Speaker 1: it had to be it did not realize it didn't
Speaker 1: see tens of thousands of pictures of cats and is
Speaker 1: a cat like it could have just as easily said
Speaker 1: that is a bucket. That's a different meme entirely. Yeah, no,
Speaker 1: walrus is Yeah. No. The amazing thing about it is
Speaker 1: that that it was able to isolate the visual concept
Speaker 1: of a cat and then be able to identify a cat,
Speaker 1: even if it was a cat that was not among
Speaker 1: the pictures that already scanned, a totally new cat in
Speaker 1: a in a video or a picture exactly so you
Speaker 1: can show it a picture of a cat and had
Speaker 1: never seen before, but because it now knew what the
Speaker 1: concept of a cat was, it could identify that as
Speaker 1: a cat. Yeah. One of the people behind this project
Speaker 1: was gave a quote saying that it basically invented the
Speaker 1: concept of a cat, which is kind of true. Like
Speaker 1: it it had to just I thought of it, and
Speaker 1: I wrote this in the video. Don't know whether you
Speaker 1: said these words or not, but I thought about it
Speaker 1: sort of in the in the terms of like a
Speaker 1: naturalist surveying new animal or plant species, you would you
Speaker 1: would look for patterns, and you're not being told what
Speaker 1: new species to look for. But by observing enough of them,
Speaker 1: you'd say, Okay, I'm seeing all these similarities with slight variations,
Speaker 1: this must be a species. And here's another species that's
Speaker 1: kind of the same but different. And you're creating these
Speaker 1: categories in your mind without direction from the top. You're
Speaker 1: just taking in lots of data and figuring out what
Speaker 1: to do with it. And that's what these neural networks
Speaker 1: are really good at. So now what they're not necessarily
Speaker 1: good at as being super energy efficient, because we're talking
Speaker 1: thousands of computers. Was sixteen thousand computers to figure out
Speaker 1: what a cat was something like it was, So we're
Speaker 1: talking about a very energy hungry approach here. Because each
Speaker 1: computer is behaving as a neuron. It's not like we're
Speaker 1: we've you know, distilled the neuron down to an element
Speaker 1: that's on a transistor. No, we're talking about individual computers
Speaker 1: acting as a neuron. So you can also simulate them
Speaker 1: within a computer, but this is far less powerful. Right,
Speaker 1: if you simulate within a computer, you better have packed
Speaker 1: a lunch and for the next like several months. No,
Speaker 1: it's incredibly slow simulating these things, making virtual brains, which
Speaker 1: people have tried to do even on a small scale
Speaker 1: requires an enormous amount of processing power, because again, that
Speaker 1: simulation is resting upon a classical computer approach. It's not
Speaker 1: something that's broken free of that Von Neumann architecture. It's
Speaker 1: just resting on top of it. Um. One of the
Speaker 1: guys working on, or at least working with some of
Speaker 1: the people who are working on Google Brain is our
Speaker 1: friend R. Kurtzwell Singularity Dude. Yeah, so, yeah, a pioneer
Speaker 1: in the in the field of voice recognition software as
Speaker 1: well as other artificial intelligence developments, and he's working with Google,
Speaker 1: not necessarily on Google Brain. At least the interview I read,
Speaker 1: it seemed to me that he was working, you know,
Speaker 1: some of his colleagues were working on Google Brain, and
Speaker 1: perhaps he had worked on some things that are are
Speaker 1: related to it. His natural language work is something that
Speaker 1: could be very useful in UH and certain approaches with
Speaker 1: a neural network, so you know, it's related to but
Speaker 1: not necessarily directly overlapping the project. Another project that I
Speaker 1: wanted to mention along these lines is an open source
Speaker 1: one called NEST, which stands for Neural Simulation Technology that
Speaker 1: started coming together in the mid nineteen nineties and and
Speaker 1: studies large at works like the brain and and tries
Speaker 1: to write flexible emulators for the brain for for general
Speaker 1: research use. It's something that I ran across while I
Speaker 1: was in the rabbit hole of all of this research,
Speaker 1: and I thought that it was really neat that it's
Speaker 1: you know, we're working towards giving researchers better tools for
Speaker 1: studying this kind of stuff. That's pretty cool, totally. Okay,
Speaker 1: let's let's zoom in a little bit, because we were
Speaker 1: talking about these sort of artificial constructs like neural artificial
Speaker 1: neural networks and you know, take place plus across a
Speaker 1: lot of computers or in some virtual simulation inside a computer.
Speaker 1: Can we make the actual hardware in your device work
Speaker 1: physically more like a brain does? That is the million
Speaker 1: dollar question, and in fact is the thing that I
Speaker 1: think is the most important if you want to capture
Speaker 1: the energy efficiency angle here, right, if it works more
Speaker 1: like a brain, then it's going to require far less
Speaker 1: power than trying to simulate what a brain does using
Speaker 1: this other architecture. Well, based on some really cutting edge research,
Speaker 1: I think the answer is, yeah, overall, there's there's a
Speaker 1: project at IBM right now. Yeah, the neurosynaptic chips. We
Speaker 1: mentioned that just the beginning in the timeline, But we're
Speaker 1: looking at things like collections of six thousand transistors copying
Speaker 1: the electrical spiking behavior of a neuron. So six thousand
Speaker 1: transistors is nothing. I mean, you look at a micro
Speaker 1: processor right now, and there could be more than a
Speaker 1: billion transistors on that single one inch silicon chip. But
Speaker 1: usually transistors are not modeling spiking behavior. No they aren't.
Speaker 1: But what I'm the only point I'm making is that
Speaker 1: we're talking much smaller now. Before before we were talking
Speaker 1: massive networks of individual computers. Now we're talking about discrete
Speaker 1: elements that you would find within a computer that are
Speaker 1: mimicking the behavior of a neuron specifically because of the
Speaker 1: way they've been engineered. It's not like you know, you've
Speaker 1: hit a little switch like, oh, now, the transistor things
Speaker 1: like a brain. No, it's because it's been specifically engineered
Speaker 1: that way. So yeah, it copies this electrical spiking behavior,
Speaker 1: and the collection of transistors are wired together to make
Speaker 1: a system of synthetic rons. So using these chips was
Speaker 1: a little complicated. When they first showed them off. The
Speaker 1: team had to simulate a physical chip with a virtual
Speaker 1: chip inside a computer program then port that configuration over
Speaker 1: onto the physical hardware that they had built, and that
Speaker 1: chip allowed programmers to create protocols so the computer could
Speaker 1: learn how to recognize handwritten numbers or play pong. This
Speaker 1: is the pong playing chip we talked about at the
Speaker 1: top of the podcast. So, but they could also learn
Speaker 1: like if if you show a computer, like you've got
Speaker 1: some optical recognition software, this is something else Kurts while
Speaker 1: worked on optical recognition software in your computer, and you
Speaker 1: show it a picture of a six, the number six
Speaker 1: that has been printed in a specific font, and then
Speaker 1: show it another number six that either someone has handwritten
Speaker 1: or it has been printed in a different font. The
Speaker 1: computer may have trouble identifying it because it's not exactly
Speaker 1: what you showed it the first time. But let's say
Speaker 1: it really really wants to leave a spam com in
Speaker 1: the comments section on your article. How can it get
Speaker 1: past this problem? Well, using this approach with neurosynaptic chips,
Speaker 1: it could really help because, like I said, they were
Speaker 1: able to use this this network to create a program
Speaker 1: that was able to recognize digits that had been drawn,
Speaker 1: even if they had totally different people drawing them in
Speaker 1: different styles, they were still able to have this machine
Speaker 1: recognize what that number was. So really, when you look
Speaker 1: at it, this particular type of machine was end end
Speaker 1: of which, by the way, the people who created Capture
Speaker 1: are pretty much okay with because they said that the
Speaker 1: yeah and only it was a stop gap. It was
Speaker 1: something that pushed forward the development of artificial intelligence because
Speaker 1: they said, we had to create a thing that was
Speaker 1: difficult for machines to do but easy for humans to do.
Speaker 1: And really, what that does is it points out, hey,
Speaker 1: machines are really bad at this one particular task. How
Speaker 1: can we make sheens better at doing that? And so
Speaker 1: while it's kind of irritating for anyone who wants to
Speaker 1: have controls on their site so they don't get hit
Speaker 1: by spam content, uh, it is very reassuring for anyone
Speaker 1: in the artificial intelligence field because it shows that progress
Speaker 1: is being made. So double edged sword, I guess we
Speaker 1: could say, but at any rate, Uh. The whole idea
Speaker 1: was to emulate the brain's ability to make lots of
Speaker 1: those synaptic connections at once, and the current state of
Speaker 1: the art involves organizing neuro synaptic cores onto a grid
Speaker 1: to create a synthetic cortex. So IBMS neuromorphic programming architecture
Speaker 1: is based off modular blocks of code called core lets,
Speaker 1: and programmers can choose core lets from a predetermined menu
Speaker 1: and choosing the type of program for specific tasks like
Speaker 1: image recognition or audio recognition. So that stuff, you know,
Speaker 1: the stuff has already been built by IBM, so that
Speaker 1: people who want to work with this kind of technology
Speaker 1: don't have to invent from scratch. They can actually take
Speaker 1: the stuff, tweak it to their needs. It's own, you know.
Speaker 1: It's it's a very well, I mean, it's a very
Speaker 1: modular approach. It's really innovative. Yeah. Qual Calm was also
Speaker 1: working on neuromorphic chips for for sort of learning machines, right, Yeah,
Speaker 1: they were looking at it. FEXTELR. There's a program called
Speaker 1: the Zeroth program zero, after the Asimov Zeroth Law of robotics, Yeah,
Speaker 1: which is cause no harm to humanity, nor allow humanity
Speaker 1: to come to harm. That's sort of the principle behind
Speaker 1: law number one, which is don't harm a person. Yeah. Yeah,
Speaker 1: this in this case, it's saying don't harm people in general,
Speaker 1: like all of people don't do any harm to them,
Speaker 1: so qual it comes approach isn't necessarily to protect humanity.
Speaker 1: That that's not the that's not the issue here. A
Speaker 1: little bit grand. Yeah, their their their goal is to
Speaker 1: make a large scale commercial platform for neuromorphic computing. So
Speaker 1: they want to incorporate this into things like handheld devices
Speaker 1: so they can recognize certain things or maybe even wearable
Speaker 1: stuff like medical uh phenomena, like anything that it could
Speaker 1: detect that something's going wrong with a patient, it could
Speaker 1: alert you or just you know, context aware sort of stuff.
Speaker 1: Was it Qualcom that created the learning rolling robot? I
Speaker 1: think it was along the same lines as the Pong game,
Speaker 1: So you had the computer based on neuromorphic technology that
Speaker 1: that learned how to play pong without being told. Uh.
Speaker 1: There was a company, I believe it was Qualcom that
Speaker 1: used neon neuromorphic technology to get a robot to figure
Speaker 1: out where to go in a room without telling it
Speaker 1: where to go. It's the same kind of principle. They
Speaker 1: just let it roll around and when it went to
Speaker 1: the right colored tiles on the floor, they give it
Speaker 1: positive feedback and say, yeah, that's right, that's what you
Speaker 1: want to do. So in this case, you're looking at
Speaker 1: like a device that could give you a notifications and
Speaker 1: and advice in certain situations, like if you've ever heard
Speaker 1: of Google Now, which is a an option on Android phones,
Speaker 1: android devices where you get Actually, I think I Os
Speaker 1: might even have it now as well, but I know
Speaker 1: Android does because that's what I have. It brings up
Speaker 1: little things based upon things like you're you're browsing history.
Speaker 1: So as long as I'm logged into a a Chrome
Speaker 1: browser and I'm browsing around, it starts to keep track
Speaker 1: of that stuff, and it might let me know contextually
Speaker 1: when that stuff would become important. Let's say that I
Speaker 1: did some research on a place that I want to
Speaker 1: go and visit sometime. It might pull up more information
Speaker 1: that I could use to look into that. It's kind
Speaker 1: of in that semantic web approach. But this is all
Speaker 1: done on the back end for Google, right. This is
Speaker 1: all done in the cloud. So what qualcom is looking
Speaker 1: at is creating these neuromorphic machines that would actually be
Speaker 1: doing it natively on the device itself, not being related
Speaker 1: out to some cloud computing network of sixteen thousand computers,
Speaker 1: but rather happening in real time in your hand at
Speaker 1: that moment. Along this a lines. Bioengineers at Stanford University
Speaker 1: have developed a circuit board based on the brain synup
Speaker 1: structure that they're calling the neuro grid. It's it contains
Speaker 1: sixteen custom design chips that together they say can simulate
Speaker 1: one million neurons and billions of synapse connections. The whole
Speaker 1: thing is about the size and energy efficiency of an
Speaker 1: iPad um and they they say it's about a hundred
Speaker 1: thousand times more energy efficient than a regular computer simulation
Speaker 1: of of that many neurons, but still way less efficient
Speaker 1: than the human brain um, you know, which contains about
Speaker 1: eighty thousand times more neurons than that and runs on
Speaker 1: just three times as much power. So, but it's really
Speaker 1: cool and and right now they're they're focusing on the
Speaker 1: goal of giving researchers who are working in robotic press
Speaker 1: theses tools that they can use to to simulate human
Speaker 1: reactions without needing to know intimately how the brain works,
Speaker 1: you know, Let those let those people focus on the
Speaker 1: robotics programming that they're really good at, and this machine
Speaker 1: translating that into a quick and efficient commands. Interesting. Yeah,
Speaker 1: so so the robotic prosthetic can actually learn that's kind
Speaker 1: of interesting. Yeah, or you know that that it it
Speaker 1: can think the way that your brain does, so that
Speaker 1: it can you know, help you solve that problem of
Speaker 1: picking up a cup, which is an incredibly easy thing
Speaker 1: for a human person to do, but really difficult for
Speaker 1: a robot to determine. Maintaining your balance if it's like
Speaker 1: a robotic leg which again something that as long as uh,
Speaker 1: you know, as long as you're not impaired in some ways,
Speaker 1: fairly easy for a person to do, assuming you're on
Speaker 1: level ground and everything, but can be tricky if you are,
Speaker 1: feel if you have to wear a prosthetic. So interesting stuff,
Speaker 1: I mean, really taking over the fact. You know, we
Speaker 1: talked about embodied cognition in the last podcast and the
Speaker 1: idea that that are are thinking could extend beyond our brains,
Speaker 1: that it incorporates the entire nervous system which stretches through
Speaker 1: our entire body. Well, if you are missing at them,
Speaker 1: clearly that would mean that you know, with a prosthetic
Speaker 1: you're replacing that. You have to build that kind of
Speaker 1: intelligence into the limb if you expect to have the
Speaker 1: same sort of utility as the functionality. Granted, you know
Speaker 1: this this one single device that they've got right now
Speaker 1: cost some forts to build, but the team thinks that
Speaker 1: it could be mass manufactured for one that like four
Speaker 1: hundred bucks of pop. That's pretty impressive. Well, how about
Speaker 1: we try a different approach. So we talked about neuromorphic machines.
Speaker 1: What about synaptic transistors? Have you guys heard about these?
Speaker 1: So this is super Harvard project, right, yeah, Harvard project.
Speaker 1: It's some light reading. If you want to read through
Speaker 1: the Harvard project, it's it's a little dense, but here's
Speaker 1: the here's the takeaway. So the neural networks and the
Speaker 1: neuromorphic transistors are both attempting to do the same thing,
Speaker 1: just at different scales, which is to simulate the relationship
Speaker 1: between neurons in the nervous system and to try and
Speaker 1: take advantage of that relationship the way that they communicate
Speaker 1: with each other through. What do those neurons communicate through
Speaker 1: the synapsis? Uh? The synapsis synapsis is again the the
Speaker 1: little gap between one neuron and the other neuron where
Speaker 1: you're having the information pass from one cell to the next.
Speaker 1: So a synaptic transistor isn't just simulating neurons. What they're
Speaker 1: trying to do is simulate the actual process through which
Speaker 1: neurons pass information. So it's not the relationship that they're
Speaker 1: they're aiming at. They're not building synthetic neurons that will
Speaker 1: behave like biological neurons. They're building synthetic synapsis, and they
Speaker 1: are still dependent upon things like you know, ion exchanges,
Speaker 1: except we're talking about oxygen now instead of sodium and potassium. So, uh.
Speaker 1: The other thing that's really interesting in this, and this
Speaker 1: is where you can really break away from the von
Speaker 1: Neumann architecture, is that you're no longer dependent upon a
Speaker 1: on or off stage for your base unit. You know,
Speaker 1: your your binary unit. Your zero or one can either
Speaker 1: be of those two states, and that's it. Um This
Speaker 1: could actually have a whole spectrum because it's dependent upon
Speaker 1: the conductance. So it's like the strength of a connection. Well,
Speaker 1: not strength, we're talking you know, conductance, it's it's it's
Speaker 1: the ability to conduct electricity, and the conductance of these
Speaker 1: synaptic transistors can actually change and it's an analog continuous change.
Speaker 1: So think of it like a sign wave, and it
Speaker 1: could be the value of that particular state could be
Speaker 1: anything along that sign wave, So not just a zero
Speaker 1: or one, but anything along that entire wave. It's kind
Speaker 1: of the same way if you think about it, that
Speaker 1: the cubit, the quantum bit of quantum computers is said
Speaker 1: to be both zero and one at the same time
Speaker 1: and all values in between, which doesn't make a whole sense,
Speaker 1: you know, intuitively, But it's kind of similar to that.
Speaker 1: This idea that it it allow us to have a
Speaker 1: completely different approach to computer science. It would it would
Speaker 1: require a lot of different work to really make this useful.
Speaker 1: I mean, it's the possibilities are are pretty phenomenal, but
Speaker 1: it does mean that you're not programming the way anyone
Speaker 1: has programmed up to this point using the traditional computer sciences.
Speaker 1: So very exciting, very weird, and uh yeah, we'll we'll
Speaker 1: do a link. I'll try and write up a blog
Speaker 1: post with a link to the Harvard article because you know,
Speaker 1: it's it goes into a lot more depth um, and
Speaker 1: it's very difficult to summarize in a way that doesn't
Speaker 1: trivialize it um. And I certainly don't have the expertise
Speaker 1: to go into a lot of depth on the subject
Speaker 1: without stepping in lots of holes and revealing my ignorance,
Speaker 1: uh speaking of holes. Yeah, I think we should talk
Speaker 1: about whole brain simulation. I'm so proud of I shave
Speaker 1: my head and I'll teach you the ukulele later. Uh. Yeah,
Speaker 1: So there are quite a few simulated brain projects that
Speaker 1: have happened, but but two or that are in the
Speaker 1: process of happening. There's actually been a few different attempts
Speaker 1: at simulating the brain. Most of them are ongoing. Um,
Speaker 1: i'd imagine, I mean, this is not something we can
Speaker 1: do yet. No, I mean we've seen some projects that
Speaker 1: simulated part of a brain, as in like a collection
Speaker 1: of neurons that would represent just a fraction of the brain.
Speaker 1: But even then you're talking about on a time scale
Speaker 1: that stretches into days for that would match up two
Speaker 1: seconds in an actual brain. Yeah. I know one that
Speaker 1: I read about years ago, it must have been going
Speaker 1: on for a long time was the Blue Brain project. Yeah,
Speaker 1: it's a reverse engineering effort. Really, it's meant to understand
Speaker 1: more about the brain, uh, more than anything else. So
Speaker 1: I think a lot of these whole brain things are Yeah.
Speaker 1: So it's not necessarily let's make a computer that can
Speaker 1: think like a brain. It's that's that's a far feature
Speaker 1: step right now. They're kind of like, let's learn some stuff. Yeah,
Speaker 1: let's learn what the brain. Let's learn what the brain do.
Speaker 1: That's really what it comes down to. So they're looking
Speaker 1: at synthesizing of mammalian brain, specifically human brain down to
Speaker 1: the molecular level. You just the whole idea of like
Speaker 1: recreating a whole brain and what it does. It's like, congratulations,
Speaker 1: you have synthesized compulsive behavior and confirmation bias. Exactly, good job.
Speaker 1: Their goals also include reconstructing the human brain and building
Speaker 1: a virtual brain in a supercomputer. It began the project
Speaker 1: began back in two thousand five, and each simulated neuron
Speaker 1: requires the power of a laptop computer, not necessarily meaning
Speaker 1: that a neuron is a laptop computer, but that's the
Speaker 1: it's the equivalent to a laptop computer to simulate a
Speaker 1: single neuron. U So it's successfully created a virtual model
Speaker 1: of a rat cortical column. That's a collection of about
Speaker 1: ten thousand neurons. Now, these are the basic unit of
Speaker 1: a cortex. So according to the team, a single rat
Speaker 1: cortical column might be devoted to a whisker, So every
Speaker 1: whisker a rat would have its own dedicated cortical column.
Speaker 1: But that's progress, whisker. We have solved the equation of life. Yeah,
Speaker 1: now another project. Sorry, that didn't mean to downplay their success.
Speaker 1: I mean that's pretty cool. It is cool. Is it
Speaker 1: does illustrate how far we have to go to get
Speaker 1: It's one of those one small step kind of situation. Yeah. Yeah,
Speaker 1: one of those things where you really realize how big
Speaker 1: a problem this is, how big a challenge it is.
Speaker 1: The Human Brain Project is another one. This one launched
Speaker 1: in two thousand thirteen at a conference in Switzerland, and
Speaker 1: the project is expected to cost a total of around
Speaker 1: one point six billion. Do it's a joint effort of
Speaker 1: the entire European Union, Yeah, yeah, it's not. It's not
Speaker 1: something that is just being taken on by a you know,
Speaker 1: a guy with a guy in a dream and a
Speaker 1: song in his heart and a brain on his drawing board. Now,
Speaker 1: at any rate, it's um. The very first decade of
Speaker 1: this project is just dedicated to learning more about the
Speaker 1: human brain and how we see, think, learn and all
Speaker 1: that before we try and build the the computer model
Speaker 1: of it. Because the argument is that we could try
Speaker 1: and go about building the building blocks and then putting
Speaker 1: it all together, but without this understanding of what's going
Speaker 1: on inside our brains, it's not very meaningful. It's almost
Speaker 1: you know, it's not quite as bad, but it's almost
Speaker 1: like you open up a a a door that's going
Speaker 1: to be your new office in your new house, and
Speaker 1: you throw in a bunch of transistor chips and and
Speaker 1: chords and a monitor and a keyboard and you close
Speaker 1: the door and you think, I have a computer. Now
Speaker 1: it's not not No, it's not doing anything meaningful yet.
Speaker 1: So over here in the U s um, we've got
Speaker 1: the Brain Initiative. That's the Brain Research through Advancing Innovating
Speaker 1: Neurotechnologies Initiative. Yeah, rolls right off the tongue. Um. It's
Speaker 1: a it's a one billion dollar project with a primary
Speaker 1: focus on the ways that individual cells and and complex
Speaker 1: neural circuits interact in both space and time. Um So,
Speaker 1: So it's talking about the architecture and the signal paths
Speaker 1: within the brain right now. Um uh. The initiative was
Speaker 1: announced in April, and it's being run by collaborative efforts
Speaker 1: from the National Institutes of Health, DARPA, and the National
Speaker 1: Science Foundation. They've got a kind of crazy collaboration coming
Speaker 1: up with the Human Brain project, Like they just announced
Speaker 1: it this March, and they haven't announced what they're collaborating on.
Speaker 1: But it's cool that everyone's playing together. So I think
Speaker 1: that's really cool. But I also have a kind of weird, spooky,
Speaker 1: semi philosophical question about what it means to create or
Speaker 1: simulate a whole human brain, because we've talked before on
Speaker 1: this podcast about the question of whether an artificial intelligence
Speaker 1: program that seems to act like a person actually has consciousness.
Speaker 1: What we mean is, like, you know, an experience. Is
Speaker 1: there such a thing as what it's like to be
Speaker 1: that program? And should it have rights? Should we have
Speaker 1: considerations for it? Right now, we don't generally worry about this.
Speaker 1: It just doesn't you know, we have no way to
Speaker 1: prove it doesn't have a conscious existence, but it just
Speaker 1: doesn't seem like it intuitively to us. Yeah, it's not
Speaker 1: like when that IBM neuromorphic transistors playing pong that it
Speaker 1: suddenly gets mad if you beat it. It doesn't like
Speaker 1: slam the paddle to one side to the other. Well,
Speaker 1: and even if it did, I mean you could think
Speaker 1: about that, well, yeah, that's its programmed behavior. You know
Speaker 1: that that's how it's behaving, But you don't think that
Speaker 1: these programs are actually having an experience that deserves recognition.
Speaker 1: I wonder, though, if we're able to simulate a whole brain,
Speaker 1: especially if we are able to simulate it physically. Somehow,
Speaker 1: that seems to be getting closer to the realm where
Speaker 1: I'd wonder, Wait a second, are we creating something that's
Speaker 1: having a conscious experience? I mean, we don't know, but
Speaker 1: could that emerge from that kind of hardware? Yeah, that's
Speaker 1: exactly what I mean. I mean it was so we
Speaker 1: don't know what the origin of consciousness is. We think that,
Speaker 1: you know, mammals with complex brains, uh and more advanced
Speaker 1: thinking seemed to exhibit it. We don't really know anybody
Speaker 1: has it except us. We just have to assume they probably,
Speaker 1: I'm not sure about myself some days, honestly, And by us,
Speaker 1: I don't mean like our species. Yeah, I mean like
Speaker 1: the the solipsism problem. It's like, I know I have
Speaker 1: a conscious experience. You can't prove anybody else does. You
Speaker 1: just have to assume they do, right, Um, So, yeah,
Speaker 1: what do y'all think about that? If we build a
Speaker 1: brain that works in a one for one way, exactly
Speaker 1: like a real organic brain. Is there such a thing
Speaker 1: as what it's like to be that synthetic brain. That's
Speaker 1: an impossible question to answer right now because we have
Speaker 1: not done that and so and we don't fully understand
Speaker 1: consciousness as far as it applies to us, So being
Speaker 1: able to answer that question it would essentially be total guesswork. Well,
Speaker 1: although I mean, to to create something like that, you
Speaker 1: would technically be having to create a body like like
Speaker 1: we talked about in our first podcast, your your neural
Speaker 1: network is a lot more than just your brain meets um.
Speaker 1: You've got a lot of other neurons and and it's
Speaker 1: tied pretty intrinsically or or so a lot of thought
Speaker 1: goes into into what you touch and what you taste
Speaker 1: and how you feel in your body temperature. So in
Speaker 1: order to create a one for one simulation, we would
Speaker 1: need to create a body for it. And at that point,
Speaker 1: I think absolutely there is an experience of being that machine. Well,
Speaker 1: and I mean you could you could have it have
Speaker 1: an intelligence that's just not a human intelligence or not
Speaker 1: human like. So if you did make it a completely
Speaker 1: disembodied brain type thing, uh, you could have it where
Speaker 1: it has some form of intelligence. It just wouldn't necessarily
Speaker 1: be be be comparable to a human experience because, like
Speaker 1: you point out, we have the body the whole embodied
Speaker 1: cognition theory approach that would not apply to something that
Speaker 1: doesn't have that kind of input, which might just mean
Speaker 1: that you have a computer that won't stop screaming. But
Speaker 1: I mean, I don't. I honestly don't know the answer
Speaker 1: because personally, I think if I'm getting down to like
Speaker 1: what my personal beliefs are, I think that the mind
Speaker 1: is completely dependent upon physical matter is the whole nervous
Speaker 1: system approach, and so therefore, if you were to be
Speaker 1: able to completely simulate uh, the nervous system of a
Speaker 1: complex living being, uh, I don't. I don't see why
Speaker 1: there couldn't be some form of experience or consciousness within
Speaker 1: that synthetic being. Yeah, I I think that's sort of
Speaker 1: entailed by the belief if you have a basically physical
Speaker 1: idea about what the mind is, as long as you
Speaker 1: don't believe in wonder tissue or you don't think that
Speaker 1: there's something magic happening in the brain you have, you
Speaker 1: really do have to wonder, like, man if if we're
Speaker 1: synthetically putting together a material construction that's pretty much like
Speaker 1: what brains do. It may very well be having an
Speaker 1: experience there, may very well be what it's like to
Speaker 1: be this computer. Maybe. I mean it's hard to say. Like,
Speaker 1: I think we wouldn't be able to identify it until
Speaker 1: it was as close to us as we could possibly
Speaker 1: make it, including that entire body experience all right up
Speaker 1: until you know it says like hey, stop poking me,
Speaker 1: or hey I am self aware, or hey, share your pizza,
Speaker 1: or hey, really, seriously, I don't want to go in
Speaker 1: the maze again. I know the way out. Stop play.
Speaker 1: You share your pizza with the synthetic brain fairly, because
Speaker 1: that sucker is going to know if you end up
Speaker 1: giving it the small piece. Please separate my hemispheres and
Speaker 1: cram that hot pizza right in between them and then
Speaker 1: seal it back up. Honestly, that's what I want to
Speaker 1: do with pizza sometimes the brain right, so the cheese
Speaker 1: gonna pools up over the cerebellum. No, I just have
Speaker 1: a I have a different point of of that I
Speaker 1: would like to talk about before we sign off here,
Speaker 1: which is, do you think is it possible? And I
Speaker 1: expect it is that building computers that can work like
Speaker 1: a brain, you know, whether you call it thinking or not,
Speaker 1: they work like a brain, that they're more energy efficient.
Speaker 1: Do you think that we'll end up seeing those type
Speaker 1: of computers being very good for things that we talked about,
Speaker 1: like recognizing visual representations, audio, the sensory stuff, recognizing patterns,
Speaker 1: that kind of thing. It will be good at that.
Speaker 1: Do you think that they will lag behind traditional computers
Speaker 1: with the things that traditional computers are really good at,
Speaker 1: in other words, like a quantum computer. A quantum computer
Speaker 1: is really good at solving parallel problems, but not a
Speaker 1: classical computer serialized problem. So I wonder, because you know,
Speaker 1: our our brains are really good at this other stuff
Speaker 1: and computers aren't, I wonder if the computer version of
Speaker 1: our brains will also be really good at what we do,
Speaker 1: but lousy what traditional computers do. Yeah, I can totally
Speaker 1: see that. I mean, computers are the way they are,
Speaker 1: or one of the reasons they are the way they
Speaker 1: are is that they're they're quantized. You know that they're
Speaker 1: dealing with specific quantities of digital data, and our brains
Speaker 1: just don't work that way. I mean, we can do math,
Speaker 1: but we're still juggling concepts. Yeah, it's all really poly
Speaker 1: up in there. It's not a series of equations that
Speaker 1: we're solving. I mean, I mean it is, but well
Speaker 1: it's fuzzy. It's fuzzy, and in that fuzziness is a
Speaker 1: is a good thing, um, because it is what allows us,
Speaker 1: like we were talking about earlier, to deal with like
Speaker 1: your brain can actually deal with the picture. It doesn't
Speaker 1: have to turn as far as we know, it doesn't
Speaker 1: have to turn the picture into a bit map. You
Speaker 1: can just look at it and and for all we
Speaker 1: can tell what's happening is okay, that's a lamp. Well,
Speaker 1: I mean not just a picture, but looking at the
Speaker 1: world around us. I mean that the same sort of
Speaker 1: thing is that it's not it's not turning everything around
Speaker 1: us into one's zeros um. I mean, that's the way
Speaker 1: any sort of computer that's using a camera ultimately that's
Speaker 1: digitizing the information. Uh, you know, it's it's the same
Speaker 1: sort of thing. So you know, yeah, I would say
Speaker 1: that we're you know, this is not going to be
Speaker 1: a replacement for the kind of computers that we have
Speaker 1: right now. But but the computers that we have right
Speaker 1: now are really good at you know, playing cat videos
Speaker 1: and uh, running tax software. And that's not what we
Speaker 1: would ask this other kind of computer to do. No,
Speaker 1: we would ask the other computer to be our friend
Speaker 1: or at least to help us recognize more pictures of
Speaker 1: you know, whatever it is we're interested in, to wench
Speaker 1: with us about taxes and find the cool cat videos
Speaker 1: for us, right, you know, at least to invent the
Speaker 1: concept of a cat over and over and it does.
Speaker 1: Maybe wonder if that Google Google computer just became obsessed
Speaker 1: with YouTube and just started watching cat videos all day long. Um,
Speaker 1: if so, what a terrible thing we have brought into
Speaker 1: this world. It must be stopped. This is what an
Speaker 1: angry young man ranting into his webcam looks like. No
Speaker 1: shortage of that. Well, I'm gonna I'm gonna say that
Speaker 1: we've wrapped up this topic for the sake of my sanity.
Speaker 1: But yeah, if you guys have any suggestions for future
Speaker 1: topics that we should cover here on forward Thinking. Maybe
Speaker 1: there's something about the future you've always wanted to know
Speaker 1: more about, or you're curious of a particular science fiction
Speaker 1: technology has any basis and science fact, let's know, send
Speaker 1: us an email or addresses f W Thinking at discovery
Speaker 1: dot com or drop us a line on Facebook, Twitter,
Speaker 1: or Google Plus, or handle it. All three is f
Speaker 1: W thinking and we will talk to you again really soon.
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