Speaker 1: Get in touch with technology with tech Stuff from how
Speaker 1: stuff works dot com. Hey there, and welcome to tech Stuff.
Speaker 1: I'm your host, Jonathan Strickland. I'm an executive producer at
Speaker 1: how Stuff Works and I love all things tech. And
Speaker 1: here is another special episode brought to you from the
Speaker 1: IBM Think two thousand and eighteen conference. I've been going
Speaker 1: to this all week in Las Vegas, Nevada. It is Thursday,
Speaker 1: March twenty two as I record this episode, and you
Speaker 1: probably heard me speak a little bit about a lot
Speaker 1: of different topics that I've heard about here at the
Speaker 1: Think two thousand eighteen conference, and uh pretty much wrapped
Speaker 1: everything up. I am technically attending the conference today. However,
Speaker 1: I'm also checking out of my hotel room today, which
Speaker 1: means I will not have a dedicated place to sit
Speaker 1: down and record. So I might record another episode after this,
Speaker 1: depending upon what I counter today, but that might have
Speaker 1: to wait until I get back to the studio because
Speaker 1: I may not be able to find a corner to
Speaker 1: sit down and record and that is suitable for such
Speaker 1: a thing. However, I do have that opportunity right now,
Speaker 1: and I figure I would have this chance to kind
Speaker 1: of talk to you about a few different speakers I
Speaker 1: had the chance to to witness and listen to while
Speaker 1: I was here at think two thousand eighteen, and uh,
Speaker 1: these were several talks given by various experts, including a
Speaker 1: few who have achieved celebrity status beyond just uh, the
Speaker 1: the tech conference level. And I don't think I could
Speaker 1: talk about any of these presentations as a full episode
Speaker 1: without doing a ton more research to flesh it out,
Speaker 1: to give background, because these presentations were each about forty
Speaker 1: minutes thirty forty minutes long, and so you know, to
Speaker 1: make a what usually ends up being about a forty
Speaker 1: five minute long podcast about a thirty minute talk, you
Speaker 1: can't do that without patting it, which I'm not gonna do.
Speaker 1: That's an insult to you, guys. You deserve better than that,
Speaker 1: or without doing a lot of background information to flesh
Speaker 1: things out to provide context. And while I could, in
Speaker 1: theory do that, there's only so many hours in the day.
Speaker 1: So what we're gonna do instead is I'm going to
Speaker 1: tell you about three of the speakers I've seen and
Speaker 1: the stuff that they covered, and that will be each
Speaker 1: of the three segments of this podcast. And I think
Speaker 1: it's pretty interesting. They were a fascinating group of people.
Speaker 1: And up first was someone who is kind of a
Speaker 1: rock star in science. That would be Neil Degrass Tyson.
Speaker 1: I'm sure most of you have heard about Neil de
Speaker 1: Grass Tyson. Maybe you've watched television programs that he's hosted,
Speaker 1: or heard him chime in on scientific discussions on various venues,
Speaker 1: whether it's online video, radio shows, TV, all sorts of stuff.
Speaker 1: He's uh of course, also made cameo appearances, or at
Speaker 1: least a character that is Neil de Grass Tyson made
Speaker 1: cameo appearances and epic rap battles of history. It wasn't
Speaker 1: actually Neil Degrass Tyson was obviously an actor playing him.
Speaker 1: But he's not just an accomplished scientist. He's a famous
Speaker 1: science communicator. So he uses his knowledge and humor and
Speaker 1: other uh social uh strategies to talk about science with
Speaker 1: the general public and to explain it and to to
Speaker 1: get enthusiasm behind it. He presented a talk here at
Speaker 1: THINK two thousand eighteen called the Knowledge of Nature and
Speaker 1: the Nature of Knowledge. Now, his presentation didn't speak directly
Speaker 1: to the theme of technology, but much of what he
Speaker 1: had to say was relevant to tech, particularly the way
Speaker 1: people who work in tech communicate their work to the public.
Speaker 1: And he began by talking about what we are able
Speaker 1: to observe directly using our five primary senses, those of
Speaker 1: course being site, hearing, smell, taste, and touch. Now he
Speaker 1: acknowledged that we actually have other senses on top of
Speaker 1: these five. You could argue a whole list of different senses,
Speaker 1: like the ability to sense pain, which is related to
Speaker 1: but not completely encompassed by touch. But these are generally
Speaker 1: agreed upon as the five basic senses, and they obviously
Speaker 1: have limits. So let's take an example. Our site is limited.
Speaker 1: Not only can we not focus to an infinite distance,
Speaker 1: Eventually the focal point will exceed what even the keenest
Speaker 1: eyed person can see. We can also only see a
Speaker 1: band of the spectrum of light. We call it the
Speaker 1: visible spectrum. Obviously, you know that's that's all the light
Speaker 1: that human beings are capable of perceiving with their sight.
Speaker 1: But there's obviously light outside of that spectrum. There's ultraviolet light,
Speaker 1: there's infrared light, and we know it exists, but we
Speaker 1: cannot directly observe it with our eyes. And beyond that
Speaker 1: there are other electromagnetic frequencies that we know exist, but
Speaker 1: again we cannot observe them using only our five senses.
Speaker 1: There are similar cases for our other senses, right, Like
Speaker 1: there's limits to what we can hear. We can hear
Speaker 1: at a certain spectrum of sound frequencies. Typically we call
Speaker 1: it twenty hurts to twenty thou hurts and anything in between.
Speaker 1: That that's the the average range of human hearing. And
Speaker 1: of course that range decreases as we get older. As
Speaker 1: we get older, our ability to hear to perceive those
Speaker 1: upper level frequencies, the ones that are at the twenty
Speaker 1: level um, that decreases over time. So we're are our
Speaker 1: range narrows as we get older, and we know obviously
Speaker 1: that there are frequencies outside of that. So he was
Speaker 1: pointing out that we have limitations. It's not that our
Speaker 1: senses are total crap. He was pointing out that there's
Speaker 1: way more out there in the universe than we can
Speaker 1: observe directly, and that, uh, we know this through science
Speaker 1: and technology. We cannot directly experience it as human beings
Speaker 1: in ways that we can make sense of using the
Speaker 1: five basic senses, but we've learned a lot about it
Speaker 1: using science and technology, and you can even think of
Speaker 1: technology as applied science, because without science, there's really no technology.
Speaker 1: With these scientific tools, we can then extend our understanding
Speaker 1: of the universe. We can make observations, we can develop hypotheses,
Speaker 1: we can test those hypotheses to see if they have merit,
Speaker 1: and we can have other people test those hypotheses. We
Speaker 1: can even have them use different methods to test those hypotheses,
Speaker 1: and as long as those results keep coming back, then
Speaker 1: we can be reasonably confident that we've hit on something.
Speaker 1: This is the basis of the scientific method. In fact,
Speaker 1: Neil deGrasse Tyson took some time to talk about the
Speaker 1: scientific method and stress the importance of testing ideas and
Speaker 1: have others test those same ideas, both with an approach
Speaker 1: that is similar to the one you took when you
Speaker 1: first tested it and with other entirely different approaches to
Speaker 1: make sure that they come to the same result. And
Speaker 1: if everyone gets more or less the same answer through
Speaker 1: their various tests, and those tests are considered to be
Speaker 1: well designed, you can feel reasonably sure that the idea
Speaker 1: you had, the observation you made, the conclusion you came
Speaker 1: to is close to an actual truth as close as
Speaker 1: we can reasonably get, and therefore we just kind of
Speaker 1: say it's true, because I mean, otherwise you're being really,
Speaker 1: really particular. And if you argue that we can never
Speaker 1: know the absolute truth, you might be right, but it's
Speaker 1: also kind of impractical. So at some point you just say,
Speaker 1: all right, this is good enough, this is close enough
Speaker 1: to truth for us to call it that. He was
Speaker 1: also careful to differentiate the concept of proof from evidence.
Speaker 1: He talked about proofs in mathematical terms and said, no
Speaker 1: good scientists would ever use the word proof when talking
Speaker 1: about his or her work. You can find evidence to
Speaker 1: support your claims, but not proof. Proofs are for math,
Speaker 1: not for science. This goes back to something else I
Speaker 1: had mentioned and that I had witnessed earlier in the
Speaker 1: h the conference where Chilia Bosquini was talking about lattice
Speaker 1: based cryptography and mathematical proofs, and the person she was
Speaker 1: talking to was interpreting proof as evidence. So he was saying,
Speaker 1: isn't the fact that computers have had so much trouble
Speaker 1: with large factors, large number of factoring that that's proof
Speaker 1: that it is a hard problem. And so they were
Speaker 1: both using a word, but they were using it in
Speaker 1: different ways, and there was a disconnect there. There was
Speaker 1: a miscommunication going on, and I didn't want to butt
Speaker 1: in because it didn't want to be a smarty pants.
Speaker 1: But it was interesting to see this breakdown in communication
Speaker 1: because they're using two separate sets of terminologies that share
Speaker 1: a common a common lexicography, like they're all lexicons that
Speaker 1: are exactly the same, but the meanings are different and
Speaker 1: that's the problem. So then Neil Degrass Tyson went on
Speaker 1: to talk about the difference between accuracy and precision. He
Speaker 1: used a simple question, and that question is what time
Speaker 1: is it? So his first answer was it's two thousand eighteen,
Speaker 1: which is accurate, or at least it's accurate as of
Speaker 1: the recording of this podcast. I'm recording it on March eighteen.
Speaker 1: But even though the answer is accurate, it's not very precise.
Speaker 1: It doesn't go to a very good level of precision.
Speaker 1: It's not useful. Typically, like if I ask you what
Speaker 1: time is it because I need to figure out if
Speaker 1: I'm going to be late to a meeting I'm rushing
Speaker 1: off to, and you just give me the year. That
Speaker 1: doesn't give me enough information to know whether or not
Speaker 1: I need to take a car or if I can
Speaker 1: take a bike ride to get there. However, you could
Speaker 1: also air the other way, where you have a lot
Speaker 1: of precision but not a lot of accuracy. So his
Speaker 1: second answer said, oh, it's oh, seven hours, forty four minutes,
Speaker 1: twenty two seconds and thirty seven milliseconds. Now that answer
Speaker 1: is precise, but it could also be wrong. What if
Speaker 1: it's forty five minutes, so it's off by a whole minute. Yeah,
Speaker 1: you had this huge level of precision, but your accuracy
Speaker 1: was off. Moreover, because you went to such a level
Speaker 1: of precision down to the millisecond, you cannot possibly be
Speaker 1: accurate because by the time you finish telling someone what
Speaker 1: time it is, it will be a different time. And
Speaker 1: so when was it four minutes, twenty two seconds and
Speaker 1: thirty seven milliseconds? Was it when you started telling the
Speaker 1: person what time it was? Was it? The end of
Speaker 1: that precision without accuracy is not very useful either, was
Speaker 1: his point. And uh, his this was to say that
Speaker 1: accuracy and precision are both important, and the level of
Speaker 1: precision you should use when you're communicating something to others
Speaker 1: is dependent upon what you're trying to accomplish. You could
Speaker 1: always get more precise, but sometimes that reaches a level
Speaker 1: that is no longer of any real use. And I
Speaker 1: can identify with this idea. See when I construct an
Speaker 1: episode of tech stuff, when I'm researching and writing up
Speaker 1: my notes, I frequently have to figure out where should
Speaker 1: I start? Where's my starting point for the episode, based
Speaker 1: upon whatever topic it is that I'm covering. Now, Rarely
Speaker 1: do I start right at the quote unquote beginning of
Speaker 1: a technology's creation. And that's because it's usually helpful to
Speaker 1: understand things that were happening before someone, more likely a
Speaker 1: string of someone's invented the technology. Without having that understanding
Speaker 1: of what preceded the invention, you don't really have enough
Speaker 1: context to understand what is going on and why it's important.
Speaker 1: Neil deGrasse Tyson went on to use an example of
Speaker 1: his own as he spoke about the Earth's orbit. The
Speaker 1: shorthand description of the Earth's orbit around the Sun. Something
Speaker 1: that you might have learned in elementary school is that
Speaker 1: it's an elliptical orbit. But that's not entirely accurate. First
Speaker 1: of all, the Earth's orbit is only a slight deviation
Speaker 1: from a circle. You could argue that it's closer to
Speaker 1: being a circular orbit than it is an elliptical orbit.
Speaker 1: But despite this, many textbooks exaggerate the path that the
Speaker 1: Earth's orbit takes to show that's not a perfect circle.
Speaker 1: But even if you were to say it's more circular
Speaker 1: than elliptical, that's not entirely precise because the Earth's Moon
Speaker 1: affects the Earth's orbital path. Now, the Moon does not
Speaker 1: actually revolve around the Earth. Instead, the Earth and the
Speaker 1: Moon are in a dance together. They rotate around a
Speaker 1: common center of gravity between the two. This is true
Speaker 1: for any two objects that are revolving around one another.
Speaker 1: They revolve around a common point of center of gravity. Now,
Speaker 1: as it happens because of the masses of the two
Speaker 1: the two bodies, the Earth and the Moon, the center
Speaker 1: of gravity happens to be located below the Earth's surface.
Speaker 1: But it's not at the center of the Earth. If
Speaker 1: it if it were at the center of the Earth,
Speaker 1: then you could say while the Moon essentially revolves around
Speaker 1: the Earth. Instead, it's about a thousand miles below the
Speaker 1: Earth's surface on whichever side the Moon is on at
Speaker 1: that time. So, in other words, the center of gravity
Speaker 1: moves around the Earth's interior as the Moon continues through
Speaker 1: its orbit, which has the effect of creating a little
Speaker 1: wobble in the Earth's orbit. So you can really think
Speaker 1: of the Earth's orbit around the Sun as a bit
Speaker 1: of a a circle that's made up of a kind
Speaker 1: of squiggly line. Then that squiggly line is the little
Speaker 1: jiggle that the Earth has because of the the Moon,
Speaker 1: the Moon's poll on the Earth. But even that is
Speaker 1: not truly precise, because the Sun in our Solar system
Speaker 1: is orbiting around the galactic center of the Milky Way
Speaker 1: galaxy and it's not in the same plane as Earth's orbit.
Speaker 1: So while from one perspective you could say the Earth
Speaker 1: has a wobbly circular orbit, if you were looking at
Speaker 1: the Sun directly in front of it as it was
Speaker 1: moving through its galactical orbital path, you would see that
Speaker 1: the planets of our Solar system are moving around and
Speaker 1: what it amounts to a spiral Because take any arbitrary
Speaker 1: starting position of the Earth with respect to its position
Speaker 1: with the Sun, and you say, all right, this is
Speaker 1: the day zero, and we're gonna go three days around
Speaker 1: so that we return to our point of origin. The
Speaker 1: thing is you'll never return to that exact fixed point
Speaker 1: in space, because the Solar system itself is uh rotating
Speaker 1: around this galactic center in the Milky Way. So while
Speaker 1: you will return to your same relative position in reference
Speaker 1: to the Sun, you won't be in the same fixed
Speaker 1: point in space because the whole system has been moving
Speaker 1: this entire time. So you can't really even say it's
Speaker 1: a circle. It's more like a spiral when you when
Speaker 1: you take your reference point as the galaxy as opposed
Speaker 1: to just the Solar system. Uh. Again, the whole purpose
Speaker 1: of this was to give an example to say we
Speaker 1: have to make choices when we communicate information to others.
Speaker 1: At what point do we need to draw the line
Speaker 1: and say, okay, that's enough detail. Anything more than that
Speaker 1: is either confusing or it's boring, or it's both. And
Speaker 1: then we make those determinations and hope that we can
Speaker 1: inspire people to look further into subjects. Later on, Neil
Speaker 1: deGrasse Tyson's presentation concluded with a discussion about how to
Speaker 1: convey information to your audience in a way that matters
Speaker 1: to them. He showed examples of science incorporated into pop culture.
Speaker 1: He specifically played a clip from the movie Frozen and
Speaker 1: then he danced around on stage as Elsa sang frozen
Speaker 1: fractals all around because the word fractal was included in
Speaker 1: a Disney film and sung by a Disney princess. And
Speaker 1: he said, this is amazing progress. He said, you need
Speaker 1: to find out what your audience cares about and then
Speaker 1: find ways to use that as a means of explaining
Speaker 1: scientific or technological ideas to them. And he used football
Speaker 1: as his own example, showing off a series of fairly
Speaker 1: recent tweets he had made during football games to get
Speaker 1: across scientific principles. Now, those tweets sparked a lot of
Speaker 1: conversation and humor and jokes around his followers and other
Speaker 1: people who saw those messages, and it meant that for
Speaker 1: a short while at least, people were talking about actual science.
Speaker 1: For people in the technology space, that could be a
Speaker 1: very valuable lesson because technology gets super complicated, and not
Speaker 1: only can the actual mechanics of technology get complex, the
Speaker 1: way it works and the intricacies of how it works.
Speaker 1: The way we describe technology can also be really confusing
Speaker 1: because we use a lot of jargon, a lot of
Speaker 1: terminology that is not in the common parlance, and we
Speaker 1: use a lot of shorthand to communicate complicated ideas. To
Speaker 1: a newcomer, this comes across as incredibly intimidating, very dense,
Speaker 1: and difficult to understand. Before you can even get to
Speaker 1: the point where you comprehend the actual concepts that underlie technology,
Speaker 1: you first have to get a grip on the language
Speaker 1: that's being used. Then you have to struggle with that
Speaker 1: language to understand what is actually happening with any technological,
Speaker 1: uh product, or service. And if technologies can use analogies
Speaker 1: or stories or examples to explain their work, If if
Speaker 1: technologists rather can use those tools to explain what it
Speaker 1: is they're doing and what they're they're the things they
Speaker 1: work on, what they do, then the general public may
Speaker 1: end up having a more enthusiastic response because they'll have
Speaker 1: a touchstone to understand what exactly is going on. Now,
Speaker 1: that's easier said than done, because you you gotta you
Speaker 1: gotta walk a line. You don't want to simplify matters
Speaker 1: to the point where people are going to misunderstand what
Speaker 1: you're saying. So with some subjects like artificial intelligence or
Speaker 1: quantum computing, going too broad or too simple will make
Speaker 1: people think that the technology is akin to magic because
Speaker 1: you've you've abstracted it and you've simplified it so much
Speaker 1: that it just sounds like it does things that nothing
Speaker 1: could truly possibly do, or you give them the idea
Speaker 1: that this technology is capable of doing things that we
Speaker 1: really can't do yet. So, like the science lesson about
Speaker 1: the Earth's orbit, there's a balance we have to achieve
Speaker 1: when we're explaining these things to someone new to the
Speaker 1: subject matter. And that's true across every industry. It's not
Speaker 1: just science or technology. I mean, there are plenty of
Speaker 1: discussions about business and particularly finance. Law is another example.
Speaker 1: Whenever I read any stories about finance or law or business,
Speaker 1: I get to a point where I realize I've read
Speaker 1: a paragraph and I have absolutely no idea what that
Speaker 1: paragraph meant. I can reread it and I can say
Speaker 1: to myself, I individually I understand what all of these
Speaker 1: words mean, but collectively I don't get what they're trying
Speaker 1: to say. And you know that that comes with the
Speaker 1: territory of having specialization in any field. Eventually get to
Speaker 1: a point where the language you use is extremely efficient.
Speaker 1: If you're talking to someone else who shares your knowledge,
Speaker 1: you can have very deep very quick conversations based upon
Speaker 1: this shared language. But if you try to communicate to
Speaker 1: anyone outside of that, you get the deer in the
Speaker 1: headlights look. Or at least that's what I get whenever
Speaker 1: I try and talk to anyone about finance or law
Speaker 1: or anything like that. So that's why I try to
Speaker 1: structure episodes the way I do. It's not that I
Speaker 1: don't have faith in my audience. I have a great audience.
Speaker 1: I have a really smart and engaging audience. It's that
Speaker 1: I never know when it might be someone's first introduction
Speaker 1: to that specific subject matter, and I want to make
Speaker 1: sure that I can build a foundation before diving more
Speaker 1: deeply into any given topic. When we come back from
Speaker 1: the break, i'll tell you about the presentation a famous
Speaker 1: futurist gave at IBM Think. But first let's take a
Speaker 1: break and thank our sponsor. The next famous person I
Speaker 1: saw give a presentation at Think two thousand eighteen was
Speaker 1: Dr michio Aku. Dr Cocku is a theoretical physicist and
Speaker 1: a professor at City College of New York. He's also
Speaker 1: an author. A futurist, UH that would be someone who
Speaker 1: predicts how technology and advancements and science will change our
Speaker 1: world in the decades to come very close to my heart.
Speaker 1: Since I used to host a show called Forward Thinking.
Speaker 1: In fact, I often would read about Minchio Okaku's work
Speaker 1: as a host of Forward Thinking, I would refer to
Speaker 1: that as part of the research I did for lots
Speaker 1: of different topics. He's a frequent guest or host on
Speaker 1: television series and radio programs, and he talks a lot
Speaker 1: about his work and predictions regularly. He has specifically studied
Speaker 1: quantum mechanics and string theory. Now, I've talked an awful
Speaker 1: lot about quantum mechanics already in this mini series, so
Speaker 1: I'm not going to rehash that. But what the heck
Speaker 1: is string theory? Well, string theory is a family of
Speaker 1: theories that attempt to describe why fundamental forces and particles
Speaker 1: found in nature behave the way they do. They go
Speaker 1: into topic in depth. Uh, it would require a whole
Speaker 1: suite of episodes to explain this. And there are multiple
Speaker 1: types of string theory. There's not just one string theory.
Speaker 1: There are competing theories. Um. I would rapidly find myself
Speaker 1: out of depth if I tried to tackle the entire field,
Speaker 1: and even sum summarize it in uh, you know, in
Speaker 1: a way that was at least semi comprehensive, but I
Speaker 1: can give a very super high level idea. In general,
Speaker 1: these theories describe forces and particles as one dimensional strings
Speaker 1: that vibrate in a way that gives those forces and
Speaker 1: particles their properties. Some of them are closed loop strings
Speaker 1: that are like rubber bands. Dr Cocu has talked about
Speaker 1: supersymmetric string theory in particular. That's one of the flavors
Speaker 1: of string theory, and it's really fascinating stuff that at
Speaker 1: least I'm told makes sense from a mathematical perspective. Now
Speaker 1: I say I'm told because the math is far too
Speaker 1: complex for me to understand, so I certainly can't look
Speaker 1: at and say, oh, yeah, no, that holds up. But
Speaker 1: as of right now and for the foreseeable future, we
Speaker 1: have no way to directly observe and test string theory,
Speaker 1: which has led some people to say that's not so
Speaker 1: much a scientific theory as it is a philosophy because
Speaker 1: you cannot test or observe it. But we'll set that
Speaker 1: whole argument aside. Dr Kaku, who took the main stage
Speaker 1: here at IBM focus largely unwary thinks humanity will be
Speaker 1: headed in the next few decades with the space the
Speaker 1: space missions in particular, he pointed out that today's computers
Speaker 1: still depend upon silicon chips and Moore's law, but he
Speaker 1: also talked about the ultimate limits of that technology, and
Speaker 1: as I mentioned in this series, you can only shrink
Speaker 1: components down so far using traditional silicon chip technology. Once
Speaker 1: you shrink below certain thresholds, quantum effects begin to override
Speaker 1: your classical computer design, and electrons no longer follow the
Speaker 1: pathways you've made for them, and our electronics are tod
Speaker 1: in it upon electrons doing what we want them to do.
Speaker 1: At the quantum level, electrons do lots of strange things,
Speaker 1: things we can't necessarily control or predict, and that means
Speaker 1: our electronics and computers that are built on these components
Speaker 1: cannot really work properly. They will will become unreliable, they'll
Speaker 1: become error prone. We can't we can't keep going this
Speaker 1: route indefinitely. We will eventually hit that fundamental physical level
Speaker 1: based upon how we build things today. So something has
Speaker 1: to change now. Dr Cocu said, if we cling to
Speaker 1: silicon as the basis for our technology moving forward, then
Speaker 1: eventually Silicon Valley could become a new rust belt. We
Speaker 1: will hit that ultimate limit of what we're capable of
Speaker 1: doing using that technology, and we will progress no further.
Speaker 1: So we'll need to shift to a new paradigm. Dr
Speaker 1: Kaku mentioned several technologies who felt were going to be
Speaker 1: instrumental in pushing us past the end of the silicon
Speaker 1: chip age and Moore's law, and he talked about the
Speaker 1: four great technological revolutions. The first of those would the
Speaker 1: development of the steam engine, which led to an incredibly
Speaker 1: productive period and the Industrial Revolution. Understanding and leveraging the
Speaker 1: laws of thermodynamics allowed us to create new ways to
Speaker 1: do work and travel. We were able to create engines
Speaker 1: that could harness the power of thermodynamics and translated into work.
Speaker 1: About at eight decades after the invention of the steam engine,
Speaker 1: physicists were getting a much better understanding of some other
Speaker 1: scientific principles like magnetism and electricity, which led to inventions
Speaker 1: like radio and then television. Decades after that, engineers and
Speaker 1: scientists invented the transistor, the third of the technological revolutions.
Speaker 1: That transistor made micro computers possible, as well as other
Speaker 1: devices like lasers, and it allowed us to go to space.
Speaker 1: It created the space industry. In fact, the space industry
Speaker 1: pretty much gave the incentive to development of transistors and manaturization.
Speaker 1: We had a goal, we wanted to send people to
Speaker 1: the Moon, and that meant that we were going to
Speaker 1: have to make some big advancements in science and technology
Speaker 1: in order to make that possible. Space, as in the
Speaker 1: space inside of a capsule, is at a premium. You
Speaker 1: want to make sure things are lightweight, you want to
Speaker 1: make sure things are compact, and that meant that we
Speaker 1: couldn't rely upon the massive circuitry of the past. We
Speaker 1: had to create other means to manaturize things and make
Speaker 1: it more practical for applications like space travel. And uh,
Speaker 1: mitchio Kaku is very excited about space travel. He's very
Speaker 1: excited the fact that we are dedicating ourselves to going
Speaker 1: back to the Moon, first with an unmanned probe that
Speaker 1: should land on the Moon in two thousand nineteen, then
Speaker 1: eventually with a a base in orbit around the Moon
Speaker 1: which will act as sort of a launching ground for
Speaker 1: missions towards Mars. And he talked about how that's very
Speaker 1: important and also mentioned Elon Musk's follow up to the
Speaker 1: Falcon nine Heavy Rocket, which is called the b f R.
Speaker 1: B stands for big, our stands for rocket, and F
Speaker 1: as Dr CaCu says, stands for well, let your imagination
Speaker 1: take over. The Fourth Revolution is the one that's happening
Speaker 1: right now. Dr CaCu says. It involves stuff like biotechnology, nanotechnology,
Speaker 1: artificial intelligence, and related technologies. It's also about computing models
Speaker 1: like neural networks, which mimic the neural structure of brains.
Speaker 1: The neural network is made up of nodes that behave
Speaker 1: like neurons, like the neurons the neural cells we have
Speaker 1: in our brains. There are interconnections with those nodes to
Speaker 1: other artificial neurons, and processing information with a neural network
Speaker 1: allows you to do some interesting things like machine learning.
Speaker 1: Computers relying on an artificial neural network can learn from data,
Speaker 1: and they can get more proficient at handling that information
Speaker 1: as they get more experience, so they improve themselves. Over time,
Speaker 1: the most efficient pathways start to win now over the
Speaker 1: least efficient pathways, and you can teach computers. Now I've
Speaker 1: talked about this before, about teaching computers how to recognize
Speaker 1: what a cat is, for example, which is a somewhat
Speaker 1: trivial version of this, but neural networks are being used
Speaker 1: across multiple industries to solve very difficult problems. He also
Speaker 1: mentioned artificial intelligence, which will augment our abilities to make
Speaker 1: decisions and take actions, and he stressed that he felt
Speaker 1: AI was not poised to replace human beings, but help them.
Speaker 1: He laid out the two big arguments that tend to
Speaker 1: be be presented in AI, sort of the Zuckerberg argument
Speaker 1: versus the Elon Musk argument, and the Zuckerberg argument is
Speaker 1: AI is going to be a huge help. It's going
Speaker 1: to benefit us. It's going to let us do what
Speaker 1: we want to do better and faster. It's going to
Speaker 1: create opportunities that do not exist right now that we
Speaker 1: can't even predict because we aren't in that age yet.
Speaker 1: Musk says it's going to lead to killer robots and
Speaker 1: we're all gonna die now. I am are simplifying on
Speaker 1: both sides, more on Musk than on Zuckerberg for a
Speaker 1: comedic effect. Musk says AI could potentially pose an existential
Speaker 1: threat to the human race, saying that if you were
Speaker 1: to create AI um and you don't have good controls
Speaker 1: in place, then it could be the end of humanity.
Speaker 1: It could eventually decide that we are bad and that
Speaker 1: we need to be stopped. Well, CaCu seems to fall
Speaker 1: more on Zuckerbird's side than Musk's side on this. He
Speaker 1: said we should always be aware of how we implement
Speaker 1: AI and how we design it, but that we shouldn't
Speaker 1: worry in the near term about AI going all terminator
Speaker 1: on us and wiping us out. As we develop AI
Speaker 1: and as it becomes more sophisticated, we can talk about
Speaker 1: how to build in fail safes that act on the
Speaker 1: AI as sort of a limitter so that it doesn't
Speaker 1: cause harm. The whole field of thought is fascinating to me,
Speaker 1: and it gets way more complicated than just put a
Speaker 1: chip in the computer's brain so that turns off if
Speaker 1: it thinks bad thoughts. But I'm gonna leave that whole
Speaker 1: discussion for another show because I think that that's something
Speaker 1: that needs to be an actual discussion. I need to
Speaker 1: get people on the show and we can all talk
Speaker 1: about these implications and kind of argue it all out,
Speaker 1: not to have a fight, but rather to just kind
Speaker 1: of see all sides of the issue and Of course,
Speaker 1: he also brought up quantum computing, because everything at IBM
Speaker 1: Think ten seems to be about quantum computing at some point,
Speaker 1: and how that could be a huge help for human
Speaker 1: efforts moving forward. Though out of all these topics, he
Speaker 1: was really most interested in discussing neural networks and artificial intelligence,
Speaker 1: I would say, and much of his presentation was in
Speaker 1: fact about space travel. Dr Cocus stress that we need
Speaker 1: healthy and robust space program, not just so that we
Speaker 1: can learn more about our Solar system and beyond, though
Speaker 1: that is very important, but also for the actual survival
Speaker 1: of the human race long term, because it's only a
Speaker 1: matter of time before we have a cataclysmic event on
Speaker 1: the Earth, sort of like a giant meteor or a
Speaker 1: comment that collides with the Earth. That will happen eventually.
Speaker 1: It might not happen for thousands of years rs, but
Speaker 1: it will happen. It's statistically a certainty that at some
Speaker 1: point it will happen. It happened before and it will
Speaker 1: happen again, and that could be an extinction level events.
Speaker 1: So we need a robust space program so that we
Speaker 1: can do stuff like detect such threats decades before they
Speaker 1: are imminent and then take measures to deflect those threats
Speaker 1: so that we are actually safe. It would also help
Speaker 1: ensure the survival of humans if we spread out a bit,
Speaker 1: if we started to colonize other planets. Spreading ourselves out
Speaker 1: ensures the possibility that the human race survives if something
Speaker 1: goes wrong on any one given location, and at that
Speaker 1: point we have an insurance policy against extinction events. Dr
Speaker 1: Cock who touched on augmented reality as well. He talked
Speaker 1: about glasses and headsets that allow you to see information
Speaker 1: overlaid on top of the world around you. He also
Speaker 1: talked about voice recognition tools that let you speak directly
Speaker 1: to this technology, calling up information you need for any
Speaker 1: given situation. You could have specific data sets and use
Speaker 1: those data sets with use cases. For people who have
Speaker 1: specialized trades, for example, like electrical engineers or heart surgeons,
Speaker 1: they could have very carefully customized data sets that aid
Speaker 1: them in their specific occupations. They also talked about how
Speaker 1: in the future will have contact lenses that will be
Speaker 1: able to do this this idea of having augmented reality contacts.
Speaker 1: You put the contact in it acts like a computer screen.
Speaker 1: It can overlay digital information on top of the view
Speaker 1: you have of the world around you in real time,
Speaker 1: and it will tell you all about stuff that's around you.
Speaker 1: You can learn about, let's say, a building that you're
Speaker 1: looking at, or they can help explain a scientific principle
Speaker 1: that you are encountering. Maybe you're at a talk like
Speaker 1: the one that Dr Cocku was giving and your augmented
Speaker 1: reality contact lens is giving you illustrations that augment that
Speaker 1: talk and help you better understand what he's trying to express.
Speaker 1: Or you might be at a party in your contact
Speaker 1: lens is telling you which people at the party are important,
Speaker 1: so you know who to suck up to. That was
Speaker 1: actually Dr Cocku's example. He said, this will be a
Speaker 1: great tool if you're ever at a cocktail party and
Speaker 1: you want to know who's important so that you can
Speaker 1: sidle up to them and kiss their butt. Um. I
Speaker 1: thought it was a clever and funny example. Dr Cocku,
Speaker 1: by the way, is a very funny person. Hearing him
Speaker 1: um make jokes as he's talking about futurism was very refreshing.
Speaker 1: Dr cock who also talked about how display technology has
Speaker 1: become far more advanced and we have displays now that
Speaker 1: are flexible and that there are paper thin with things
Speaker 1: like oh lad displays, and then the future will have
Speaker 1: digitized paper everywhere, so you could have a sheet of
Speaker 1: paper that could be literally anything because it can be
Speaker 1: a display. It's not really paper, it's a display that
Speaker 1: could show any potential any potential information you need. You
Speaker 1: can have a wall coded in digitized paper and be
Speaker 1: digitized wallpaper, and that wall becomes a computing surface that
Speaker 1: you can use. So let's say you've got this wallpaper display.
Speaker 1: You could walk up to the wall all you could
Speaker 1: talk to it. You could pull up information about any
Speaker 1: given subject, and you could interact with an AI chatbot
Speaker 1: to answer questions, or you help make decisions, kind of
Speaker 1: like Jarvis in Iron Man. Dr Cock who gave an
Speaker 1: example of someone waking up at night and they have
Speaker 1: chest pains and they start talking to the wall, kind
Speaker 1: of having a robo doctor right there to find out
Speaker 1: if the symptoms they are suffering from indicate something serious
Speaker 1: like a heart attack, and if so, the digital assistant
Speaker 1: helps by reaching out to emergency medical personnel and getting
Speaker 1: things ready so that that the the the person in
Speaker 1: question can get medical attention, or if it's something more
Speaker 1: benign like just indigestion. Dr Cock who also talked about
Speaker 1: trying to foil aging, both through genetic approaches and through
Speaker 1: digital ones. He talked about identifying the genes that are
Speaker 1: responsible for aging and finding a way to deactivate them
Speaker 1: so that the aging process stops once you reach adulthood.
Speaker 1: From an external point of view, you would always appear
Speaker 1: to be, say thirty years old. However, Dr Cock, who
Speaker 1: also had not acknowledged that this wouldn't necessarily mean you
Speaker 1: never die of old age, because your organs could continue
Speaker 1: to deteriorate just from use over years and years and years.
Speaker 1: You might live much longer, but eventually stuff would start
Speaker 1: to wear out, and that would require us to find
Speaker 1: ways to build new organs. Now we've started to do
Speaker 1: that already, but we've got other major organs that we
Speaker 1: haven't yet been able to build, like a liver, but
Speaker 1: that might preserve someone's life well beyond today's lifespans, perhaps
Speaker 1: even indefinitely. And then he mentioned digital immortality. This is
Speaker 1: a very different approach. This is where you somehow capture
Speaker 1: all the information that makes a person who he or
Speaker 1: she is. Think of it like a three D scan,
Speaker 1: except for who you are, not what you look like
Speaker 1: all of your memories and your thoughts and your emotional
Speaker 1: responses to anything, the way you think, the way you
Speaker 1: come up with ideas, all of that would end up
Speaker 1: being capt shared, and you'd end up with a simulation
Speaker 1: of you or all practical purposes, and it would behave
Speaker 1: as you would in any given situation, assuming that the
Speaker 1: simulation was as close to perfect as possible. Now, would
Speaker 1: you then say that that construct is in fact you, Well,
Speaker 1: if you think of yourself in terms of your fleshy existence, no,
Speaker 1: you wouldn't. The version that you would be looking at
Speaker 1: would be a simulation, and you, however, would eventually age
Speaker 1: and die. But if you think of yourself as your
Speaker 1: collection of experiences and your thoughts and your emotions, then
Speaker 1: maybe you might say the simulation is you, but it's
Speaker 1: another instance of you. Now, that doesn't solve the problem
Speaker 1: that your own experience of being you would someday come
Speaker 1: to an end, and a simulated jerk face version of
Speaker 1: you would keep on going as if no one cared,
Speaker 1: as long as no one unplugged them jerk face. Then
Speaker 1: Dr Cocu said something I found really fascinating, and I
Speaker 1: hadn't really considered before He said that if you could
Speaker 1: digitize a person, boil their essence down into pure information,
Speaker 1: and then you encode that information using a laser beam,
Speaker 1: you could fire that laser beam at distant targets like
Speaker 1: other planets in our Solar system, or even beyond. Let's
Speaker 1: say you shoot it toward Alpha Centauri. Within four years,
Speaker 1: you would get there. You could have minds travel vast
Speaker 1: distances at the speed of light. Matter can't travel at
Speaker 1: the speed of light, but information can. Of course, I'm
Speaker 1: not sure what you could do once you've got to
Speaker 1: where you were going, because if you're information encoded on
Speaker 1: a medium, I can't see any way you could meaningfully
Speaker 1: do anything at all or perceive anything. You would just
Speaker 1: have just information shooting out there, kind of like how
Speaker 1: radio waves can radiate out into space. That doesn't necessarily
Speaker 1: mean we can do anything with them. A program isn't
Speaker 1: running if you don't execute it right, Like if you
Speaker 1: have a computer game on a disk. Imagine an old
Speaker 1: disc you've got, you put it in your computer, and
Speaker 1: you don't run the program. You just put it in
Speaker 1: the computer, but the computer is not engaging the disk
Speaker 1: at all. It's not like the game is actually running
Speaker 1: on your machine. It's not like it's making any sort
Speaker 1: of activity, but still, this is an intriguing idea. It
Speaker 1: could be a new form of space exploration, assuming we
Speaker 1: figure out how to make that information useful, so that
Speaker 1: can either communicate back to us about what it has discovered,
Speaker 1: or it can somehow go about and take actions of
Speaker 1: its own across the universe. I just don't know how
Speaker 1: that would work. Dr Kko's final section was about brain
Speaker 1: computer interfaces. Now, these are the technologies that allow us
Speaker 1: to communicate directly with technologies through thought. It's kind of
Speaker 1: a technological telepathy. It's still a very early field, but
Speaker 1: there are examples out there and have been for a
Speaker 1: decade or more. These are not perfect methods, and each
Speaker 1: method has advantages and disadvantages. The best way to get
Speaker 1: high resolution data from a person that is consistently reliable,
Speaker 1: or at least more reliable than the alternatives, is to
Speaker 1: embed sensors directly onto that person's brain. So it requires
Speaker 1: brain surgery. Then you have the problem of figuring out
Speaker 1: how do you send signals from the sensors you've implanted
Speaker 1: in the brain to the target technologies. Now, you could
Speaker 1: include wires or an antenna that protrudes from the brain
Speaker 1: through the skull. But this also presents a problem because
Speaker 1: you've created the potential for contaminants to infect the brain.
Speaker 1: Like anytime you've got something that's breaking that blood brain barrier,
Speaker 1: that's bad news. So you have to be super careful
Speaker 1: with this. And the skull is pretty thick. It's thick
Speaker 1: enough to make it tricky to send signals through the skull.
Speaker 1: So a wireless solution, while it might be possible, is
Speaker 1: not ideal. It might end up being hard to detect
Speaker 1: legitimate signals. Or you could go the non invasive route
Speaker 1: and you could use an e G headset to pick
Speaker 1: up brain waves. Now those don't require surgery, but sometimes
Speaker 1: are not terribly accurate. They can pick up noise that
Speaker 1: looks like brain waves, but it's actually just interference. Uh,
Speaker 1: they can be false positives, or it can fail to
Speaker 1: register legitimate commands as something that's meaningful. So you might
Speaker 1: be thinking, now type the word dog, but it's nothing's
Speaker 1: happening because it hasn't picked it up as a legitimate command.
Speaker 1: So this doesn't require a surgeon to operate on you,
Speaker 1: but it also does not give you as accurate a
Speaker 1: response as an intercranial approach would. However, we do have
Speaker 1: some examples of these technologies that allow people who otherwise
Speaker 1: couldn't control things with their bodies to be able to
Speaker 1: have control over things. And a lot of it comes
Speaker 1: into communication, right, using a computer interface to communicate with
Speaker 1: other people, you think, and then the computer either speaks
Speaker 1: or types things out on your behalf and it opens
Speaker 1: up a channel of communication where otherwise you would be
Speaker 1: unable to do so. Or we can pair that type
Speaker 1: of technology with something like an exoskeleton, and of someone
Speaker 1: who has lost mobility the chance to regain it. They
Speaker 1: can use their mind to send signals to the exoskeleton
Speaker 1: and use that to move them around. One day, we
Speaker 1: might extend this so that we have robotic bodies that
Speaker 1: we can control remotely using our minds, giving us telepresence
Speaker 1: into distant locations. Of course, the further out we go,
Speaker 1: the more we'll have to deal with the limitation of
Speaker 1: remote control. Because even if we had a perfect system
Speaker 1: that let us communicate with a robot that's light years
Speaker 1: away from us, so we can send signals to it
Speaker 1: and we can receive signals back from it, somehow there
Speaker 1: would still be tremendous lag because information is still only
Speaker 1: going to travel at the speed of light. So if
Speaker 1: a robot is light years away, that means it literally
Speaker 1: takes years for a command you send to be received
Speaker 1: by the robot, and years again for the robot to
Speaker 1: send the results back to you. So it would be
Speaker 1: like the longest game of mail chess, like where you're
Speaker 1: mailing your move back to your opponent and over again,
Speaker 1: but still super cool. Dr Cocku ended his talk by
Speaker 1: reminding us that without pursuit of these new forms of technologies,
Speaker 1: we could find ourselves clinging to diminishing returns with classical computing,
Speaker 1: and that was a really interesting presentation. I do have
Speaker 1: one more speaker to talk about, but first let's take
Speaker 1: another quick break to thank our sponsor. The last person
Speaker 1: I want to talk about in this episode is a
Speaker 1: remarkable young man and his name is Tonme boks Sheet.
Speaker 1: He's a programmer. He's an expert on deep learning and
Speaker 1: artificial intelligence, and he's fourteen freaking years old. Box She's
Speaker 1: father is a computer programmer, and ten May began learning
Speaker 1: about coding and programming when he was just five years old,
Speaker 1: and he loved it, and so he studied it intently.
Speaker 1: When he was seven, he started up a YouTube channel
Speaker 1: and he began to upload videos about how to code
Speaker 1: and develop for the web. He started to developing for
Speaker 1: iOS when he was eight, and Apple published his first
Speaker 1: app when he was nine. That was an app, by
Speaker 1: the way, that taught multiplication to kids. When he was eleven,
Speaker 1: he caught the attention of IBM. See box She saw
Speaker 1: a video about IBM Watson and how that platform was
Speaker 1: advancing artificial intelligence, and he found it absolutely fascinating. So
Speaker 1: he also started to work with an alpha build of
Speaker 1: a tool that IBM had developed that was meant to
Speaker 1: convert documents from one format into other formats. Box She
Speaker 1: discovered a bug in the code, and he sent a
Speaker 1: bug report to the development team to let them know
Speaker 1: about it. Some IBM developers ended up reaching out to
Speaker 1: box She in response, and a new partnership began to form.
Speaker 1: Box She, by the way, isn't truly an IBM partner
Speaker 1: because he does not get compensated. He doesn't get paid
Speaker 1: for this, but he does work with IBM on a
Speaker 1: lot of projects. Right now. Uh he's creating novel approaches
Speaker 1: to computing and he's finding new ways to leverage data,
Speaker 1: and since his communications with IBM, he has ended up
Speaker 1: going on tours around the world to talk about the
Speaker 1: next advancements and data processing and data analysis, as well
Speaker 1: as artificial intelligence and brain computer interfaces and the work
Speaker 1: he's doing in those fields. He decided to talk about
Speaker 1: three of the projects that he was interested in, all
Speaker 1: three relating to healthcare, and he said that healthcare is
Speaker 1: one of those fields where deep learning and big data
Speaker 1: are really important because there's tons and tons and tons
Speaker 1: of information in the healthcare sphere and it's useful information
Speaker 1: in theory. In practice, however, it can often not be useful. Now,
Speaker 1: the reason I say that is, imagine that you are
Speaker 1: a doctor. Some of you probably are doctors. For the
Speaker 1: rest of you. Imagine that you are a doctor, and
Speaker 1: you're a good doctor. You're you're you're good at what
Speaker 1: you do. You understand your field. However, there are always
Speaker 1: more advancements being made in the pioneering edge of medicine.
Speaker 1: They you may or may not be familiar with. So
Speaker 1: part of your job isn't just treating your patients. It's
Speaker 1: learning more about developments in medicine. So you have to
Speaker 1: go and seek out that information. You have to learn
Speaker 1: about it, you have to comprehend it. Then you have
Speaker 1: to put it into practice if it makes sense as
Speaker 1: as your role as doctor. And while you're doing this,
Speaker 1: while you're learning about the latest and greatest stuff, pioneers
Speaker 1: are finding even more breakthroughs in medicine. And you have
Speaker 1: to keep doing this. You have to keep learning. It's
Speaker 1: a never ending process. And if you are a doctor
Speaker 1: working in a in a in a large city that
Speaker 1: has access to a lot of uh like cutting edge
Speaker 1: research centers, then you have those assets at your disposal.
Speaker 1: You might be able to reference or refer rather a
Speaker 1: patient to one of those specialists who can then use
Speaker 1: their their expertise and the latest and greatest information to
Speaker 1: help that patient. But the further out you are from
Speaker 1: those centers, the less of that support you're going to get,
Speaker 1: and you'll have to do more and more of this
Speaker 1: on your own, which leaves it up to you to
Speaker 1: learn everything and that is just not humanly possible. That's
Speaker 1: where deep learning and big data come in. So with
Speaker 1: deep learning, you can design computer algorithms that are looking
Speaker 1: deeply into the results of any given scenario. So let's
Speaker 1: take drugs as a as a as A as an example,
Speaker 1: clinical trials are great. Clinical trials are how we determine
Speaker 1: whether or not a drug is effective, whether or not
Speaker 1: it poses the potential to give patients UH adverse side effects.
Speaker 1: These are really important things, and all drugs have to
Speaker 1: go through lengthy clinical trials before they can be used
Speaker 1: in medical applications. However, clinical trials are not perfect because
Speaker 1: it is impossible to have a truly representative population of
Speaker 1: volunteers in these clinical trials. You're never going to get
Speaker 1: a full demographically representative population of people testing out your drug. UH. Eventually,
Speaker 1: you're going to get approval. Let's say that the drug
Speaker 1: is effective and doesn't show any UH implications of truly
Speaker 1: adverse sight effects. You get it approved, it goes out
Speaker 1: into the market. Suddenly, it is now out in the
Speaker 1: world where more than seven billion people live. And because
Speaker 1: more than seven billion people live there, they're all sorts
Speaker 1: of different body types, body chemistryes, potential drug interactions going
Speaker 1: on because people could be taking other drugs that could
Speaker 1: change the way your new drug interacts with their bodies.
Speaker 1: And suddenly you've got things that are happening where you
Speaker 1: aren't sure if your drug is responsible for maybe some
Speaker 1: adverse side effects or not. In fact, you can't even
Speaker 1: really be sure in clinical trials. If you observe it
Speaker 1: frequently enough, you could say, well, chances are the drug
Speaker 1: is causing these side effects, but you can't just say
Speaker 1: that based upon one instance because there are too many
Speaker 1: unknown variables. It's like that way with humans. We humans
Speaker 1: are complicated people. And maybe that the person who was
Speaker 1: in the clinical trial had taken some aspirin that morning
Speaker 1: and the aspirin interacted with the drug in a way
Speaker 1: that was unexpected, and thus the person in the trial
Speaker 1: had an adverse side effect. But if they hadn't taken
Speaker 1: the aspirin, they would have been fine. Like they're They're
Speaker 1: all these unknowns that can take place during clinical trials.
Speaker 1: So what Bakshi was arguing is that if you use
Speaker 1: deep learning, you can look across the results of real
Speaker 1: life cases of people taking drugs and reporting any sort
Speaker 1: of side effects they might have felt, and used that
Speaker 1: to start to draw more conclusions. You could even do
Speaker 1: this by looking at social media. So in other words,
Speaker 1: you could look at instances of a drug being mentioned
Speaker 1: in social media and looking at any possible descriptions of
Speaker 1: how a person felt after they took that drug and
Speaker 1: mine the entire sphere of social media for instances like
Speaker 1: that and add that into a database. Now, this would
Speaker 1: be way too much for any human to handle, but
Speaker 1: if you put it into the realm of computers, they
Speaker 1: could look around, look for data points, try and see
Speaker 1: how many of those data points there are. Is there
Speaker 1: a significant number? Is there enough to suggest that perhaps
Speaker 1: there is something actually going on here, or is it
Speaker 1: an outlier that isn't indicative of anything meaningful. Computers can
Speaker 1: help do this, and that can help people start to
Speaker 1: consider what drugs they want to prescribe to patients. People
Speaker 1: being doctors, So a doctor could look at their patient
Speaker 1: and let's say the doctor she's looking at her patients,
Speaker 1: she says, well, I want I want to prescribe medication
Speaker 1: to treat you, and I've got a couple of different options.
Speaker 1: So based upon who you are, your body type, your
Speaker 1: body chemistry, the other medications you might be on, I'm
Speaker 1: going to take a look at this information that has
Speaker 1: been curated by algorithms that have mined this huge data
Speaker 1: set and brought back all these results, and based upon
Speaker 1: this information, I am more likely to prescribe drug b
Speaker 1: to you because too, from what I see here, it
Speaker 1: is the least likely to cause any really bad side
Speaker 1: effects and the most likely to be efficacious in treating
Speaker 1: your disease or whatever. And that was one of the
Speaker 1: three ways that tom may backshe was citing as being
Speaker 1: an important way to use dev learning really just kind
Speaker 1: of finding out ways to avoid having negative drug interactions.
Speaker 1: This idea of leveraging information that is out there that
Speaker 1: otherwise is you know, it's it's useful, but it's unstructured
Speaker 1: and it's not being harnessed in any meaningful way. Changing
Speaker 1: that by using a neural network and using open source
Speaker 1: databases like FDA Drug Adverse Database and social media to
Speaker 1: try and predict if any one particular person will suffer
Speaker 1: negative events from any particular drug. I found that really fascinating.
Speaker 1: The idea of not just using the official databases, the
Speaker 1: medical resources that have accumulated over the course of decades
Speaker 1: of work, but also the more anecdotal evidence and I
Speaker 1: hesitate to use those two words together, but the more
Speaker 1: anecdotal accounts, let's say, of people who are using those drugs,
Speaker 1: who are reporting, you know, just casually on what it
Speaker 1: is they're experiencing and using that information to help kind
Speaker 1: of create a much larger, more informal clinical trial in
Speaker 1: the real world that can end up having real impact
Speaker 1: on the future of how that those particular drugs are
Speaker 1: are used in the future. I used the future a
Speaker 1: couple of times there, but that's just because I'm thinking
Speaker 1: about it so much. Then bok she transitioned to talk
Speaker 1: about another project he's been working on that is called
Speaker 1: the Cognitive Story, and this is a story about augmenting
Speaker 1: people's lives with the power of cognitive computing and artificial
Speaker 1: intelligence and brain computer interfaces. So this kind of ties
Speaker 1: into what Dr Mitchiokaku had talked about earlier during the conference.
Speaker 1: Uh In this in this particular case, uh Tom may Bak,
Speaker 1: she was talking about a woman named Boo, and the
Speaker 1: woman has Rhetz syndrome, which has made her almost completely
Speaker 1: non communicative. She's quadriplegic and almost incapable of communicating except
Speaker 1: to the those who know her best, who can interpret
Speaker 1: what Boo is trying to communicate, and they can therefore
Speaker 1: help her throughout the day. But anyone who doesn't know
Speaker 1: Boo with that level of familiarity is not going to
Speaker 1: be able to understand what she's trying to communicate at
Speaker 1: any given time. And so Bakshi and the team he's
Speaker 1: working with have been trying to develop a tool that
Speaker 1: allows Boo to communicate with other people, and it requires
Speaker 1: several steps. The first of those steps is finding a
Speaker 1: way to interpret booze brain waves so that Boo doesn't
Speaker 1: have to struggle, she just thinks, and then those thoughts
Speaker 1: can be communicated to the outside world. Um and so
Speaker 1: that would require doing what I had talked about previously,
Speaker 1: finding that brain computer interface. In this case, they went
Speaker 1: with the e G model, the noninvasive model. They had
Speaker 1: two three D print a specific headset for Boo so
Speaker 1: that it could comfortably sit on her head. Rett syndrome
Speaker 1: means that she's very, very sensitive, and you can't have
Speaker 1: something heavy or bulky on her because it would be
Speaker 1: caused so much discomfort that it would not be of
Speaker 1: any real help. It would actually it would hurt her physically.
Speaker 1: And then then you have to train the technology. It's
Speaker 1: not just enough that you are able to detect brain waves.
Speaker 1: It's not like all humans have brain waves that follow
Speaker 1: the exact same patterns. And therefore, if I think yes
Speaker 1: or no, and you think yes or no, it's exactly
Speaker 1: the same as it in my brain as it is
Speaker 1: in your brain. So they have to train it with
Speaker 1: booze own brain waves. And that also means they have
Speaker 1: to rely heavily upon Booze Mother for the interpretation of
Speaker 1: Booze communications, because again, without knowing Boo in this level
Speaker 1: of familiarity, they can't know what any particular brain wave represents.
Speaker 1: They can detect the brain waves, but detecting it is
Speaker 1: one thing. Knowing what it means is something else. So
Speaker 1: they work with Booze Mother, who has been given the
Speaker 1: title of intimate interpreter, to interpret what those brain waves mean.
Speaker 1: And right right now, they're able to kind of go
Speaker 1: with a binary approach, a yes or no, but they
Speaker 1: want to extend that so that Boo can communicate more
Speaker 1: complicated thoughts and not just respond to a series of
Speaker 1: yes no questions. To narrow down what it is she wants. Um,
Speaker 1: this is going to require lots and lots of data gathering.
Speaker 1: You have to do a lot of sessions with Boo
Speaker 1: wearing the e G headset, thinking things, measuring them, interpreting
Speaker 1: what those those measurements mean, and then mapping that to
Speaker 1: the system. They want to use neural networks and deep
Speaker 1: learning to detect which of those signals represent actual commands,
Speaker 1: which ones might be noise or interference or just a
Speaker 1: random little blip from the brain. And you have to
Speaker 1: differentiate those if you want it to be a meaningful experience,
Speaker 1: if you want to make sure that the thing you
Speaker 1: are recording is in fact an actual command and not
Speaker 1: just random information, because otherwise, obviously you would be acting upon, uh,
Speaker 1: just blips like blips that don't that don't indicate any
Speaker 1: actual request or question or response. You have to cut
Speaker 1: out all that noise. Um Box. She also gave us
Speaker 1: an example, showed us a black screen and on that
Speaker 1: black screen, occasionally a red square would appear, and Box
Speaker 1: she said, well, when that black screen is black and
Speaker 1: you're just concentrating on it, eventually your mind kind of
Speaker 1: goes at rest and your alpha waves will start to spike,
Speaker 1: because that's those are the brain waves that are associated
Speaker 1: with relaxation and uh, you know, just just relaxed focus.
Speaker 1: And then when the red square appears, you would expect
Speaker 1: to see those alpha waves drop because you were shown something,
Speaker 1: you were stimulated by the red square you. You had
Speaker 1: stimuli there and your brain reacted and box. She said.
Speaker 1: The thing is sometimes you'll get spikes even when there's
Speaker 1: no red square. So that's the trick is figuring out
Speaker 1: how to eliminate those those outliers, those false hits, and
Speaker 1: make sure that the information you're gathering is actually real stuff.
Speaker 1: The last thing that bok She touched upon was in
Speaker 1: concern of mental health. UM He's in a collaboration with
Speaker 1: several other people to create an early warning system for depression.
Speaker 1: He started with looking at depression in teens and also
Speaker 1: to create a therapist chatbot that would help teens deal
Speaker 1: with depression and prevent teen suicide. He cited a very
Speaker 1: troubling statistic and said that in Australia, more than forty
Speaker 1: of teenagers calling helplines per year don't even get another
Speaker 1: person on the phone. The call goes unanswered, which means
Speaker 1: there are thousands of kids who need help but they
Speaker 1: are not getting it because the helpline can't afford to
Speaker 1: pay enough staff to staff all the phones all the time.
Speaker 1: In the United States, of the teens who committed suicide
Speaker 1: were uh found later to have given off signs or patterns,
Speaker 1: but humans aren't always good at picking up on that.
Speaker 1: Like in hindsight, we could say, oh, there was this sign,
Speaker 1: this tragic sign that I did not notice, and if
Speaker 1: I had, if I had noticed, maybe I could have
Speaker 1: done something. This is a horrible tragedy for everybody. Obviously,
Speaker 1: there's a a kid who's lost his or her life,
Speaker 1: and then of course the people around that kid who
Speaker 1: may blame themselves for not having picked up on subtle
Speaker 1: signs that we humans just typically are not very good
Speaker 1: and noticing. Tom They talked about creating algorithms that can
Speaker 1: mine social media again looking for these signs, looking for
Speaker 1: warning flags that someone may be going through depression. Maybe
Speaker 1: there they are experiencing anxiety and stress, and maybe they
Speaker 1: are are entertaining thoughts of self harm or suicide. And
Speaker 1: there are a lot of different indicators that could potentially
Speaker 1: lead someone to have that sort of concern about a person.
Speaker 1: So the idea would be to design these algorithms that
Speaker 1: could send an alert to someone who could step in
Speaker 1: and intervene and perhaps give help to someone who needs
Speaker 1: it because the indicators all show with that person is
Speaker 1: on a really bad pathway. And also this idea of
Speaker 1: a therapist chat bot UH is really intriguing. The idea
Speaker 1: of a chat butt that is responsive in a way
Speaker 1: that's meaningful. It doesn't feel like you're just talking to
Speaker 1: a robot, that you're talking to an uncaring entity that's
Speaker 1: just asking very tough or or very you know, dry
Speaker 1: questions that wouldn't be helpful at all. It has to
Speaker 1: be something that seems to evoke empathy. It seems to
Speaker 1: be UH listening and caring about the person. And the
Speaker 1: nice thing is that chatbots, of course never get tired,
Speaker 1: they never have to go on break, They can always
Speaker 1: be available. They can always provide some guidance and perhaps
Speaker 1: lead someone to resources that might give them more help
Speaker 1: and support when they need it most. And um and
Speaker 1: they talked about how they had rolled this out for teenagers,
Speaker 1: but now they're looking at using it for for veterans,
Speaker 1: for people who have served in the armed forces and
Speaker 1: maybe dealing with depression and other related UH problems and
Speaker 1: feel like they don't have the resources around them to
Speaker 1: help them cope with these issues. So I thought, I
Speaker 1: found that really interesting to this idea of using not
Speaker 1: just very powerful technologies to do cool stuff, but very
Speaker 1: compassionate uses of technology. So that's all three of the
Speaker 1: presenters that I wanted to cover for this episode. Like
Speaker 1: I said before, I've got one more full day here
Speaker 1: at Think eighteen. You never know what I might encounter.
Speaker 1: I might end up having enough to to talk about
Speaker 1: yet another episode. Um, we will see. I can't guarantee
Speaker 1: that that will happen, but I'm going to explore, see
Speaker 1: who I can talk to, see what I can find out,
Speaker 1: and so maybe we'll get one more out of these,
Speaker 1: but if not, it has been a pleasure attending this conference.
Speaker 1: I got to talk to a lot of really smart
Speaker 1: people and learn a lot about these topics that I've
Speaker 1: always been interested in and had some understanding in, but
Speaker 1: this time I felt like I was being absolutely immersed
Speaker 1: in the ideas and it was really, really fascinating. It's
Speaker 1: also mentally exhausting. I don't know how everyone is holding
Speaker 1: up so well, because I feel like my brain is
Speaker 1: slowly turning into sludge being exposed to all this really
Speaker 1: really hyper detailed, complicated information. I hope you guys have
Speaker 1: enjoyed this mini series. I hope to do more of these,
Speaker 1: not just for tech conferences, but related events things that
Speaker 1: touch upon technology. Obviously, because it is tech stuff. But
Speaker 1: I hope to go to more of these sort of
Speaker 1: things and record special episodes that do not again, they
Speaker 1: don't replace our normal episodes of tech stuff, they just
Speaker 1: augment them. It's like that augmented intelligence thing. If you
Speaker 1: guys have suggestions for future episodes of tech Stuff, maybe
Speaker 1: there's a particular technology I need to cover, a person
Speaker 1: who's important in technology, a company that's instrumental in tech.
Speaker 1: Maybe there's a conference or an event that you think
Speaker 1: I absolutely need to go to because you've always wanted
Speaker 1: to learn more about it and you think it would
Speaker 1: be ideal for tech Stuff to attend. Let me know,
Speaker 1: send me a message. The email address for the show
Speaker 1: is text stuff at how stuff works dot com, or
Speaker 1: drop me a line on Twitter or Facebook that sometimes
Speaker 1: it is the fastest way to get in touch with me.
Speaker 1: Those uh they handle for both of those is text
Speaker 1: stuff hs W. Remember we've gotten Instagram account. You can
Speaker 1: go follow that and see behind the scenes stuff and
Speaker 1: other interesting tidbits. And every week on Wednesdays and Fridays,
Speaker 1: I record the normal episodes of tech Stuff and I
Speaker 1: usually stream it live on twitch tv, so you can
Speaker 1: go to twitch dot tv slash text stuff. You can
Speaker 1: watch me record episodes live that means I make mistakes
Speaker 1: and you can watch me do it and stumble over myself.
Speaker 1: There's also a chat room there so you can chat
Speaker 1: with me, and during breaks, I do like to chat
Speaker 1: with everybody in the chat room, see what's going on,
Speaker 1: see if there's anything that they want me to cover
Speaker 1: in specific or in particular I guess I should say,
Speaker 1: And it's always a joy. So please come on by,
Speaker 1: jump in the chat room, introduce yourself. I'd love to
Speaker 1: see there, and I'll talk to you again really soon.
Speaker 1: For more on this and thousands of other topics. Is
Speaker 1: that how stuff works dot com