Three Cool Talks at Think2018

to this all week in Las Vegas, Nevada. It is Thursday, March twenty two as I record this episode, and you probably heard me speak a little bit about a lot of different topics that I've heard about here at the Think two thousand eighteen conference, and uh pretty much wrapped everything up. I am technically attending the conference today. However, I'm also checking out of my hotel room today, which means I will not have a dedicated place to sit

down and record. So I might record another episode after this, depending upon what I counter today, but that might have to wait until I get back to the studio because I may not be able to find a corner to sit down and record and that is suitable for such

a thing. However, I do have that opportunity right now, and I figure I would have this chance to kind of talk to you about a few different speakers I had the chance to to witness and listen to while I was here at think two thousand eighteen, and uh, these were several talks given by various experts, including a few who have achieved celebrity status beyond just uh, the

the tech conference level. And I don't think I could talk about any of these presentations as a full episode without doing a ton more research to flesh it out, to give background, because these presentations were each about forty minutes thirty forty minutes long, and so you know, to make a what usually ends up being about a forty five minute long podcast about a thirty minute talk, you can't do that without patting it, which I'm not gonna do.

That's an insult to you, guys. You deserve better than that, or without doing a lot of background information to flesh things out to provide context. And while I could, in theory do that, there's only so many hours in the day. So what we're gonna do instead is I'm going to tell you about three of the speakers I've seen and the stuff that they covered, and that will be each of the three segments of this podcast. And I think it's pretty interesting. They were a fascinating group of people.

And up first was someone who is kind of a rock star in science. That would be Neil Degrass Tyson. I'm sure most of you have heard about Neil de Grass Tyson. Maybe you've watched television programs that he's hosted, or heard him chime in on scientific discussions on various venues, whether it's online video, radio shows, TV, all sorts of stuff. He's uh of course, also made cameo appearances, or at least a character that is Neil de Grass Tyson made

cameo appearances and epic rap battles of history. It wasn't actually Neil Degrass Tyson was obviously an actor playing him. But he's not just an accomplished scientist. He's a famous science communicator. So he uses his knowledge and humor and other uh social uh strategies to talk about science with the general public and to explain it and to to get enthusiasm behind it. He presented a talk here at THINK two thousand eighteen called the Knowledge of Nature and

the Nature of Knowledge. Now, his presentation didn't speak directly to the theme of technology, but much of what he had to say was relevant to tech, particularly the way people who work in tech communicate their work to the public. And he began by talking about what we are able to observe directly using our five primary senses, those of course being site, hearing, smell, taste, and touch. Now he acknowledged that we actually have other senses on top of

these five. You could argue a whole list of different senses, like the ability to sense pain, which is related to but not completely encompassed by touch. But these are generally agreed upon as the five basic senses, and they obviously have limits. So let's take an example. Our site is limited. Not only can we not focus to an infinite distance, Eventually the focal point will exceed what even the keenest eyed person can see. We can also only see a band of the spectrum of light. We call it the

visible spectrum. Obviously, you know that's that's all the light that human beings are capable of perceiving with their sight. But there's obviously light outside of that spectrum. There's ultraviolet light, there's infrared light, and we know it exists, but we cannot directly observe it with our eyes. And beyond that there are other electromagnetic frequencies that we know exist, but again we cannot observe them using only our five senses.

There are similar cases for our other senses, right, Like there's limits to what we can hear. We can hear at a certain spectrum of sound frequencies. Typically we call it twenty hurts to twenty thou hurts and anything in between. That that's the the average range of human hearing. And of course that range decreases as we get older. As we get older, our ability to hear to perceive those upper level frequencies, the ones that are at the twenty

level um, that decreases over time. So we're are our range narrows as we get older, and we know obviously that there are frequencies outside of that. So he was pointing out that we have limitations. It's not that our senses are total crap. He was pointing out that there's way more out there in the universe than we can observe directly, and that, uh, we know this through science

and technology. We cannot directly experience it as human beings in ways that we can make sense of using the five basic senses, but we've learned a lot about it using science and technology, and you can even think of technology as applied science, because without science, there's really no technology. With these scientific tools, we can then extend our understanding of the universe. We can make observations, we can develop hypotheses, we can test those hypotheses to see if they have merit,

and we can have other people test those hypotheses. We can even have them use different methods to test those hypotheses, and as long as those results keep coming back, then we can be reasonably confident that we've hit on something.

This is the basis of the scientific method. In fact, Neil deGrasse Tyson took some time to talk about the scientific method and stress the importance of testing ideas and have others test those same ideas, both with an approach that is similar to the one you took when you first tested it and with other entirely different approaches to

make sure that they come to the same result. And if everyone gets more or less the same answer through their various tests, and those tests are considered to be well designed, you can feel reasonably sure that the idea you had, the observation you made, the conclusion you came to is close to an actual truth as close as we can reasonably get, and therefore we just kind of say it's true, because I mean, otherwise you're being really,

really particular. And if you argue that we can never know the absolute truth, you might be right, but it's also kind of impractical. So at some point you just say, all right, this is good enough, this is close enough to truth for us to call it that. He was also careful to differentiate the concept of proof from evidence. He talked about proofs in mathematical terms and said, no good scientists would ever use the word proof when talking about his or her work. You can find evidence to

support your claims, but not proof. Proofs are for math, not for science. This goes back to something else I had mentioned and that I had witnessed earlier in the h the conference where Chilia Bosquini was talking about lattice based cryptography and mathematical proofs, and the person she was talking to was interpreting proof as evidence. So he was saying, isn't the fact that computers have had so much trouble with large factors, large number of factoring that that's proof

that it is a hard problem. And so they were both using a word, but they were using it in different ways, and there was a disconnect there. There was a miscommunication going on, and I didn't want to butt in because it didn't want to be a smarty pants.

But it was interesting to see this breakdown in communication because they're using two separate sets of terminologies that share a common a common lexicography, like they're all lexicons that are exactly the same, but the meanings are different and that's the problem. So then Neil Degrass Tyson went on to talk about the difference between accuracy and precision. He used a simple question, and that question is what time

is it? So his first answer was it's two thousand eighteen, which is accurate, or at least it's accurate as of the recording of this podcast. I'm recording it on March eighteen. But even though the answer is accurate, it's not very precise. It doesn't go to a very good level of precision. It's not useful. Typically, like if I ask you what time is it because I need to figure out if I'm going to be late to a meeting I'm rushing

off to, and you just give me the year. That doesn't give me enough information to know whether or not I need to take a car or if I can take a bike ride to get there. However, you could also air the other way, where you have a lot of precision but not a lot of accuracy. So his second answer said, oh, it's oh, seven hours, forty four minutes, twenty two seconds and thirty seven milliseconds. Now that answer is precise, but it could also be wrong. What if

it's forty five minutes, so it's off by a whole minute. Yeah, you had this huge level of precision, but your accuracy was off. Moreover, because you went to such a level of precision down to the millisecond, you cannot possibly be accurate because by the time you finish telling someone what time it is, it will be a different time. And so when was it four minutes, twenty two seconds and thirty seven milliseconds? Was it when you started telling the

person what time it was? Was it? The end of that precision without accuracy is not very useful either, was his point. And uh, his this was to say that accuracy and precision are both important, and the level of precision you should use when you're communicating something to others is dependent upon what you're trying to accomplish. You could always get more precise, but sometimes that reaches a level that is no longer of any real use. And I

can identify with this idea. See when I construct an episode of tech stuff, when I'm researching and writing up my notes, I frequently have to figure out where should I start? Where's my starting point for the episode, based upon whatever topic it is that I'm covering. Now, Rarely do I start right at the quote unquote beginning of a technology's creation. And that's because it's usually helpful to understand things that were happening before someone, more likely a

string of someone's invented the technology. Without having that understanding of what preceded the invention, you don't really have enough context to understand what is going on and why it's important. Neil deGrasse Tyson went on to use an example of his own as he spoke about the Earth's orbit. The shorthand description of the Earth's orbit around the Sun. Something that you might have learned in elementary school is that

it's an elliptical orbit. But that's not entirely accurate. First of all, the Earth's orbit is only a slight deviation from a circle. You could argue that it's closer to being a circular orbit than it is an elliptical orbit. But despite this, many textbooks exaggerate the path that the Earth's orbit takes to show that's not a perfect circle. But even if you were to say it's more circular than elliptical, that's not entirely precise because the Earth's Moon

affects the Earth's orbital path. Now, the Moon does not actually revolve around the Earth. Instead, the Earth and the Moon are in a dance together. They rotate around a common center of gravity between the two. This is true for any two objects that are revolving around one another.

They revolve around a common point of center of gravity. Now, as it happens because of the masses of the two the two bodies, the Earth and the Moon, the center of gravity happens to be located below the Earth's surface. But it's not at the center of the Earth. If it if it were at the center of the Earth, then you could say while the Moon essentially revolves around the Earth. Instead, it's about a thousand miles below the Earth's surface on whichever side the Moon is on at

that time. So, in other words, the center of gravity moves around the Earth's interior as the Moon continues through its orbit, which has the effect of creating a little wobble in the Earth's orbit. So you can really think of the Earth's orbit around the Sun as a bit of a a circle that's made up of a kind of squiggly line. Then that squiggly line is the little jiggle that the Earth has because of the the Moon,

the Moon's poll on the Earth. But even that is not truly precise, because the Sun in our Solar system is orbiting around the galactic center of the Milky Way galaxy and it's not in the same plane as Earth's orbit.

So while from one perspective you could say the Earth has a wobbly circular orbit, if you were looking at the Sun directly in front of it as it was moving through its galactical orbital path, you would see that the planets of our Solar system are moving around and what it amounts to a spiral Because take any arbitrary starting position of the Earth with respect to its position with the Sun, and you say, all right, this is the day zero, and we're gonna go three days around

so that we return to our point of origin. The thing is you'll never return to that exact fixed point in space, because the Solar system itself is uh rotating around this galactic center in the Milky Way. So while you will return to your same relative position in reference to the Sun, you won't be in the same fixed point in space because the whole system has been moving this entire time. So you can't really even say it's

a circle. It's more like a spiral when you when you take your reference point as the galaxy as opposed to just the Solar system. Uh. Again, the whole purpose of this was to give an example to say we have to make choices when we communicate information to others. At what point do we need to draw the line and say, okay, that's enough detail. Anything more than that is either confusing or it's boring, or it's both. And then we make those determinations and hope that we can

inspire people to look further into subjects. Later on, Neil deGrasse Tyson's presentation concluded with a discussion about how to convey information to your audience in a way that matters to them. He showed examples of science incorporated into pop culture. He specifically played a clip from the movie Frozen and then he danced around on stage as Elsa sang frozen fractals all around because the word fractal was included in a Disney film and sung by a Disney princess. And

he said, this is amazing progress. He said, you need to find out what your audience cares about and then find ways to use that as a means of explaining scientific or technological ideas to them. And he used football as his own example, showing off a series of fairly recent tweets he had made during football games to get

across scientific principles. Now, those tweets sparked a lot of conversation and humor and jokes around his followers and other people who saw those messages, and it meant that for a short while at least, people were talking about actual science. For people in the technology space, that could be a very valuable lesson because technology gets super complicated, and not only can the actual mechanics of technology get complex, the way it works and the intricacies of how it works.

The way we describe technology can also be really confusing because we use a lot of jargon, a lot of terminology that is not in the common parlance, and we use a lot of shorthand to communicate complicated ideas. To a newcomer, this comes across as incredibly intimidating, very dense, and difficult to understand. Before you can even get to the point where you comprehend the actual concepts that underlie technology, you first have to get a grip on the language

that's being used. Then you have to struggle with that language to understand what is actually happening with any technological, uh product, or service. And if technologies can use analogies or stories or examples to explain their work, If if technologists rather can use those tools to explain what it is they're doing and what they're they're the things they work on, what they do, then the general public may end up having a more enthusiastic response because they'll have

a touchstone to understand what exactly is going on. Now, that's easier said than done, because you you gotta you gotta walk a line. You don't want to simplify matters to the point where people are going to misunderstand what

you're saying. So with some subjects like artificial intelligence or quantum computing, going too broad or too simple will make people think that the technology is akin to magic because you've you've abstracted it and you've simplified it so much that it just sounds like it does things that nothing could truly possibly do, or you give them the idea that this technology is capable of doing things that we

really can't do yet. So, like the science lesson about the Earth's orbit, there's a balance we have to achieve when we're explaining these things to someone new to the subject matter. And that's true across every industry. It's not just science or technology. I mean, there are plenty of discussions about business and particularly finance. Law is another example.

Whenever I read any stories about finance or law or business, I get to a point where I realize I've read a paragraph and I have absolutely no idea what that paragraph meant. I can reread it and I can say to myself, I individually I understand what all of these words mean, but collectively I don't get what they're trying to say. And you know that that comes with the territory of having specialization in any field. Eventually get to a point where the language you use is extremely efficient.

If you're talking to someone else who shares your knowledge, you can have very deep very quick conversations based upon this shared language. But if you try to communicate to anyone outside of that, you get the deer in the headlights look. Or at least that's what I get whenever I try and talk to anyone about finance or law or anything like that. So that's why I try to structure episodes the way I do. It's not that I don't have faith in my audience. I have a great audience.

I have a really smart and engaging audience. It's that I never know when it might be someone's first introduction to that specific subject matter, and I want to make sure that I can build a foundation before diving more deeply into any given topic. When we come back from the break, i'll tell you about the presentation a famous futurist gave at IBM Think. But first let's take a break and thank our sponsor. The next famous person I saw give a presentation at Think two thousand eighteen was

Dr michio Aku. Dr Cocku is a theoretical physicist and a professor at City College of New York. He's also an author. A futurist, UH that would be someone who predicts how technology and advancements and science will change our world in the decades to come very close to my heart. Since I used to host a show called Forward Thinking. In fact, I often would read about Minchio Okaku's work as a host of Forward Thinking, I would refer to that as part of the research I did for lots

of different topics. He's a frequent guest or host on television series and radio programs, and he talks a lot about his work and predictions regularly. He has specifically studied quantum mechanics and string theory. Now, I've talked an awful lot about quantum mechanics already in this mini series, so I'm not going to rehash that. But what the heck is string theory? Well, string theory is a family of theories that attempt to describe why fundamental forces and particles

found in nature behave the way they do. They go into topic in depth. Uh, it would require a whole suite of episodes to explain this. And there are multiple types of string theory. There's not just one string theory. There are competing theories. Um. I would rapidly find myself out of depth if I tried to tackle the entire field, and even sum summarize it in uh, you know, in a way that was at least semi comprehensive, but I

can give a very super high level idea. In general, these theories describe forces and particles as one dimensional strings that vibrate in a way that gives those forces and particles their properties. Some of them are closed loop strings that are like rubber bands. Dr Cocu has talked about supersymmetric string theory in particular. That's one of the flavors of string theory, and it's really fascinating stuff that at

least I'm told makes sense from a mathematical perspective. Now I say I'm told because the math is far too complex for me to understand, so I certainly can't look at and say, oh, yeah, no, that holds up. But as of right now and for the foreseeable future, we have no way to directly observe and test string theory, which has led some people to say that's not so much a scientific theory as it is a philosophy because you cannot test or observe it. But we'll set that

whole argument aside. Dr Kaku, who took the main stage here at IBM focus largely unwary thinks humanity will be headed in the next few decades with the space the space missions in particular, he pointed out that today's computers still depend upon silicon chips and Moore's law, but he also talked about the ultimate limits of that technology, and as I mentioned in this series, you can only shrink

components down so far using traditional silicon chip technology. Once you shrink below certain thresholds, quantum effects begin to override your classical computer design, and electrons no longer follow the pathways you've made for them, and our electronics are tod in it upon electrons doing what we want them to do.

At the quantum level, electrons do lots of strange things, things we can't necessarily control or predict, and that means our electronics and computers that are built on these components cannot really work properly. They will will become unreliable, they'll become error prone. We can't we can't keep going this route indefinitely. We will eventually hit that fundamental physical level based upon how we build things today. So something has

to change now. Dr Cocu said, if we cling to silicon as the basis for our technology moving forward, then eventually Silicon Valley could become a new rust belt. We will hit that ultimate limit of what we're capable of doing using that technology, and we will progress no further. So we'll need to shift to a new paradigm. Dr Kaku mentioned several technologies who felt were going to be instrumental in pushing us past the end of the silicon chip age and Moore's law, and he talked about the

four great technological revolutions. The first of those would the development of the steam engine, which led to an incredibly productive period and the Industrial Revolution. Understanding and leveraging the laws of thermodynamics allowed us to create new ways to do work and travel. We were able to create engines that could harness the power of thermodynamics and translated into work.

About at eight decades after the invention of the steam engine, physicists were getting a much better understanding of some other scientific principles like magnetism and electricity, which led to inventions like radio and then television. Decades after that, engineers and scientists invented the transistor, the third of the technological revolutions. That transistor made micro computers possible, as well as other devices like lasers, and it allowed us to go to space.

It created the space industry. In fact, the space industry pretty much gave the incentive to development of transistors and manaturization. We had a goal, we wanted to send people to the Moon, and that meant that we were going to have to make some big advancements in science and technology in order to make that possible. Space, as in the space inside of a capsule, is at a premium. You want to make sure things are lightweight, you want to make sure things are compact, and that meant that we

couldn't rely upon the massive circuitry of the past. We had to create other means to manaturize things and make it more practical for applications like space travel. And uh, mitchio Kaku is very excited about space travel. He's very excited the fact that we are dedicating ourselves to going back to the Moon, first with an unmanned probe that should land on the Moon in two thousand nineteen, then eventually with a a base in orbit around the Moon which will act as sort of a launching ground for

missions towards Mars. And he talked about how that's very important and also mentioned Elon Musk's follow up to the Falcon nine Heavy Rocket, which is called the b f R. B stands for big, our stands for rocket, and F as Dr CaCu says, stands for well, let your imagination take over. The Fourth Revolution is the one that's happening right now. Dr CaCu says. It involves stuff like biotechnology, nanotechnology,

artificial intelligence, and related technologies. It's also about computing models like neural networks, which mimic the neural structure of brains. The neural network is made up of nodes that behave like neurons, like the neurons the neural cells we have in our brains. There are interconnections with those nodes to other artificial neurons, and processing information with a neural network allows you to do some interesting things like machine learning.

Computers relying on an artificial neural network can learn from data, and they can get more proficient at handling that information as they get more experience, so they improve themselves. Over time, the most efficient pathways start to win now over the least efficient pathways, and you can teach computers. Now I've talked about this before, about teaching computers how to recognize what a cat is, for example, which is a somewhat trivial version of this, but neural networks are being used

across multiple industries to solve very difficult problems. He also mentioned artificial intelligence, which will augment our abilities to make decisions and take actions, and he stressed that he felt AI was not poised to replace human beings, but help them. He laid out the two big arguments that tend to be be presented in AI, sort of the Zuckerberg argument versus the Elon Musk argument, and the Zuckerberg argument is AI is going to be a huge help. It's going

to benefit us. It's going to let us do what we want to do better and faster. It's going to create opportunities that do not exist right now that we can't even predict because we aren't in that age yet. Musk says it's going to lead to killer robots and we're all gonna die now. I am are simplifying on both sides, more on Musk than on Zuckerberg for a

comedic effect. Musk says AI could potentially pose an existential threat to the human race, saying that if you were to create AI um and you don't have good controls in place, then it could be the end of humanity. It could eventually decide that we are bad and that we need to be stopped. Well, CaCu seems to fall

more on Zuckerbird's side than Musk's side on this. He said we should always be aware of how we implement AI and how we design it, but that we shouldn't worry in the near term about AI going all terminator on us and wiping us out. As we develop AI and as it becomes more sophisticated, we can talk about how to build in fail safes that act on the AI as sort of a limitter so that it doesn't

cause harm. The whole field of thought is fascinating to me, and it gets way more complicated than just put a chip in the computer's brain so that turns off if it thinks bad thoughts. But I'm gonna leave that whole discussion for another show because I think that that's something

that needs to be an actual discussion. I need to get people on the show and we can all talk about these implications and kind of argue it all out, not to have a fight, but rather to just kind of see all sides of the issue and Of course, he also brought up quantum computing, because everything at IBM Think ten seems to be about quantum computing at some point, and how that could be a huge help for human

efforts moving forward. Though out of all these topics, he was really most interested in discussing neural networks and artificial intelligence, I would say, and much of his presentation was in

fact about space travel. Dr Cocus stress that we need healthy and robust space program, not just so that we can learn more about our Solar system and beyond, though that is very important, but also for the actual survival of the human race long term, because it's only a matter of time before we have a cataclysmic event on the Earth, sort of like a giant meteor or a comment that collides with the Earth. That will happen eventually. It might not happen for thousands of years rs, but

it will happen. It's statistically a certainty that at some point it will happen. It happened before and it will happen again, and that could be an extinction level events. So we need a robust space program so that we can do stuff like detect such threats decades before they are imminent and then take measures to deflect those threats so that we are actually safe. It would also help ensure the survival of humans if we spread out a bit,

if we started to colonize other planets. Spreading ourselves out ensures the possibility that the human race survives if something goes wrong on any one given location, and at that point we have an insurance policy against extinction events. Dr Cock who touched on augmented reality as well. He talked about glasses and headsets that allow you to see information

overlaid on top of the world around you. He also talked about voice recognition tools that let you speak directly to this technology, calling up information you need for any given situation. You could have specific data sets and use those data sets with use cases. For people who have specialized trades, for example, like electrical engineers or heart surgeons, they could have very carefully customized data sets that aid

them in their specific occupations. They also talked about how in the future will have contact lenses that will be able to do this this idea of having augmented reality contacts. You put the contact in it acts like a computer screen. It can overlay digital information on top of the view you have of the world around you in real time, and it will tell you all about stuff that's around you.

You can learn about, let's say, a building that you're looking at, or they can help explain a scientific principle that you are encountering. Maybe you're at a talk like the one that Dr Cocku was giving and your augmented reality contact lens is giving you illustrations that augment that talk and help you better understand what he's trying to express. Or you might be at a party in your contact lens is telling you which people at the party are important, so you know who to suck up to. That was

actually Dr Cocku's example. He said, this will be a great tool if you're ever at a cocktail party and you want to know who's important so that you can sidle up to them and kiss their butt. Um. I thought it was a clever and funny example. Dr Cocku, by the way, is a very funny person. Hearing him um make jokes as he's talking about futurism was very refreshing.

Dr cock who also talked about how display technology has become far more advanced and we have displays now that are flexible and that there are paper thin with things like oh lad displays, and then the future will have digitized paper everywhere, so you could have a sheet of paper that could be literally anything because it can be a display. It's not really paper, it's a display that

could show any potential any potential information you need. You can have a wall coded in digitized paper and be digitized wallpaper, and that wall becomes a computing surface that you can use. So let's say you've got this wallpaper display. You could walk up to the wall all you could talk to it. You could pull up information about any given subject, and you could interact with an AI chatbot to answer questions, or you help make decisions, kind of

like Jarvis in Iron Man. Dr Cock who gave an example of someone waking up at night and they have chest pains and they start talking to the wall, kind of having a robo doctor right there to find out if the symptoms they are suffering from indicate something serious like a heart attack, and if so, the digital assistant helps by reaching out to emergency medical personnel and getting things ready so that that the the the person in question can get medical attention, or if it's something more

benign like just indigestion. Dr Cock who also talked about trying to foil aging, both through genetic approaches and through digital ones. He talked about identifying the genes that are responsible for aging and finding a way to deactivate them so that the aging process stops once you reach adulthood. From an external point of view, you would always appear

to be, say thirty years old. However, Dr Cock, who also had not acknowledged that this wouldn't necessarily mean you never die of old age, because your organs could continue to deteriorate just from use over years and years and years. You might live much longer, but eventually stuff would start to wear out, and that would require us to find

ways to build new organs. Now we've started to do that already, but we've got other major organs that we haven't yet been able to build, like a liver, but that might preserve someone's life well beyond today's lifespans, perhaps even indefinitely. And then he mentioned digital immortality. This is a very different approach. This is where you somehow capture all the information that makes a person who he or

she is. Think of it like a three D scan, except for who you are, not what you look like all of your memories and your thoughts and your emotional responses to anything, the way you think, the way you come up with ideas, all of that would end up being capt shared, and you'd end up with a simulation of you or all practical purposes, and it would behave as you would in any given situation, assuming that the

simulation was as close to perfect as possible. Now, would you then say that that construct is in fact you, Well, if you think of yourself in terms of your fleshy existence, no, you wouldn't. The version that you would be looking at would be a simulation, and you, however, would eventually age and die. But if you think of yourself as your collection of experiences and your thoughts and your emotions, then maybe you might say the simulation is you, but it's

another instance of you. Now, that doesn't solve the problem that your own experience of being you would someday come to an end, and a simulated jerk face version of you would keep on going as if no one cared,

as long as no one unplugged them jerk face. Then Dr Cocu said something I found really fascinating, and I hadn't really considered before He said that if you could digitize a person, boil their essence down into pure information, and then you encode that information using a laser beam, you could fire that laser beam at distant targets like other planets in our Solar system, or even beyond. Let's say you shoot it toward Alpha Centauri. Within four years,

you would get there. You could have minds travel vast distances at the speed of light. Matter can't travel at the speed of light, but information can. Of course, I'm not sure what you could do once you've got to where you were going, because if you're information encoded on a medium, I can't see any way you could meaningfully do anything at all or perceive anything. You would just have just information shooting out there, kind of like how

radio waves can radiate out into space. That doesn't necessarily mean we can do anything with them. A program isn't running if you don't execute it right, Like if you have a computer game on a disk. Imagine an old disc you've got, you put it in your computer, and you don't run the program. You just put it in the computer, but the computer is not engaging the disk at all. It's not like the game is actually running on your machine. It's not like it's making any sort

of activity, but still, this is an intriguing idea. It could be a new form of space exploration, assuming we figure out how to make that information useful, so that can either communicate back to us about what it has discovered, or it can somehow go about and take actions of its own across the universe. I just don't know how that would work. Dr Kko's final section was about brain computer interfaces. Now, these are the technologies that allow us

to communicate directly with technologies through thought. It's kind of a technological telepathy. It's still a very early field, but there are examples out there and have been for a decade or more. These are not perfect methods, and each method has advantages and disadvantages. The best way to get high resolution data from a person that is consistently reliable, or at least more reliable than the alternatives, is to embed sensors directly onto that person's brain. So it requires

brain surgery. Then you have the problem of figuring out how do you send signals from the sensors you've implanted in the brain to the target technologies. Now, you could include wires or an antenna that protrudes from the brain through the skull. But this also presents a problem because you've created the potential for contaminants to infect the brain. Like anytime you've got something that's breaking that blood brain barrier, that's bad news. So you have to be super careful

with this. And the skull is pretty thick. It's thick enough to make it tricky to send signals through the skull. So a wireless solution, while it might be possible, is not ideal. It might end up being hard to detect legitimate signals. Or you could go the non invasive route and you could use an e G headset to pick up brain waves. Now those don't require surgery, but sometimes are not terribly accurate. They can pick up noise that

looks like brain waves, but it's actually just interference. Uh, they can be false positives, or it can fail to register legitimate commands as something that's meaningful. So you might be thinking, now type the word dog, but it's nothing's happening because it hasn't picked it up as a legitimate command. So this doesn't require a surgeon to operate on you, but it also does not give you as accurate a

response as an intercranial approach would. However, we do have some examples of these technologies that allow people who otherwise couldn't control things with their bodies to be able to have control over things. And a lot of it comes into communication, right, using a computer interface to communicate with other people, you think, and then the computer either speaks or types things out on your behalf and it opens up a channel of communication where otherwise you would be

unable to do so. Or we can pair that type of technology with something like an exoskeleton, and of someone who has lost mobility the chance to regain it. They can use their mind to send signals to the exoskeleton and use that to move them around. One day, we might extend this so that we have robotic bodies that we can control remotely using our minds, giving us telepresence into distant locations. Of course, the further out we go, the more we'll have to deal with the limitation of

remote control. Because even if we had a perfect system that let us communicate with a robot that's light years away from us, so we can send signals to it and we can receive signals back from it, somehow there would still be tremendous lag because information is still only going to travel at the speed of light. So if a robot is light years away, that means it literally takes years for a command you send to be received by the robot, and years again for the robot to

send the results back to you. So it would be like the longest game of mail chess, like where you're mailing your move back to your opponent and over again, but still super cool. Dr Cocku ended his talk by reminding us that without pursuit of these new forms of technologies, we could find ourselves clinging to diminishing returns with classical computing, and that was a really interesting presentation. I do have one more speaker to talk about, but first let's take

another quick break to thank our sponsor. The last person I want to talk about in this episode is a remarkable young man and his name is Tonme boks Sheet. He's a programmer. He's an expert on deep learning and artificial intelligence, and he's fourteen freaking years old. Box She's father is a computer programmer, and ten May began learning about coding and programming when he was just five years old, and he loved it, and so he studied it intently.

When he was seven, he started up a YouTube channel and he began to upload videos about how to code and develop for the web. He started to developing for iOS when he was eight, and Apple published his first app when he was nine. That was an app, by the way, that taught multiplication to kids. When he was eleven, he caught the attention of IBM. See box She saw a video about IBM Watson and how that platform was

advancing artificial intelligence, and he found it absolutely fascinating. So he also started to work with an alpha build of a tool that IBM had developed that was meant to convert documents from one format into other formats. Box She discovered a bug in the code, and he sent a bug report to the development team to let them know about it. Some IBM developers ended up reaching out to box She in response, and a new partnership began to form. Box She, by the way, isn't truly an IBM partner

because he does not get compensated. He doesn't get paid for this, but he does work with IBM on a lot of projects. Right now. Uh he's creating novel approaches to computing and he's finding new ways to leverage data, and since his communications with IBM, he has ended up going on tours around the world to talk about the next advancements and data processing and data analysis, as well as artificial intelligence and brain computer interfaces and the work

he's doing in those fields. He decided to talk about three of the projects that he was interested in, all three relating to healthcare, and he said that healthcare is one of those fields where deep learning and big data are really important because there's tons and tons and tons of information in the healthcare sphere and it's useful information in theory. In practice, however, it can often not be useful. Now, the reason I say that is, imagine that you are

a doctor. Some of you probably are doctors. For the rest of you. Imagine that you are a doctor, and you're a good doctor. You're you're you're good at what you do. You understand your field. However, there are always more advancements being made in the pioneering edge of medicine. They you may or may not be familiar with. So part of your job isn't just treating your patients. It's learning more about developments in medicine. So you have to go and seek out that information. You have to learn

about it, you have to comprehend it. Then you have to put it into practice if it makes sense as as your role as doctor. And while you're doing this, while you're learning about the latest and greatest stuff, pioneers are finding even more breakthroughs in medicine. And you have to keep doing this. You have to keep learning. It's

a never ending process. And if you are a doctor working in a in a in a large city that has access to a lot of uh like cutting edge research centers, then you have those assets at your disposal. You might be able to reference or refer rather a patient to one of those specialists who can then use their their expertise and the latest and greatest information to

help that patient. But the further out you are from those centers, the less of that support you're going to get, and you'll have to do more and more of this on your own, which leaves it up to you to learn everything and that is just not humanly possible. That's where deep learning and big data come in. So with deep learning, you can design computer algorithms that are looking deeply into the results of any given scenario. So let's take drugs as a as a as A as an example,

clinical trials are great. Clinical trials are how we determine whether or not a drug is effective, whether or not it poses the potential to give patients UH adverse side effects. These are really important things, and all drugs have to go through lengthy clinical trials before they can be used in medical applications. However, clinical trials are not perfect because it is impossible to have a truly representative population of volunteers in these clinical trials. You're never going to get

a full demographically representative population of people testing out your drug. UH. Eventually, you're going to get approval. Let's say that the drug is effective and doesn't show any UH implications of truly adverse sight effects. You get it approved, it goes out into the market. Suddenly, it is now out in the

world where more than seven billion people live. And because more than seven billion people live there, they're all sorts of different body types, body chemistryes, potential drug interactions going on because people could be taking other drugs that could change the way your new drug interacts with their bodies. And suddenly you've got things that are happening where you aren't sure if your drug is responsible for maybe some adverse side effects or not. In fact, you can't even

really be sure in clinical trials. If you observe it frequently enough, you could say, well, chances are the drug is causing these side effects, but you can't just say that based upon one instance because there are too many unknown variables. It's like that way with humans. We humans

are complicated people. And maybe that the person who was in the clinical trial had taken some aspirin that morning and the aspirin interacted with the drug in a way that was unexpected, and thus the person in the trial had an adverse side effect. But if they hadn't taken the aspirin, they would have been fine. Like they're They're all these unknowns that can take place during clinical trials.

So what Bakshi was arguing is that if you use deep learning, you can look across the results of real life cases of people taking drugs and reporting any sort of side effects they might have felt, and used that to start to draw more conclusions. You could even do

this by looking at social media. So in other words, you could look at instances of a drug being mentioned in social media and looking at any possible descriptions of how a person felt after they took that drug and mine the entire sphere of social media for instances like that and add that into a database. Now, this would be way too much for any human to handle, but if you put it into the realm of computers, they could look around, look for data points, try and see

how many of those data points there are. Is there a significant number? Is there enough to suggest that perhaps there is something actually going on here, or is it an outlier that isn't indicative of anything meaningful. Computers can help do this, and that can help people start to

consider what drugs they want to prescribe to patients. People being doctors, So a doctor could look at their patient and let's say the doctor she's looking at her patients, she says, well, I want I want to prescribe medication to treat you, and I've got a couple of different options.

So based upon who you are, your body type, your body chemistry, the other medications you might be on, I'm going to take a look at this information that has been curated by algorithms that have mined this huge data set and brought back all these results, and based upon this information, I am more likely to prescribe drug b to you because too, from what I see here, it is the least likely to cause any really bad side effects and the most likely to be efficacious in treating

your disease or whatever. And that was one of the three ways that tom may backshe was citing as being an important way to use dev learning really just kind of finding out ways to avoid having negative drug interactions. This idea of leveraging information that is out there that otherwise is you know, it's it's useful, but it's unstructured

and it's not being harnessed in any meaningful way. Changing that by using a neural network and using open source databases like FDA Drug Adverse Database and social media to try and predict if any one particular person will suffer negative events from any particular drug. I found that really fascinating.

The idea of not just using the official databases, the medical resources that have accumulated over the course of decades of work, but also the more anecdotal evidence and I hesitate to use those two words together, but the more anecdotal accounts, let's say, of people who are using those drugs, who are reporting, you know, just casually on what it is they're experiencing and using that information to help kind of create a much larger, more informal clinical trial in

the real world that can end up having real impact on the future of how that those particular drugs are are used in the future. I used the future a couple of times there, but that's just because I'm thinking about it so much. Then bok she transitioned to talk about another project he's been working on that is called the Cognitive Story, and this is a story about augmenting people's lives with the power of cognitive computing and artificial

intelligence and brain computer interfaces. So this kind of ties into what Dr Mitchiokaku had talked about earlier during the conference. Uh In this in this particular case, uh Tom may Bak, she was talking about a woman named Boo, and the woman has Rhetz syndrome, which has made her almost completely non communicative. She's quadriplegic and almost incapable of communicating except to the those who know her best, who can interpret what Boo is trying to communicate, and they can therefore

help her throughout the day. But anyone who doesn't know Boo with that level of familiarity is not going to be able to understand what she's trying to communicate at any given time. And so Bakshi and the team he's working with have been trying to develop a tool that allows Boo to communicate with other people, and it requires several steps. The first of those steps is finding a way to interpret booze brain waves so that Boo doesn't have to struggle, she just thinks, and then those thoughts

can be communicated to the outside world. Um and so that would require doing what I had talked about previously, finding that brain computer interface. In this case, they went with the e G model, the noninvasive model. They had two three D print a specific headset for Boo so that it could comfortably sit on her head. Rett syndrome means that she's very, very sensitive, and you can't have something heavy or bulky on her because it would be caused so much discomfort that it would not be of

any real help. It would actually it would hurt her physically. And then then you have to train the technology. It's not just enough that you are able to detect brain waves. It's not like all humans have brain waves that follow the exact same patterns. And therefore, if I think yes or no, and you think yes or no, it's exactly the same as it in my brain as it is in your brain. So they have to train it with

booze own brain waves. And that also means they have to rely heavily upon Booze Mother for the interpretation of Booze communications, because again, without knowing Boo in this level of familiarity, they can't know what any particular brain wave represents. They can detect the brain waves, but detecting it is one thing. Knowing what it means is something else. So they work with Booze Mother, who has been given the title of intimate interpreter, to interpret what those brain waves mean.

And right right now, they're able to kind of go with a binary approach, a yes or no, but they want to extend that so that Boo can communicate more complicated thoughts and not just respond to a series of yes no questions. To narrow down what it is she wants. Um, this is going to require lots and lots of data gathering. You have to do a lot of sessions with Boo wearing the e G headset, thinking things, measuring them, interpreting what those those measurements mean, and then mapping that to

the system. They want to use neural networks and deep learning to detect which of those signals represent actual commands, which ones might be noise or interference or just a

random little blip from the brain. And you have to differentiate those if you want it to be a meaningful experience, if you want to make sure that the thing you are recording is in fact an actual command and not just random information, because otherwise, obviously you would be acting upon, uh, just blips like blips that don't that don't indicate any actual request or question or response. You have to cut

out all that noise. Um Box. She also gave us an example, showed us a black screen and on that black screen, occasionally a red square would appear, and Box she said, well, when that black screen is black and you're just concentrating on it, eventually your mind kind of goes at rest and your alpha waves will start to spike, because that's those are the brain waves that are associated with relaxation and uh, you know, just just relaxed focus.

And then when the red square appears, you would expect to see those alpha waves drop because you were shown something, you were stimulated by the red square you. You had stimuli there and your brain reacted and box. She said. The thing is sometimes you'll get spikes even when there's no red square. So that's the trick is figuring out how to eliminate those those outliers, those false hits, and make sure that the information you're gathering is actually real stuff.

The last thing that bok She touched upon was in concern of mental health. UM He's in a collaboration with several other people to create an early warning system for depression. He started with looking at depression in teens and also to create a therapist chatbot that would help teens deal with depression and prevent teen suicide. He cited a very troubling statistic and said that in Australia, more than forty of teenagers calling helplines per year don't even get another

person on the phone. The call goes unanswered, which means there are thousands of kids who need help but they are not getting it because the helpline can't afford to pay enough staff to staff all the phones all the time. In the United States, of the teens who committed suicide were uh found later to have given off signs or patterns, but humans aren't always good at picking up on that.

Like in hindsight, we could say, oh, there was this sign, this tragic sign that I did not notice, and if I had, if I had noticed, maybe I could have done something. This is a horrible tragedy for everybody. Obviously, there's a a kid who's lost his or her life, and then of course the people around that kid who may blame themselves for not having picked up on subtle signs that we humans just typically are not very good

and noticing. Tom They talked about creating algorithms that can mine social media again looking for these signs, looking for warning flags that someone may be going through depression. Maybe there they are experiencing anxiety and stress, and maybe they are are entertaining thoughts of self harm or suicide. And there are a lot of different indicators that could potentially lead someone to have that sort of concern about a person.

So the idea would be to design these algorithms that could send an alert to someone who could step in and intervene and perhaps give help to someone who needs it because the indicators all show with that person is on a really bad pathway. And also this idea of a therapist chat bot UH is really intriguing. The idea of a chat butt that is responsive in a way

that's meaningful. It doesn't feel like you're just talking to a robot, that you're talking to an uncaring entity that's just asking very tough or or very you know, dry questions that wouldn't be helpful at all. It has to be something that seems to evoke empathy. It seems to be UH listening and caring about the person. And the nice thing is that chatbots, of course never get tired, they never have to go on break, They can always

be available. They can always provide some guidance and perhaps lead someone to resources that might give them more help and support when they need it most. And um and they talked about how they had rolled this out for teenagers, but now they're looking at using it for for veterans, for people who have served in the armed forces and maybe dealing with depression and other related UH problems and feel like they don't have the resources around them to

help them cope with these issues. So I thought, I found that really interesting to this idea of using not just very powerful technologies to do cool stuff, but very compassionate uses of technology. So that's all three of the presenters that I wanted to cover for this episode. Like I said before, I've got one more full day here at Think eighteen. You never know what I might encounter. I might end up having enough to to talk about

yet another episode. Um, we will see. I can't guarantee that that will happen, but I'm going to explore, see who I can talk to, see what I can find out, and so maybe we'll get one more out of these, but if not, it has been a pleasure attending this conference. I got to talk to a lot of really smart people and learn a lot about these topics that I've always been interested in and had some understanding in, but this time I felt like I was being absolutely immersed

in the ideas and it was really, really fascinating. It's also mentally exhausting. I don't know how everyone is holding up so well, because I feel like my brain is slowly turning into sludge being exposed to all this really really hyper detailed, complicated information. I hope you guys have enjoyed this mini series. I hope to do more of these, not just for tech conferences, but related events things that

touch upon technology. Obviously, because it is tech stuff. But I hope to go to more of these sort of things and record special episodes that do not again, they don't replace our normal episodes of tech stuff, they just augment them. It's like that augmented intelligence thing. If you guys have suggestions for future episodes of tech Stuff, maybe there's a particular technology I need to cover, a person who's important in technology, a company that's instrumental in tech.

Maybe there's a conference or an event that you think I absolutely need to go to because you've always wanted to learn more about it and you think it would be ideal for tech Stuff to attend. Let me know, send me a message. The email address for the show is text stuff at how stuff works dot com, or drop me a line on Twitter or Facebook that sometimes it is the fastest way to get in touch with me. Those uh they handle for both of those is text

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There's also a chat room there so you can chat with me, and during breaks, I do like to chat with everybody in the chat room, see what's going on, see if there's anything that they want me to cover in specific or in particular I guess I should say, And it's always a joy. So please come on by, jump in the chat room, introduce yourself. I'd love to see there, and I'll talk to you again really soon. For more on this and thousands of other topics. Is that how stuff works dot com