Max Tegmark: "Life 3.0: Being Human in the Age of AI" | Talks at Google

Video Statistics and Information

Video
Captions Word Cloud
Captions
JOHN BRACAGLIA: Hello, my name is John Bracaglia and I work at Verily, which is Google's life sciences company. I also lead a group called The Singularity Network, which is an internal organization composed of more than 3,000 Googlers, focused on topics about the future of artificial intelligence, for which we are here today. And it's my pleasure to be here today with Dr. Max Tegmark. As a brief introduction, Max Tegmark is a renowned scientific communicator and cosmologist, and has accepted donations from Elon Musk to investigate the existential risk of advanced artificial intelligence. His research interests include consciousness, the multiverse, advanced risk from AI, and formulating an ultimate ensemble theory of everything. Max was elected fellow of the American Physics Society in 2012, won "Science" magazine's Breakthrough of the Year in 2003, and has written over 200 publications, nine of which have been cited more than 500 times. Max Tegmark, everyone. [APPLAUSE] MAX TEGMARK: Thank you so much. It's a really great honor to be back here at Google and to get to talk in front of so many old friends, and so much human-level intelligence and idealism. Does anyone recognize? NASA EMPLOYEE: 20 seconds and counting-- MAX TEGMARK: This was, of course, the Apollo 11 moon mission that put Neil Armstrong, Buzz Aldrin, and Michael Collins on the moon. NASA EMPLOYEE: Ten, nine, MAX TEGMARK: This-- NASA EMPLOYEE: Ignition sequence starts. MAX TEGMARK: --mission was not only successful, but I think it was very inspiring because it showed that when we humans manage technology wisely, we can do things that our ancestors could only dream of, right? Now there are some important lessons I think we can learn from this, as well, so I want to devote the rest of this talk to another journey, powered by something much more powerful than rocket engines, where the passengers are not just three astronauts, but all of humanity. So let's talk about our collective journey into the future with AI. My friend Jaan Tallinn likes to emphasize that just as with rocketry, it's not enough to just make our technology powerful. We also have to focus on figuring out how to control it and on figuring out where we want to go with it. And that's what we're going to talk about. I think the opportunities are just so awesome, if we get this right. During the past 13.8 billion years, our universe has transformed from dead and boring to complex and interesting, and it has the opportunity to get dramatically more interesting in the future if we don't screw up. About 4 billion years ago, life first appeared here on Earth, but it was pretty dumb stuff, like bacteria, that couldn't really learn anything in their lifetime. I call that life 1.0. We are what I call life 2.0 because we can learn things. Which, of course, in geek speak means we can upload new software modules. If I want to learn Spanish, I can study Spanish, and now I have all these new skills uploaded in my mind. And it's precisely this ability of humans to design their own software rather than be stuck with what the software evolution gave us, which has enabled us to dominate this Earth, give us what we call cultural evolution. We seem to be gradually heading towards life 3.0, which is life that can design not just its software, but also its hardware. Maybe we're at 2.1 right now because we can get cochlear implants and artificial knees, and a few minor things like this. But if you were robots that were able to think as cleverly as right now, of course, there would be no limits whatsoever to how you could upgrade yourselves. So let's first talk about the power of the technology. Obviously, the power of AI has improved dramatically recently. I'm going to define intelligence itself, just very broadly, as the ability to accomplish complex goals. I'm giving such a broad definition because I want to be really inclusive and include both all forms of biological intelligence and all forms of artificial intelligence. And as you guys here at Google all know, obviously, a subset of artificial intelligence is machine-learning, where systems can improve themselves by using data from their environment, much like biological organisms can. And another subset of that is, of course, deep learning, in which we use neural-net architectures. And if you look at older breakthroughs in AI, like when Gary Kasparov got his posterior kicked by IBM's Deep Blue, the intelligence here was, of course, mainly just put in by human programmers. And Deep Blue beat Kasparov just because it could think faster and remember better. Whereas, in contrast, the recent stuff that you've done here at Google, like this work by Ilya Sutskever's group, there's almost no intelligence at all put in by the humans, right? They just trained a simple neural-- with a bunch of data. And you put in the numbers that represent the pixel colors, and it puts out this caption-- "a group of young people playing a game of Frisbee," even though the software was never taught anything about what a Frisbee is, or what a human is, or what a picture is. And the same stuff, if you put in other images, it gives other captions, which are often quite impressive. I find it even more striking how cool things can be done with video. So this is Google DeepMind, of course, learning to play Atari games. And for those of you-- those few of you who haven't seen this before, you need to remember that this neural network, here, with simple reinforcement-learning built in, had no idea what a game was, what a paddle was, what a ball was, or anything like that. And just by practicing, gradually it starts to miss the ball less often and got to a point where it hardly missed it at all, and plays much better than I could play this. And the real kicker is that, of course, the people at DeepMind, they actually didn't know that there was this clever trick you can do when you play Breakout, which is always aim for the corners and try to build a little-- do a little tunnel, there. So once this little deep-learning software figured that out, it's just every single time the ball comes back-- look how just uncannily precise it is. It's just putting it right back there in the corner and playing. I can only dream to play this well. Now this is, of course, a very, very simple environment, that little two-dimensional game world. But if you're a robot, you can think of life as a game, just a more complex one. And you could ask yourself to what extent these sort of techniques might enable you to learn more interesting things. And so DeepMind more recently, had three-dimensional robots in a simulated world, and just asked to see if they could learn to do things like walk. And this is what happened. [MUSIC PLAYING] This software had never, ever seen any videos of walking. It knew nothing about the concept of walking. All the software was doing was sending random commands to how to bend the different joints, and it got rewarded every time this creature managed to move a little bit forward. And it looks a bit funky, maybe a little awkward, but hey, it actually learns interesting stuff. So this raises this very interesting question of how far can AI go? How much of what we humans can do will machines, ultimately, be able to do, if we use not just the techniques that we know of so far, but factor in all sorts of additional progress that you people in the room and elsewhere are going to do? I like to think about this in terms of this landscape. I drew this-- I made this picture inspired by a paragraph in one of my favorite books by Hans Moravec from many years ago, where the height, here, represents how difficult it is for a computer to do a certain task. And the sea level represents how good computers are at doing them right now. So what we see here is that certain tasks, like chess-playing and arithmetic have, of course, long been submerged by this slowly rising tide of machine intelligence. And there are some people who think that there are certain tasks, like art and book-writing, or whatever, that machines will never be able to do. And then there are others who think that the old goal of AI to really solve intelligence and do everything that we do will mean that sea levels will eventually submerge everything. So what's going to happen? There have been a lot of interesting polls of AI researchers and the conclusion is very clear-- we don't know. A little bit more specifically, though, what you find is there are some people in the techno-skeptic camps who think that AI research is ultimately doomed. We're never going to get there. Or maybe we're only going to get there hundreds of years from now. But actually, most AI researchers think it's going to happen more in a matter of decades. And some people think that we don't have to worry so much about steering this rocket, metaphorically speaking, because it's not going to happen, that we'll ever get powerful enough that we have to worry about this-- but that's a minority. And then there are people who think we don't have to worry about steering because it's guaranteed that the outcome is going to be awesome. I call such people digital Utopians. And I respect that point of view. And there are also people who think it's guaranteed that things are going to suck, so there's no point in worrying about steering because we're screwed anyway. But most of the people in surveys tend to land more here, in the middle, in what I've called the beneficial AI movement, where you're really motivated, actually, to ask, what can we do right now to steer things in a good direction? Because it could be awesome, or it could be not so great, and it depends on what we do now. I put this web page up, AgeofAI.org. We did a survey there for people from the general public could answer these same questions. You can go there and do it, too. And I was actually very interested that the general public response was almost exactly the same as AI researchers have done in recent polls. This is from something I analyzed this weekend with 14,866 respondents. And you see most people think maybe we're decades away from human-level AI, maybe it'll be good, maybe there'll be problems. So this is maximally motivating to think about how we can steer this technology in a good direction. So let's talk about steering. How can we control-- how can we learn to control AI to do what we want it to do? NASA EMPLOYEE: Lift-off. MAX TEGMARK: To help with this, my wife Meia, who's sitting there, and I, and some other folks, founded The Future of Life Institute. And you can see we actually have the word 'steer' up here in our mission statement. Our goal is simply to do what we can to help make sure that technology is beneficial for humanity. And I'm quite optimistic that we can create a really inspiring future with technology, as long as we win this race between the growing power of the technology and the growing wisdom with which we manage it. But I think if we're going to win this race, we actually have to shift strategies, because technology is gradually getting more powerful. And when we invented less powerful tech, like fire, we very successfully used the strategy of learning from mistakes. We invented fire-- oopsy-- and then invented the fire extinguisher. We invented the car-- oopsy-- and then we invented the seat belt, the airbag, the traffic light, and things were more or less fine. But when you get beyond the certain point of the power of the technology, this idea of learning from mistakes is just really, really lousy, right? You don't want to make mistakes if one mistake is unacceptably many. And when we talk about nuclear weapons, synthetic biology, and certain superhuman AI, I feel we're at the point where we really don't want to make mistakes. We want to shift strategy from being reactive to being proactive, which is exactly the slogan you said you're also using for your work here at Google, earlier. I'm optimistic that we can do this if we really focus on it and work for it. Some people say, nah, don't talk about this because it's just Luddite scaremongering when you talk about things that could go wrong. I don't think it's Luddite scaremongering. I think it's safety engineering. We started by talking about the Apollo moon mission. When NASA thought through, very carefully, everything that could possibly go wrong when you put three astronauts on top of this 100-meter tall rocket full of highly explosive fuel, that wasn't Luddite scaremongering. What they were doing was precisely what ultimately led to the success of the mission. And this is what I think we want to be doing with the AI as well. I think so far, what we've learned from other technologies here is that we need to up our game a little bit because we haven't really absorbed this idea that we have to switch to being proactive. Today is a very special day in terms of nuclear weapons, because we came pretty close to September 26 being the 34th anniversary of World War III. In fact, it might have ended up this way if this guy, Stanislav Petrov, hadn't just, on gut instinct, ignored the fact that his early warning system said that there were five incoming Minuteman US missiles that should be retaliated against. So how can we do better? How can we win this wisdom race? I'm very, very happy that the AI community has really started to engage with these issues a lot in recent years. And thanks to a lot of people who are in this room here, including Peter Norvig, and with The Future Life Institute, we organized a couple of conferences in Puerto Rico. And then earlier this year in Asilomar, California, where there was really quite remarkable consensus around a number of very constructive things we can do to try to develop this wisdom and steer things in the right direction. And I want to spend just a little bit of time hitting some highlights of things here, from this list of 23 Asilomar Principles, which has now been signed by over 1,000 AI researchers around the world. First of all, it says here on item one, that we should define the goal of AI research not to be just making undirected intelligence, but to make beneficial intelligence. So in other words, the steering of the rocket is part of the design specs. And then there was also very strong consensus that, hey, if we have a bunch of unanswered questions that we need to answer, we shouldn't just say, oh yeah, we should answer them. Well, we should answer them the way we scientifically know is the best way to answer hard questions, namely, to research them, to work on them. And we should fund this kind of research as just an integral part of computer science funding, both in companies and in industry. And I'm actually very, very proud of Google for being one of the founding members of the partnership on AI, which aims very much to support this kind of AI research-- AI safety research. Another principle here that was very broad agreement was the shared prosperity principle, that the economic prosperity created by AI should be shared broadly to benefit all of humanity. What do I mean by that? Obviously, technology has kept growing the economic pie. It's been growing our GDP a lot in recent decades, as you can see if you look at the top line in this plot, here. But as you're also generally aware of, this pie hasn't been divvied up quite equally. And in fact, if you look at the bottom 90% of income earners, their income has stayed flat, almost since I was born. Actually, maybe it's my fault. And the 30% poorest in the US have actually gotten significantly poorer in real terms, in recent decades, which has created a great deal of anger, which has given us the election of Donald Trump. It's given us Brexit. And it's given us a more polarized society in general. And so there was a very strong consensus among AI researchers that if we can create so much more wealth and prosperity, and have machines help produce all these wonderful goods and services, then if we can't make sure everybody gets better off from this, shame on us. Some people say, well, this is just nonsense because something magical is going to change in these statistics soon. And the jobs that get automated away are going to be replaced by much better, new jobs that don't exist yet. But actually, if you look at this data, it doesn't support that. We could have made that same argument 100 years ago, when much more people worked in farming, that all those jobs that were lost were going to be replaced by new jobs that didn't exist yet. And this is what actually happened. This is-- I made this little pie chart, here, of all the jobs in the US by size. And you can start going down the list-- managers, drivers, retail salespersons, cashiers, et cetera. Only when you get down to 21st place do you get to a job category that didn't exist 100 years ago, namely, software developers. Hi, guys. So clearly what happened is not that most farmers became software developers. What instead happened was people who lost, generally, from the Industrial Revolution and onward, jobs where they were using their muscles to do work, went into other jobs where they could use their brains to do work. And these jobs tended to be better paid, so this was a net win. But they were jobs that already existed before. Now what's happening today, which is driving the growth in income inequality, is similarly that people are getting switched into other jobs that had existed before. It's just that this time, since the jobs that are being automated away are largely jobs where they'll use their brains, they often switch to new jobs that existed before that pay less, rather than pay more. And I think it's a really interesting challenge for all of us to think about how we can best make sure that this growing pie makes everybody better off. Another item here on this list is principle number 18-- the AI arms race. This was the one that had the highest agreement of all among the Asilomar participants. "An arms race in lethal autonomous weapons should be avoided." Why is that? Well, first of all, we're not talking about drones, which are remote-control vehicles where a human is still deciding who to kill. We're talking here about systems where the machine itself, using machine-learning or whatever, decides exactly who is going to be killed, and then does the killing. And first, whatever you think about them, the fact is, although there's been, of course, a huge amount of investment in civilian uses of AI recently, it's actually dwarfed by talk about military spending here, recently. So if you look in the pie, there's a real risk that the status quo will just mean that most of the loud sucking noise trying to recruit AI graduates from MIT and Stanford and elsewhere, will be to go to military places rather than to places like Google. And most AI researchers felt that that would be a great shame. Here's how I think about it. If you look at any science, you can always use it to develop new ways of helping people, or new ways of harming people. And biologists fought really, really hard to make sure that their science is now known as new ways of curing people, rather than for biological weapons. They fought very hard and they got an international ban on biological weapons passed. Similarly, chemists managed to get the chemical weapons banned by really speaking up as a community and persuading politicians around the world that this was good. And that's why you associate chemistry now mainly with new materials. And it's very stigmatized to have bioweapons. So even if some countries cheat on them, it's so stigmatized that Assad even gave up his chemical weapons to not get invaded. And if you want to buy some chemical weapons to do something silly, you're going to find it really hard to find anyone who's going to sell them to you because it's so stigmatized. What there is very widespread support for in the AI community is exactly the same thing here, to try to negotiate an international treaty where the superpowers get together and say, hey, the main winners of having an out-of-control arms race and AI weapons is not going to be the superpowers. It's going to be ISIS and everybody else who can't afford expensive weapons, but would love to have little cheap things that they can use to assassinate anybody with anonymously, and basically drive the cost of anonymous assassination down to zero. And this is something that if you want to get involved in, the United Nations is going to discuss this in November, actually. And I think the more vocal the AI community is on this issue, the more likely it is that that AI rocket here is going to veer in the same direction as the biology and chemistry rockets went. Finally, let me say a little bit about the final Asilomar Principles here. I find it really remarkable that even though a few years ago, if you started talking about superintelligence or existential risks, or whatever, many people would dismiss you as some sort of clueless person who didn't know anything about AI. These words are in here, and yet this is signed by Demis Hassabis, the CEO of DeepMind. It's signed by Peter Norvig, who's just sitting over there, by your very own Jeff Dean, and by, really, a who's-who of AI researchers, over 1,000 of them. So there's been a much greater acceptance of the fact that, hey, this is part of this theory of maybe AI is actually going to succeed and maybe we need to take these sort of things into account. Let me just unpack a little bit what the deal is with all of this. So first of all, why should we take it seriously at all, this idea of recursive self-improvement and superintelligence? We saw that a lot of people expect we can get to human-level AI in a few decades, but why would that mean that maybe we can get AI much smarter than us, not just a little? The basic argument for this is very eloquently summarized in just this paragraph by IJ Good, from 1965, a mathematician who worked with Alan Turing to crack codes during World War II. I think you've mostly heard this all before. He basically says that if we have a computer, a machine, that can do everything as well as we can, well, one of the things we can do is design AI systems, so then it can, too. And then you can hire-- instead of hiring 20,000 Google employees to do work for you, you can get 20 million little AI things working for you, and they can work much faster. And that the speed of AI development will no longer be set by the typical R&D time scale of humans, or years, but by how fast machines can help you do this, which could be way, way faster. And if it turns out that we have a hardware overhang, where we've compensated for the fact that we really are kind of clueless about how to do the software of human-level AI by having massive amounts of extra hardware, then it might be that you can get a lot of, through improvements first, even just by changing the software, which is something that can be done very, very quickly, without even having to build new stuff. And then from there on, things could get-- you might be able to get machines that are just dramatically smarter than us. We don't know that this will happen, but basically, what we see here is that, for linear researchers viewing this, is at least a possibility that we should take seriously. Another thing which you see here is existential risk. So more specifically, it says here, "risks posed by AI systems, especially existential risks, must be subject to the planning and mitigation efforts commensurate with their expected impact." And existential risk is a risk which basically can include humanity just getting wiped out altogether. Why would you possibly worry about that? There are so many absolutely ridiculous Hollywood movies with terminator robots or whatever, that you can't even watch without cringing. So what are the serious reasons that people like this sign on to something that talks about that? Well, the common criticism that you hear is that, well, machines-- there's no reason to think that intelligent machines would have human goals if we built them. And after all, why should they have sort of weird, alpha-male goals of trying to get power, or even self-preservation? My laptop doesn't protest when I try to switch it off, right? But there's a very interesting argument here I just want to share with you in the form of this silly little fake computer game I drew for you here. Just imagine that you are this little blue, friendly robot whose only goal is to save as many sheep as possible from the big bad wolf. You have not put into this-- this robot does not have the goal of surviving, or getting resources, or any stuff like that. Just sheep-saving. It's all about these cute little sheepies, OK? It's going to-- very quickly, if it's smart-- figure out that if it walks into the bomb here and blows up, then it's not going to save any sheep at all. So a subgoal that it will derive is-- well, actually, let's not get blown up. It's going to get a self-preservation instinct. This is a very generic conclusion if you have a robot, then you program it to walk to the supermarket and buy you food and cook you a nice dinner, it's going to, again, develop the subgoal of self-preservation because if it gets mugged and murdered on the way back with your food, it's going to not give you your dinner. So it's going to want to somehow avoid that, right? Self-preservation is an emergent goal of almost any goal that the machine might have, because goals are hard to accomplish when you're broken. And also, if the robot-- the robot might develop, find, have an incentive to get a better model of the world that's in here, and discover that there is actually a shortcut it can take to get to where the sheep are faster, then it can save more. So trying to understand more about how the world works is a natural subgoal you can get, no matter whatever fundamental goal you program the machine to have. And then resource acquisition, too, can emerge, because when this little robot here discovers that when it drinks the potion, it can run twice as fast, then it can save more sheep. So it's going to want the potion. It'll discover that when it takes the gun, it can just shoot the wolf and save all the sheep-- great. So it's going to want to have resources. As I've summarized in this pyramid, here, this idea, which has been very eloquently-- it was mentioned first by Steve Omohundro, who lives here in the area, and is talked a lot about in Nick Bostrom's book. The idea is just that whatever fundamental goal you give a very intelligent machine, if it's pretty open-ended, it's pretty natural to expect that it might develop subgoals of not wanting to be switched off, and try to get resources. And that can be fine. There's not necessarily a problem, being in the presence of more intelligent entities. We all did that as kids, right, with our parents? The reason it was fine was because their goals were aligned with our goals. So therein lies the rub. We want to make sure that if we ever give a lot of power to machines of intelligence comparable or greater to ours, that their goals are aligned with ours. Otherwise, we can be in trouble. So to summarize, these are all questions that we need to answer, technical research questions. How can you make-- how can you have machines learn, adopt, retain our goals, for example? And let me just show you a very short video talking about these issues in superintelligence and then some. [CLICKS KEYBOARD] And let's see if we have better luck with video this time. [VIDEO PLAYBACK] - "Will artificial intelligence ever replace humans?" is a hotly-debated question these days. Some people claim computers will eventually gain superintelligence, be able to outperform humans on any task, and destroy humanity. Other people say, don't worry. AI will just be another tool we can use and control, like our current computers. So we've got physicist and AI researcher Max Tegmark back again to share with us the collective takeaways from the recent Asilomar conference on the Future of AI that he helped organize. And he's going to help separate AI myths from AI facts. - Hello. - First off, Max, machines, including computers, have long been better than us at many tasks, like arithmetic, or weaving, but those are often repetitive and mechanical operations. So why shouldn't I believe that there are some things that are simply impossible for machines to do as well as people, say, making Minute Physics videos, or consoling a friend? - Well, we've traditionally thought of intelligence as something mysterious that can only exist in biological organisms, especially humans. But from the perspective of modern physical science, intelligence is simply a particular kind of information processing and reacting, performed by particular arrangements of elementary particles moving around. And there's no law in physics that says it's impossible to do that kind of information processing better than humans already do. It's not a stretch to say that earthworms process information better than rocks and humans better than earthworms. And in many areas, machines are already better than humans. This suggests that we've likely only seen the tip of the intelligence iceberg, and that we're on track to unlock the full intelligence that's latent in nature and use it to help humanity flourish, or flounder. - So how do we keep ourselves on the right side of the flourish-or-flounder balance? What, if anything, should we really be concerned about with superintelligent AI? - Here is what has many top AI researchers concerned. Not machines or computers turning evil, but something more subtle-- superintelligence that simply doesn't share our goals. If a heat-seeking missile is homing in on you, you probably wouldn't think, no need to worry, it's not evil. It's just following its programming. No, what matters to you is what the heat-seeking missile does and how well it does it, not what it's feeling, or whether it has feelings at all. The real worry isn't malevolence, but competence. Superintelligent AI is, by definition, very good at attaining its goals. So the most important thing for us to do is to ensure that its goals are aligned with ours. As an analogy, humans are more intelligent and competent than ants, and if we want to build a hydroelectric dam where there happens to be an anthill, there may be no malevolence involved, but, well, too bad for the ants. Cats and dogs, on the other hand, have done a great job of aligning their goals with the goals of humans. I mean, even though I'm a physicist, I can't help think kittens are the cutest particle arrangements in our universe. If we build superintelligence, we'd be better off in the position of cats and dogs than ants. Or better yet, we'll figure out how to ensure that AI adopts our goals, rather than the other way around. - And when exactly is superintelligence going to arrive? When do we need to start panicking? - First of all, Henry, superintelligence doesn't have to be something negative. In fact, if we get it right, AI might become the best thing ever to happen to humanity. Everything I love about civilization is the product of intelligence, so if AI amplifies our collective intelligence enough to solve today's and tomorrow's greatest problems, humanity might flourish like never before. Second, most AI researchers think superintelligence is at least decades away. But the research needed to ensure that it remains beneficial to humanity rather than harmful might also take decades, so we need to start right away. For example, we'll need to figure out how to ensure machines learn the collective goals of humanity, adopt these goals for themselves, and retain the goals as they get ever smarter. And what about when our goals disagree? Should we vote on what the machines' goals should be? Should we do whatever the president wants, whatever the creator of the superintelligence wants, let the AI decide? In a very real way, the question of how to live with superintelligence is a question of what sort of future we want to create for humanity, which obviously shouldn't just be left to AI researchers, as caring and socially skilled as we are. [END PLAYBACK] MAX TEGMARK: So that leads to the very final point I want to make here today. To win this wisdom race, creating an awesome future with AI, in addition to doing these various things I've talked about, we really need to think about what kind of future we want, what sort of goal we want to have, where we want to steer our technology. So just for fun, the survey I mentioned that we did, we asked people also to say what they wanted for the future. And I'll just share with you here. These are from the analysis I did last weekend. Most people out of the 14,866 here, say they actually want AI to go all the way to superintelligence. Although some are saying no, here. A lot of people want humans to be in control. Most people actually want both humans and machines to be in control together. And a small fraction, [INAUDIBLE],, prefer the machines to be in control. [LAUGHTER] And then, when asked about consciousness, a lot of people said, yeah, if they have machines that are behaving as if they are as intelligent as humans, they would like to have them have a subjective experience also, so the machines can feel good. But some people said, nah, they prefer having zombie robots that don't feel conscious, that people don't have to feel guilty about switching them off or giving them boring things to do. In terms of what a future civilization should strive for, there was a large majority who felt we should either try to maximize positive experiences, or minimize suffering, or something like that. Then more people who said let the future civilization pick whatever goals they want, as long as it's reasonable. Some people said they didn't even care about it if they thought the goal that the future wanted was reasonable, even if it was pointlessly banal, like maybe turning our universe into paper clips. They were fine with just delegating it to humans. But most people actually felt that since we're creating this technology, we have the right to have some say as to where things should go. The broadest agreement of all was on this question that, actually, maybe we shouldn't just limit the future of life to forever be stuck on this little planet, but give it the potential to spread and flourish throughout the cosmos. And to get people thinking more about different futures, my wife, Meia, likes to point out that even though it's a good idea to visualize positive outcomes when you plan your own career, and then try to figure out how to get there, we kind of do the exact opposite as a society. We just tend to think about everything that could possibly go wrong and then we freak out about it. When you watch Hollywood movies, it's almost always dystopic depictions of the future, right? So to get away from this a little bit in my book, the whole chapter 5 is theories of thought experiments with different future scenarios, trying to span the whole range of what people have talked about, and other, so you, yourselves, can ask what you would actually prefer. And the most striking thing from the survey was that people disagree very strongly in what sort of society they would like. And this is a fascinating discussion that I would really encourage you all to join into. I'm just going to end by saying that I think when we look to the future, there's really a lot to be excited about. People sometimes ask me, Max, are you for AI or against AI? And I respond by asking them, what about fire? Are you for it or against it? And of course, they'll concede that they're for fire to heat their homes in the winter and against fire for arson. But it's the same with all technology, it's always a double-edged sword. The difference with AI is just it's much more powerful, so we need to put even more effort into how we steer it. If you want life to exist for beyond the next election cycle, and maybe, hopefully, for billions of years on Earth and maybe beyond, then just pressing pause on technology forever-- that's actually just a really sucky idea. Because if we do that, the question isn't whether humanity is going to go extinct. The question is just, what's going to wipe us out? Whether it's going to be the next massive asteroid strike, like the one that took the dinos out, or the next super volcano, or another one on a list of long things that we know are going to happen to Earth, that technology can create-- sorry, that technology can prevent, but technology that we don't have yet. It's going to require further development of our tech. So I, for one, think that it would be really foolish if we just run away from technology. I'm much more excited about, in the Google spirit-- and I love your old slogan, "Don't Be Evil"-- asking, what can we do to steer, to develop [? theoretic ?] technology in a direction so that life can really flourish? Not just for the next election cycle, but for a very, very long time on Earth, and maybe even throughout our cosmos. Thank you. [APPLAUSE] JOHN BRACAGLIA: Thanks so much, Max. Now we have time for questions from the audience. We have a mic over here, which we can use for questions. And also, I can pass this one around. And while we're doing that, I'll pull up the Dory. MAX TEGMARK: Great. And since you mentioned there were a lot of questions, make sure to keep the questions brief, and make sure that they actually are questions. AUDIENCE: AI risk seems to have become a much more mainstream worry in the last few years. What changed to make that happen and why didn't we do it earlier? MAX TEGMARK: I agree with you. I'm actually very, very happy that it's changed in this way, and trying to help make it change this way was the key reason we founded the Future of Life Institute and organized the Puerto Rico conference and the Asilomar conference, and so on. Because we felt that up until a few years ago, the debate was kind of dysfunctional. And what I think has really, really changed things for the better is that the AI research community itself has really engaged, joined this debate and started to own it. I think that's why it's become more mainstream, and also much more sensible. JOHN BRACAGLIA: [INAUDIBLE] MAX TEGMARK: OK, so you're the boss. Should we alternate with online, offline questions? Do you want to read the questions? JOHN BRACAGLIA: Oh, sure. "What would you most hope to see a company like Google do to ensure safety as we transition to a more AI-centric world?" MAX TEGMARK: So as I said, I think Google already has the soul to do exactly what's needed. This "Don't Be Evil" slogan of Larry and Sergey-- I interpret it as though we shouldn't just build technology because it's cool, but we should think about its uses. For those of you who know the old Tom Lehrer song about "Wernher von Braun," (SINGING IN A GERMAN ACCENT) Once the rockets go up, who cares where they come down? That's not my department, says Wernher von Braun. I view Google's "Don't Be Evil" slogan as exactly the opposite of that-- thinking mindfully about how to steer the technology to be good. And I'm also really excited again that Google is one of the founding partners in the Partnership for AI, trying to make sure that we-- that this happens not just in what Google does, but throughout the community. And I also think it's great if Google can pull all of its strings to persuade politicians all around the world to seriously fund AI safety research, because the sad fact is, even though there's a great will for AI researchers to do this stuff now, there's almost no funding for it still. What Elon Musk helped us give out 37 grants for is just a drop in the bucket of what's needed. And it makes sense that Google and other private companies want to own the IP on things that make AI more powerful and build products out of it. But these same private companies, it's better for them all if nobody patents the way to make it safe, and keep others from using it, right? That's something that's great if it's developed openly by companies who share it, or in universities, so that everybody can use the same best practices and raise the quality of safety everywhere. AUDIENCE: All right, so Max, I actually talked to you last night about a lot, like future-- really long, maybe 100 years-ish what's going to happen. But if you look at AI nowadays, not a lot of people are focusing on today, just the imminent risks. So if you think about how did Trump got elected, and how those things went wrong in the last few years, you can't really deny that AI has contributed a lot, especially in the fake news, that AI's like suggested contents. So is that like focusing on all energy into the future? So I felt there are really few people that's looking into today. So do you think that's a problem, or do you think we need to do better on that? MAX TEGMARK: Yeah, I think there's a really great opportunity for us nerds in the tech community to educate the broader public and politicians about the need to really engage with this. This is one of the reasons I wanted to write this book. I think when I watched the presidential debates for the last election, for example, completely aside from the issues they talked about, I thought it was just absolutely astonishing what they didn't talk about. None of them talked about AI at all. Hello? They're talking about jobs, they're not mentioning AI. They're talking about international security, they're not talking about AI, like, the biggest technology out there. And I think in addition to just telling politicians to pay attention, I think it's incredibly valuable, also, if a bunch of people from the tech community can actually go into government positions to add more human-level intelligence in government, to prevent the world governments from being asleep at the wheel. AUDIENCE: I mean, actually-- MAX TEGMARK: Maybe we should just-- JOHN BRACAGLIA: [INAUDIBLE] MAX TEGMARK: We can talk more afterwards, but give everybody a chance to ask first. AUDIENCE: Hello? Hi. When you introduced the concept-- when you introduced the Asilomar Treaty, you mentioned the difference between undirected intelligence and benevolent intelligence. Don't you think that if humans succeeded in creating controllable, benevolent intelligence, that they really have failed in creating intelligence? Let me rephrase-- MAX TEGMARK: I'm not sure I fully understood this question. Do you want to just repeat the punch line? AUDIENCE: I'll rephrase. Do you think that benevolent intelligence would be the intelligence that we should strive towards, or should it be general intelligence that perhaps cannot be controlled? MAX TEGMARK: So that's a great question. You asked what I think. I am trying to be very open-minded about what we actually want. And I wrote the book not-- really avoiding saying what I think the future should be, because I think this is such an important question, we just need everybody's wisdom on it. And again, I talk about all these different scenarios, some of them which correspond to some of the different options you even listed there. And I'm incredibly interested to hear what other people think would actually be good with these things. One thing that Meia and I found very striking when we discussed this was-- when I was writing the book, was even though I tried quite hard to emphasize the upsides of each scenario, there wasn't a single one there that I didn't have at least some major misgivings about. JOHN BRACAGLIA: "Do you think deep neural networks would be the way to get to artificial general intelligence? If not, do you see fundamental reasons why these do not have the potential for recursive self-improvement that can speed up the development of AGI or superintelligence?" MAX TEGMARK: All right, that's a great question. So I think that although-- let me say two things about this. First of all, our brain seems to be, of course, some kind of a recurrent neural network that's very, very complicated, and it has human-level intelligence. But I think it would be a mistake to think that that's the only route there. I think it'd also be a mistake to think, assume that that's the fastest route there. Meia likes to point out that even though, finally, a few years ago, there was a beautiful TED Talk demonstrating the first-ever successful mechanical bird, that came a hundred years after the Wright brothers built airplanes. And when I flew here yesterday-- you'll be very surprised to hear this, but I didn't come in a mechanical bird. It turned out there was a much easier, simpler way to build flying machines. And I think we're going to find exactly the same thing with human-level intelligent machines. The brain is just optimized for very different things than what your machines that you build are. The brain is-- Darwinian evolution is obsessed about only building things that can self-assemble. Who cares if your laptop can self-assemble? Evolution is obsessed about creating things that can self-repair. It would be nice if your laptop could self-repair, but it can't and you're still using it, so. And also evolution doesn't care about simplicity for humans to understand how it works, but you care lot about that. So maybe this is much more complicated than it needs to be, just so it can self-assemble, and blah, blah, whatever. My guess is that the first human-level AI will not be working exactly like the brain. That it will be something much, much simpler, and maybe we'll use that to create later-- figure out how human brains work. That said, the deep neural networks are, of course, inspired by the brain and are using some efforts-- some very clever computational techniques that evolution came up with. My guess is that the fastest route to human-level AI will actually use a combination of deep neural networks with GOFAI-- various good old-fashioned AI techniques, more logic-based things, which have a lot of their own strength for building, like for building a world model and things like this. JOHN BRACAGLIA: Live question? MAX TEGMARK: Maybe I should just add one more thing about this. Also this poses-- the increasing successes of neural networks also poses a really interesting challenge. Because when we put AI in charge of more and more infrastructure in our world, it's really important that it be reliable and robust. Raise your hand if your computer has ever crashed on you. That wouldn't have been so fun if it was the machine that was controlling your self-driving car, or your local nuclear power plant, or your nation's nuclear weapons system, right? And so we need to transform today's buggy and hackable computers into robust AI systems that we can really trust. What is trust? Where does trust come from? It comes from understanding how things work. And neural networks, I think, are a double-edged sword. They are very powerful, but we understand them much less than traditional software. So in my group at MIT, actually, we're working very hard right now on a project that I call intelligible intelligence, where-- we're trying to come up with algorithms where you can transform neural networks into things which-- where you can really understand better how they work. I think this is a challenge that I would encourage you to all think about, too. How can you combine the power of neural nets with stuff that you can really understand better, and therefore trust? AUDIENCE: So should we be afraid that AI will use its superintelligence to figure it out that its treatment by the humans is, essentially, slavery with just extra steps? MAX TEGMARK: That's a wonderful, wonderful question. I haven't talked at all about consciousness here, but the whole chapter 8 in the book is about that. And a lot of people say things like, well, machines can never have a subjective experience and feel anything at all, because to feel something, you have to be made of cells, or carbon atoms, or whatever. As a scientist, I really hate this kind of carbon chauvinism. I'm made of the same kind of up-quarks, down-quarks, and electrons as all the computers are. Mine are just arranged in a slightly different way. And it's obviously something about the information processing that's all that matters, right? And moreover, this kind of self-justifying arguments have been used by people throughout history to say, oh, it's OK to torture slaves because they don't have souls. They don't feel anything. Oh, it's OK to torture chickens today in giant factories because they don't feel anything. And of course, we're going to say that about our future computers, too, because it's convenient for us. But that doesn't mean it's true. And I think it's actually a really, really interesting question, to first figure out, what is it exactly that makes an information processing system have subjective experience? A lot of my colleagues, whom I really respect, think this is just BS, this whole question. This is what Daniel Dennett says-- I looked up in the "MacMillan Dictionary of Psychology" and it said that consciousness is that nothing worth reading has ever been written on. But I really disagree with this. And actually, let me just take one minute and explain why I think this is actually a scientifically interesting question. So look at this. OK, and ask yourself, why is it that when I show you 450 nanometer light on the left, and 650 nanometer light on the right, why do you subjectively experience it like this, [CLICK] and not like this? Why like this, and not like this? I put to you that this is a really fair-game science question that we simply don't have an answer to right now. There's nothing to do with wavelengths of light, or neurons, or anything that explains this, but it's an observational fact. And I would like to understand, why does it feel like anything, why do we have this experience? You might say, well, look, we know that there are three kinds of light sensors in a retina, the cones. And when I-- with a 450 nanometer light to activate one kind, and I have the longer wavelength to activate the other kind, and then you can see how they're connected to various neurons in the back of your brain. But that just sharpens the question, the mystery of consciousness, because this proves that it had nothing to do with light at all, because you can experience colors even when you're dreaming, when different neurons in your brain are active, when there is no light involved, right? So my guess is that consciousness-- by which I mean subjective experience-- is simply the way information feels when it's being processed in certain complex ways. And I think there are some equations that we will one day discover that specify what those complex ways are. And once we can figure that out, it'll both be very useful because we can put a consciousness detector in the emergency room, and when an unresponsive patient comes in, you can figure out if they have locked-in syndrome, or not. And it will also enable us to answer this really good question you asked about whether machines should also be viewed as moral entities that can have feelings. And above all, and I don't see Ray Kurzweil here today, but if he can one day upload himself into a Ray Kurzweil robot and live on for thousands of years, and he talks like Ray and he looks like Ray and he acts like Ray, you'll feel that that's great for Ray. Now he's immortal. But suppose it turns out that that machine is just a zombie and doesn't feel like anything to be it, he would be pretty bummed, wouldn't he? All right? And if in the future, life spreads throughout our cosmos in some post-biological form, and we're like, this is so exciting. Our descendants are doing all these great things and we can die happy. If it turns out that they're all just a bunch of zombies and all that cool stuff is just a play for empty benches, wouldn't that suck? JOHN BRACAGLIA: I'll do another question from the Dory. "What do you think is the most effective way for individuals to embrace or promote a security-engineering mentality, i.e., where not even one glitch is tolerable, when working on AI-related projects?" MAX TEGMARK: Well, first of all, I think we have a lot to learn from existing successes in safety engineering, that's why I started by showing the moon mission. It's not like this is anything new to engineers. I think it's just that we're so used to the idea that AI didn't work, that we didn't need to worry about the impact of things. And now it is beginning to have an impact, so we should think it through. And then there are also a few challenges which are really unique and specific to AI. Some of the Asilomar Principles talk about them and this Research Agenda for AI Safety Research is a really long list of specifics of safety engineering challenges that we need smart people like you to work on. And I hope we can support that. AUDIENCE: So also on the topic of security engineering, a lot of rockets blew up on the way to the moon. MAX TEGMARK: Yeah. AUDIENCE: And given the intelligence explosion, it's like we're only going to have one chance to be able to get the alignment problem correct. And I think we couldn't even align on a set of values in this room, let alone a system that would govern the world effectively, because there's certainly some drawbacks of capitalism. So I'm hopeful-- I am glad that Elon is hedging our bets by making a magic hat, but it seems like you and your group are focusing on the alignment problem, and I'm just kind of just curious what makes you optimistic that we're going to be able to get it right on the first time? MAX TEGMARK: So first of all, yeah, a lot of rockets blew up. But you will note that most of the rockets that blew up, in fact, all the-- that's the rocket that blew up in the moon mission had no people in them, right? So that was safety engineering. The high-risk stuff, they did it in a controlled environment where the failure didn't matter so much. So if you make some really advanced AI, you want to understand it really well, maybe don't connect it to the internet the first time, right? So the downsides are small. There's a lot of things like this that you can do. And I'm not saying that there's one thing that we should particularly focus on, either. I think the community has brainstormed up a really nice, long list of things, and we should really try to work on them all, and then we'll figure out some more challenges along the way. But so the main thing we need to do is just knowledge that yeah, this is valuable. Let's work on it. Then you asked also why I'm optimistic. Let me just clarify. There are two kinds of optimism. There's naive optimism, like my optimism that the sun is going to rise over Mountain View tomorrow morning, regardless of what we do. That's not the kind of optimism I feel about the future of technology. Then there's the kind of optimism that you're optimistic that this can go well if we really, really plan and work for it. That's the kind of optimism I feel here. We have in our hands to create an awesome future, but so let's roll up our sleeves and do it. AUDIENCE: Hey, Max. In the paper that you wrote entitled "Why Does Cheap and Deep Learning Work So Well?" with Lin, and now Rolnick, as well, you ask a key question and you draw a lot of connections between a deep learning and then core parts of what we know about physics. Low polynomial order, hierarchical processes, things like that. I'm just curious, what are the reactions you've received both from the physics community, and then from the AI community to that attempt to kind of draw some deep parallels? MAX TEGMARK: Generally quite positive feedback. And then also people who have pointed out a lot of additional research questions related to that, which are really worth doing. And just to bring everybody up to speed as to what we're talking about, so we don't devolve into just discussing a nerdy research paper here, we were very intrigued by the question of why deep learning works so well, because if you think about it naively, even if I just want to classify, take all the Google Images that have cats and dogs, and I want to write-- and I want to take a neural network that will take in, say, 1 million pixels and output the probability that it's a cat, right? If you think about it just a little bit, you might convince yourself that it's impossible because how many such images are there? Even if they're just black-and-white images, each pixel can be black or white, there's 2 to the power of 1 million possible images, which is much more images than there are atoms in our universe. There's only 10 to the 78, right? And for each image, you have to output a probability. So to specify an arbitrary function of images, how many parameters do you need for that? Well, 2 to the 1,000, which you can't even fit if you store one parameter on each atom in our cosmos. So how can it work so well? So the basic conclusion we found there was that, of course, the class of all functions that you can do well with a neural network that you can actually run is its almost infinitesimally tiny fraction of all functions. But then physics tells us that the fraction of all functions that we actually care about, because they're relevant to our world, is also an almost infinitesimally small fraction. And conveniently, they're almost the same. I don't think this was luck. I think Darwinian evolution gave us this particular kind of neural network-based computer precisely because it's really well-tuned for tapping into the kind of computational needs that our cosmos has dished out to us. And I'll be delighted to chat more with you later about loose ends to this, because I think there's a lot more interesting stuff to be done on that. JOHN BRACAGLIA: Take a Dory question. "Being humans in the age of AI seems like an egocentric effort that gives an undeserved special status to our species. Why should we even bother to remain humans when we could get to push our boundaries and see where we get?" MAX TEGMARK: All right. [LAUGHING] "An egocentric effort that gives us undeserved special status to our species." Well, first of all, you know, I'm totally fine with pushing our boundaries and I've been advocating for doing this. I mean, I find it very annoying human hubris when we go on a soapbox and we're like, (DEEPLY) we are the pinnacle of creation and nothing can ever be smarter than us, and we try now to build our whole self-worth on somehow human exceptionalism. I think that's kind of lame. On the other hand-- we should probably make this the last question. On the other hand, egocentric efforts-- well, we are the only ones-- it's only us humans who are in this conversation right now, and somebody needs to have it. So it's really up to us to talk about it, right? We can't use this kind of thinking as an excuse to just not talk about it and just bumble into some completely uncontrolled future. I think we should take a firm grip on the rudder and steer in whatever direction we decide to steer in. So let me thank you again so much for coming out. It's a wonderful pleasure to be here. [APPLAUSE] And If you have any more questions you didn't get in, I'll be here signing books and I'm happy to chat more. JOHN BRACAGLIA: Thank you all for coming and thanks, Max, for talking at Google. MAX TEGMARK: And thank you for having me.
Info
Channel: Talks at Google
Views: 29,050
Rating: 4.8666668 out of 5
Keywords: Terminator, Skynet, Computer Brain, AI, Artificial Intelligence, transhumanism, futureism
Id: oYmKOgeoOz4
Channel Id: undefined
Length: 62min 15sec (3735 seconds)
Published: Tue Dec 05 2017
Reddit Comments
Related Videos
Note
Please note that this website is currently a work in progress! Lots of interesting data and statistics to come.