YouSaid · the spoken record

Risto Miikkulainen

lines on the record: 113
first: 2021-04-19
most recent: 2021-04-19
sittings or episodes: 1
sources: podcast

Every line below is reproduced as it was said and linked to the record it came from. Nothing here is summarised or generated. Directory · Search · Corrections

“And here we are talking about a relatively simple physical actions, but you can take that the higher levels also to predict what the people are going to do. You need to know what their goals are, what are they trying to, are they exercising? Are they just trying to get somewhere? But even higher level, I mean, you are predicting what people will do in their career, what their life themes are. Do they want to be famous, rich, or do good? And that takes a lot more information, but it allows you to then predict their actions, what choices they might make.”
2021-04-19 · Lex Fridman Podcast · #177 – Risto Miikkulainen: Neuroevolution and Evolutionary Computation · IDENTIFIED FROM THE TRANSCRIPT · source
“I think these systems need to be able to predict what will happen, what the other agent is going to do, and then have a structure of what the goals are and whether those predictions actually meet the goals. And you can go probably pretty far with that relatively simple setup already, but to call it a theory of mind, I don't think you need to. Maybe when you start interacting and you're trying to get the other agent to do something and jointly so that you can jointly collaboratively achieve something, then it becomes more complex.”
2021-04-19 · Lex Fridman Podcast · #177 – Risto Miikkulainen: Neuroevolution and Evolutionary Computation · IDENTIFIED FROM THE TRANSCRIPT · source
“Other robot was doing, and in the end, there was a behavior where one of the robots, the most sophisticated one, sensed where the food pieces were and identified that the other robot was close to two of a very far distance. And there was one more food nearby. So it faked now I'm using anthropomorphized terms. Made a move towards those other pieces in order for the other robot to actually go and get them. Because it knew that the last remaining piece of food was close and the other robot would have to travel a long way, lose its energy and then lose the whole competition. So there was like an emergence of something like Ethereum mind, knowing what the other robot would do. Guided towards bad behavior in order to win. So we can get things like that happen in simulation as well.”
2021-04-19 · Lex Fridman Podcast · #177 – Risto Miikkulainen: Neuroevolution and Evolutionary Computation · IDENTIFIED FROM THE TRANSCRIPT · source
“And sometimes I think that it requires a theory of mind On the side of the robot that they understand what you're doing because they themselves are doing something similar. And that's a big question too. We talked about how intelligence in general and the social aspect of intelligence. And I think that's what is required, that we humans understand other humans because we assume that they are similar to us. We have one simulation we did a while ago, Ken Stanley did that. Two robots that we're competing simulation, like you said, they were foraging for food to gain energy. And then when they were really strong, they would bounce into the other robot and win if they were stronger. And we watched evolution discover more and more complex behaviors. They first went to the nearest food and then they started to plot a trajectory so they get more. But then they started to pay attention.”
2021-04-19 · Lex Fridman Podcast · #177 – Risto Miikkulainen: Neuroevolution and Evolutionary Computation · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, another interesting direction is. Learning for virtual creatures learning to walk. We did a study in simulation, obviously, that you create those creatures, not just their controller, but also their body. So you have cylinders, you have muscles, you have joints and sensors, and you're creating creatures that look quite different. Some of them have multiple legs. Some of them have no legs at all. And then the goal was to get them to move, to walk, to run. And what was interesting is that when you evolve the controller together with the body, you get movements that look natural because they are optimized for that physical setup. And these creatures, you stop believing them that they are lie because they walk in a way that you would expect somebody with that kind of a setup to walk.”
2021-04-19 · Lex Fridman Podcast · #177 – Risto Miikkulainen: Neuroevolution and Evolutionary Computation · IDENTIFIED FROM THE TRANSCRIPT · source
“Because you can afford these evolutionist dead ends, although they are not entirely dead ends in the sense that they can serve as stepping stones. When you take two of those, put them together, you get something that works even better. And that is a great example of this kind of discovery.”
2021-04-19 · Lex Fridman Podcast · #177 – Risto Miikkulainen: Neuroevolution and Evolutionary Computation · IDENTIFIED FROM THE TRANSCRIPT · source
“Reinforcement learning evolution, yes. So if you have a reinforced learning agent, it tries to be conservative because it wants to walk as long as possible and be stable. But if you have evolutionary computation, it can afford these agents that go haywire. They fall flat on their face and they could take a step and then they jump and they again fall flat. And eventually, what comes out of that is something like a falling that's controlled. And you take another step, another step, and you no longer fall. Instead, you run, you go fast. So that's a way of discovering something that's hard to discover step by step incrementally.”
2021-04-19 · Lex Fridman Podcast · #177 – Risto Miikkulainen: Neuroevolution and Evolutionary Computation · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, yeah, very much. And indeed, there are fascinating videos of that. And that's actually one of the examples where you can contrast the difference.”
2021-04-19 · Lex Fridman Podcast · #177 – Risto Miikkulainen: Neuroevolution and Evolutionary Computation · IDENTIFIED FROM THE TRANSCRIPT · source
“One the best candidate that Evolution produced. In that sense, they also apply to different kinds of problems. Now that boundary is starting to blur a bit. You can use evolution as an online method and reinforcement learning to create engineering solutions. But that's still roughly the distinction. And from the point of view of what algorithm you want to use, If you have something where there is a cost for every trial, reinforcement learning might be your choice. Now, if you have a domain where you can use a surrogate, perhaps, so you don't have much of a cost for trial. And you want to have surprises. You want to explore more broadly than this population-based method is perhaps a better choice because you can try things out that you wouldn't afford when you're doing reinforcement.”
2021-04-19 · Lex Fridman Podcast · #177 – Risto Miikkulainen: Neuroevolution and Evolutionary Computation · IDENTIFIED FROM THE TRANSCRIPT · source
“And that's how you learn. And evolution is also a mechanism like that by the different time scale because you have a population, not an individual during lifetime, but an entire population as a whole can discover what works. And there you can afford individuals that don't work out. Everybody dies and you have a next generation and it will be better than the previous one. So that's the big difference between these methods. They apply to different kinds of problems. And in particular, there's often a comparison that's kind of interesting and important between reinforcement learning and evolutionary computation. And initially, reinforcement learning was about individual learning during the lifetime. And evolution is more engineering. You don't care about the lifetime. You don't care about all the individuals that are tested. You only care about the final result.”
2021-04-19 · Lex Fridman Podcast · #177 – Risto Miikkulainen: Neuroevolution and Evolutionary Computation · IDENTIFIED FROM THE TRANSCRIPT · source
“So labeled examples, or there might be predictions, it might be weather predictions where the data itself becomes labels, what the weather was today, and what it will be tomorrow. So they are very effective deep learning methods on that kind of tasks. But there are other kinds of tasks where we don't really know what the right answer is. Game playing, for instance, but many robotics tasks and actions in the world, decision making and actual practical applications like treatments and healthcare or investment in stock market. Many tasks are like that. We don't know and we'll never know what the optimal answers were. And there you need different kinds of approach. Reinforcement learning is one of those. Reinforcement learning comes from biology as well. Agents learn during their lifetime. They berries and sometimes they get sick and then they don't and get stronger.”
2021-04-19 · Lex Fridman Podcast · #177 – Risto Miikkulainen: Neuroevolution and Evolutionary Computation · IDENTIFIED FROM THE TRANSCRIPT · source
“Yes, of course, they are very different and they address different kinds of problems. And the deep learning has been really successful in domains where we have a lot of data. And that means not just data about situations, but also what the right answers were.”
2021-04-19 · Lex Fridman Podcast · #177 – Risto Miikkulainen: Neuroevolution and Evolutionary Computation · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, and many ways of being successful. Usually enables computation, we have one go, you know, play this game really well compared to others. But in biology, there are many ways of being successful. You can build niches, you can be stronger, faster, larger, or smarter, or eat this or eat that. So there are many ways to solve the same problem of survival. And that then breeds creativity. And it allows more exploration and eventually you get solutions that are perhaps more creative rather than trying to go from initial population directly or more or less directly to your maximum fitness, which you measure as just one metric.”
2021-04-19 · Lex Fridman Podcast · #177 – Risto Miikkulainen: Neuroevolution and Evolutionary Computation · IDENTIFIED FROM THE TRANSCRIPT · source
“Comes from using statistics. It's like the rest of machine learning based on statistics. We use similar tools to guide evolutionary computation. And in that sense, it has diverged a bit from biological evolution. And that's one of the things I think we could look at again having a weaker selection, more crossover, large populations, more time. And maybe a different kind of creativity would come out of it. We are very impatient in evolution computation today. We want answers right now, quickly. And if somebody doesn't perform, kill it. And biological evolution doesn't work quite that way. It's more patient. Yes, much more patient.”
2021-04-19 · Lex Fridman Podcast · #177 – Risto Miikkulainen: Neuroevolution and Evolutionary Computation · IDENTIFIED FROM THE TRANSCRIPT · source
“Follow some principle like you collect statistics of performance and correlations and try to make mutations you believe are going to be helpful. That's where evolution computation has moved in the last 20 years. I mean, evolution competition has been around for 50 years, but a lot of the recent”
2021-04-19 · Lex Fridman Podcast · #177 – Risto Miikkulainen: Neuroevolution and Evolutionary Computation · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, so again, back to what the computational mechanisms of evolution, computation are, so the way to create variation, you can take multiple individuals, two, usually, but you could do more, and you exchange the part of the representation. You do some kind of recombination, could be crossover, for instance. In biology, you do have DNA strings that are cut and put together again. We could do something like that. And it seems to be that in biology, crossover is really the workhorse in biological evolution. In computation, we tend to rely more on mutation. And that is making random changes into parts of the chromosome. You try to be intelligent and target certain areas of it and make the mutations also.”
2021-04-19 · Lex Fridman Podcast · #177 – Risto Miikkulainen: Neuroevolution and Evolutionary Computation · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah. Yeah. So we haven't really seen that in computation yet. And there are certainly attempts to have open-ended evolution, things that could add more complexity and start selecting at a higher level, but it is still not quite the same as going from single to multi to society, for instance, in biology.”
2021-04-19 · Lex Fridman Podcast · #177 – Risto Miikkulainen: Neuroevolution and Evolutionary Computation · IDENTIFIED FROM THE TRANSCRIPT · source
“Even though it could reproduce, now it can't alone. It has to have that environment. So there's a push to another level, at least the selection.”
2021-04-19 · Lex Fridman Podcast · #177 – Risto Miikkulainen: Neuroevolution and Evolutionary Computation · IDENTIFIED FROM THE TRANSCRIPT · source
“Most likely it does, but it's quite we don't even understand it in biology very well. It's coming from, so it would be really good to look at major transitions in biology, try to characterize them a little bit more in detail, what the processes are. So like a unit, a cell is no longer evaluated alone. It's evaluated as part of a community organ.”
2021-04-19 · Lex Fridman Podcast · #177 – Risto Miikkulainen: Neuroevolution and Evolutionary Computation · IDENTIFIED FROM THE TRANSCRIPT · source
“Useful at one point, perhaps, and no longer are, but they're still there. So that process is complex. And your representation should support it. And that is quite difficult if we are limited with strings or trees. then we are pretty much limited what can be constructed. And one thing that we are still missing in evolution computation in particular is what we saw in biology major transitions so that you go from, for instance, single cell to multicells and eventually societies, the transitions of level of selection and level of what a unit is. And that's something we haven't captured in evolutionary computation yet.”
2021-04-19 · Lex Fridman Podcast · #177 – Risto Miikkulainen: Neuroevolution and Evolutionary Computation · IDENTIFIED FROM THE TRANSCRIPT · source
“And DNA, in some sense, is also a sequence and a string. So it's not that far from it. But DNA also has many other aspects that we don't take into account necessarily, like there's folding and interactions that are other than just the sequence itself. And lots of that is not yet captured. And we don't know whether they are really crucial. Evolution, biological evolution has produced wonderful things, but if you look at them, it's not necessarily the case that every piece is irreplaceable and essential. There's a lot of baggage because you have to construct it and it has to go through various stages. And we still have appendix and we have tail bones and things like that that are not really that useful. If you try to explain them now, it would make no sense, very hard. But if you think of us as productive evolution, you can see where they came from.”
2021-04-19 · Lex Fridman Podcast · #177 – Risto Miikkulainen: Neuroevolution and Evolutionary Computation · IDENTIFIED FROM THE TRANSCRIPT · source
“Yes, and that is a big question. How do you encode these individuals? So there's a genotype, which is that encoding, and then a decoding mechanism gives you the phenotype, which is the actual individual that then performs the task. And in an environment, can be evaluated how good it is. So even that mapping is a big question. And how do you do it? But typically the representations are either they are strings of numbers or they are some kind of trees. Those are something that we know very well in computer science and we try to do that.”
2021-04-19 · Lex Fridman Podcast · #177 – Risto Miikkulainen: Neuroevolution and Evolutionary Computation · IDENTIFIED FROM THE TRANSCRIPT · source
“Lot of these algorithms really do take motivation from biology, but they are Caricatures, you try to essentialize it and take the elements that you believe matter. So in evolution computation, it is the creation of variation and then the selection upon that. So the creation of variation, you have to have some mechanism that allow you to create new individuals that are very different from what you already have. That's the creativity part. And then you have to have some way of measuring how well they are doing and using that measure to select who goes to the next generation and you continue”
2021-04-19 · Lex Fridman Podcast · #177 – Risto Miikkulainen: Neuroevolution and Evolutionary Computation · IDENTIFIED FROM THE TRANSCRIPT · source
“Expectations of your opponent This was even Kasparov pointed that out that when Deep Blue was playing against Kasparov, that it was not playing the same way as Kasparov expected. And this has to do with not having the same biases. And that's really one of the strengths of AI approach.”
2021-04-19 · Lex Fridman Podcast · #177 – Risto Miikkulainen: Neuroevolution and Evolutionary Computation · IDENTIFIED FROM THE TRANSCRIPT · source
“Away. The other teams, the other programs, this expanded memory in order to take that into account until they ran out of memory and crashed. And then you win a tournament by crashing all your opponents.”
2021-04-19 · Lex Fridman Podcast · #177 – Risto Miikkulainen: Neuroevolution and Evolutionary Computation · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, that was quite a bit smaller scale than our baseline doesn't need to sleep surprise. But it was actually done by students in my class in Neural Nets Evolution Computation class. There was an assignment. It was perhaps the final project where people built game playing AI was an AI class. And it was for tic-tac-toe or five in a row in a large board. And this one team evolved a neural network to make these moves. And they set it up, the evolution. They didn't really know what would come out. But it turned out that they did really well. Evolution actually won the tournament. And most of the time when it won, it won because the other teams crashed. And then when you look at it, what was going on was that it was...”
2021-04-19 · Lex Fridman Podcast · #177 – Risto Miikkulainen: Neuroevolution and Evolutionary Computation · IDENTIFIED FROM THE TRANSCRIPT · source
“It happens all the time. I mean, evolution is so creative, so good at discovering solutions you don't anticipate. A lot of times they are taking advantage of something that you didn't think was there, like a bug in the software. There's a great paper, the community put it together about surprising anecdotes about evolution computation. A lot of them are indeed in some software environment, there was a loophole or a bug and the system utilizes that.”
2021-04-19 · Lex Fridman Podcast · #177 – Risto Miikkulainen: Neuroevolution and Evolutionary Computation · IDENTIFIED FROM THE TRANSCRIPT · source
“I think it's wonderful. I think it's great that we could do something like that. I mean, you can, there are devices that read your EEG, for instance, and humans can learn to control things using just their thoughts in that sense. And I don't think it's that different. I mean, those signals would go to limbs. They would go to thumbs. Now the same signals go through a sensor to some computing system. It still probably has to be built on human terms not to overwhelm them, but utilize what's there and sends the right kind of patterns that are easy to generate. But, oh, I think it's really quite possible and wonderful and could be very much more efficient.”
2021-04-19 · Lex Fridman Podcast · #177 – Risto Miikkulainen: Neuroevolution and Evolutionary Computation · IDENTIFIED FROM THE TRANSCRIPT · source
“And that we can switch that from vision to some other wavelength or some other kind of modality. But I think that the same processing principles probably still apply. But also indeed this ability to have information more accessible and more relevant, I think, can enhance what we do. I mean, kids today at school, they learn about DNA. I mean, things that were discovered just a couple of years ago, and it's already common knowledge, and we are building on it. And we don't see a problem where there's too much information that we can absorb and learn. Maybe people become a little bit more narrow in what they know. They are in one field. But this information that we have accumulated, it is passed on, and people are picking up on it. And they are building on it. So it's not like we have reached the point of saturation. We have still this process that allows us to be selective.”
2021-04-19 · Lex Fridman Podcast · #177 – Risto Miikkulainen: Neuroevolution and Evolutionary Computation · IDENTIFIED FROM THE TRANSCRIPT · source
“And that can make us more productive. You don't have to argue about, I don't know, what happened in that baseball game or whatever it is, because you can look it up right away. And I think in that sense we can learn to utilize tools. And that's what we have been doing for a long, long time. And we are already, the brain is already drinking from the water fire hose. Like vision, there's way more information in vision than we actually process. So, brain is already good at identifying what matters.”
2021-04-19 · Lex Fridman Podcast · #177 – Risto Miikkulainen: Neuroevolution and Evolutionary Computation · IDENTIFIED FROM THE TRANSCRIPT · source
“Exactly. You make do with what you have, but you don't have to pipe it directly to the brain. I mean, we already have devices like phones where we can look up information at any point.”
2021-04-19 · Lex Fridman Podcast · #177 – Risto Miikkulainen: Neuroevolution and Evolutionary Computation · IDENTIFIED FROM THE TRANSCRIPT · source
“And I think this good hope that these prosthetic devices, for instance, work, not because we make them so good and so easy to use, but the brain. And so, in that sense, if there's a trouble, a problem, I think that brain can be used to correct it. Now, going beyond what we have today, can you get smarter? That's really much harder to do, giving the brain more input probably might overwhelm it. It would have to learn to filter it and focus in order to use the information effectively. Augmenting intelligence with some kind of external devices like that might be difficult, I think, but replacing what's lost, I think, is quite possible.”
2021-04-19 · Lex Fridman Podcast · #177 – Risto Miikkulainen: Neuroevolution and Evolutionary Computation · IDENTIFIED FROM THE TRANSCRIPT · source
“I think there's a lot of that. The eye experiments that are done in animals like Mikan Gazur, the Matis switching the auditory and visual information going to the wrong part of the cortex and the animal was still able to hear and perceive the visual environment. And there are kids that are born with severe disorders and sometimes they have to remove half of the brain, like one half. And they still grow up. They have the functions migrate to the other parts. There's a lot of flexibility like that. So I think it's quite possible to hook up the brain with different kinds of sensors, for instance. And something that we don't even quite understand or have today on different kinds of wavelengths or whatever they are. And then the brain can learn to make sense of it.”
2021-04-19 · Lex Fridman Podcast · #177 – Risto Miikkulainen: Neuroevolution and Evolutionary Computation · IDENTIFIED FROM THE TRANSCRIPT · source
“200, 300 recipes, exploration as well as known recipes. But now we are going beyond that. And everything was pushed that limit. So we look at it and say, well, we can easily just change it. Let's have it your way. And it turns out the system discovered that Basil does not need to sleep. 24 hours, lights on, and it will thrive. It will be bigger. It will be tastier. And this was a big surprise, not just to us, but also the biologists in the team that anticipated that this is some constraints that are in the world for a reason. It turns out that evolution did not have the same bias. And therefore, it discovered something that was creative. It was surprising. It was useful, and it was new.”
2021-04-19 · Lex Fridman Podcast · #177 – Risto Miikkulainen: Neuroevolution and Evolutionary Computation · IDENTIFIED FROM THE TRANSCRIPT · source
“What do you do if you can't control everything? Farmers know a lot about how to make plants grow in their own patch of land, but if you can control everything, it's too much. And it turns out that we don't actually know very much about it. So we built a system evolution optimization system together with the surrogate model of how plants grow and let this system explore recipes on its own. And initially, we were focusing on light, how strong what wavelengths, how long the light was on. And we put some boundaries which we thought were reasonable. For instance, that there was at least six hours of darkness like night because that's what we have in the world. And very quickly, the system evolution pushed all the recipes to that limit. We were trying to grow basil and we had initially had some”
2021-04-19 · Lex Fridman Podcast · #177 – Risto Miikkulainen: Neuroevolution and Evolutionary Computation · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, algorithms absolutely can be creative. They can come up with solutions that you don't think about. I mean, creativity can be defined. A couple of requirements has to be new. It has to be useful and it has to be surprising. And those certainly are true with, say, evolution computation discovering solutions. So maybe an example, for instance, we did this collaboration with MIT Media Lab, Galeb Harvest Lab, where they had a hydroponic food computer, they called it environment that was completely computer controlled, nutrients, water, light, temperature, everything is controlled.”
2021-04-19 · Lex Fridman Podcast · #177 – Risto Miikkulainen: Neuroevolution and Evolutionary Computation · IDENTIFIED FROM THE TRANSCRIPT · source
“Even if it's a simple, it has a life of its own, has the intelligence of its own. It's beyond what you actually thought. And that is, I think it's exactly spot on. That's exactly what it's about. You created something, it has a ability to live its life and do good things. And you just gave it a starting point. So in that sense, I think that may be part of the joy, actually.”
2021-04-19 · Lex Fridman Podcast · #177 – Risto Miikkulainen: Neuroevolution and Evolutionary Computation · IDENTIFIED FROM THE TRANSCRIPT · source
“And now, if you take that one more step, you get something like evolution algorithms that discover things, they create things, they come up with solutions that you did not think of. And that just blows me away. It's so great that we can build systems, algorithms that can be in some sense smarter than we are, that they can discover solutions that we might miss. A lot of times it is because we have assumers we have certain biases. We expect the solutions to be certain way. And you don't put those biases into the algorithm, so they are more free to explore. And evolution is just absolutely fantastic explorer. And that's what really is fascinating.”
2021-04-19 · Lex Fridman Podcast · #177 – Risto Miikkulainen: Neuroevolution and Evolutionary Computation · IDENTIFIED FROM THE TRANSCRIPT · source
“I think evolution computation is the most amazing method. So what fascinates me most is that with computers is that you can get more out than you put in. I mean, you can write a piece of code and your machine does what you told it. I mean, this happened to me my freshman year. It did something very simple and I was just amazed. I was blown away that it would get the number and it would compute the result and I didn't have to do it myself. Very simple. But if you push that.”
2021-04-19 · Lex Fridman Podcast · #177 – Risto Miikkulainen: Neuroevolution and Evolutionary Computation · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, they are visible. And we can even quantify possibly their emotional state because Leave droppings behind, and there are chemicals there that can be associated with neurotransmitters. And we can separate what emotions they might have Experienced in the last 24 hours.”
2021-04-19 · Lex Fridman Podcast · #177 – Risto Miikkulainen: Neuroevolution and Evolutionary Computation · IDENTIFIED FROM THE TRANSCRIPT · source
“They've discovered something about social structures, communication, about cooperation. And it might then spill over to other things too in thousands of years in the future.”
2021-04-19 · Lex Fridman Podcast · #177 – Risto Miikkulainen: Neuroevolution and Evolutionary Computation · IDENTIFIED FROM THE TRANSCRIPT · source
“Like I said, they are intelligent, but they are not quite as intelligent as, say, baboons. Which would learn a lot and would be much more flexible. Hyenas are relatively rigid in what they can do. And therefore, you could look at this behavior like this is a breakthrough in evolution about to happen.”
2021-04-19 · Lex Fridman Podcast · #177 – Risto Miikkulainen: Neuroevolution and Evolutionary Computation · IDENTIFIED FROM THE TRANSCRIPT · source
“So, this is a great example of how together we can achieve things we couldn't otherwise. Like the hyenas, alone they couldn't, but as a team they could. And I think humans do that all the time. We're really good at that.”
2021-04-19 · Lex Fridman Podcast · #177 – Risto Miikkulainen: Neuroevolution and Evolutionary Computation · IDENTIFIED FROM THE TRANSCRIPT · source
“And that allows you to then build all the good stuff about planning, for instance, and building things and so on. So yeah, I think that very strongly humans are social and that gives us ability to structure the world. But also as a society, we can do so much more because one person does not have to do everything. You can have different roles and together achieve a lot more. And that's also something we see in computational simulations today. I mean, we have multi-agent systems that can perform tasks. There's fascinating demonstration Marco Dorrigo, I think it was. These robots, little robots that had to navigate through an environment and there were things that are dangerous, like maybe a big chasm or some kind of groove, a hole, and they could not get across it. But if they grab each other with their gripper, they formed a robot that was much longer under the team. And this way they could get across that.”
2021-04-19 · Lex Fridman Podcast · #177 – Risto Miikkulainen: Neuroevolution and Evolutionary Computation · IDENTIFIED FROM THE TRANSCRIPT · source
“To understand actions in terms of roles that can be changed, that's a basis for language, for grammar. And now you can start using symbols to refer to objects in the world. And you have this flexible structure. So there's a social structure that's fundamental for language to develop. Now again, then you have language. You can refer to things that are not here right now.”
2021-04-19 · Lex Fridman Podcast · #177 – Risto Miikkulainen: Neuroevolution and Evolutionary Computation · IDENTIFIED FROM THE TRANSCRIPT · source
“Yes, I strongly believe that's true. And yes, the communication is multifaceted. I mean, they vocalize and call for friends, but they also rub against each other and they push and they do all kinds of gesters and so on. So they noct alone. And I don't think people act alone very much either, at least normal most of the time. And social systems are so strong for humans that I think we build everything on top of these kind of structures. And one interesting theory around that, Bickerton's theory, for instance, for language origin is that where did language come from? And it's a plausible theory that first came social systems that you have different roles in a society. And then those roles are exchangeable, that I scratch your back, you scratch my back, we can exchange roles. And once you have the brain structures that allow you to...”
2021-04-19 · Lex Fridman Podcast · #177 – Risto Miikkulainen: Neuroevolution and Evolutionary Computation · IDENTIFIED FROM THE TRANSCRIPT · source
“Affiliation eventually is so strong that when they move they move together they act as a unit and they can perform that function. So there's an interesting behavior that seems to depend on these emotions strongly and makes it possible for coordinate actions.”
2021-04-19 · Lex Fridman Podcast · #177 – Risto Miikkulainen: Neuroevolution and Evolutionary Computation · IDENTIFIED FROM THE TRANSCRIPT · source
“Than anything else they do. They can band together if there's about 30 of them or so. They can coordinate their effort so that they push the lions away from a kill, even though the lions are so strong that they could kill a hyena by striking with a paw. But when they work together and precisely time this attack, the lions will leave and they get the kill. And probably there are some states like emotions that the hyenas go through. The first day they call for reinforcements. They really want that kill, but there's not enough of them. So they vocalize and there's more people. Mohenas that come around. And then they have two emotions. They're very afraid of the lion. So they want to stay away. But they also have a strong affiliation between each other. And then this is the balance of the two emotions. And also, yes, they also want to kill. It's both repelled and attractive. But then this.”
2021-04-19 · Lex Fridman Podcast · #177 – Risto Miikkulainen: Neuroevolution and Evolutionary Computation · IDENTIFIED FROM THE TRANSCRIPT · source
“Yes, it is an impossible. You can build representations and functions, I think, into these agents that act like emotions and consciousness, perhaps. So I mentioned emotions being something that allows you to focus and pay attention, filter out what's important. Yeah, you can have that kind of a filter mechanism. And it puts you in a different state. Your computation is in a different state. Certain things don't really get through and others are heightened. You label that box emotion. I don't know if that means it's in emotion, but it acts very much like we understand what emotions are. And we actually did some work like that, modeling hyenas who were trying to steal a kill from lions, which happens in Africa. I mean, hyenas are quite intelligent, but not really intelligent. And they have this behavior that's more complex.”
2021-04-19 · Lex Fridman Podcast · #177 – Risto Miikkulainen: Neuroevolution and Evolutionary Computation · IDENTIFIED FROM THE TRANSCRIPT · source
“Even if it's not logically necessarily easy to derive and you don't have time for that logical detection, you may be able to recognize the situation is dangerous and this fear kicks in and you all of a sudden perceive the facts that are important for that. And I think that's generally is the role of emotions. It allows you to focus what's relevant for your situation. And maybe a fear of death plays the same kind of role, but if it consumes you and it's something that you think in normal life when you don't have to, then it's not healthy and then it's not productive.”
2021-04-19 · Lex Fridman Podcast · #177 – Risto Miikkulainen: Neuroevolution and Evolutionary Computation · IDENTIFIED FROM THE TRANSCRIPT · source