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Ben Goertzel
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- 2020-06-22
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- 2020-06-22
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“And then it joins the global singularity net. And anyone who puts a request for services out into the singularity net, the peer-to-peer discovery mechanism will find your AI. And if it does what was asked, it can then start a conversation with your AI about whether it wants to ask your AI to do something for how much it would cost and so on. So that's fairly simple. If you wrote an AI and want it listed on like official singularity net marketplace, which is on our website, then we have a publisher portal and then there's a KYC process to go through because then we have some legal liability for what goes on on that website. So in a way, that's been an education too. There's sort of two layers. Like there's the open decentralized protocol.”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“So that's much easier. I mean, OpenCog is still a research system, so it takes some expertise and some time to get, we have tutorials, but it's somewhat cognitively labor intensive to get up to speed on OpenCOG. And I mean, what's one of the things we hope to change with the True AGI OpenCog 2.0 version is just make the learning curve more similar to TensorFlow or Torch or something. Right now, OpenCog is amazingly powerful, but not simple to deal with. On the other hand, SingularityNet, as an open platform was developed a little more with usability in mind, although the blockchain is still kind of a pain. I mean, if you're a command line guy, there's a command line interface, it's quite easy to take NAI that has an API and lives in a Docker container and put it online anywhere.”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“I mean, you have that in biology, you have that in the internet as a just networking medium. And I think that's what we're going to have. In the network of”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Will interface with other AIs doing deep neural nets or custom biology data analysis or whatever they're doing in singularity net, which is a looser integration of different AIs, some of which may be their own networks, right? And I think at a very loose analogy, you could see that in the human body. Like the brain has regions like cortex or hippocampus which tightly interconnect like cortical columns within the cortex, for example. Then there's looser connection within the different lobes of the brain, and then the brain interconnects with the endocrine system in different parts of the body even more loosely. Then your body interacts even more loosely with the other people that you talk to. So you often have networks within networks within networks with progressively looser coupling as you get higher up in that higher.”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“To a whole bunch of it could be made multi chain, important to a whole bunch of different blockchains. And there's a lot of potential and a lot of importance to putting this kind of tool set out there. If you compare the open cog, what you could see is OpenCog allows tight integration of a few AI algorithms that share the same knowledge store in real time, in RAM, right? Singularity net allows loose integration of multiple different AIs. They can share knowledge, but they're mostly not going to be sharing knowledge in RAM on the same machine. And I think what we're going to have is a network of network of networks, right? I mean, you have the knowledge graph inside the OpenCOG system. And then you have a network of machines inside distributed OpenCog mind. But then that open”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Something like a decentralized blockchain based AGI framework or narrow AI framework, it's cool, it's nice to have. On the other hand, if governments start trying to tamp down on my AI interoperating with someone's AI in Russia or somewhere, right, then suddenly having it decentralized protocol that nobody owns or controls becomes an extremely valuable part of the tool set. And we've put that out there now. It's not perfect, but it operates. And it's pretty blockchain agnostic. So we're talking to Algorand about making part of Singular run on Algorand. My good friend Tufi Saliba has a cool blockchain project called Toda, which is a blockchain without a distributed ledger. It's like a whole other architecture. So there's a lot of more advanced things you can do in the block. Singularity net could be ported”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“It is entirely possible. And part of what I think we're doing with things like SingularityNet Protocol is creating a tool set. That can be used to counteract that sort of thing. Say a similar thing about mesh networking, right? Plays a minor role now, the ability to access internet like directly phone to phone. On the other hand, if your government starts trying to control your use of the internet, suddenly having mesh networking there Can be very convenient, right? And so right now.”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Oh, that's a lot slipper than money, too, right? I mean, money is easier to regulate because it's kind of easier to define. Whereas AI is almost everywhere inside everything. Where's the boundary between AI and software, right? I mean, if you're going to regulate AI, there's no IQ test for every hardware device that has a learning algorithm. You're going to be putting hegemonic regulation on all software. I don't rule out that that adapt is.”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Digital currency makes total sense, but they would rather do it in the way that Putin and Xi Jinping have access to the global keys for everything, right?”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“I mean, currency will be on the blockchain. It'll just be on the blockchain in a way that enforces centralized control and government hegemony rather than otherwise. Like the ERMP will probably be the first currency on the blockchain. The ERUble may be next. E-Rubruble.”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“I mean, for making it distributed money, you could do that on Algar End right now. I mean, so that while Ethereum is too slow. There's Algarand, and there's a few other more modern, more scalable blockchains that would work fine for a decentralized global currency. So I think there were technical bottlenecks to that two years ago. And maybe Ethereum 2.0 will be as fast as Algar and I don't know. That's not fully written yet, right? I think the obstacle to currency being put on the blockchain is that”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Currency, the main bottleneck is politics. And the bands of armed thugs that will shoot you if you bypass their currency restrictions”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Problems which can be solved, but they're just very difficult to solve. In some cases, the individuals who started those projects were not well equipped to actually solve the problems that they wanted.”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“It's quite challenging because to build this whole decentralized blockchain based infrastructure, I mean, your competitors are like Google, Microsoft, Alibaba, and Amazon, which have so much money to put behind their centralized infrastructures, plus their solving simpler algorithmic problems,'cause making it centralized in some ways is easier, right? There are very major... Computer science challenges. And I think what you saw with the whole ICO boom in the blockchain and cryptocurrency world is a lot of young hackers who are hacking Bitcoin or Ethereum and they say, well, why don't we make this decentralized on blockchain, then after they raise some money through an ICO, they realize how hard it is. Actually, we're wrestling with incredibly hard computer science and software engineering and distributed systems.”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, yeah, exactly. In terms of data. We're partnering closely with another blockchain project called Ocean Protocol. An Ocean Protocol, that's the project of Trent McConney, who developed Big Chain DB, which is a blockchain-based database. So Ocean Protocol is basically blockchain-based big data, names at making it efficient for different AI processes or statistical processes or whatever to share large. So, by getting ocean and you have data lakes. So this is the data ocean, right? By getting ocean and singularity net to interoperate, we're aiming to take into account the big data aspect also.”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“You can put less and less in your AI and rely more and more on interactive calls to other AIs. Running in the network. And of course, that's not fully manifested yet because although we've rolled out a nice working version of SingularityNet platform, there's only 50 to 100 AIs running in there now. There's not tens of thousands of AIs. So we don't have the critical mass for the whole society of mind to be doing.”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“So, the real benefit would be if that AI wanted to outsource some cognitive processing or data processing or data pre-processing, whatever, to some other AIs in the network which specialize in something different. And this really requires a different way of thinking about AI software development, right? So just like object-oriented programming was different than imperative programming. And now object-oriented programming is all use these frameworks to do things rather than just libraries even, you know, shifting to agent-based programming where AI agent is asking other live real-time evolving agents for feedback and what they're doing. That's a different way of thinking. I mean, it's not a new one. There was loads of papers on agent-based programming in the 80s in Omwood. But if you're willing to shift to an agent-based model of development, then...”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, yeah, yeah. Those are all quite good points. So I think the... The benefit from being on the decentralized network as we envision it is that we want the AIs and the network to be outsourcing work to each other and making API calls to each other frequently.”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Using Ethereum, you can't do it. Now there's more modern and faster blockchains where you could start to do that in some cases. Two years ago, that was less so. It's a very rapidly evolving ecosystem.”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Well, the identity of each agent is on the blockchain. Ethereum blockchain. If one agent rates the reputation of another agent, That goes on the blockchain. An agent can publish what APIs they will fulfill on the blockchain. But the actual data for AI and the results of AI is not on the blockchain.”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“A proxy that lives in that container along with the AI that handles the interaction with the rest of SingularityNet. And then when one AI wants to contribute with another one in the network, they set up a number of channels. And the setup of those channels uses the Ethereum blockchain. And once the channels are set up, then data flows along those channels without having to be on the blockchain. All that goes on the blockchain is the fact that some data went along that channel. So you can do”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, yeah. Basically, an AI is just some software in Singularity. AI is just some software process living in a container.”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“At the moment, yeah, yeah, I would have loved to put the AI's operations on chain in some sense, but in Ethereum, it's just too slow. You can't do it.”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Neither and easier to use. I'm very annoyed with it by this point, but like Java, I mean, these languages are amazing when they first come out. So then I came up with the idea that turned into singularity net. Okay, let's make it decentralized agent system where a bunch of different AIs wrapped up and say different Docker containers or LXC containers, different AIs can each of them have their own identity on the blockchain. And the coordination of this community of AIs has no central controller, no dictator, right? And there's no central repository of information. The coordination of the society of minds is done entirely by the decentralized network in a decentralized way by the algorithms, right? Because the motto of Bitcoin is in math we trust, right? And so that's what you need. You need the society of minds to trust only in math, not trust only in one centralized”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Solidity scripting language, it's kind of dorky in a way, and I don't see why you need to turn complete language for this purpose. But on the other hand, this is like the first time I could sit down and start to script infrastructure for decentralized control of the AIs in a society of minds in a tractable way. Like you could hack the Bitcoin code base, but it's really annoying, whereas Salady is Ethereum scripting language”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“The blockchain wasn't there yet. So how them do this decentralized control? We sort of knew it. We knew about distributed systems. We knew about encryption. So, I mean, we had the key principles of what underlies blockchain now. But we didn't put it together in the way that's been done now. So when Vitalik Buterin and colleagues came out with Ethereum blockchain, many, many years later, like 2013 or something, then I was like, well, this is interesting. Like this is...”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Of it as part of being all part of it. That's what David and Hatz and I want to do with many Sophia and other robots. Each one has its own individual mind living on this server, but there's also a collective intelligence infusing them and a part of the mind living on the edge in each robot, right? So the thing is, at that time, as well as WebMind being implemented in Java 1.1 as like a massive distributed system.”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Organizational mode. Now, the main difference was he wanted the Individual AIs to be all incredibly simple and all the intelligence to be on the collective level. Worse, I thought that was cool, but I thought a more practical way to do it might be if some of the agents in the society of minds were fairly generally intelligent on their own. So like you could have a bunch of open cogs out there and a bunch of simpler learning systems. And then these are all cooperating and coordinating together. Sort of like in the brain, okay, the brain as a whole is the general intelligence. But some parts of the cortex, you could say, have a fair bit of general intelligence on their own, where, say, parts of the cerebellum or limbic system have very little general intelligence on their own, and they're contributing to general intelligence by way of their connectivity to other modules.”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Simming is right. Yeah. Yeah, yeah, exactly. So the idea with the global brain was, you know, maybe the AI won't just be in a program on one guy's computer, but the AI will be. You know, in the internet as a whole with the cooperation of different AI modules living in different places. So one of the issues you face when architecting a system like that Is how is the whole thing controlled? Do you have like a centralized control unit that pulls the puppet strings of all the different modules there? Or do you have a fundamentally decentralized network where the society of AIs is controlled in some democratic and self-organized way? All the AIs in that society, right? And Francis and I had a different view of many things, but we both wanted to make like a global society of AI minds with a decentralized”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Different parts of which would live on different computers all around the world, and each one would do its own thinking about the data local to it, but they would all share information with each other and outsource work with each other and cooperate, and the intelligence would be in the whole collective. And I organized a conference together with Francis Heiligen at Free University of Brussels in 2001, which was the Global Brain Zero Conference. And we're planning the next version, the Global Brain One Conference at the Free University of Brussels for next year, 2021, 20 years after. And then maybe we can have the next one, 10 years after that, like exponentially faster until the singularity comes, right?”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“He really thought the mind was more like a society than I do. I think you could have a mind that was as disorganized as a human society, but I think a human-like mind has a bit more central control than that, actually. I mean, we have this thalamus and the medulla and limbic system. We have a sort of top-down control system that guides much of much of what we do, more so than a society does. So I think he stretched that metaphor a little too far. But I also think there's something interesting there. And so in the 90s, when I started my first sort of non-academic AI project, WebMind, which was an AI startup in New York in the Silicon Alley area in the late 90s, what I was aiming to do there was make a distributed society of AIs.”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Sure, sure. Singularity net is a platform Realizing a decentralized Network of artificial intelligences. So Marvin Minsky, the AI pioneer who I knew a little bit, he had the idea of a society of minds. Like you should achieve an AI not by writing one algorithm or one program, but you should put a bunch of different AIs out there and the different AIs will interact with each other, each playing their own role, and then the totality of the society of AIs would be the thing that displayed the human level intelligence. When he was alive, I had many debates with Marvin about this idea. I think”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, plenty, of course. I mean, the challenge is to find a supervisor who wants to foster that sort of research, but it's way easier than it was when I got my PhD degree.”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“OpenCog, first of all, is open source project. There's a Google group discussion list. There's a GitHub repository. If anyone's interested in lending a hand with that aspect of AGI, introduce yourself on the OpenCog email list. And there's a Slack as well. I mean, we're certainly interested to have inputs into our... Redesign process for a new version of OpenCog, but also we're doing a lot of very interesting research. I mean, we're working on data analysis for COVID clinical trials. We're working with Hansen Robotics. We're doing a lot of cool things with the current version of OpenCognile. So there's certainly opportunity to jump into OpenCog or various other open source AGI-oriented projects.”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, I think that's probably what will happen. And maybe the AGI will help us do better brain imaging that will then let us build artificial humans, which is very, very interesting. Us because we are humans, right? I mean, building artificial humans Is super worthwhile. I just think it's probably not the shortest path to AGI.”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Of course, it's a valid research direction. You can try to understand more and more. And we are measuring more and more about what happens in the brain now than ever before. So it's quite interesting. On the other hand, I sort of got more of an engineering mindset about AGI. I'm like, well, okay, we don't know how the brain works that well. We don't know how birds fly that well yet either. We have no idea how a hummingbird flies in terms of the aerodynamics of it. On the other hand, We know basic principles of like flapping and pushing the air down. And we know the basic principles of how the different parts of the brain work. So let's take those basic principles and engineer something that embodies those basic principles. But is well designed for the hardware that we have on hand right now?”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“So, how did these Lex Friedman neurons, how do they coordinate with the distributed representation of Lex Friedman I have in my cortex, right? There's some back and forth between Cortex and Hippocampus that lets these discrete symbolic representations in hippocampus correlate and cooperate with the distributed representations in cortex. This probably has to do with how the brain does its version of abstraction and quantifier logic, right? Like you can have a single neuron and hippocampus that activates a whole distributed activation pattern in cortex. Well, this may be how the brain does symbolization and abstraction, as in functional programming or something. But we can't measure it. We don't have enough electrodes stuck between the cortex and the hippocampus in any known experiment to measure it. So I got frustrated with that direction, not because it's impossible.”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Wire them together to co-train and learn them together. That would be an approach to creating an AGI. One could implement something like that efficiently on top of our true AGI, like OpenCog 2.0 system once it exists. Although obviously Google has their own highly efficient implementation architecture. I think that's a decent way to build AGI. I was very interested in that in the mid-90s. I mean, the knowledge about how the brain works sort of pissed me off. Like it wasn't there yet. Like, you know, in the hippocampus, you have these concept neurons, like the so-called grandmother neuron, which everyone laughed at. It's actually there. Like, I have some Lex Friedman neurons that fired differentially when I see you and not when I see any other person, right?”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“I mean, by now the automated theorem proven community has gone way, way, way beyond anything Google was doing. But still, but anyway, if that community was going to make an AGI, probably one way they would do it was take 25 different neural modules architected in different ways, maybe resembling different parts of the brain. Like a basal ganglia model, cerebellum model, a thalamus model, a few hippocampus models, number of different models representing parts of the cortex, right? Take all of these and then”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“And you put that together with Google Brain, which granted they're not working that closely together now. But my oldest son Zarathustra is doing his PhD in machine learning applied to automated theorem proving in Prague under Joseph Urban. So the first paper, Deep Math, which applied deep neural nets to guide theorem proving was out of Google Brain.”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, yeah. So I think if a group like Deep Mind or OpenAI were to build AGI, and I think DeepMind is like a thousand times more likely from where I could tell, because they've hired a lot of people Broad minds and many different approaches and angles on AGI, whereas open AI is also awesome, but I see them as more of like a pure deep reinforcement learning shop.”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“You do learn to say why, and some of it's bullshit, but some of it isn't, right? Some of it is learning to map sensory knowledge into declarative and linguistic knowledge. Yet without necessarily making the sensory system itself use a transparent and an easily communicable representation.”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Because if you're composing, you may want to see how you did it and then declaratively morph that in some way that your fingers wouldn't think of, right? But the physiological movement may come out of some opaque like cerebellar reinforcement learned thing, right? And so that's, I think. Trying to milk the structure of a neuron net by treating it as an oracle, maybe more like how you're declarative mind post-processes, what your visual or motor cortex. I mean, in vision, it's the same way. You can recognize beautiful art. Much better than you can say why you think that piece of art is beautiful. But if you're trained as an art critic,”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Maybe didn't have a declarative representation. Same way with music I will hear something in my head. I'll sit down and play the thing like I heard it, and then I will try to study what my fingers did to see what did you just play? How did you do that?”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Have the symbolic representation inside it. I tend to think what the brain is doing is more like using the deep neural net type thing as an oracle. Like I think the visual cortex or the cerebellum are probably learning a non-semantically meaningful opaque tangled representation. And then when they interface with the more cognitive parts of the cortex, the cortex is sort of using those as an oracle and learning the abstract representation. So if you do sports, say, take, for example, serving in tennis, right? I mean, my tennis serve is okay, not great, but I learned it by trial and error, right? And I mean, I learned music by trial and error too. I just sit down and play. But then if you're an athlete, which I'm not a good athlete, then you'll watch videos of yourself serving and your coach will help you think about what you're doing and you'll then form a declarative representation. But your cerebellum.”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, yeah, yeah. So that's one way is you use a structured learning algorithm, which is symbolic, and then you use the deep neural net as an oracle to guide the structure learning algorithm. The other way to do it is like Infogam was trying to do and try to tweak the neural network to...”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“I don't know. It might be that backpropagation just won't work for it because the gradients are too screwed up. Maybe you could get to work using CMAES or some flowing point evolutionary algorithm. We tried, we didn't get it to work. Eventually, we just paused that rather than gave it up. We paused that and said, well, okay, let's try more innovative ways to learn what are the representations implicit in that network without trying to make it grow inside that network. And I described how we're doing that in... language you can do similar things in vision right so”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“The grammar out of the neural network that doesn't have it in there. So I think the thing is these neural nets are not getting a semantically meaningful representation internally by and large. So one line of research is to try to get them to do that. And Infogam was trying to do that. So like if you look back like two years ago, there was all these papers like Edward, this probabilistic programming neural net framework that Google had, which came out of Infogan. So the idea there was you could train an infogan neural net model, which is a generative associative network to recognize and generate faces, and the model would automatically learn a variable for how long the nose is. And automatically learn a variable for how wide the eyes are or how big the lips are or something, right? So it automatically learn these variables, which have a semantic meaning.”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Is we're using a symbolic grammar learning algorithm, but we're using the transformal neural network as a sentence probability oracle. So if you have a rule of grammar and you aren't sure if it's a correct rule of grammar or not, you can generate a bunch of senses using that rule of grammar and a bunch of senses violating that rule of grammar. And you can see the transformer model doesn't think the senses obeying the rule of grammar are more probable than the senses disobeying the rule of grammar. So in that way, you can use the neural model as a sense probability oracle to guide symbolic grammar learning process. That seems to work better than trying to milk.”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“But I mean, what you get is that the representation is horribly ugly and is scattered all over the network and doesn't look like the rules of grammar that you know are the right rules of grammar, right? It's kind of ugly. What we're actually doing”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source