YouSaid · the spoken record

Ben Goertzel

lines on the record: 334
first: 2020-06-22
most recent: 2020-06-22
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

“But I don't think it was yet a technical term when we started calling this a generalized hypergraph. But in any case, it's a weighted labeled generalized hypergraph or weighted labeled metagraph. The weights and labels mean that the nodes and links can have numbers and symbols attached to them. So they can have Types on them. They can have numbers that represent, say, a truth value or an importance value for a certain purpose.”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“A graph in this sense is a bunch of nodes with links between them. A hypergraph is like a graph but links can go between more than two nodes. You have a link between three nodes. And in fact, OpenCog's Adam space would properly be called a metagraph because you can have links pointing to links or you could have links pointing to whole subgraphs, right? So it's an extended hypergraph or a metagraph.”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Atom space, yeah, yeah, not Adam, like Adam and Eve, although that would be cool too. So you have a hypergraph, which is like a, so...”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Like in Yeah, let me first tell about OpenCog as a software platform, and then I'll tell you the specific AGI R&D we've been building on top of it. So, the core component of OpenCog is a software platform is what we call the Atom space. Which is a weighted labeled hypergraph.”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Open COG. On the one hand, as a software framework could be used to implement a variety of different AI architectures and algorithms. But in practice, there's been a group of developers which I've been leading together with Lenus Vepstas, Nil Geisweiler, and a few others, which have been using the OpenCog platform and infrastructure to implement certain ideas about how to make an AGI. So there's been a little bit of ambiguity about opencog, the software platform versus OpenCOG, the AGI design. Because in theory, you could use that software to use it to make a neural net. You could use it to make a lot of different”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“At birth. Yeah. I mean, SingularityNet is a separate project and a separate body of code. And you can use SingularityNet as part of the infrastructure for distributed OpenCOG system. But there are different layers.”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Sure. Open COG is an open source software project that I launched together with several others in 2008 and Probably the first code written that was written in 2001 or two or something that was developed As a proprietary cool base within my AI company Novamente LLC, then we decided to open source it in 2008, cleaned up the code throughout some things, added some new things, and”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“And you're thinking about these ideas. I would say. Open COG two point zero is a term worth throwing around sort of tongue in cheek because the existing open cog system that we're working on now is not remotely close to what we'd consider a 1.0, right? I mean, it's an early, it's been around. 13 years or something, but it's still an early stage research system, right? And actually, we are... Going back to the beginning in terms of theory and implementation, because we feel like that's the right thing to do. A huge amount in common with the current system. I mean, we all still like the general approach.”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“And there's a lot to be learned from that. And I want to incorporate that knowledge appropriately in our OpenCog 2.0 system. On the other hand, I also think current deep neural net architectures as such will never get you anywhere near AGI. So I think you want to avoid the pathology of... Throwing the baby out with the bathwater and like saying, Well, these things are garbage because foolish journalists overblow them as being the path to AGI and a few researchers overblow them as well. There's a lot of interesting stuff to be learned there, even though those are not the golden path.”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“And I doubt it's necessary now, although I think it will be a super major enhancement. But I'm also now in the middle of embarking on a complete rethink and rewrite from scratch of our open cog AGI system, together with Alexei Podopov and his team in St. Petersburg, who's working with me in singularity net. So now we're trying to like go back to basics, take everything we learned from working with the current OpenCog system, take everything everybody else has learned from working with their proto-AGI systems and design the best framework for the next stage. And I do think there's a lot to be learned from the recent successes with deep neural nets and deep reinforcement systems. I mean, people made these essentially trivial systems work. Much better than I thought they would”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“And the media wants to pigeonhole you as an avatar of a certain idea. I've changed my mind on a bunch of things. When I started my career, I really thought quantum computing would be necessary for AGI.”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“The world, well, that one point is that your brain doesn't want to. The other part is that the world doesn't want you to, like the people who have followed your ideas get mad at you if you change your mind.”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“One aspect like a cognitive architecture or a deep learning approach, it can be hard once you're old and have made your career doing one thing. It can be hard to mentally shift gears. I mean, I try quite hard to remain flexible minded.”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Or what I was going to say is it didn't happen in terms of bringing the lions of cognitive architecture together with the lions of deep learning. It did work in the sense that a bunch of younger researchers have had their heads filled with both of those ideas. This comes back to a saying my dad who was a university professor often quoted to me, which was a science advances one funeral at a time. Which I'm trying to avoid. Like, I'm 53 years old, and I'm trying to invent amazing, weird-ass new things that nobody ever, ever thought about, which we'll talk about in a few minutes. But there is that aspect, right? Like the people who've been at AI a long time and have made their career at developing.”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“This was clear to me a while ago, and one of my hopes with the AGI community was to sort of bring people from those two directions together. That didn't happen much in terms of”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Probably dentist to saw us with thinking about is like part of Cortex and part of Hippocampus because hippocampus has a spatial map and when he was a neuroscientist he was doing a bunch on Cortex hippocampus interconnection. So there the DNC would be an example of folks from the deep neural net world trying to take a step in the cognitive architecture direction by having two neural modules that correspond roughly to two different parts of the human brain that deal with different kinds of memory and learning. But on the other hand it's super super super crude from the cognitive architecture view, right? Just as what John Laird and Sor did with neural nets was super super crude from a learning point of view because the learning was like off to the side not affecting the core representations, right? I mean you weren't learning the representation. You were learning the data that feeds into the abstractions of perceptual data to feed into the representation that was not learned, right? So yeah this was”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, that doesn't quite work. You could look at some of the stuff DeepMind has done, like the differential neural computer or something that sort of has a neural net for deep learning perception. It has another neural net, which is like a memory matrix. It stores the map of the London subway or something.”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Can't just do that without totally rebuilding what is happening on both the cognitive architecture and the learning side. So I mean, they tried to do that in SOR, but what they ultimately did is like... Deep neural nut or something for perception, and you include it as one of the black boxes.”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“10 years ago before this whole commercial deep learning explosion was on the one hand you had these cognitive architecture guys who were working closely with psychologists and cognitive scientists who had thought a lot about how the different parts of a human like mind should work together. On the other hand you had these learning theory guys who didn't care at all about the architecture but were just thinking about like how do you recognize patterns and large amounts of data and in some sense What you needed to do was to get the learning that the learning theory guys were doing and put it together with the architecture that the cognitive architecture guys were doing and then you would have what you needed now you can't unfortunately when you look at the details”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, I mean, I had a lot to do with that community. And, you know, Paul Rosenblum, who was one of the, and John Laird, who built the SOR architecture, our friends of mine. And I learned SOR quite well in Aktar and these different cognitive architectures. How I was looking in the AI world about...”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“And when you can do that, it's interesting. I mean, that's how a car is built, right? But on the other hand, that's clearly not how biological systems are made. The parts covs was to adapt and work together.”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Are necessary for this kind of work. Yeah, yeah, necessary for this sort of intelligence. What types of learning went well with these different types of memory? And then how do you connect all these things together, right? And of course, the human brain did it incrementally through evolution because each of the sub-networks of the brain, I mean, it's not really the lobes of the brain, it's the subnetworks, each of which is widely distributed, which of the subnetworks of the brain co-evolves with the other subnetworks of the brain, both in terms of its patterns of organization and the particulars of the neurophysiology. So they all grew up communicating and adapting to each other. It's not like they were separate black boxes that were then glommed together, right? Whereas as engineers, we would tend to say, let's make the declarative memory box here and the procedural memory box here and the perception box here and wire them together.”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“And limbic system, there's specifics of spatial and temporal modeling connected with memory, which has to do with hippocampus and thalamus connecting to cortex. And the basal ganglia, which influences goals. So we have specific memory of what goals, sub-goals, and sub-sub-goals we wanted to perceive in which context in the past. Human brain has substantially different subsystems for these different types of memory and substantially differently tuned learning, like differently tuned modes of long-term potentiation to do with the types of neurons and neurotransmitters in the different parts of the brain corresponding to these different types of knowledge. These different types of memory and learning in the human brain, I mean you can back these all into embodied communication for controlling agents and worlds of solid objects. So if you look at building an AGI system, one way to do it”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Systems that are supposed to deal with physical objects. So if you look at You look at cognitive psychology, you can see there's multiple types of memory which are to some extent represented by different subsystems in the human brain. So we have episodic memory, which takes into account our life history and everything that's happened to us. We have declarative or semantic memory, which is like facts and beliefs abstracted from the particular situations as they occurred in. There's sensory memory, which to some extent is sense modality specific, and to some extent is unified across sense modalities. There's procedural memory, memory of how to do stuff. How to swing the tennis racket, right? Which is there's motor memory, but it's also a little more abstract than motor memory. It involves cerebellum and cortex working together. Then there's memory linkage with emotion, which has to do with linkages, of course.”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Cognitive science. It was cross-disciplinary among engineering, math, psychology, philosophy, linguistics, computer science. But yeah, we were teaching psychology students to Try to model the data from human cognition experiments using multilayer perceptrons, which was the early version of a deep neural network. Very, very recurrent back prop was very, very slow to train back then, right?”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“In the University of Western Australia, I was focused on cognitive science of memory and perception. Actually, I was teaching neural nets and deep neural nets, and it was multilayer perceptrons, right?”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“In a system that has limited memory and limited speed of processing, but whose general intelligence will be biased toward controlling a solid object agent which is mobile in a solid object world for manipulating solid objects and communicating via language with other similar agents in that same world, right? Starting from that, you're starting to get a requirements analysis for human level general intelligence. And then that leads you into cognitive science. And you can look at, say, what are the different types of memory that the human mind and brain has. And this has matured over the last decades. And I got into this a lot. So after I get my PhD in math, I was an academic for eight years. I was in departments of mathematics, computer science, and psychology. When I was in the psychology department,”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, yeah. What I meant was, you know, what are we humans evolved for? You can say being human, but that's very abstract, right? I mean, our minds control individual bodies, which are autonomous agents moving around in a world that's composed largely of solid objects, right? We've also evolved to communicate via language with other solid object agents that are going around doing things collectively with us in a world of solid objects. And these things are very obvious, but if you compare them to the scope of all possible intelligences or even all possible intelligences that are physically realizable, that actually constrains things a lot. So if you start to look at how would you realize some specialized or constrained version of universal general intelligence?”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“He's just using the term. I love Yoshua to pieces. He's by far my favorite of the lines of deep learning. He's such a good hearted guy and creative thinker.”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“So that's about the hardware side and the software side, which follows from that. Then regarding what are the requirements, I wrote the paper years ago on what I called the embodied communication prior, which was quite similar in intent to Yoshua Benjio's recent paper on the consciousness prior, except I didn't want to wrap up consciousness in it because to me the qualia problem and subjective experience is a very interesting issue also, which we can chat about. But I would rather keep that philosophical debate distinct from the debate of what kind of biases do you want to put in a general intelligence to give it human-like general intelligence?”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Way I would want to implement AGI on a bunch of neurons in a vat that I could rewire arbitrarily is quite different than the way I would want to create AGI on, say, a modern server farm of CPUs and GPUs, which in turn may be quite different than the way I would want to implement AGI on whatever quantum computer we'll have in 10 years, supposing someone makes a robust quantum Turing machine or something, right? So I think there's been co-evolution of the patterns of organization in the human brain and the physiological particulars of the human brain over time. And when you look at neural networks, that is one powerful class of learning algorithms, but it's also a class of learning algorithms that evolve to exploit the particulars of the human brain as a computational”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Okay, cool. But yeah, going back to your original question. The way I look at Human level AGI is how do you specialize? Unrealistically inefficient superhuman brute force learning processes to the specific goals that humans need to achieve and the specific resources that we have. And both of these, the goals and the resources and the environment, all this is important. And on the resources side, it's important that the hardware resources we're bringing to bear. Very different than the human brain. So the way”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“I mean, they're cute Which is why we put up with their. Repetitiveness and stupidity. And they have what the Zen guys would call a beginner's mind, which is a beautiful thing. But that doesn't necessarily correlate with a high level of intelligence.”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“I don't think it's more complex than that In some sense, a young child. Is less biased and the brain has yet to sort of crystallize into appropriate structures for processing aspects of the physical and social world. On the other hand, the young child is very tied to their sensorium, whereas we can deal with abstract. Mathematics, like 750 dimensions, and the young child cannot because they haven't grown what Piaget called the formal capabilities. They haven't learned to abstract yet, right? And the ability to abstract gives you a different kind of generality than what a baby has. So there's both more specialization. And more generalization that comes with the development process, actually.”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“How do you specialize them so that they can operate within the resource constraints that you have, but will achieve the particular things that you care about? Because we humans are not maximally general intelligences, right? If I ask you to run a maze in 750 dimensions, you'll probably be very slow. Whereas in two dimensions, you're probably way better, right? So, I mean, because our hippocampus has a two-dimensional map in it, right? And it does not have a 750-dimensional map in it. So, I mean, we... We're in a peculiar mix of generality and specialization, right?”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, there's a long history of thinking about evolution that way also, right? Well, my point is that what we're thinking of as a human level general intelligence, if you start from narrow AIs like are being used in the commercial AI field now, then you're thinking, okay, how do we make it more and more general? On the other hand, if you start from AXE or Schmid-Uber's girdle machine or these infinite infinitely powerful but Practically infeasible AIs than getting to a human level AGI is a matter of specialization. It's like how do you take these maximally general learning processes and how do you”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“So Exactly. I mean, you're Russian heritage is showing whether Alexander Vityev, I mean, and Peter Anokin and so on. I mean, there's a...”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Take sorting, for example. You can have a generic program for sorting lists. But what if all your lists you care about are length, 10,000 or less? You can run an automated program specializer on your sorting algorithm, and it will come up with the algorithm that's optimal for sorting lists of length or 10,000 or less, right?”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“You formalize those requirements in some formal specification language, you should then be able to run automated program specializer on AXETL specialize it to the computing resource constraints and the particular environment and goal. And then it will spit out like the specialized version of AXTL to your resource restrictions in your environment, which will be your AGI, right? And that, I think, is how our super AGI will create new AGI systems, right? But that's a very...”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“It's searching through the space of all possible computer programs in between each action and each next action. Now, XCTL. Searches through all possible computer programs that have runtime less than t and length less than L. Which is still an impracticably humongous space, right? So. What you Like to do to make an AGI and what will probably be done 50 years from now to make an AGI is say, okay, well, we Some constraints. We have these processing power constraints. And we have space and time constraints on the program. We have energy utilization constraints. And we have this particular class environment, class of environments that we care about, which may be, say, manipulating physical objects on the surface of the earth, communicating in human language. I mean, whatever our particular annihilating humanity happen to be.”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, yeah, yeah. So, how AXC works basically is each Each action that it wants to take before taking that action, it looks at all its history. And then it looks at all possible programs that it could use to make a decision. And it decides which decision program would have let it make the best decisions according to its reward function over its history. And he uses that decision program to make the next decision, right?”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, so now these are all analogable by a basic theory of aerodynamics, right? So one issue with AGI is we don't yet have the analog of the theory of aerodynamics. And that's what Marcus Hooder was trying to make with the AXE and his general theory of general intelligence, but that theory in its most clearly articulated parts really only works for either infinitely powerful machines or insanely Impractically powerful machines. So, I mean, if you were going to take a theory based approach to AGI, what you would do is say, well, let's take what's called, say, AXETL, which is Hutter's AXE machine that can work on merely insanely much processing power rather than infinitely much power.”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, that's a very interesting topic that I thought about for a long time. And I think Are many, many different approaches that can work for getting to human level AI. So I don't think there's one golden algorithm, one golden design that can work. And I mean flying machines is the much worn analogy here, right? Like, I mean, you have airplanes, you have helicopters, you have balloons, you have stealth bombers that don't look like regular airplanes. You've got all blimps”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“People are forced to take it seriously because that's the way modern society works. So it's still not as mainstream as cancer research, just as AGI is not as mainstream as automated driving or something. But the degree of mainstreaming that's happened. The last 10 to 15 years is astounding to those of us who've been at it for a while.”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Know 15 years ago, there's been an amazing mainstreaming. You could say the same thing about super longevity research, which is one of my application areas, and I'm excited about. I mean, I've been talking about this since the 90s, but working on this since 2001. And back then, really to say you're trying to create therapies to allow people to live hundreds or thousands of years, you were way, way, way, way out of the industry academic mainstream. But now Google had Project Calico, Craig Venter had human longevity incorporated. And once the suits come marching in, right? I mean, once there's big money in it.”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Dream I mean, Google Brain has a lot of amazing AGI oriented people also. And I mean, so I'd say from a... Public marketing view DeepMind and OpenAI are the two large well funded organizations that have put the term and concept AGI out there sort of as part of their public image. But I mean they're certainly not, there are other groups that are doing research that seems just as AGI-ish to me. I mean, including a bunch of groups in Google's main mountain view office. So yeah, it's true. AGI is somewhat Away from the mainstream now, but if you compare it to where it was.”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Well, they have billions of dollars behind them. So, I mean, in the public mind, that certainly carries some right. I mean, my also”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Something which is the pursuit of like crazed mavericks, crackpots, and science fiction fanatics, to being a marketing term for large corporations and national leaders, which Astounding transition, but yet in the course of this transition, I think a bunch of sub-communities have formed. Is certainly one of them. It hasn't grown as big as I might have liked it to. On the other hand, sometimes a modest sized community can be better for making intellectual progress also. You go to a society for neuroscience conference. You have 35 or 40,000 neuroscientists on the one hand, it's amazing. On the other hand, Not going to talk to the leaders of the field there if you're an outsider.”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“You couldn't really, it would be very, very edgy then to give a university department seminar the mentioned AGI or human-level AI. It was more like you had to talk about something more short-term and immediately practical than in the bar after the seminar, you could bullshit about AGI in the same breath as time travel or the simulation hypothesis or something, right? Whereas now AGI is not only in the academic seminar room, like you have Vladimir Putin knows what AGI is. And he's like, Russia needs to become the leader in AGI, right? So national leaders and CEOs of large corporations. I mean, the CTO of Intel, Justin Ratner, this was years ago, Singularity Summit Conference, 2008 or something. He's like, we believe Ray Kerswell, the singularity will happen in 2045. And it will have Intel inside. So it's gone from being”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, yeah, yeah. Well, certain architectures for learning using neural networks. So yeah, the AGI conferences are sort of now the main concentration of. People not obsessed with deep neural nets and deep reinforcement learning, but still interested in AGI. Not the only ones. I mean, there's other little conferences and groupings interested in human-level AI and cognitive architectures and so forth. But yeah, it's been a big shift. Back then.”
2020-06-22 · Lex Fridman Podcast · #103 – Ben Goertzel: Artificial General Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source