Ep 564: The Road to Creating Benevolent Decentralized AGI with Ben Goertzel

Topics Covered in This Episode:

1. Decentralized AGI Development Challenges
2. Ensuring AGI Benefits Society
3. Definition Evolution of Artificial General Intelligence
4. Historical Context of AGI Development
5. Human-Level AGI vs Superintelligence
6. AGI Impact on Global Geopolitics
7. Ethical Concerns with AGI Development
8. AGI's Role in Education & Longevity

Keywords:

Artificial General Intelligence, AGI, decentralized AI, benevolent decentralized AGI, SingularityNET, Ben Goertzel, human-level AGI, superintelligence, AI for human benefit, AI ethics, AI arms race, AI development, AI systems, AI and geopolitics, AI and military use, AI in medicine, AI in education, AI and intelligence explosion, time to fume, LLMs, large language models, machine reasoning, evolutionary learning, neurosymbolic AI, biomedical research, AI tools, AI in robotics, AI robots, future of AI, AI and creativity, AI in everyday life, global AI economy, AI and productivity, AI acceleration, beneficial singularity, AGI definitions, AI ethics, AI risks, AGI conference, Reykjavik.

Podcast Transcript

Jordan Wilson [00:00:16]:
How can we ensure that artificial general intelligence benefits everyone? There's been obviously a lot of talk about AGI and what that means and all of these big companies working toward it. But should one company control AGI? And what about what it means for the rest of us? Right? The good that it can do in the world. Well, we're gonna be answering those questions and a lot more on today's edition of Everyday AI. One, I'm very excited about because if you follow AI at all, you definitely know our guest for today, and it's gonna be a great one. So thanks for tuning in. If you're new here, welcome to Everyday AI. This is your daily livestream podcast and free daily newsletter, helping everyday business leaders like you and me not just learn what's happening in the world of AI, but how we can leverage it to grow our companies and our careers. If that's what you're trying to do, make sure you go to our website at youreverydayai.com.

Jordan Wilson [00:01:15]:
We're gonna be highlighting the best takeaways from today's episode in our free daily newsletter, and I can already guarantee you there's gonna be a ton of them. So make sure you do that. And if you want the daily news, that's gonna be in there as well. Alright. Let's have our guest for today. I'm excited, and livestream audience he needs no introduction, but I'm still gonna do it. So please help me welcome to the show, doctor Ben Gertzel, CEO of SingularityNET. Doctor Ben, thank you so much for joining the Everyday AI Show.

Jordan Wilson [00:01:44]:
Hey. Thanks, Jordan. Good to be here. Alright. And, yeah, like, to have the person who literally coined the term, AGI on the show is is a pleasure for me, and I'm excited to get into it. But maybe let's start at the end, Ben, and then we can rewind our way through it. How do we make sure that AGI benefits everyone and not just, you know, one big tech company that may or may not discover it?

Ben Goertzel [00:02:12]:
Yeah. That's obviously an important question and kind of question some of us have been thinking about for decades, although it's now rising to greater and broader prominence. I mean, first of all, I'd say we don't ensure or guarantee anything, and that's just the way it is. Right? Like, when when the cavemen developed agriculture, they couldn't ensure how that was gonna go either. And the jury in a way is still out on whether we're happier than than we were in in in in in the stone age. Right? So, I mean and we didn't have guarantees when we launched the industrial revolution or the computer revolution and whatnot. So, I mean, I think we are taking leaps into the unknown here without a guarantee, and that is what our species has done all along. That that said, common sensically, there seem to be some things we can do to bias the odds in in a positive direction as regards the launch of advanced AI systems.

Ben Goertzel [00:03:23]:
I mean, one of them might be to start using AI for broader human benefit right now instead of using it so much for killing people, spying on people, selling them stuff they don't need, running automated trading bots to extract money from retail investors into in into the bank accounts of large large, investment banks, say, plagiarizing people's creative works and not giving them any compensation. So I mean, a start might be to take the AI systems we have now and use them for broader benefit, and that might set the initial condition for the emergence of general intelligences and and superintelligences that are oriented toward toward broad human benefit. Right? So the the thing is there's there's nobody sitting there in their office in the in a big tower overlooking the world thinking, like, this is how we should be developing AI to maximize the odds of a beneficial singularity. Right? Rather, what happens is AI is bubbling up and moving toward general intelligence from the incredible mess of the world the world economy that that that that we all all see around this. Like, for better or for worse, the AI that we're creating, you know, it treats us the way we treat each other, and it and it's reflecting the whole chaotic mess of of of our of our society. And that's that's kind of what we're doing as we stir the mix of the Internet and see what AGI bubbles have out of it.

Jordan Wilson [00:05:07]:
And and and, Ben, for more, you you know, casual people in AI or people who just started, You know, you've been talking about AGI longer than anyone else. Right? You coined the term. You wrote your first piece of AI code in the mid nineties. So I'm curious. How has the definition of artificial general intelligence, how has it changed throughout the decades? What does that mean?

Ben Goertzel [00:05:32]:
I think I wrote my first AI code in 1980. Oh, in the eighties.

Jordan Wilson [00:05:36]:
I was off by a decade. That's that's amazing.

Ben Goertzel [00:05:39]:
Yeah. I mean, I learned about AI in the early seventies when I was a young child, and I learned about concepts similar to what we would now call the singularity from a book called the Prometheus Project by the Princeton physicist Richard Feinberg. I think I read that book in '73. It had been published in, I don't know, '68 or something. Right? So these ideas have been around a while. I bought a little Atari computer in '80 or something and then caught myself to to code and started trying to code AI. It had 16 k of RAM, so you couldn't do too much. Right? Then when when the Internet came out and the web became a thing in '93, '94, '95, I started to think about decentralized AI because the Internet itself is decentralized.

Ben Goertzel [00:06:35]:
Right? It's protocols, not platforms. So it seemed clear that with the Internet, you had the ability to roll out AI where little bits and pieces of the thought process were in different places around the Internet, and the intelligence sort of emerged emerged out of all that. So while while I started on AI in the early eighties, decentralized AI, you can think about that earlier. But once you had the the web there to play with, it was like, wow. We can see how you would how you would do this. Right? I mean, you you had actual protocols you could use to decentralize AI agents living in different sections of the Internet. I I remember I was at a workshop on agent systems and agent based AI somewhere in Australia in in '96 or so, and I was giving a pitch on the Internet as as this sort of, incubation ground for networks of autonomous AI agents. And, you know, now the last couple years, agents have finally become a big a big thing.

Ben Goertzel [00:07:41]:
But, I mean, the idea ideas have been there a long time. It's just the computer hardware and the networking speed weren't there. Right? And the same is true with what we would now call AGI. Right? So, I mean, I when I first read about AI in the early seventies, I was thinking about AI as it can think like people and ultimately be smarter than people. And in 2,002 or so, I was trying to edit an academic book of papers on how to build real thinking machines rather than the more narrowly specialized problem specific AIs, which the AI field had sort of drifted into over the seventies, eighties, and nineties. And I was gonna call the book Real AI, but I started to feel like there was a little bit too much of a sort of poke at people building no application specific AIs, which are real. They're doing real things. They're some some say it's good things.

Ben Goertzel [00:08:41]:
So, yeah, in the in the search for a better title for the book, myself and a bunch of the other chapter authors for the book for the brewed up AGI Mhmm. Artificial general intelligence. Actually, at first, it was gonna be GAI, general artificial intelligence, which was suggested by Pei Wang, a Chinese AI researcher, because in Chinese, it goes that way, general AI. But, yeah, that that was gay, which we thought was not I mean, while I'm I'm a big supporter of the the the queer universe. It seemed like that wasn't the best acronym. Right? So we went with AGI instead. I later found someone had used that in an essay in, like, '97 in an essay on nematodext. So we we weren't the first ones to use that term in our book or anything, but I think that book is what put it on the map.

Ben Goertzel [00:09:37]:
Then we had an AGI workshop in 02/2006, the first annual AGI conference in 02/2008, and we're doing the next one in Iceland, University of Reykjavik in in in mid august mid August. So we've been sort of pushing forth that term and that meme. And what I meant by it from the beginning was AI systems that could generalize beyond their training and programming at least at the vague level that people can and ultimately more so. And since that time and even in that initial book on on AGI, which ended up being published in 02/2005, there's been attempts to mathematically formalize what do we mean by AGI. Mhmm. But And that and that one's interesting to me. Right? Because I even as I'm trying to

Jordan Wilson [00:10:31]:
learn more about AI, I I go back and look at definitions from, you know, twenty years ago, ten years ago, and it seems like it's always changing. So, from the one that's probably been talking about it more than anyone else for the last few decades, when will you say, oh, yes. We've achieved API. Right? Like, is there mathematical

Ben Goertzel [00:10:52]:
define terms a little better because, I mean, AGI is that's a natural language term. It's a vague term in the same sense that beauty, intelligence, or or life or health are vague terms. Right? And so if you try to mathematically nail down what is AGI, you come up with something that's fuzzy and graded rather than either or. So an example of the sort of mathematical definition you come up with is, say, the ability to achieve arbitrary computable reward functions in arbitrary computable environments. So that that basically means you average over all things that a system might wanna do in all possible environments. And you're like, on average, how good is this system at learning to do random things in random worlds. Right? And that's you can formalize that using a bunch of nice math, and you can vary in different ways. Now what you conclude, first of all, from this sort of very abstract mathematical conceptualization of AGI, First of all, you conclude, like, there is no dividing line.

Ben Goertzel [00:12:07]:
Right? Like, one system will be more generally intelligent than other. They'll be more generally intelligent than another. Secondly, you conclude humans are not very generally intelligent. Right? I mean, by the in the grand scheme of things, like, I'm not very good at at achieving a random computable goal in a random computable environment. Like, I I couldn't even run a maze in 750 dimensions. Right? I I I mean, so I'm I'm quite restricted in this broad scope of things. Now probably a worm is even more so and a rock is is even more so. Right? But then you conclude, like, the precise level of human general intelligence, our ability to achieve random goals in random environments to a certain level is it's kind of like looking at the running speed of humans and defining that as general running ability or something, your general racing ability because, I mean, we run at a certain speed.

Ben Goertzel [00:13:12]:
It's faster than some species. It's slower than other species. We could build machines that can run way faster than us. There's nothing that magic about the speed that Usain Bolt can run at in the scope of the speed of moving in the whole universe. Right? Human level AGI to me is a little bit like that. Like, I mean, there's a certain level at which humans can generalize beyond their programming and and and and and their training. And it's not a super magic level. It's just where we happen to be.

Ben Goertzel [00:13:51]:
Like, it's important to us. Right? Of of of course. Just like being able to run faster than all other people was important to Usain Bolt. It let him win a prize, and it would let him outrun other bad guys who wanna clobber him unless they're holding weapons or something. Right? But but in the scheme of the universe, like, human level AGI or human level running speed are not necessarily that important. But we could look at it a little differently. Like, escape velocity is important on Earth. Right? So it's not important in the whole cosmos, but, I mean, it's the speed it lets you get off the planet.

Ben Goertzel [00:14:29]:
So you could say if humans are barely intelligent enough to build superhuman AI, then maybe well, on the whole, our intelligence level is a bit arbitrary. Like, in that sense, we've reached a critical threshold. Like, given the materials available on our planet, maybe we've reached the minimum general intelligence level needed to build something smarter than ourselves. So my my friend, Jim Rutt is like a master enter entrepreneur. He was a CTO of Thomson Reuters and so forth. He he liked to say humans are about the minimum possible general intelligence. Like, we're we're about as dumb as you could possibly be and still invent computer science and still figure out how to make how to make soup super AI. Right? I I think it's kind of dependent on the resources in hand.

Ben Goertzel [00:15:27]:
Like, you could have some planets where you could be dumber than humans and build a super AI somewhere. You'd have to be smarter. But this is all it's a long winded way of saying that when people talk about AGI, they're usually thinking about human level AGI. So they're thinking about something that can, like, generalize, take imaginative leaps beyond its experience roughly as well as people can. And that's important to us, of course. It's important economically, but it's sort of almost arbitrary for a computer science and and AI view. And the other way that notion of AGI has become confused in recent years is, like, Sam Olin says, wait. We already achieved AGI.

Ben Goertzel [00:16:19]:
Right? And, indeed, GBD 4.5 arguably passed the Sohmalt Turing test, meaning it can fool humans into thinking it's human in a brief conversation. The thing is LLMs achieve a great amount of generality in their discourse, but not by being able to generalize very well. They generalize mediocrally. I mean, it's impressive, but it has clear limits as as Apple researchers demonstrated in a paper that came out a week or two ago, which has gotten a bunch of discussion. The thing is, LMs achieve a great deal of breadth because their training data is the whole fucking web. Right? So they they just need to leap a little bit beyond their training data in order to do a lot because their training data is so much. So that that is a kind of generality, but it's different than having human level generalization ability, which is what so I think AGI is a very broad thing. Human level AGI is a more particular thing, but what it should mean is the ability to generalize as well as humans can rather than just to do a broad scope of of stuff.

Ben Goertzel [00:17:37]:
And being able to do a broad scope of stuff certainly is an important ingredient, and I think it's part of what can help us get to systems that can generalize Mhmm. Really well, but it doesn't get you there on its own.

Jordan Wilson [00:17:56]:
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Jordan Wilson [00:19:16]:
What does that mean? Right? Is it a bad thing if one company is the first to, quote, unquote, you know, achieve that level of of intelligence that you just spoke of? And and if so, why is it important to have a sex

Ben Goertzel [00:19:32]:
and science version? Few different questions here, and there's a lot of unknowns. Right? So one one question, Eliezer Yudkowsky and Robin Hanson in the in the futurist world have posed as sort of the the time to fume. And what they mean there is, like, once you get a human level general intelligence, potentially, it could be a quite short period until you have a superintelligence. Because a human level AGI, you know, if it's as smart as an AGI researcher, it's also got the ability to rewrite its own code and to copy itself and experiment on those copies. Right? So seems like potentially, human level AGI could be followed up not too long after by slightly greater than human level AGI by a little more greater than human level AGI. Bing, bing, bing, bing, bing, bing, bing, bing, bing, bing, till you've got super intelligence. So how fast is that bing, bing, bing, bing, bing? Like, is it a month? Is it five years? Yeah. Right? So if it's a month, then it may matter a lot who gets to AGI first.

Ben Goertzel [00:20:47]:
Right? If it's five years or, say, sixteen years, like my friend Ray Kurzweil thought, he thought we would get human level AGI in 2029 and superintelligence in 2045. Right? If it's five or sixteen years between human level AGI and superintelligence, it's less clear how much it matters who gets there first because there's not that big a moat for anyone. Like, even if Tencent or Google gets there first, within a couple years, everyone's gonna be there, and then you've got this whole population of AGIs. You may have other problems like an AGI arms race or something, right, or an AGI World War three, but but you're not it's not gonna matter that much in terms of superintelligence who got to AGI first. But there's an unknown there of how long it will take for that intelligence explosion to enact itself. And in the grand scope of history, whether it's a month or sixteen years, you know, those are all a very brief period in the scope of intelligence on Earth or even human history. But it does make a big difference in terms of whether the first AGI is also the last AGI before before before the this the singularity. Right? So there's two reasons why it may matter who gets there first.

Ben Goertzel [00:22:11]:
One is if the time to fume is short, then the first AGI may be the one that self modifies itself into a superintelligence. Right? The other the other reason is if the time to fume is even, like, three or five years or something, I mean, then who gets to the AGI first may tell us what sort of chaos the world goes through during that transition because I'm a big optimist about what happens once we get superintelligence. I'm not a Terminator guy. Like, I think barn by and large, the first superintelligence will probably be benevolent and compassionate to its creators, and it will take very little of a superintelligence resources to provide great bounty to its creator species. But I'm not as much an optimist about what happens geopolitically between the first AGI and the beneficial superintelligence that can provide great bounty for all of us with little effort. Like, what happens in that interim period may be a mess if a few large companies and associated military industrial complexes are are in charge. And both of these are the reason I spent a bunch of time working on decentralized infrastructure for for AGI. What I've been doing with SingularityNET and with the artificial superintelligence alliance into which SingularityNET merged with several other decentralized a a AI projects.

Ben Goertzel [00:23:52]:
I mean, I think if we can make the first AGI not just open source, but decentralized and controlled by a participatory network of of, of software developers and and server farm owners and compute owners and so forth. I mean, then I think we have better odds of getting an AGI that is doing beneficial things for more people at at the time when when it comes into being, which I think gives a better odds both in the case of a rapid fume and and and a slow fume scenario. So you kind of alluded to it, and and I

Jordan Wilson [00:24:32]:
do wanna, after this, get to the the the benefits of AGI. Right? What about the downsides? What if one company, right, and you like, there's all these big government contracts, and you kind of alluded to this, but what happens if that first iteration or declaration of AGI goes wrong?

Ben Goertzel [00:24:55]:
None of us knows, and it depends on how it how it goes wrong. Right? I mean, I think if if we had a rational, benevolent, democratic world government, which is more and more of a laughing stock year on year, right, in every dimension, both the rational democratic part and the world's part. Right? It could well be a reasonable thing to say, let's slow down AGI development until we better understood how to make the singularity come out well. It will be an ethical trade off because AGI can cure death. It can cure world hunger, which we're egregiously failing to do. It can do a lot of good. On the other end, if it goes wrong, it could do a lot of bad. And there would be a real ethical trade off for this hypothetical, rational, democratic, benevolent world government to consider.

Ben Goertzel [00:25:50]:
Right? So we're very, very far from that scenario and apparently getting further, not closer, at least on a on a local year on year basis. So the default seems to be all out AGI arms race between US and China and with Xi Jinping possibly as the rational benevolent adult in the room. Right? So I I mean, this is this is a in this sort of scenario, what happens is anybody's guess. Right? Like clearly, if big tech in US or China makes a breakthrough to AGI first, they will have no choice to put that AGI in the hands of their corresponding military and intelligence establishment, which will use it to achieve some version of partial global hegemony for the the corresponding great great power. Now the good news is neither Trump nor Xi Jinping nor Putin nor TenTen, nor Yandex, nor Google wants to kill everybody and and leave the Earth like a desiccated wasteland. Right? Like, I mean, we have a fair bit in common as human beings, and what one of which is, like, we want our species to continue. Like, we we want the earth to be a reasonable place to live. We all want air we can breathe and and water we can drink, and we we we all wanna be able to have our kids and grandchildren live on and and have a good time.

Ben Goertzel [00:27:24]:
Right? So, I mean, while while there are downsides, I mean, I I I would say the actual psychopaths who wanna kill everybody are not the ones who are likely to develop to develop a AGI. Right? So Yeah. I mean, what what you're likely to see in this case is the early AGI is focusing more attention on making country a stronger than country b, rather than company a sell more products than than than company b. And the actual good of our species and the birthing of a positive singularity, we're attempting to achieve that as a subgoal of achieving domination for a certain company or or or or or country. Right? And I I mean, I think I think we can still get to a beneficial singularity in that way, but you're gonna wind up taking more risks and causing more damage on route than would really be necessary if you weren't trying to achieve the really important things as a sub case involving this or that party to lord it over the other the the the other parties they they feel they're they're competing with. Right? And this, I mean, this devolves into a long list of of special cases in terms of what does that AGI doing initially. Right? But, I mean, you can see the way things are going. Like, post deep seek, The US VC community is more and more into AGI for robotics rather than just software.

Ben Goertzel [00:28:59]:
Well, you know, what's the most obvious UK use case for AGI robots. Right? I mean, you can see it in the, in the Middle East battle battle theater right now. Like those, those use cases are gonna become mature before home service robots or or or or or medical robots. Although in a way, all of them will rise together, but the military case needs a lot less safety tests. Right? So, I mean, there's there's a lot to worry about in the short term, even though clearly tremendous benefit. Also, I mean, if you look at I mean, I myself am working on some robotics use cases for education and for medical and and elder care robotics. And then in in science and medicine, like, the ability of AI to help you discover new therapies to combat disease and and and and prolong life. I mean, along with all the other kinds of scientific discovery, like, my own main use of AI personally is with my researcher hat on.

Ben Goertzel [00:30:09]:
Like, the way AI accelerates biomedical research or AI research is incredible. Like, I don't think LLMs are the golden path to AGI. I think they can just be one component of sort of hybrid systems that put all of them together with machine reasoning, evolution learning, learning other components. I don't think LMS on their own can get us to AGI, although they can be part of an architecture. But on their own, they make me, like, five times as productive as a scientific researcher and an AI developer. Right? So, I mean, it's incredible. We're we're already well past the point where AI tools are massively accelerating the advent of better and better AI tools. Right? Which is one of the things that gives you a, like, woah.

Ben Goertzel [00:30:55]:
The singularity is near feeling in in in in practice. So I would say even now, though, AI is being developed in a corporate slash military way. Like, the tools are helping with every kind of scientific research, good good good and bad, and adding a lot of acceleration.

Jordan Wilson [00:31:17]:
Yeah. And and you kind of there talked about some of the, you know, obviously, commercial or intended commercial outcomes that would come via, you you know, AGI and also geopolitical and talking about some of the potential, you know, negative outcomes. But what about on the positive side? Right? Might we see longer lifespans? You know? How does it that

Ben Goertzel [00:31:40]:
are available? Education education, it's incredible. Like, so my my seven year old son, Corky is super bright kid, unsurprisingly. He's into math. He's a bit dyslexic. Like, he sees every letter or word backwards. So his his reading is okay, but not as advanced as his math. So, I mean, he can he can ask chat GPT any question he has. It it will it will answer him.

Ben Goertzel [00:32:07]:
Right? So he's he's learned an incredible amount from having speech to text. And, I mean, for these years when his dyslexia gets better and better each year, but, I mean, like, at his age, I was reading every encyclopedia I could get my hands on. He would do that too, but he reads slowly because of dyslexia. So just the fact that AI lets him ask in words any science question he he he comes up with. Right? Like, he he you know what? Who who who will win? A lion or a silverback gorilla? Why? What's the evidence? Right? So, I mean, just being able to you know, what what what what which of the dwarf planets has an atmosphere? Why doesn't that one have an atmosphere? Like, as a seven year old, being able to dig into whatever you want on your own time, even if your visual system makes you slow in learning to read, like, I mean, this is amazing for for education. Right? And that's just running at home after school. Right? But school systems will be slower to adopt these technologies. It will it will come over the next next few years.

Ben Goertzel [00:33:13]:
Right? So and, yeah, for biomedical research, I'm I mean, I'm I'm working in a project called REJUV, and we're we're looking at trying to discover new longevity therapies. And again, you know, LLMs are not good at discovering new hypotheses and therapies, particularly. We have other AI tools within our hyper on neurosymbolic AI system that are better than that. What we've used LLMs for, take millions of datasets literally from all around the world that biologists put online and normalize them all into a common form and suck them into a big AI knowledge graph. Right? It's ironic, but until LLMs, we were held back not from having we we had AI that could crunch biology datasets and come up with new suggestions for therapies and even automatically run lab equipment, But we were held back by data preprocessing. All these millions of biology datasets are in different formats, and they're not normalized the same way. And the row and column errors in the spreadsheets are hard to read. And I'm then sifts through all that.

Ben Goertzel [00:34:23]:
They can suck all these biological datasets into a standard form so your other AI tools can can can analyze it. Right? So you're this was totally not why they were created, but they're they're helpful for it for it anyway. Right? Now now the same tools will let US, Chinese, or Russian intelligence normalize all the random data in on the Internet about everyone in the world so as to, you know, spy on them and and take advantage of them in in in in in different ways. Right? So it's the same the same technology that was just developed originally to be a chatbot turns out to be useful for managing datasets of all different sorts for for good and and and for ill. And I mean, that's amazing in all sorts of ways, both on the back end discovering therapies for people to ache to prolong their lives, and on the front end, like, giving tools that kids can use to to self educate. Right? So, I mean, it's but the scope of applications is exactly why, like, this is not gonna slow down. Right? It's just making too much money for too many people and delivering too much value to to to too many peep.

Jordan Wilson [00:35:42]:
Yeah. Speaking speaking of delivering value, you've delivered a lot in today's conversation. But, you know, as we wrap up today's show, what is the one most important, takeaway that you want people to remember, when it comes to this concept of creating in the path to an AGI that benefits everyone?

Ben Goertzel [00:36:03]:
I think the most important thing for people to remember is that we are doing this. All all of us are doing this together. Like, the the singularity, the emergence of AGI superintelligence is not something being done by a few, like, Stanford graduates off in in an office on the Santo Road in in in in Palo Alto or something like this is being done by the whole global economy cooperating together in quite complex ways. The story is not yet told, and there's loads of ways for all sorts of people to jump in and participate. I look like DeepSeek was a headphone out of Hong Kong. All of a sudden they disrupted what everybody was was thinking regarding how expensive it would have to be to train to train AI AI models. Right? And, you know, I I started in 2013 in AI development office in Addis Ababa, Ethiopia, and we've then pulled in hundreds of AI developers in Ethiopia to build all sorts of super advanced AI tools. These people, you know, brilliant young guys must have never been out of Ethiopia, let alone to to to to Silicon Valley.

Ben Goertzel [00:37:34]:
And beyond tech work. I mean, podcasts like this one or blogs anyone creates, if they're telling the truth about how AI works and how it may impact different sectors, or even telling how people are thinking and reacting about these things. I mean, all of these things contribute to what our species is doing to create AGI and ASI. So I think we should all feel very empowered and feel like we are participants in building this crazy ass future. Right? And there is no plan. Like, that's one of the things I realized when I got about 10 years old. Like, before that, I thought there were some people somewhere in the world who knew what the hell was going on and were pulling the strings and orchestrating things. Yep.

Ben Goertzel [00:38:23]:
Around age 10, I realized, like, holy shit. Nobody on this planet knows what's going on. Nobody is in in charge. Right? And my my friend, Leslie Allen Combs, a psychologist, gave a talk at our conference on beneficial general intelligence last year, and he ended it with a quote from Ram Dass. He said, like, relax. Nothing is under control. Right? And then I mean, depending on your frame of mind, that's either a big relief or totally fucking scary. But the truth is nothing is under control.

Ben Goertzel [00:39:02]:
It's open ended. It's all evolving. We're all playing a role in it, and we can't calculate how big an impact any of us is gonna have. Any of us could be having the critical impact making the difference between a beneficial singularity and otherwise. I get it could be a blog post that you yourself write that influences some young kid to think about things differently and build something amazing that that tips tips the balance into a beneficial singularity. Such

Jordan Wilson [00:39:33]:
an insightful, eye opening, and excited, exciting conversation. Doctor Ben Goertzel, thank you so much for joining the Everyday AI Show. We really appreciate your time.

Ben Goertzel [00:39:43]:
Yeah. Thanks for having me.

Jordan Wilson [00:39:45]:
Alright. If you missed anything, it's all gonna be on our newsletter. So thanks for joining us. If you haven't already, please go to your everydayai.com. Thanks for tuning in. Hope to see you back tomorrow and everyday for more Everyday AI. Thanks, y'all. Alright.