How AI Could Solve Humanity’s Biggest Problems

a huge difference between the original intent of building thinking machines and building narrow AI. A few of us got together and and said we thought the time is right now to go back to the original dream of AI. Today we have a guest who literally coined the term AGI, artificial general intelligence. He's a mastermind who took a software company from his garage to a 400 employee company, taking it public in just 7 years. He's a serial entrepreneur, engineer, and AI pioneer with a lifelong mission to bring human level AI to the world. Currently, he's building a company with a direct path to AGI called IGO AI. Please join me in welcoming Peter Boss, CEO and chief scientist at Igo AI. What does that even mean, AGI? Yeah, the the term has really changed a lot since we coined it. Three of us coined it in in 2002. I'll give you a little bit of the history of why we actually coined the term when the term AI was coined uh 69 years ago. The original idea was to build thinking machines. Machines that can think and learn and reason the way humans do. You know, can learn a wide variety of things that a smart human can. That was the original intent and they they figured that they could solve this problem in like two or three years. Now, of course, it turned out to be much much harder, you know, now 70 almost 70 years later. So what happened the field of AI really turned into the field of narrow AI where you pick one particular problem that sort of requires human intelligence human level intelligence and you then figure out how you can write a program to solve that problem. So a perfect example is deep blue the IBM world champion chess uh program. So building narrow AI because in narrow AI it's actually the external intelligence. It's the intelligence of the programmer that solves the problem and how to get a computer to play chess. You know what particular algorithms to write and how to optimize it to play chess and then you want to do something else uh container optimization or medical diagnosis. And again it's the intelligence of the programmer or the data scientist. The field of AI really didn't live up to the promise. So in 2002 a few of us got together and and said we thought the time is ripe now to go back to the original dream of AI to build thinking machines. So we decided to write a book on that on that topic but we felt we wanted a different term and we came up with artificial general intelligence. Um in fact agi the G also respond corresponds to in cognitive psychology uh little G really uh refers to IQ or or general intelligence. So it's having a system that can by itself figure out how to play chess or how to do container optimization or to do medical research or whatever. That's really what AGI is. a system that has the general ability to learn lots of different things by itself uh largely by itself. So that that was the meaning of AGI. Now of course in the last few years with chat GPT and the massive amount of money coming in the term AGI has sort of picked up again. People picked up on that and say that's what we do. Now it's turned into into a big mess because now it's become a marketing term, a term used for funding. you know, we can do AGI, but then you have ridiculous statements like Sam Alman saying, "Oh, we'll have AGI soon, but it's not going to be a big deal." Which makes no sense. If you have AGI, it's a big deal. And um so there's this confusion really about what AGI is. And AGI really requires a system that can adapt to changing circumstances. And that's also we can talk more about that but that's also the reason why large language models cannot be the path to AGI because they cannot adapt. AGI means a machine can conduct independent thinking. Yes. It can specifically that it can learn new things and adapt to changing circumstances. So it means it needs to be able to kind of think on the fly. You know like we having a conversation now you know I'm learning from you, you're learning from me. we are kind of updating our model and we come up with new ideas you know and and and we learn incrementally as as time goes by. Now of course AGI is more is is also about uh solving difficult problems you know being cancer researcher or you know a master programmer or whatever but the system can basically teach itself so the autonomy not having to have constantly a human in the loop to and Igo's approach is cognitive AI and how is cognitive AI different from the large language models DAPA actually a few years ago they came out um with with a nice sort of categorized ization where they talk about the three waves of AI. So the first wave people now refer to as good old-fashioned AI which is basically largely logical based approaches uh and again deep deep blue would be a good example of of that you know so you have lots of logical rules maybe also some statistics but that's basically the first wave of AI which pretty much the first 60 years like we're more or less at certainly 70s ' 80s ' 90s was sort of all wave one was there any application of that expert some expert systems you know Um but they're sort of more or less handcrafted decision trees and then of course like again a deep deep blue a chess chess playing program you know container optimization and when did that come out deep blue I think was uh either late 90s or around 2000. So you know were sort of sporadic successes in in narrow applications for for AI in the sort of first wave. Now the second wave hit us like a tsunami about 12 13 years ago when people started to figure out how they could use masses amounts of data and masses amounts of compute to actually do useful things. So the big companies like you know like Google have a lot of data have a lot of compute. So that was kind of how can we leverage that and and and that is basically the second wave is statistical big data approaches. So where you have a lot of data, a lot of compute and you number crunch all the data you have and end up with kind of some kind of a model you know that the in in the early days that was sort of deep learning really and um tremendous success with improving speech recognition you know translation image recognition and that kind of really opened the door towards self-driving cars and and you know things like that. So deep learning that was really kind of a a big advance in the field of AI this second wave big data approach and that is now over the last few years of course uh moved into generative AI and chat GPT which took this to a whole another level by scaling even bigger and bigger systems and you know having this sort of huge oracle that has masses amounts of knowledge uh that that's built into that's trained into into the model. So that's the second wave of AI is big data statistical approaches and generative AI chat GPT is very much part of that and even the the sort of the latest what they call agentic AI is still very much part of that because it's it's all based on big data and now in the last few years it's all been transformer technology you know which is just a particular way of number crunching and and uh the the data now so that's the second wave the third wave according to Diaper is really this adaptive uh adaptive AI that can learn by itself. It doesn't need masses amounts of of data. It works more the way our our human brain works. Our mind works. You know, you you have to consider just how different that is when you think that our brain uses 20 watts of power, not 20 gawatt. We don't need a nuclear power station to power our brain, you know, and a child can learn with maybe a few a few million words, if that. Some people argue even humans can be like LLMs. We're always predicting the next word, what to say. Yeah. But I mean, we have higher level processes. We know when we don't know something, whereas an LLM doesn't know when it doesn't know something. It'll just make up stuff and sound very confident in this. Yeah. Hallucinate. And it's very confident in telling you something that's complete garbage. You know, a couple of differences we can go into, but really the biggest difference is that cognitive AI is much more like the human brain that it can learn incrementally in real time with much less data. And is that the third wave? Yeah, that's a third wave of AI. So that's cognitive AI and that's the approach we've been following really since I coined the term um you know that that sort of it makes sense for a system to be able to learn as you go along. So you know while these large language models are are very powerful and very useful many things they can never be reliable because they don't know they they don't know what they don't know you know and the other problem is they can't adapt to changing circumstances and I I'll give I'll give you a very kind of clear example of just how severe that limitation is you know we had recently had presidential elections and the day before the elections we didn't know who was going to win by the you know by the time the results were out suddenly a lot of things change. So your model has to be updated. So now with large language models you now number crunch for for 2 months or so before you have a new model with a new situation. But even that wouldn't be up to date because the cut off of the data will be whenever you start your training. So it's it's really they're not predict it's it's not at all the way humans you know I mean as soon as we know the result something major changes in in the world we start to think through the implications and kind of we update our model what we now expect uh to to happen you know they simply large language models second wave of AI simply cannot be updated in real time it's impossible in fact I authored a paper where we reviewed more than 200 research papers to to see what is the state-of-the-art you know people trying to get large language models to learn incrementally there wasn't a single one that can update the core model itself can't be done that's why companies spend hundreds of millions of dollars I mean Elon Musk just on grock he spent $5 billion to build a data center I don't know how much Grock cost but many hundreds of millions of dollars to to to to train it maybe half a billion or billion who knows um but then you use this for a few months and then it becomes outdated and you throw it away. If they could update them, they wouldn't be throwing them away and building new models every time, you know, so it can't be done. So it's a very severe limitation. So the third wave cognitive AI is really makes much more sense and it's ultimately what you require to get to AGI. How would one even go about that? Is that a trade secret? Like is that something you guys No, no. I mean I have written not you know not in total detail. We're not open source or so, but the way we go about it is we have a a knowledge representation. Um, it's a vector graph database that that we've developed. A very high performance. Uh, our graph database is literally a thousand times faster than any commercially available graph database. So, you have this knowledge representation that is specifically designed that it can be updated in real time incrementally, which means also it isn't opaque. It isn't a complete black box and it it uses mechanisms actually similar to the way our brain does you know is that you you know we can hear one sentence and we can learn and we can think through the uh the implications some other so I can talk about a few of the technical details so the knowledge representation is really important having that in a way that can learn incrementally it's also important to have your knowledge representation in what people refer to as neuro symbolic so the neuro part is really the way um large language models work. So they they have fuzzy pattern matching basically you know where they kind of this pattern seems similar to another one and that's what they use to predict the next word. So you have this fuzzy pattern matching but then you also need high level logical thinking that is what's called symbolic logic basically. So you have neuros symbolic in our approach the data representation for both the neuro aspects of it and the symbolic aspect is one uniform database. So our system can switch between the two different modes of thinking. And in fact there's kind of a parallel in cognitive psychology. You may have heard of system one and system two thinking. Uh Daniel Canaman is famous for developing that thinking fast and slow. Exactly. Yeah. That's exactly it. where you know system one is sort of the automatic responses we have that's much more like a large language model but then we have our upper level our metacognition our you know logical thought that supervises that and then that kind of check oh no I'm not sure here or I need to think a bit more and it directs our you know thought cognitive architecture is also in in can operate in those two modes you know it can operate in sort of system one mode or system two mode they're not two separate systems It's more like the mode you're operating in. I'm just trying to imagine the world where this cognitive AI exists right now. Okay. Chat GPD is a chat box people can wrap their heads around. It's very simple. You prompt question you get an answer in the world with cognitive AI like is there some application that you have envisioned that would exist that could be used by the masses. Oh, absolutely. And to me cognitive AI what we focusing on in our company right now is to get to full AGI which means adult level intell general intelligence. Over the years we've alternated our company has alternated between development developing the core technology and then commercializing it. You know that's to try and make money and get investments. We had to commercialize systems that weren't really anywhere near AGI yet, but also already using a cognitive architecture. But now our company is 100% focused on taking the the core technology all the way to human level AGI. The implications of AGI, the benefit of AGI to humanity is just tremendous. I can I can give you kind of three areas. They're more more ways of carving it up, but let me explain it in in sort of three different buckets. Uh the first bucket would be an AGI can be a very very powerful researcher uh in any field really but let's take for example um cancer research. So an AGI can teach itself to become a PhD level cancer researcher. Actually let me go back one step and and just sort of sketch out what an AGI would be sort of the generic AGI would be capable of. Think of it a smart college graduate kind of general training. you know, some statistics, some mathematics, some you know, a kind of general knowledge, but you know, very small smart, highly motivated college graduate. That's sort of your baseline of of AGI. But once you have that, that AGI can now teach itself like a like a human except much quicker uh to become say a cancer researcher. But it could also be a researcher in battery technology or you know nuclear power pollution control or you know what, whatever. But you now have this AGI cancer researcher. You can now make a million copies of that. Now you have a million PhD level cancer researchers chipping away at the problem, pursuing different kinds of avenues of of research, but also communicating with each other much more effectively than humans. They don't have egos getting in the way and you know they can kind of copy whole bits of knowledge that they've acquired. So imagine just how much faster the progress will be for us to conquer diseases and and and solve technical problems you know in you know whether it's nanotechnology or you know energy or pollution that would be the biggest use case like finding cure to cancer figuring out how we can um use nuclear energy yeah absolutely I mean just think of the benefits of problems that humanity has been trying to solve for a long time uh that will just be accelerated tremendously so the benefits we to that. So that's kind of the one bucket which is super exciting on how that will improve human the human condition. Yeah. The second area is kind of more obvious is that any deskbound job cognitive job can now be automated at a much much lower cost than humans. So you have a dramatic reduction in the cost of goods and services which means everything is becoming cheaper because you know it can can be automated in in in an effective way because again this smart college graduate can teach itself to become you know an accountant um you know a programmer a manager you know what whatever so that's kind of the second second bucket that's very obvious. The third one I'm actually almost most excited about and that is what I call a personal personal assistant. Now, why two personals? Yeah, why two personal? Two different meanings of the word personal. The the one is that you own it. Serves your agenda, not some mega corporation's agenda. So, it it it's there for you. That's the first personal. You own it. You control it. Serves your agenda. The second personal is it's hyperpersonalized to you. It learns over time whatever you want to teach it, you know, about your history, your goals, you know, your personality, your likes, your dislikes, who your friends are, what you're trying to achieve and so on. So imagine having this kind of personal personal assistant that is with you all the time and you can use it. It's almost like an exocortex, like an extension of your own brain. Then why I'm so excited about that is it'll make us better people. It'll help us think things through and mistakes that we might make otherwise, you know, getting into a wrong business relationship or personal relationships, you know, whatever it it might be, we will have a personal personal assistant to help us think things through and give us advice apart from doing the dirty work for us like dealing with insurance company and banks and and and stuff like that, you know. And that's AGI. It I mean, immensely powerful and immensely beneficial to humanity. That's why I can't think of wanting to work on anything. It's extremely exciting. It's extremely scary. I have to say that it's extremely scary. I just cannot believe that we will have so many new jobs created that will fill in the demand for 8 billion people. I think the next big question is like what will humans be doing? Let me turn that around because you know people think of unemployment as a as as a scary thing, but let me turn that on its head. If you ask most people, you know, I ask you, would you like to win the lottery? Yes. Yes. You know, everyone is there. Of course, you almost everybody maybe a billionaire will say, well, no, I don't care. Most people would like to win the lottery. Why do they want to win the lottery? Because then they can do the work they want to do or not work. You know, whether they want to travel, spend all their friends and family and kids or do education or, you know, do some research, educate themselves, build something, whatever. You have that total freedom. then if you don't have to work anymore because the amount of wealth created through AGI will allow people to do that. If you look at history actually the work week has shrunk tremendously you know when when people work yes uh very solid statistics people would you know on on farms would you know work 14 hours every day to to make a living I just feel like the concept of money will change like you know how we had a barter system yeah then now we have the financial system I just feel like it's going to change the overall like I know people are experimenting with universal basic income but I personally don't know if uh What's going to be the next phase? We'll have AGI to also help fig figure it out. There will still be money. There will still be value because there only so many houses on top of Beverly Hills. Only so many people who can come come to a live performance of your favorite artist or or whatever. So there will always be scarcity and that scarcity will will basically people will be exchanging money, exchanging value uh for that. But people will not have to work nearly as many hours if if any hours at at all to to be able to afford a very good if you want to do it that way. And different people will have different degrees of authority and and uh autonomy that they want to retain. Some people will say look just kind of help me manage my life you know and I'll just watch TV. And this sounds extremely exciting because the CEO of Nvidia, Jen Senuang, came out and said humanoid robots are having their chat GPT moment right now where every household will have a humanoid robot like helping them with the basic chores that we find mundane and then each robot can become impacted with this AGI. Robotics will come after AGI because uh I mean at the moment robotics is sort of the big hot thing that's raising a lot of money. Robots aren't you're not going to put a robot in your house until it has human level general intelligence because it needs to learn on the fly. Yeah. How to deal with you got a new pet, you know, or you got a new child, you've got visitors coming, how you treat different kinds of visitors in the house, things change, something breaks in the house. You can't pre-program all of that. The system has to have general intelligence to be able to know what it doesn't know. So robotics will be fine in the factory or in very confined kind of situations. But to have a robot in the home as a personal robot, you will need to have AGI first. And AGI will actually help to make that happen. So a lot of these new models, they claim that they have a lot of reasoning capabilities. And how does that going to differ from the reasoning capabilities of the AGI model? First of all, that that reasoning ability is actually very brittle. it only really works well within the particular things that it's been trained on. Again, it doesn't know what it doesn't know and in and you know, some of the bloopers it makes are really really bad. There's also a big distortion in the in how to assess large language models that they've been trained on a lot of the benchmarks and the benchmarks are very impressive like the law firm exam or the Right. Right. But they kind of they know what the answers are. You know, it's it's you you don't know how much contamination there is and there's a lot of contamination where basically well they just know the know the answer because they got it in their training training data and the reasoning is similar. There many many examples where they fall down dramatically on that. In fact, recently there was a paper that the top large language models cannot learn how to multiply numbers. they break down. I've used uh like one that's one thing I actually have to talk about like how come it's so bad at mathematics and it's never I have to always double check and most of the times it's incorrect because they don't they don't learn things in the way we learned you know a child learns or we learn mathematics they do it through this sort of pattern recognition and based on the frequency on how many times they've seen a particular thing so they don't know that to multiply two bigger numbers you actually have to go through this procedure like we do in our in our heads, like we've learned to do that. And they don't even know that they should use a calculator, which they could, to solve the problem. They simply don't have a an understanding of what they know, what they don't know, and when to use when to use a tool or when to use this. That's called metacognition. They really don't have metacognition. They don't have control over their own thought processes. And how did you think about the new approach by Deep Seek compared to open open AI? Oh, Deepseek is really not uh not that different. you know they've just been very smart to combine different techniques and to do some take some clever shortcuts what they can achieve is really no better than you know it's just they require less less power this compute but they don't do anything that other large language models can can do one one of the applications at iiggo was u building the voice agents currently and is there any product that people can use right now or everything is in beta right now yeah so we we had a commercial uh venture where we took our core engine and then built it out specifically for call center operation. Our best known customers 1800 Flowers and Harry and David group of companies. It's about 12 companies. And in fact, last Valentine's Day, we replaced over 3,000 agents that they normally had to hire just for one week with our technology. It wasn't AGI, it was cognitive AI, but there was a lot of human input in terms of rules that we put in, grammar rules and reasoning rules that were kind of handcrafted, you know, programmed. So, didn't have the flexibility to to learn that. Now, um, and seven months ago, we decided to put our commercial business completely on hold to concentrate on building AGI. Why should we go after, you know, scaling to 10 million or even a hundred million revenue with a commercial business? AGI is a multi-t trillion dollar business, which it literally is. Yeah. Because a lot of companies are chasing the lowhanging fruit, you know. Yeah. Well, of course, it makes sense. You know, as a startup, there are a lot of opportunities to take large language models and tune them to do a particular job. Yan Lakun, the uh chief scientist of Meta, he put it very strongly and I agree with him. large language models are an off-ramp to AGI, a distraction, a dead end. That's how strongly he puts it. In fact, he tells his students, don't bother learning large language model technology. That's not the future. You know, the future is something more like cognitive. He doesn't use those. Clearly, what he we're referring to is essentially the third wave and cognitive AI that can learn and reason, you know. And how can someone go about learning the next wave? Cuz I'm interested in it now. I'm thinking why why should I focus on my energy of learning how LLMs work when this third wave is going to it it's really hard because at the moment all the money is still I mean there was another announcement somebody is raising another $1 billion at a $30 billion valuation for large language models you know there is still so much momentum it's sucking all of the all of the oxygen out of the air for any other so you you won't find any university courses for the third wave or cognitive AI they used to have them like 20 years ago. You have a whole generation of computer scientists and AI scientists that don't know anything other than big data brute force approaches, statistical approaches. They don't know anything other than second wave. And the same is true for VCs and investors and customers kind of that's that's what people know. So to learn about cognitive AI is really hard. I mean there are only like a handful of companies in the in the world that are really dedicated to that kind of approach. And how do you you must be struggling a little bit for sure hiring people to build this. Oh, no. Quite the opposite. Quite the opposite. Yeah. Because we we are not looking for the same people at all. We're not looking people with big data experience. And in fact, that that's a negative. If that's what they've been trained in, they're not going to be useful to us because what we're looking for is is for people who can think about the problem from a cognitive psychology point of view. you know understanding um what what do what does IQ do IQ tests measure that's important what is special about human intelligence how does our intelligence differ from animal intelligence understanding language understanding education how children learn so half of our team is what we what what I call AI psychologist it's a profession I invented so they they think how people think well they think that's their starting point but now they their job is to understand the mind of the AI of the AGI. So to figure out what curriculum what training system do we need to give the AI for it to learn language for it to learn reasoning. Uh so they build the curriculum then then build tests and evaluate it and say okay we need to improve memory or we need to improve reasoning in a in a particular manner understanding that then the other half of our team basically on the coding side but again the coding that that's done is sort of difficult algorithms that we do it's nothing to do with big data and GPUs and and stuff like that so we don't need that expertise so we're not competing other companies you know all um secondwave companies, generative AI companies, we're not really competing for their their their staff. The people who read about our approach and say, "Wow, this makes a lot of sense to me. This is the way to AGI." They're actually super keen to work on our project. So, we have a lot of people, you know, apply to work on our project and you know that it's so No, it's it's not actually hard. It's we don't have the funding to hire enough people. you know, that's um we're only 12 people in the company and for us to get to AGI, we believe uh we need to hire another 45 people to be able to get there within two years. You know, we have a particular road map of of what we uh what we're developing. And it's ridiculous because the kind of money we're looking for is like a rounding error compared to the money that's being spent on large language models. We don't need massive amounts of data. We don't need massive amounts of compute. Our system is trained on an off-the-shelf computer, you know, a single computer. You know, it's it's a very very different approach. As I say, it's much closer to the 20 watts that our brain has and not 20 gawatt of nuclear power stations that need to be recommissioned. You know, you said two years. Do you have a road map for the next two years? Yes. We we believe we can get to AGI with with our approach in two years. Yeah. Because we've you know, we've we've done a lot of work over the last 20 years, you know, with a small team. So, we have commercialized it. So we have proof points in terms of scaling the system and you know core technology. So there's a lot of stuff we've learned and figured out already. So at at the moment we just we need to scale up our system basically to get to this college graduate level. One thing I wanted to ask you I read something about INSA. It's um integrated neurosymbolic architecture. We've been steadily moving towards the beneficial goal of humanity by leveraging our INSA which is an integrated neurosy symbolic architecture which facilitates realtime incremental autonomous learning. I would love to know more about this. You know as I as I said earlier the the approach that we have is a neuros symbolic approach and the reason I call it INSA integrated neuros symbolic architecture unlike other cognitive architectures there have been other cognitive architectures especially like 20 years ago there was quite a big thing and now hardly anybody's working on cognitive architectures at this stage what people worked on like 20 years ago they were very modular so you'd have a cognitive architecture that would would have maybe one module for natural language passing and then have another module for reasoning, another module for memory, you know, and and so on. So they have a whole bunch of different modules and that didn't work very well because these modules didn't really talk to each other effectively. Whereas the way our brain works, you know that this is all integrated and that's what we have. We have a integrated neural symbolic as I mentioned earlier our system can work on a sort of neural network basis where it's pattern matching where it can do fuzzy pattern matching but then it can also operate in a symbolic mode which is logical thinking. So which you know again between system one and system two is that that kind of can switch between you're building the system two basically because the pattern matching has its flaws. Yeah. But the the pattern matching the system one also needs to be able to learn incrementally in real time. So you you can't just take a large language model in LLM and somehow connect it with a reasoning engine. That won't work. You need a uniform an integrated and that's why we call it INSA integrated. You need an integrated system where the knowledge representation between these two modalities is uniform and the system can easily switch between the different modes. In fact, the two modes need to kind of work to together. And I I can give you I can give you an example that I think many people are familiar with if you've ever learned to play an instrument or ride a bicycle or ride a horse or whatever. But you know, when you start playing guitar, initially it's it's the sort of more symbolic, you know, you say, okay, this finger goes here, that finger goes there, and then you strum this string. And then after a while, that becomes automatic. So you can now do that. So that now it's moved into system one where you don't have to think about what you're doing anymore. So now you can concentrate while you you're playing your guitar, you can now concentrate on inonation, you know, and changing emphasis and how exactly you strike and and so on. Once that becomes automated, then you can concentrate on the audience. So you know, but these are two, you know, two systems and then something happens, something goes wrong, a string breaks on, you know, on your guitar or something. System two kicks in, right? what do I what do I do now you know do I continue playing and without the string or you know what do I do so you need that integrated architecture uh for for AGI and that that's our approach and that's why we call it INSA integrated euros symbolic architecture in that's incredible systems too I think why are these other companies not building the system too or they're also building system or they just have a different approach well you can't go from a a deep learning you can't go from a transformer-based system to an incremental learning system. There just is no way. They're trying to sort of emulate this the system to thinking through these, you know, reasoning chain of thought things, but the chain of thought is also pre-trained. It's not generated automatically, which it really needs needs to be, you know, to to different things. It's it's really going back to Yan Lun. Large language models are an offramp to AGI, a distraction, a dead end. There's a lot of money flowing into it. there's a lot of money to be made and people kind of, you know, jump are jumping on that bandwagon, but it really is the wrong path. You think of it, if you're trying to go somewhere north, but you're heading east, it doesn't matter how far you go or how fast you go, not you're not going to get to north and and that's kind of the momentum is there and and it is doing useful things at making people money. And do you feel the AI should be open sourced like these models should be open source or closed source? It's a difficult decision uh to to make because open source is obviously a lot harder to raise money people you know investors not monetize it the well it's just to raise money even you know in investors want that proprietary they want that competitive advantage I mean we're seeing that with open AI right now they can't raise the kind of money that they want with a supposedly open you know I mean they were never really open AI to start with but you know as a basically a nonprofit they they can't raise the kind of I I do believe though that AGI with the right approach will not require these massive data centers. It'll run on an off-the-shelf computer which which will make it available really to everyone in the world. In fact, as hardware improves and these, you know, AGI improves, it it literally will be able to run on your phone. So, you'll have your personal personal assistant on your phone. It'll be available, affordable to just about everybody. then it will become automatically become open source because people will figure out how to do it, how to reverse engineer it and how to different ways of you know achieving cognitive AI. Basically from an investment point of view we want to be the company that achieves AGI. Obviously for our investors there will be a huge payday but also very quickly the benefits will really spread across all of humanity you know through the research on the one hand through reducing the cost of goods and services and through giving us these personal assistant. I love that. Have you seen the movie her? There is no it's not a it's a no-brainer that we're going towards that direction, right? except it'll be your personal personal assistant that you you see there it's was running in the cloud and was obviously ultimately I mean the the end of the story was it wasn't your own personal assistant thousand people's exactly and you don't want that you want your personal personal assistant that you own is dedicated to you that really becomes an extension of your own mind you know with with our technology and our approach uh that's exactly what you'll get if you're an investor Professor, please contact Peter cuz he's on to something here. We are. We are. We are indeed. But it really requires visionary. We are currently looking for a series A. Um, you know, up to now we've been funded on a safe with money I've put in and we've had other investors come come in, but we're looking for the right kind of partner for a series A. Now, we're only looking for 25 million, which is like a rounding error compared to the amount of money that's going in there. As I say, because we don't need masses amounts of compute, our compute budget is is is is minimal, but we're looking for the right kind of investor who shares our vision, you know, unlike where open AI are talking about monetizing it through advertising and stuff and no, we are not we are not going to do that, you know, we are not going to be another Alexa or you know Siri or something that is owned by some mega corporation and they control you know what what it will can can tell you. We are looking for an aligned investor, a visionary who sees the benefit of AGI and sees the benefit of us getting there soon as possible with the right kind of technology, you know, that we can that we can offer. Yeah, absolutely. Um, if you if you think you are that person, we are looking for for a partner to take us to AGI. One last thing I wanted to ask you. You've been an entrepreneur for like five decades now and I was on your LinkedIn. I saw like you've been a founder and chief scientist for so many different companies. What's one advice you would give to entrepreneurs building in the AI space or just generally? So generally if I have to think back one of the biggest regrets I have is I started my first company at 25 and I wish I'd started it earlier because there's nothing like actually you know being a co-founder or being responsible for a company and actually doing that and gaining that experience you know. Yeah. Uh so any and it's not for everyone brutal starting a business. I mean the ups and downs you have maybe an occasional company that a company that started where it's all plain sailing from the get-go usually it's brutal it is that um so the sooner you can learn the dynamics of of a company you know the marketing finding partners employees customers you know all of that learn learn that um that's that's sort of my big advice just go out and do it and if you can find a good partner it makes it a lot easier you If you have like the technical person and you have a salesperson, usually these are very different personalities. You know, the person who who loves to go out and talk to people and, you know, smoo at parties and and and whatever, finds the investors, you know, that's that's a certain kind of personality and it's extremely valuable to have uh in in a partnership. And then, you know, you have potentially the money the the accountant or manager or manager type and then technical person. It it makes it a lot easier if you combine it. Obviously, it's always risky. You may, you know, so you need to think about how a divorce would work if it doesn't work out. Sometimes dynamic just doesn't work out. But if you can find a partner or two, it it makes it a lot easier to to run a business. It really does. And I can also tell that you read a lot from the way you talk. What's some books you would recommend to people? Like what are some books you give other people? Some books that have changed your life. Um they could be technical books. There are so so many books. Yeah. I literally have uh actually when I sold the shares of my first company that I took public, I took off five years just to study intelligence, you know, all different aspects and philosophy, cognitive psychology and child development, psychometric tests and and so I read a lot of books then one of the books that that always comes comes to mind is Douglas Hoffata the mind's eye is a collection of short stories which is kind of a futurist really very cool stories on different ways of thinking, thinking about what intelligence is, what identity, what personality, you know, if we could teleport or if we could clone ourselves, who would we be, you know, and and and really really some neat very neat stories. Yeah, that's probably the the first one that comes to mind. It was great having you, Peter. How can people find you and uh what you're building like what's can you tell them guide them in the right direction? I know the website is iggo.ai, but how can they find you? Yeah, very easy. Peter Voss and I'm on LinkedIn. very easy to find. Also, you can email me peteriggo.ai. Uh, I'm on Twitter. Yeah. So, you know, between the website um and and and LinkedIn and Twitter, you can easily find me. Thank you, Peter, for coming on the show. I really appreciate your time. All right. Well, thanks for good questions.