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this week on the futurists Ben gertzel you could say that the primary use cases for AI in the planet today are selling killing spying and crooked gambling AKA Wall Street which is perhaps not what you really want the first generation of transhuman intelligence to have on its mind

hi welcome back to the futurists I’m Rob tercik with my co-host Brett King I’m here too great to see you Brett and this week we’ve got a great guest this is going to be Kevin gurtzel from Singularity Nets Ben has been at the Forefront of research into artificial intelligence for many years and old acquaintance both of ours great to have you back then great to be here good to have you man um before we jump into that there’s a couple news items so if you like I can just jump in man the news from the future

really two things as a follow-up to last week’s recording we talked a little bit about the China and some of the changes that are happening there um but the stock market has been uh frowning upon recent moves in China to consolidate power around president zeed and as a result the Chinese tech stocks have collapsed uh they’re they’re down significantly the uh NASDAQ Dragon China index has dropped by 20 percent uh since those announcements from the Chinese Communist Party Congress that happened just recently Alabama is down 32 percent uh below its September IPO 1914 uh sorry 2014 IPO and the Chinese currency the raminbi has dropped to its lowest level since 2007. so it looks like China is getting uh seriously slammed uh for the consolidation of power that’s how the market seemed to be responding because I don’t want to buy r b if you’re interested it’s uh the other news is that the United States is looking pessimistic about the future there’s news today from Gallup uh the research agency and they report that 42 just 42 percent of Americans of American adults think that it’s either very or somewhat likely that today’s youth will have a better living standard Better Homes and a better education than the previous generation now that’s always been kind of framed as the American dream but it looks like that dream is sour this is uh an 18 percentage Point drop since 2019. it’s almost 20 drop in optimism um and it’s tied with the previous low which was in 2011. uh one of the things that’s driving that interestingly is uh is two things wealthier people people with higher income around a hundred thousand dollars are way more pessimistic than people with lower incomes uh which I don’t know what that’s meant to tell you and then the other thing is that it’s also a very strongly politically partisan uh view in the future yeah uh which is to say that Republicans are way more pessimistic than Democrats um and the Democrats uh perspective on the future hasn’t changed that much but there’s been a gigantic swing um when Donald Trump was elected president in 2016 Republican optimism swing up 29 percent and then when Joe Biden was elected last year it fell 33 points do you see like a 30 swing in optimism and that’s one of the things that’s driven that’s crazy uh too low since 2011. so there’s the news uh markets reaction yeah I just got one one out of my life that um you know um Department of Education is tracking homelessness for students prior to the pandemic 2018 2019 school year it was about 38 000 um students were homeless Across America that’s ballooned to 1.3 million last year and could Exceed 2 million this year um so we’re looking at a um you know a national increase in homelessness of like five to six hundred percent because of the pandemic on the evictions free market can’t fix this I wonder if people are going to look back at this and and say like remember that time in American history when we thought it was okay to step over someone who was living on the sidewalk it seems pretty common in big American cities we can’t afford to have homeless people it costs like thirty five thousand dollars a year anyway that’s and that’s for a future episode Ben welcome welcome back or welcome to the show um the you know the obvious thing to start with is is um you know how are you and how are your your you’ve had a couple of kids since um you know a couple of children since oh it last came on the show how are they going uh I’m I’m I’m all good I’ve been having it having a great time uh these last last few years and I you know I was living in Hong Kong for 10 years right exactly and I I moved back to the US in early 2020 at the start of the of the pandemic mostly not because of the political changes in Hong Kong which were not having an impact on everyday life there for me so much but because it looked like they were gonna impose more Draconian covered restrictions and I felt I felt like that I felt like dealing with it so yeah I’ve been living on a small rural island in the Puget Sound off the coast of of Seattle so hanging out in the woods and on the beach with my family in a lot of uh AI development and the blockchain research done and it’s been it’s been the productive time this fall I’ve been starting to do business travel again because conferences have been opening up right right you actually had mixed feelings about it because I was being very productive sitting home at the computer uh I can’t understand getting getting stuff done right and it’s uh so it’s been an interesting time yeah I did the I had my fifth child uh during the during the pandemic she’s a year and a half year and a half old now and uh it’s been so productive productive in a number of accounts you know I would say the pandemic and Associated Mayhem probably had the less impact on what I’ve been doing in AI than on many parts of the economy because I mean we’re Singularity net open Cog the various projects I’m involved in we’re we’re a bunch of AI Geeks and programmers already mostly working from home from various locations all over the world already coordinating by on online mechanisms and for the people who aren’t familiar with your work can you tell us about opencog and Singularity Network what are they doing absolutely so my own my own career since the 1980s has been centrally focused on AI and in particular on AGI artificial general intelligence so trying to make taking machines that can really think like people now that that’s the long-term long-term research project which is getting closer and closer as we’ll talk about but along the way I’ve been working on application of AI in a variety of different you know vertical areas both because one more staff one is called doing useful things not not just playing the research lab and also I mean this is has helped for making money to pay to pay programmers and get real data into AI systems and so forth so open Cog is an open source project aimed at laying the foundations for real artificial general intelligence for thinking machines a good thing learn imagine generalized like people first version of open clock system was launched in 2008 we’re now working on and almost from the ground rewrite called open called hyper arm which is aimed to let open Cog really scale up in a way that takes advantage of modern Computing infrastructure then Singularity net a project I found in 2017 is a blockchain based platform that allows AIS of any kind to run decentralized in a whole bunch of computers owned by a whole bunch of different people without any Central owner or Central controller so you can you can run an open Cog on that you can run neural Nets and that you can run a lot of different a lot of different different things on there so I’ve been I’ve been sort of working it multiple levels in in building an AI oriented Tech stack and then also working on applications of these AI Tools in a variety of areas including longevity medicine crypto Finance robotics we got the Sofia robot and of course yes her little sisters grace and grace and uh and Dez Demona and that that piece has been more annoyingly influenced by the pandemic just because you got Hardware instead of just software you got to get parts and pieces and so forth very interesting about AI so you you are um with Singularity net you’re focused on using a blockchain to decentralize AI that sounds really interesting and promising tell me why that’s important um you know is there another group that’s centralizing is AI tend to tend to be centralized and is that important to differentiate from that I mean at at the moment AI is very heavily centralized in its practical deployment and it’s it’s centralized within a small number of large companies and and a few a few large large governments right and that said good and bad aspects there’s a certain efficiency of course that that that comes with that I I think there’s also been a tendency for AI to be channeled according to the the business models of these large companies interests of these governments and I I think that does have bad aspects as well as good I mean the good aspects is you know Google tencent Facebook Microsoft they were pretty efficient organizations that at moving Technologies from research to deployment right so they’re they’re good they’re good at doing stuff and they’re making some amazing things happen on the other hand you could say that the primary use cases for AI and the planet today are selling killing spying and and crooked gambling AKA Wall Street perhaps not what you really want the first generation of transhuman intelligence to have on its mind as it moves moves beyond the beyond the human level right I definitely want to get into the transhuman intelligence idea that’s that’s something as well but um so let me just um sort of take us back to Singularity net and and and then talk about the developments in the last few years because last time we spoke was was pre-pandemic and you know at that time you know you were doing a lot of work at Singularity net to try and build these um various components of artificial intelligence or competencies with a view that at some point we would be able to aggregate this up into ATI but the last few is because of the advances in deep learning you know these general purpose AIS that could lead us to AGI seem to be getting a lot of traction so how is the overall thesis behind um you know AGI changed over the last few years so my own overall thesis regarding AGI and my own thinking about how to get to AGI has changed rather little in the last few years and the ways in which it’s changed are pretty in-depth and mathematical and unrelated to the advances that that you mentioned I mean I posted a paper online called the general theory of general intelligence which outlines some of the work I’ve been doing kind of the unified different cognitive algorithms in this in the standard a standard math framework so my thinking about how to get to machines that can really think hasn’t changed too much I mean one thing that’s changed over the last few years is that very large neural net models trained on huge amounts of data have done more and more cool things right and this is exciting it’s bringing money and attention to the AI world and it’s doing useful stuff what’s the relation of this work to the quest to build real thinking machines is a different question and some my friend Gary Marcus who would be a great guy for your podcast if you haven’t had them yet but so again Gary Marcus if you look at his online articles and videos he’s he’s made a pretty coherent well thought out case that these recent advances in deep learning using large neural models constitute extremely little progress toward real general intelligence because pretty much they don’t understand what the hell is going on I I mean I mean I mean they’re they’re they’re they’re they’re they’re they’re faking it they’re faking it in the very interesting way because they have so much data to use to to drive they’re faking it and in some domains that will work pretty well so like if you look at generating art or something yeah yeah I’m just gonna say that I mean these programs are not they’re never going to be Van Gogh they’re never going to be Andy Warhol they’re not going to come up with some new thing that hadn’t ever been done before because what they’re doing is looking at surface level patterns and compositing them together in on the other hand that’s kind of what many commercial graphic artists or even some well-known fine artists are doing right like you can you can you can you can get some mileage that way so part of the discovery here is like how much of what humans do which we consider impressive and then the lucrative and intelligent how much of it can be just faked by gathering a bunch of stuff and glamoring it together in this sort of a sort of Artful way so that that’s interesting that synthetic imagery and synthetic art is kind of a parlor trick uh it’s like a neural net parlor trick well it’s that’s that’s unfair to it because parlor tricks don’t solve real world problems and they they they don’t make you huge amounts of money so I think it’s it’s a new category of of entity right so it’s it’s more than the Parlor trick and and less than less than and and AGI right because there’s no path there towards uh general intelligence I don’t think so like so in the domain of Music which I’ve been playing a bunch with generative models and music because I one of the things I’ve done the last few years is started playing the keyboard again we started rock band with a robot as as a lead vocalist so we we’ve been playing with AI for generating singing and and generating music and I mean it’s it’s genuinely cool as a musician right like the AI will come up with new melodies or new vocal stylings that as far as me as a musician to play different things that than I would otherwise right so I mean it’s it’s it’s all good it’s not just a cheap demo on the other hand like no way does it come at music with the passion and inventiveness of a of you know a really outstanding original human musician or composer or improviser or something right so it’s uh back to Brett’s question you your Brett was asking whether you see a point where these different strands of Technology will will be integrated or complicated yeah and then somehow maybe is emerging from that is that I do think there’s a path to AGI in which multiple components architected according to different architectures Co-op operate together and you get some emerging intelligence out of it but I think among those components needs to be something with some fundamental capability for abstraction generalization creativity and and Imagination and I think that’s don’t don’t really have this so you can’t I don’t think you can just take a bunch of narrow AIS serving some vertical application functions Network those together in singular argument or anything else and general intelligence

so you need to take a new new run first approach is that what you suggest no I think there’s a number there are many approaches that one could take I mean the approach we’re taking in open Cog actually is you have components that are doing symbolic logical reasoning you have components that are simulating Evolution for creativity and and are doing doing evolutionary learning and you have components that are doing neural net pattern recognition and you can you can network network all of these together but I think I think well that’s the approach that we’re taking which is using some advanced math to structure a variety of different algorithms I think you could also take a more biological approach and try to do you know deep dive girl and gleal and astrocyte modeling and to try to take the biology biology more seriously than than current neural Nets are doing I think there’s there’s a variety of approaches that could be taken to to general intelligence but I don’t think the current deep neural Nets that have sort of fine-tuned to meet narrow application goals based on training and large data sets these alone these alone cannot do it actually or massive data sets are going to be critical right I don’t know I mean the smarter your AI is the smaller a data set you can get away with I mean part of the reason you need such huge data sets is because there’s no generalization happening so you you need sizable data sets but I mean you know mid-journey has seen more images that than I have a gpt3 has seen more language than than I have that amount of that amount of data isn’t really isn’t really necessary so I I do anyway I do think you can get multiple components cooperating together to sort of emerge emerge in AGI but when people hear the word emergent I know what some of the people listening are going to say some of the people are listening are going to say hang on a second you’re saying if you aggregate all these different Technologies and different approaches and combine them together then something magic will happen that’s what emergence is something magic occurs and now suddenly we have intelligence and they’re like I don’t think it’s magic I mean when you put hydrogen and oxygen together to get water I mean the water is what the hydrogen oxygen were not wet but that’s not Magic it’s just yeah but we’re not combining molecules here right chemistry right I mean and this is the same thing in the brain I mean if you put we’re putting the hippocampus and the cortex together and they do stuff that neither hippocampus or cortex does on their own but that okay so you’re saying and replicate that main functions it’s emergence yeah okay so what I’m hearing you say is that if you can replicate certain brain functions in algorithms uh and combine those together then we might have the generalization that you’re referring to that or or am I missing something you talked about an abstraction layer I’m trying to figure out where and when that gets developed and introduced well I think there are going to be many different approaches to building general intelligence and I’m not sure there’s only one only one golden path and I I I I I do think there are paths that are more closely tied to human biology and their paths are less closely tied to human biology so I think one can look at human cognition and the different kinds of learning and memory the human cognition deals with declarative memory episodic accessory memory you know action attention you can look at the cognitive functions that human mind carries out and figure out you know clever mathematical algorithms to do each of these cognitive functions maybe in a quite different way than the human brain does then have a network that combines together different agents performing mathematical algorithms corresponding to different key cognitive functions in in the human mind and there could be you don’t need a neuron in there you don’t necessarily need any any simulation of anything in the brain on the other hand I think you could also simulate the non-linear dynamics of neurons and glia and and chemical and electrical diffusion for the brain maybe even the quantum Dynamics in the water Mega molecules in the brain like you could you could you could you could go all out and do a detailed biological model and you could then get it generalization and and creativity and Imagination from from that route so I think there’s going to be many paths to general intelligence which could lead to different kinds of of intelligence it just happens that deep neural Nets as they exist right now for commercial applications I don’t I don’t think I don’t think those are one of the many pests to general intelligence interesting that that can work all right well that’s a good place for us to take a little bit of a break here we’re going to have a break but just before we jump to that Brett wants to ask you the quick fire questions so take it away my friend okay here’s the quick fire lightning Ram Ben what was the first science fiction you remember being exposed to Star Trek the original Star Trek TV show very cool um what name a futurist or an entrepreneur or scientist that has influenced your thinking and why uh Gerald Steinberg the Prometheus project he wrote a book outline the singularity in 1968 which I read in 73 or so which kind of kind of blew my mind he outlined another technology AGI and immortality and said we had to decide whether to use them for stupid commercialism or expanding human consciousness so I read that at like age seven and I was like so pretty yeah

um this is a maybe a tougher question what’s the best prediction an entrepreneur futurist or sci-fi author has ever made do you think so the the best predictions ever made well I mean Cyrano de Bergerac said we go to the Moon that was a pretty good one mate yeah hundreds of years ago in in the modern era actually my buddy Ray Kurzweil has not done badly I mean he has his track record is not quite as good as he as he is as he markets but it’s it’s pretty pretty good yeah it’s still pretty good pretty good yeah um and then uh finally just before break what science fiction story do you think is most representative of the future you hope for oh of the future I I hope I hope for well I I I I don’t know that I have a good answer for that one actually okay that’s cool that one that one uh may may have may have yet to be written all right good well um that’s uh that’s the first segment done we’re just going to take a quick break and have some words from our sponsors and we’ll be back to talk about how living with alternative intelligences and AIS AI is going to change the future of humanity after the break

provoked media is proud to sponsor produce and support the futurist podcast provoke.fm is a global podcast Network and content creation company with the world’s leading fintech podcast and radio show Breaking Banks and of course it’s spin-off podcast breaking Banks Europe breaking Banks Asia Pacific and the fintech 5. but we also produce the official phenovate podcast Tech on reg emerge everywhere the podcast of the Financial Health Network and next-gen Banker from information about all our podcasts go to provoke.fm or check out breaking Banks the world’s number one fintech podcast and radio show

welcome back to the futurists with myself Brett King and Rob turc as co-host and we have Ben gertzel live from the the coast of Seattle where did you say it was that you’re living these days I’m actually on vassan island which is in the Puget Sound office okay Puget Sound beautiful area of of the country um but Ben spent an extensive period in Hong Kong working in southern China and so that is sort of the subject for news from the future Deep dive today let’s get to it uh so um there’s been a lot of debate um in the U.S press over the the last uh a few news cycles of the progress being made in artificial intelligence in um China um that is a big driver towards the recent uh you know which we spoke about in the last Deep dive in terms of chipset controls and things like that for export and import um that the US has put on China but Nick Chalan the U.S department of state’s first ever software Chief was forced to resign in September of 2021 after he claimed that the United States had no competing Fighting Chance against China in the next 15 to 20 years based on artificial intelligence and in the National Defense report from September of this year so very recent the United the special competitive studies project released a new study called mid-decade challenges to National competitiveness identified that China is continuing to invest and has surpassed the U.S in three key areas semiconductor development artificial intelligence and 5G and this was from the CEO of the defense writers group saying that the U.S has just one budget cycle to get this right uh badge Qatari

umkatari said if we don’t get our act together in these three core Battlegrounds in terms of Bio in terms of Next Generation computing power in terms of Next Generation inventions it’s not going to happen in the countries that the Forefront of democracies today everything will happen in China now we tried to stop them on 5G but the U.S you know clearly is significantly behind on 5G we’re significantly behind on edge Computing and and in terms of artificial intelligence development the later statistics show that China produces somewhere between and it depends you know depends on the stats that you read three PhD to five or eight PhD graduates in the fields of artificial intelligence for every one that the U.S produces so they’re just able to throw a lot more bodies at this problem Eric Schmidt has been one of the real voices of concern in terms of AI development he says the real issues compression of time these systems are going to have to make decisions faster than human decision-making time frames and that’s where the boundary is going to be and we’re going to have a serious conversation about that in society so these short-term concerns you can see playing out the major voice in terms of the AI threat to the United States Market has not been from the market itself but from U.S defense department which sees this as a big strategic threat so how is the U.S going to compete well my call on this is stopping China from developing these Technologies we tried that with 5G and we found Huawei is dominant in terms of 5G standards and Technologies right now globally the only way for us to compete in the United States is by making AI based and stem-based education across the board free because that’s the core infrastructure that you need to develop competitiveness of these skills and while we continue to have this pay for play you know free market approach to education we are restricting the lifeblood force of AI development in in China we have the investment we just don’t have the skills that’s my deep dive for this week Ben I would love to hear your thoughts on this sure so I I did I lived in Hong Kong for 10 years and I had many dozens of visits in into Mainland to meet people in various universities and and companies in in China doing doing all sorts of different work I mean I I think that you know that the push of China into the AI field has been substantial and impressive but still has serious limitations I would say that vast majority of significant AI Innovations have come from the U.S up until today still and with Western Europe following second and China way way behind in terms of really like wild new ideas coming up trying to probably behind Russia or Japan for that matter if you want to spend account then we hear all about what so that is scalable deployment of of AI right so the algorithms the ideas have mostly come from U.S and Western European universities and PhD students not even from Western companies but then the typical pipeline has been professors and PhD students come up with new stuff they prototype it Western companies roll it out first proving it can be done commercially then Chinese companies take it and roll it out better at larger scale and sort of mass Master the the real world deployment right and so that that’s that’s the story as we’ve seen it so far and then when when ethnic Chinese researchers emigrate to the west and embed themselves in Western companies and universities then you often see the Innovation level of what they produce go way up because the the social the social context is just is just different like China hasn’t managed to make a Google Deep Mind or an open AI or say an open car or or singularity in that right but they which is cooking up like wild New Frontier ideas but deployment is also important and they they’ve certainly shown incredible Mastery there now so there’s been this flood of research papers uh you know Brett pointed out this week and we spoke about it with a previous guest as well uh some people say there’s an exponential increase as they always do about anything that goes up and to the right an exponential increase in the number of uh AI research papers being produced by Chinese researchers my question for you is uh there’s a yeah there’s a large number of uh filings but patent filings as well as papers um but there’s a difference between quantity and quality so Ben what’s your perception on the quality of the research that’s being done in China is that significantly better just because they’re throwing more people at the problem I think quality also has to be drilled down into a a little bit I mean I I think there’s a lot of very high quality research coming out of China and also some some lower quality research I think that on the whole the Chinese system is even more biased in the western system toward research which is improving incrementally on previously published stuff according to easily Quantified metrics like getting a couple percent higher accuracy on this Benchmark for for uh you know in image recognition or or language understanding or something and what you’re still not seeing coming out of China is the next radical new innovation that that nobody ever thought of I mean you you’re seeing a lot of papers that do one after the other after the other after the other incremental improvements on on already published results and some well-defined area and that’s that’s not to say that’s bad quality it can be great great quality work but I would say the Chinese system has not yet solved the problem of incubating radical Innovation now they they may not need to do that in order to conquer and Prevail from an economic or even a military standpoint for that matter right but it’s so it’s it’s still a point to understand right like that so one of the things you’re pointing out there’s a distinction between basic science like you know basic scientific research and applied science um and and in the case of applications it looks like the Chinese are actually moving ahead a good example of that is Chinese metal wallets actually and the approach that players like alipay or ant group have taken to aggregating wallet capabilities you know I’d be very surprised if most of the world isn’t using Chinese mobile wallets by 2030 because of their approach to this which you know is it you know for MasterCard and Visa that’s got to be a huge threat but that’s a a different angle unless unless they’re using crypto wallets which are developed outside of China Banning crypto because of their their need to continue of course well the cbdc because they see that crypto as a computer against that but so Ben I want to dive a little bit into um this concept you know you’ve been involved in humanity plus and and other areas of the transhumanist movement for for many years um so you know we had Zoltan Estefan on a few weeks ago uh talking talking about um that movement but just let me ask you this question is how do you think we’re going to absorb um you know first of all alternative intelligences into our sort of worldscape you know um as as humanity and you know how we’re going to respond to you know superhuman intelligence over time you know how is that going to change the way we view intelligence itself do you think the the emergence of AGI

I think our view of intelligence right now is is very primitive and and crude it is overfitted to ourselves right so I mean I mean and there’s then a very Broad mathematical theory of general intelligence is Marcus Hooter in his book Universal AI but that’s that’s not closely connected to our theory of intelligence in in Psychology with IQ tests and whatnot so certainly having a variety of different generally intelligent Minds to study and build and interact with and think about I mean this will give us a greatly expanded model of intelligence and indeed it may lead us to new Concepts besides intelligence I mean we may decide that intelligence is not not the most interesting quantity to be thinking about anyway I mean there’s a bit of a parallel in biology where like what is life has never been pinned down precisely and with synthetic biology you’re screwing around with with constructs that are at the border between life and non-life but in the end like how living is this is not such an interesting question to ask if you’re if you’re if you’re a synthetic biologist you’re interested in like what what what can the system do what properties does it have right and uh I mean the I I love you about this Concepts right right so so one of the things you mentioned earlier in the show is uh is revisiting some of the biological thinking about um intelligence and and as it turns out there is a an alien intelligence here on the planet Earth it just happens to be underwater I’m talking about octopuses um I have have Robotics and AI researchers learned anything from other biological forms of intelligence is that in any way an inspiration um I would be very curious to hear about your thoughts on octopus intelligence interesting when you said there was a non-human form of Intelligence on the planet I thought I thought you were referring to multinational corporations so you could I mean I mean yeah you could I I I think that non-human intelligences have certainly served as conceptual inspiration to AI researchers just in terms of showing you that there are other ways to do things so we don’t need to be slavishly tied to the precise human architecture right like you can look at dolphin language it seems that semi-dolphins can transmit to each other details about the 3D you know architecture of the bottom of the ocean and then the flows of water so maybe they’re like sending map information in continuous variable Transmissions or something and that that makes you think well you know when two AIS communicate with each other it doesn’t have to be by discrete symbols arranged in a sequence like like humans messages yeah yeah something like it like an octopus appears to have a less centralized mode of intelligence than that being with the different technicals

limited autonomy the way they coordinate there’s a bit more of a flavor of you know complex self-organization non-linear dynamical emergence and and blah blah blah which can be can be how people on the soccer team coordinate that uh actually more so than than how than how the parts of your your body tend to coordinate so and another example of non-human intelligence is the um I I’m trying to think of the name of it it’s the um it’s the mushroom or fungi uh that extend through a forest and yeah

bacteria I mean I remember in the 80s reading papers or maybe early 90s papers on the uh intelligence of bacterial colonies as well as they can they can do some reinforcement learning to figure out figure out communicate by secretion yeah thank you I would say at the high level the existence of this whole field and constellation of different kinds of intelligences that inspires one not to be too rigorously tied to exactly how human beings do it I mean I think it helps drive home the point that like humans humans are one among a pretty broad class of possible Intel intelligent systems and each of these different kinds of intelligence systems has different properties so they’re not it’s it’s more different than Apples and apples and oranges right and they evolve for different contexts different ecosystems yeah okay let me do another question sorts of things and what finding a common measure of intelligence to compare these different systems is not necessarily interesting but going back to the science fiction if you read the the sun Salem novel Solaris from I guess the 60s I mean I mean I mean that that that’s about an intelligent alien ocean that clearly has a superhuman level of complex and intelligence in some sense but it’s just so weird an alien people can’t pin it down and there there’s certainly a possibility that AGI that emerges on Earth will have that characteristic like some kind of oceanic quality some kind of development intelligence will come about by a combination of engineering and emergence and its own unsupervised learning okay let me ask you a related question it would be a super smart mind fabric but we won’t be able to

let’s talk about the hardware limitations and capabilities that exist one of the reasons that we’ve had seen we’ve seen such rapid increase in the application of machine learning in the last 10 years has been because the cost of computing has dropped gpus have gotten to be way more powerful and they continue to get more powerful so you know the the usual story is like well we’ve got better algorithms we’ve got much bigger data sets and now we’ve got much more affordable compute power with gpus tell me about that as you project forward into the future like how important is the hardware factor I mean the next step there so my friend uh Rachel Sinclair who you should also interview she’s we should yeah simulized simulate that Ai and we’re we’re actually working together to make an AGI board that we hope can do for for AGI architectures what gpus have done for deep deep neural net so we got ah interesting we got a box of I designed this A specialized chip for open pattern matching she designed the chip for what’s called hyper Vector math which underlies certain kinds of neural net neural net implementation so we’re making a board you put a GPU with CPU open open Cog pattern matching chip a hyper Vector chip with fast processor processor interconnect so it seems like the way we’re going is specialized chips corresponding to particular classes of AI algorithms why why are them tightly together on a single board then just make you huge rhapsodies in the server farm and then then ultimately you scale them down and pack them into into embedded devices and then so that that we’re working on now those should roll out in a few years but then the next step after that obviously is quantum hardware and then there’s tremendous tremendous progress I’m really interesting but it seems like it see I mean it’s not going to happen as fast as more advanced specialized AI silicon ships classical ones but I mean 10 years from now I really think we’re going to have quite powerful forms of quantum AR Hardware again perhaps general purpose Quantum Turing machines isn’t going to be the main thing but specialized Quantum ai ai ai circuits carrying out particular particular functions right so I mean I think Hardware will keep exploding exponentially along with the software and enabling a greater and greater variety of AI algorithms to scale and how does the blockchain fit into this into your vision of uh of AGI well blockchain allows you to roll out massive scale deploy the AI systems without a central owner or controller and this is a little bit like steady AI a seti in a sense steady at home if if uh is that a fine person you look more broadly the internet and the Linux operating system are two examples of decentralized networks without a central owner or controller which have been highly highly influential in part due to their decentralized nature which has made it hard for them to be pulled into some particular organization’s narrow goals and we we want to see the network of deployed AI mind components be more like the internet or Linux than like for example mobile or or Windows right I think that that sort of opened this has has a lot a lot of different implications awesome now now um you know you you talked about Quantum AI this is something that is really interesting um is this is this a hardware function or is you know or is the development of quantum AI more about us learning to do things like deep learning on quantum computers because at the moment right right now it’s gated by Hardware we just don’t have enough qubits on the machines I mean of course there’s more and more math to do but the actually the math of chrome AI has really Advanced tremendously in the in the last few years so now there’s way more awesome fleshed out Quantum AI algorithms than we can run until we get a lot more qubits on the machines awesome well um at this point in the show um we like to wrap up with some sort of Big Sky thinking you know looking out far into the future before we wrap up so let me let me ask you this um you know over the next 30 to 50 years um you know as you look forward into Humanity um what do you think are going to be the biggest changes that happened to humanity and what are you most optimistic about for the future I mean the biggest change that will happen to humanity is the Advent of artificial general intelligence with capability beyond the human level which will do two things it will do many things actually I mean it will it will abolish material scarcity of our everyday human needs it will abolish death and disease except for those who who happen to desire them and it will give humans the ability to transcend their ordinary Human Condition by merging themselves into some sort of distributed supermind so these will be rather substantial changes to the Human Condition although you know Amish style people who want to retain Legacy Humanity I I hope will still be able to I think you know that that sort of speciation that gap between augmented humans and and natural humans is is a given because some people will choose uh choose not to to do that but the the concept of the super intelligent AIS that can solve problems that we can’t conceive Solutions of is of course fascinating in terms of where it takes us um yeah how do you you do you have any thoughts on what the motivations of AI might be in this world you know I think the motivation like intelligence or life is a legacy concept that will seem less interesting if a few a few decades from now I mean human beings you know each human intelligence evolved to control a particular body and so we have very particular goals like you know the the four apps from biology right right I mean what we want to eat we want to not want to get caught every what we want to reproduce and and so forth right so I I think an AI is going to be more heterogeneous the the the than that and it’s gonna have a lot of a lot of different sort of gradients along with along which it’s it’s evolving if we can get some core human values like compassion you know Joy Choice growth and expansion if we can get some core human values codified yeah like in the and trained and then taught and into the into the AI right then then and these help to guide the ongoing evolution of of the AI that I think I think things will come out will come out quite well these are not necessarily the core values being put into large scale systems at presents so in that regard it’s probably fortunate the current large-scale commercial AI systems don’t have that much potential to to evolve directly into General intelligences fantastic well Ben it’s time for us to wrap up I’m you know I gotta respect your time um I I will just ask before we wrap up how do people follow your thinking and the work you’re doing at open Cog and singularity.net and um the AGI Society yeah check out my own website uh girls.org putting weird [ __ ] on the internet since 1995 and then the singularity net Dot IO which is a more professional and structured website which has links in to work on open Cog and the various other projects we’ve discussed fantastic well Ben gertzel thank you for joining us on the futurists a fascinating conversation as always and and we wish you all the best thanks for having me that’s it for the futurists this week if you like the show and I’m sure you did fascinating content make sure to give us a shout out on social media give us a five star review on your podcasting platform of choice and just generally share the uh the crap out of the show so more people uh get it we are in the top one percent of podcasts globally now so we’re making phenomenal progress since our launch in April but we can always do better our thanks to Sylvie Johnson Kevin hershen and Elizabeth severins and Carlo Navarro who support the team at provoke media for on the production side but that’s it for the futurists this week we’ll definitely see you next week with more a good future focused content until then we’ll see you in the future

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