what I think happened in the last year really is the starting gun was fired we spent a trillion dollars on 5G is AI more impactful than 5G of course it is do we actually know what’s going on inside the models we can measure everything and still we don’t really quite understand mechanically what’s happening inside them they’re not engineered and designed they’re kind of grown AI is clearly more capable than humans now we know this is going to get better when does it slow down right now it’s a few companies doing this but we need standards around it and we need the expansion and again every country will invest in this every company will invest this we’re here to talk about what happened I almost to say WTF just happened in the past year because a lot has happened imod you were on stage with me a year ago and things were amazing then but a lot has happened so I want to split this into a couple of Parts like what just happened the last year and
[00:01:00] then I want to talk about how how far forward can you see what do you expect is going to happen uh you know I’d love to talk about the companies the models the dramas what did Ilia see I think that’s going to become a meme um Nat do you want to kick us off what what what’s the big things that happened in the last calendar year in your mind um well yeah it’s it’s been kind of an amazing year I I do think for people who just started paying attention to the sort of new Ai and deep learning revolution in November of 22 when chat GPT came out uh things probably seemed very fast because we had um stable diffusion come out over the summer previously in 22 we had chat GPT in November and then just a few months later GPT 4 came out sort of demonstrated a new level of capabilities that you know were shockingly improved over what was there previously and so I do think there’s a set of people who
[00:02:01] sort of um extrapolated from those three data points in terms of the progress that was going to happen from there and it’s incredible actually how quickly people can adapt to these new things and there was a point at which kind of chat gbt blew everyone’s minds and then a few months later uh it was sort of just something we accepted as part of the world as part of reality and there and we can very quickly enter this kind of slump where’s the new model gpt3 is four is boring now when do we get GPT 5 come on Sam put it out already what are you waiting for you know this kind of thing so I think we had a little bit of that this year and then uh over the course of the year though what we started to see and and um I’d been waiting this for a while for this for a while but we started to see real adoption taking off and so um you know back in 2020 gpt3 had come out in 21 when I was running GitHub we put out GitHub co-pilot and uh was I guess one of the
[00:03:01] first llm commercial products and I thought that immediately after that there would be this rapid wave of commercialization of large language models because developers would have seen how capable they were what they could do and start building products with them but it really took this kind of chat gbt moment before entrepreneurs developers and product people started to do this and so what I think happened in the last year really is the starting gun was fired on the actual um exploitation of these ra capabilities the building of products the working of them into companies and organizations and now what that’s what we see the adoptions been incredible chat gbt is rumored to have gone from basically Z to2 billion doar in Revenue in just about a year um that’s amazing there’s uh you know organizations like stability that put out models that have you know hundreds of millions of users um you have you have mid Journey you have now Google in the game finally with Gemini I thought it was exciting to see them not only
[00:04:01] release Gemini but very quickly follow up with Gemini 15 and so there’s a way in which Google is clearly internalized the pace here and the need to iterate and ship and so that I don’t think a lot of people realize how far ahead Google was for a decade yeah right so Google had really developed a lot of the the early Tech here and said it’s not ready for release right they were being responsible uh to a great degree and then and then how do you not release after chat p goes gets released they’re shipping pilt now they’re going to ship they’re going to going to ship yes um Iman um the open model uh story that you told us last year has really blossomed and in fact it’s become sort of a an ethos in the organization right it’s a Elon is like really twisting the knife in with uh with Sam and then says but you know grock’s going to be open now can you talk about what’s happened in last year in in open will open Win do
[00:05:03] you think and I’m curious for both you guys and and describe what open is versus closed for the audience here yeah so proprietary AI is you don’t see the code the weights anything and it’s provided as a service whereas open models and code are ones that you can adapt yourself you can take bring to your own data and you own and that’s important CU these models are something a bit different they’re like extensions of our mind and so the best analogy I’ve said is that these are actually like graduates so they can code they can write they can say they sometimes go a bit crazy when they try a bit too hard you know we’ll give them better education and so open models are like graduates that you hire and then proprietary models are like Consultants you bring in interesting and in the early stages we all needed Consultants now people saying well I want this for my own data I want this for that and open allows for Innovation to happen so we’ve had now 330 million downloads of our models on hugging face wow um so we just and what is hugging face for folks it’s GitHub but for AI models so it’s where you go as a developer download the
[00:06:00] models and so people millions and millions of developers are using this technology but then you look at the language model side people are taking it adapting it and optimizing it um to have innovations that now catching up with even proprietary guys but they’re complimentary you will have your own team and then you’ll bring in the Specialists and I think that’s the best way to think about this because you can’t Outsource your internal intelligence from a personal company or even country level to models that you don’t know what the provenant are and this is one of the debates that we saw over the last year the first step was oh my God exponential extrapolation you know as Nat said kind of actually the foundations for this were dug like maybe a decade ago and now we’ve been filling in the cement and now we’re all building houses and we’re like well the houses will become skyscrapers so it was like well they could kill us all and those and those are valid things discussed then it became about sovereignty and well gp4 is amazing but I want my own version as well for my own private data and so I think as we advance proprietary models will always beat open models proprietary will always be what open models open okay because you can take
[00:07:01] the open model and bring your private data to it and optimize it CU open models are like generalized graduates as it were but both of them need to exist and I think we’ll see both of them continue to take off do we actually know what’s going on inside the models I was listening to a conversation with the chief scientist at uh uh anthropic who will be on stage with us next year um and he’s saying we actually don’t understand really how these models work is that is that a fair statement yeah we I I mean I just just so you understand all right it’s like they’re amazing but we actually don’t understand yeah how how the weights and connections and all are really working I mean we we understand it at this like very micro level you know we can see the multiplications happening and we can see the signal moving to the next layer in the neural network but um it’s actually sort of similar although not the same mechanisms to the way we don’t have a perfect understanding the way thinking happens in our brains um we don’t even
[00:08:01] though in the case of our brains when we try to you know do the neuroscience and understand what’s happening at the neuron level and the organel level um we’re limited by our measurement right we can’t measure the state of every single neuron in your brain while you’re thinking that would that’s not something we have the sensing technology to do now that’s not a problem we have with these AI Minds that we’re building we can measure everything and still we don’t really quite understand mechanically What’s happen happening inside them they’re not engineered and designed they’re kind of grown you know they’re the products of us actually because we have the internet which digitized the world and put the sort of all the data we’ve produced online and there’s a way in which that process of building the internet was like a bootstrapping process for building AI because it was a precondition to making AI we digitized the world we put all the contents online and then we could use all of that to sort of grow and train these AI models but we don’t quite know how they work
[00:09:00] now there’s a new field um new not in time but but relatively small field uh called interpretability which is about trying to understand what’s going on in these things what are they doing how do they make the decisions that they make obviously there’s benefits if you can do that to making them better in all sorts of ways you can make them smarter maybe and make make better decisions and you can also hope to make sure they do the thing you want them to do and not not something else um but it’s new Elon put a tweet out which I read during my opening remarks that said by 20 yeah we’re going to have human level AI next year and by 2029 AI will be equivalent to all 8 billion people uh do you agree with that emod um do you think it’s going to move that fast I think we had a big discussion about generalized AI that can do everything and that was the focus of Deep Mind and open AI but for specific tasks AI is clearly more capable than humans now so for example
[00:10:00] uh Google’s Gemini Ultra model has a million 10 million token context window what does that mean it means that someone can upload themselves debugging a piece of code in the code base and it’ll correct it and no human could ever do that you know Ram isn’t big enough you know on image now we can generate images faster than anything songs in seconds and other things so let’s talk let’s talk about that a second um Sora was I mean pretty like holy moment right um and someone is doing it right at open AI with chat GPT and then Sora uh but I remember last year you told us it used to take like 30 seconds to generate on stability a single image and then it was down to a second and you’ve Advanced the technology orders magnitude since then yeah so I think um if we can put the thing up but yeah put up the slide um stable image yeah and then if we actually go to the next slide talk about speed here um I’ll should I click this let’s see oh
[00:11:03] we can click this to give an idea of where it’s gone so image was kind of one of the things that kicked us off in 2022 all of these images can be generated on a MacBook on a MacBook just from description and now we’ve perfected text and other things but the next step after you can generate all these beautiful images is that you want to move to control so the models are just the first step you have to have chat GPT and other things to make them useful but then you want to be able to take that guy and say upscale and that’s all we said and it upscaled him you want to say replace a lion with a cat and we can now do that real time you know or tiger with a unicorn replace background with a forest forest so all of this you can do pretty much real time because if you look at the next slide from this okay I’ll I said it was 20 seconds to 1 second this is live real time you can just type and it automatically adjusts the cat it gives them a hat but then if you look at the optimization of that with the next slide oh back one we missed it this one okay well we missed it go back one slide
[00:12:00] sorry with this process here we’re just releasing a new distillation model today we’ve got it to 200 cats with Hats per second 200 cats with Hats per second so that’s your speed of of image generation that’s a new unit of image generation 50 milliseconds per image yeah exactly it’s cuz there’s not enough cats on the internet right so we’re going to add more cats to the internet oh 5 milliseconds 5 millisecs 5 milliseconds yeah but I think with the new chips that are coming and video is going to announce another one we’ll get up towards 1 th000 images per second so that’s real live that’s live video times times times 30 yeah and so open ai’s Innovation there were two major things there was the Transformer architecture the language models and diffusion that drive the media models they combine the two together so our new image model stable diffusion 3 which is the best performing image model combines those together as well so if you go forward a few slides from here skip um another one another one that’s the upscaling you know so you’re basically said upscale this image on the left and you get you get the image on the right you’ll have that real time in
[00:13:00] a couple of years so your boxy video games will look a lot more realistic so just literally as you’re you can take an old game and play it in real life yes but if you go to the next slide these videos with our video model all generated on a consumer graphics card with 5 GB of vram so I mean the the point is it’s this is in everybody’s hands it’s in everyone’s hands and again we haven’t even optimized the data so in an hour uh next slide we’re releasing in the world’s most advanced 3D model so all of these are generated just from descriptions and so the fastest version of this does it in 1 second so you can generate that Dragon but then the next step is you’ll be able to control every element make its horns bigger make these adjustments so how long would that take for a normal creative person in kind of industry a huge amount of time but now it works on the edge and it works even faster in the cloud so a future here where you can be describing the video game you want created and it’s generating all the
[00:14:02] characters and generating the play yeah and so if you go back a few slides to that thing with all the nodes I think this is an important thing sorry I didn’t do the slides properly I think one of the things that we’re having right now is this is a system we buil called comfy UI that’s used by just about everyone now so you take the face the pose the dress and then you have the output but if I share that image with you it reconstructs the entire flow because last year was the year of creation model that create then chat GPT com UI allowed you to control and compose them and the next bit is bringing that all together because chat GPT all the knowledge that you build from writing your speech you don’t have files anymore you have flows with these models and assets there and I think that’s the next step because you know when you go beyond just spitting out ideas to be able to control them like that that’s a huge deal amazing uh and you’re announcing this in an hour uh the 3D models releasing in an hour open source to everyone give it up for emod
[00:15:03] here um you really have uh I now don’t know if you’re able to say this I mean um uh you’re uh driving revenue and Su net profitability what’s your financial I can’t say that publicly you can’t okay but it’s going well we’re ahead of forecasts as can say all right he told me backstage was good yeah okay do all right yeah everybody I want to take a short break from our episode to talk about a company that’s very important to me and could actually save your life or the life of someone that you love company is called Fountain life and it’s a company I started years ago with Tony Robbins and a group of very talented Physicians you know most of us don’t actually know what’s going on inside our body we’re all optimists until that day when you have a pain in your side you go to the physician or the emergency room and they say listen I’m sorry to tell you this but you have this stage three or four going on and you know it didn’t start that morning it probably was a
[00:16:01] problem that’s been going on for some time but because we never look we don’t find out so what we built at Fountain life was the world’s most advanced diagnostic Centers we have four across the us today and we’re building 20 around the world these centers give you a full body MRI a brain a brain vasculature an AI enabled coronary CT looking for soft plaque a dexa scan a Grail blood cancer test a full executive blood workup it’s the most advanced workup you’ll ever receive 150 GB of data that then go to our AIS and our physicians to find any disease at the very beginning when it’s solvable you’re going to find out eventually might as well find out when you can take action Fountain life also has an entire side of Therapeutics we look around the world for the most Advanced Therapeutics that can add 10 20 healthy years to your life and we provide them to you at our centers so if this is of interest
[00:17:02] to you please go and check it out go to Fountain life.com back/ Peter when Tony and I wrote Our New York Times bestseller life force we had 30,000 people reached out to us for Fountain lifee memberships if you go to Fountain life.com Peter will’ll put you to the top of the list really it’s something that is um for me one of the most important things I offer my entire family family the CEOs of my companies my friends it’s a chance to really add decades onto our healthy lifespans go to fountainlife decomp it’s one of the most important things I can offer to you as one of my listeners all right let’s go back to our episode um I am curious about the idea of how far are we from having an AI that I can have a conversation with and say I’d really like to create a new business that does this this this and this and
[00:18:01] just have that like a brainstorm partner Ai and it will do the incorporation um it will write the code it will generate the marketing materials um and it will be able to you know we’re all entrepreneurs here that’s all we you know find a problem build a business find a problem build a business how far are we from that that reality of an AI created well I’ll come back to the second part in a minute but an AI that can that can be your thought partner and really sta up a company I think it’s it’s already happening gradually um and then maybe suddenly so we have I think companies will these models are neural they’re neural networks and so I think the right way to think about it is that companies in the future will just be increasingly neural and there will be more and more of the company and what used to be the Departments of the company that are single models or swarms of models that are doing work I think it’ll you know at some point probably very soon we’ll look back on 2024 and
[00:19:02] say God do you remember when companies had like hundreds of people in the finance department doing accounts payable and just like transforming information from one form of text to another essentially and doing this coordination and um it so I think you will have some form of both existing companies that just adopt more and more AI because it gives them advantages it makes them more efficient maybe it gives them better customer service people enjoy the responsiveness intelligence politeness Clarity Etc of the AI counterparty and um you’ll also have new companies that’ll be AI native started from scratch neural from the beginning so we you some people may remember when Instagram was acquired by Facebook for a billion dollars everyone was just marveling at the fact that a a small company 13 employees 13 people could be worth a billion dollars how is that possible and the joke was you know gosh could you ever get down to a one person company that’s worth a billion dollars
[00:20:01] and while like this is clearly going to happen maybe eventually a zero person company that’s so we talked about this getting ready and I said when are we going to have a scenario where uh a government and we have some governance in the room here uh says we’re going to create a new regulation that allows an AI to incorporate on its own in our jurisdiction I think it’s I think it’s a winning scenario because all of a sudden the AI incorporates you get the tax base and every AI Incorporated company will move there I think Wyoming’s done that what’s that Wyoming’s done that they have a new structure called aduna for a decentralized autonomous organization I’m s who has Wyoming Wyoming Wyoming all places yeah yeah we have some Wyoming fans here in the audience yeah so technically that could happen today amazing uh I mean I I do think that that is a so the speed of wealth creation um how how should F so let’s talk about
[00:21:02] what are the wow moments that might be possible in the next year what are some crazy wow moments that might you might see I I think one of the most important things is uh the accuracy and then these long context windows so explain what the long context window is again so you’re absorbing information right now and it’s quite high definition because you can see everything here and other stuff but you’re still writing it down and the reason organizations get big is because text is a lossy trans transmiss format we lose so much context the final PDF loses all of that stuff now with the new Google models Nat has some amazing companies in this space as well you can upload hundreds of thousands of words thousands of documents and the AI can interpret them all at once there doesn’t need to be trained on it so you can upload all of your ideas and say build a business based on this and it will do that or you can upload like a whole bunch of movies and then tell it to write a script that incorporates all of that and it’ll do that in reference time that again is something super human but
[00:22:01] we all have these massive like repositories of all these ideas we’ve had being able to dump that now and then the AI without having to be trained spit back answers ideas and things like that I think is a really huge step along with that composition step that kind I’ve discussed before I think yeah I totally agree with that I think there will be a couple things coming probably pretty soon um it’s hard to predict exact timelines on these things uh sometimes things happen faster than you expect effect and sometimes a little slower but one of the clearly amazing clearly possible now U products to build is a voice too model that’s indistinguishable from talking to a human maybe for a conversation of up to a couple minutes where what happens after a couple of minutes well maybe you can kind of just tell somehow that it’s not quite human after a couple of minutes uh I’m I’m setting a milestone that I think is achievable this year maybe when if you can do 2 minutes you can do 12 minutes I I don’t know um but uh yeah you know you would it’s it’s
[00:23:00] actually about all the Technologies there it all just has to be integrated and so you need the sort of the ability to recognize speech is there the ability to interpret it with a language model and generate responses is there and then the ability to turn that text into incredibly realistic voices there and kind of putting that all together into a package that has very low latency that’s talking the way we talk where you can kind of interrupt me and maybe there’s an avatar that’s giving you this human like I mean Aristotle was very impressive but I knew that that was not a real person right and so um I think we could yeah he was a real person yeah that’s right and then I think the other thing that’s a very big deal is this this idea of autonomy and agents um there’s been a lot of talk of it with AI over the last year today these things are not agents they’re tools they’re call and response you go to chat gbt you type something you hit enter you watch the response kind of scream back and I think what people don’t necessarily understand is that when these language models are responding to you that it’s it’s almost like a rap battle they have
[00:24:00] a fixed amount of time to generate each word and so they can’t sort of sit there and Ponder for a minute you know what they’re going to say they have to talk to a metronome and um so that’s why when you see the words that they’re writing out that’s not like they’ve thought about it a lot and then you see the words it’s actually the thinking is happening during the output would you say that’s how a human does it too I often a lot of times sometimes I’m really pissed off at what came out my mouth cuz I didn’t think about it in advance yeah yeah I think so I mean sometimes you have a conversation with a smart friend and you just forming the words to respond you have a better idea and so I think we we definitely do that too but um the agent the autonomy is about kind of increasing the unit of work you can trust the AI to do without making a mistake so right now you can ask it one question get a response you interpret it you figure out the next thing but what if it could go do 10 or 100 steps you’re talking about an AI business you know do you trust it to come up with the title of the blog post
[00:25:00] that you’re going to post or do you do you trust it to actually come up with the whole idea of the marketing campaign right come up with a strategy for how to execute it all of the content it’s going to generate the partners it will reach out to and negotiate advertising or or whatever it’s going to do have those conversations that’ll multi-step successfully all the way to like measuring its results without supervision and I think that agency thing we’re starting to see it in programming there was a very impressive demo a week or two ago of a company called cognition that um I think it was maybe the it was arguably the first really impressive demo of working agents in Ai and and they did it with gp4 so not with a new brand new model they did it by being very clever about the way they squeeze and distill the intelligence out of gp4 by repeatedly calling it and and analyzing and evaluating its results and choosing the best ones and so sort of going from rap battle to like draft and redraft and sort of think and ponder about it a
[00:26:00] little bit harder mode and uh it can do hundreds of steps in programming successfully so okay I was going to ask you one quick question then please jump into that how far out can you imagine right now about three years three years is a Max yeah how about you well you can dream uh you can dream yeah so um I actually the thing I I I find easier is to think about the long term because the the where the ASM toote of where we’re yeah and that’s the key question of our time is the ASM toote it’s like we know this is going to get better when does it slow down and what’s the shape of the curve to get there you mind I I cut you off apologies uh no it’s fine uh I think probably the thing to look forward to is the last couple of year F the first year was about the technology and the breakthroughs and the research last year was about the models next year going forward no one give a
[00:27:00] down about the models it’s all about what you can do with the models bringing them together because the models have satisficed they’ve got good enough fast enough and cheap enough they will get even better and there’s probably two orders of magnitude Improvement still in the speed and reliability of the model but this will really become about and the stories of the next year will be about we used this model to do this and it was amazing you know and so I think that’s one of the things to look at and that’s what Nat said about tying them all together you’ve got the ingredients now what are the recipes we’re going to make very quickly capital and regulation uh I was in a conversation with the CEO of one of the major um AI companies and uh just he was saying he’s raising three billion and I said you know have a venture fund I said great and and I said what’s your minimum and he said probably 100 million I said okay well that’s out of my ballpark um but uh and and how Quil you think I’m you’re going to raise it he goes next month I mean there is a
[00:28:01] lot of capital flowing in yeah I mean have you ever seen a capital Rush faster than this um no no I mean I think there are a couple of comparisons you can look at um I think the railways at when Railway infrastructure was being laid in the UK I think it was some double digit percent of GDP investment um I think the solar explosion that’s happening right now is actually pretty amazing it’s something like half a point of global GDP is being invested in solar so those are pretty big um we’re nowhere near that yet with AI and so I I actually think if AI keeps working which it seems like it will um you should expect the future to look more like that Railway or solar situation where there’s you measuring the investment in intelligence because it’s so valuable intelligence AI is intelligence intelligence is power power is valuable it’s power over nature
[00:29:00] it’s power over others and so you’ll probably measure the amount of investment in it in points of global GDP and whether that happens in two years or 10 years I’m I’m not sure but it seems likely I mean to put this in context less money has been spent on private AI companies than the Los Angeles San Francisco Railway that hasn’t started yet there you go wow right but that’s almost done I heard so really fantastic they haven’t even started we wrote down on it the ai ai right now isn’t infrastructure but it should be which talk about your vision there because I find it very powerful um your your your vision there in education in health talk to me this is the next Generation human operating system because these AIS extend our capabilities and again you’ll need the AIS that are open and the ones that are proprietary to have the best outcome for everyone cuz all of our companies here are all information systems and we’ve seen how better it can be so the total amount of spending in this will be trillions of dollars we spent a trillion dollars on 5G is AI
[00:30:00] more impactful than 5G of course it is should it be infrastructure of course it should be should the data be transparent of course it should be because you need to know how our Railways are made the information knowledge Super Highway of the future right now it’s a few companies doing this but we need standards around it and we need the expansion and again every country will invest in this every company will invest in this I mean is anyone here who runs a company not investing in this in some way at least your time right yeah multiplied by every company in the world why where are we percentage wise at the investment in AI are we at a fraction of 1% still I would say so yes how about you Nate seems likely so a lot of upside still opportunity uh it is crazy though I saw a tweet the other day I mean I live through do and um all these you know little attack Bubbles and um uh I saw I saw a tweet the other day from someone that said if you don’t secure equity in an AI startup now then your children will be chattles slaves for the machine God for all eternity and I
[00:31:02] remember thinking I don’t remember anyone saying that during like then you switched around and bought some more G right this is somehow a little bit more extreme than the prior bubbles so I mean look I think web 3 kind of received a lot without any results whereas now you’re seeing impact here yes but again you multiply this by the number of people it effects the value created it’s insane because it will go all abstraction you’ve had the base the found found ations now the base now you’re building the houses you’re building a whole ecosystem around this and the transformation that that has and the amount of capital is bigger than anything we’ve seen let me ask the question we’re going to be debating today on the ASM toote How concerned are you about digital super intelligence I’m defining this for a purpose of conversation as AI a billionfold more advanced than the human being um how do you think about that what’s your position in that
[00:32:00] debate Pro con anywh I can kick off my belief is that humans can break the atom or we can go to Mars and so my vision is every single nation person company country culture has their own AI data sets and self- Sovereign needs to figure out the governance of this because it’s important and then that AI is our collective intelligence it’s the human Colossus so it isn’t controlled by anyone individual it isn’t embodied like that but again it’s amplifying all of us and it’s solving every single problem that we can have I think that’s a positive version of the future I think it seems really likely that I mean it seems undoubtable to me actually that intelligence is just a material process like muscle strength we have organs called muscles we use them to to move we have an organ called the brain and we use it to think and so if you look at if you just sort of ask what that means well we’ve managed to exceed muscle strength with artificial artificial machines with with machines
[00:33:02] uh hundreds of years ago and um you know we’re going to do that with brains too we’re going to have artificial Minds that are much more powerful than ours it’s um and you it’s almost like you just have to be an AI doubter not to believe that or you have to not believe in human Ingenuity all the most brilliant people in the world are now working on this and there’s a huge amount of capital going into to it in a way we’ve just started and so the idea that it wouldn’t improve seems hard to believe did you know that your microbiome is composed of trillions of bacteria viruses and microbes and that they play a critical role in your health you know research has increasingly shown that microbiomes impact not just digestion but a wide range of health conditions including digestive disorders from IBS to Crohn’s disease metabolic disorders from obesity to type 2 diabetes autoimmune disease like rheumatoid arthritis and multiple sclerosis mental health conditions like
[00:34:02] depression and anxiety and cardiovascular disease you know viome has a product I’ve been using for years called full body intelligence which collects just a few drops of your blood saliva and stool and can tell you so much about your health they’ve tested over 700,000 individuals and used their AI models to deliver key critical guidelines and insights about their members Health like what foods you should eat what foods you shouldn’t eat what supplements or probiotics to take as well as your biological age and other deep Health insights and as a result of the recommendations that viome has made to their members the results have been Stellar as reported in the American Journal of Lifestyle medicine after just 6 months members reported the following a 36% reduction in depression a 40% reduction in anxiety a 30% reduction in diabetes and a 40 8% reduction in IBS listen I’ve been using viome for 3 years
[00:35:02] I know that my oral and gut health is absolutely critical to me it’s one of my personal top areas of focus best of all viome is Affordable which is part of my mission to democratize healthcare if you want to join me on this journey and get 20% off the full body intelligence test go to vi.com Peter when it comes to your health knowledge is power again that’s vom.com SL Peter here’s the challenge it feels like potentially win or take all scenarios where if all of a sudden my company is able to utilize the most advanced Ai and build out the next generation of systems and I’m doing it with you know me and my my versions of Haley and and and Aristotle that there’s you know they’re working 24/7 I’m feeding them as as many gpus as possible and I have a chance to really
[00:36:00] outrun um outrun the competition and it used to be that in the early days in the mechanical world if you were using mechanical systems to outrun the horse that was a local phenomenon right but now this is a global phenomenon because my my bits are reaching around the world and you know when you say yeah it can do my marketing do my marketing campaigns it can do my scientific analysis it can do everything and you know I keep on hearing that is like not 10 years from now not 5 years from now but that’s like a three-year scenario I don’t know how to think about that I think the the pace of change you know is going to be very high and um Global GDP growth has been kind of in the 2% range and we have a society and civilization that’s able to adapt to that amount of change per year mostly not entirely it’s been a little bit higher before um maybe it’s going to be much higher very soon and uh whether
[00:37:04] there’s a huge spread of outcomes that come from that I mean even if you don’t have the extermination scenarios the extinction scenarios in mind um I think I think you actually should have other scenarios in mind what does it mean to be human uh is the economy human human dominated you know 10 years from now or 15 where are decisions made and and who makes them um and then just uh you know this this vision of of sovereign AI you know it’s a new level of potentially cooperation and competition between countries and sources of this kind of power and so um a lot of changes coming I think you should I think uh you know we went through this before we had the sort of five big inventions of like the 1880s to the through the 1920s and that was a a wild time of transformation it’s like almost unimaginable people who grew up with horse carts you know and up riding on jet planes and um we’re going
[00:38:01] to have at least probably much more than that amount of change happen this time I go to uod next but when you’re you’re about to go after this to part two of that tool and I want you to be thinking about how can AI help you solve the challenges you listed how will AI threaten the dominant positions or the strengths that you have and how will AI this year help you meet your 20 24 goals right that’s that’s the goal to putting it to use you might do you want to comment on that and I want to also talk about medicine and you know you’re dedicated to using AI models to solve autism and cancer and death um or let’s just say sick sickness yeah sickness yeah I mean uh I just had a thought actually you know we had Steve Jobs qu earlier maybe the AI is jet planes for the mind right there you go here we go but the kind of the impact here there’s a sociological impact we
[00:39:01] can extrapolate forward but an ASI is just something we can’t think if you’ve got super intelligence if you’ve got too infinite supply of graduates what do the existing graduates do if the floor is raised you don’t need to hire as much you become more efficient what are the new jobs of the future in a knowledge based economy we have to think about that because as Nat alluded to it’s like slowly slowly then all at once like a turkey you know at Thanksgiving I think if you look at this though there’s the negative side but then there’s a positive side so I think mentioned kind of last year Google’s medpar model outperforms human doctors in medical diagnosis accuracy but also empathy and we have medical models like that anyone here who’s had someone who’s had multiple sclerosis Alzheimer’s autism something like that you don’t have comprehensive authority to up toate knowledge and nobody to guide you through that process and you lose agency well guess what we will make open source models and they’ll be available to everyone and you’ll never be alone again through that and then be used for diagnosis and organizing all lar so describe that a little bit more so
[00:40:01] people can can feel what that feels like it means again anyone’s experienced that someone comes to you and says you have a diagnosis of Alzheimer’s for your father or Autism for your child you lose agency because you’re like what now there’s no cure there’s no treatment where do I even go for information a lot of our stuff is a coordination issue whereas if we have a specialized medical model for that topic and retrieval augmentation and tie it into all these systems you can have all that knowledge at your fingertips and it can help guide you empathetically through that it can literally talk to you it can connect you with other people going through the same process and then the next step is it can organize all the knowledge in this area because there’s so many promising treatments but what how does anyone here find out all the treatments about autism or cancer you used to be patients like me but it’s hasn’t survived but this is supercharges that and then you do that once and it’s infrastructure for the 50% of people around the world that will have a diagnosis of cancer and again you’ve transformed it because there will always be someone with every single
[00:41:00] person that can connect them to the right information at the right time so this is the positive view of the future and that creates boundless new potential in terms of both addressing our health but science and again most of our Science is based on files a PDF and we throw away all the stuff that doesn’t work if you look at that knowledge flow of that image with a different things and again if I drag and send you that image it’ll reconstruct the whole flow I think most of our things will go from files to CL so we can remix our knowledge so that we can find out what works and what doesn’t work and that’s how we get breakthroughs and is it true that most the large language models were built on top of all the social media Facebook websites and so but not crawling science magazines and most of the scientific databases out there science was a part of it but again what we did is Big compute was a substitute for bad data it was like cooking a steak for too long now we see that high quality data is even better but we’re only understanding what high quality means and this is why we need
[00:42:00] specialized models which are transparent especially for things that affect things like health education and others so I think data set transparency will be a big deal and this goes to Nat’s point about interpretability as well like you were given that example of ariv earlier right um science you know one of the things that I think is amazing is the potential for these AIS to help us discover new science Beyond interpolation or extrapolation new laws of physics new understandings of fundamental biology and chemistry uh do you think that is possible when are we going to see that and speak one second to the vvus challenge that you oh sure yeah um I think definitely they will um which by the way for me is like the most exciting thing if you could unleash these AI models and say go and create room temperature superconductors go and cre you know uh life extension processes so there’s uh in biology
[00:43:02] especially biology you know there’s this sort of analogy that’s been put out there that uh the language of of you know physics was was mathematics the language of biology might be machine learning because you’re dealing with enormously complex systems machine learning is great at sort of understanding those and so the potential for AI to transform biology is enormous I think it’s all in the very earliest stages right now and we have not yet had the kind of gpt3 let alone on the chat GPT moment for biology and AI I think it’ll come soon since Google is our sponsor I say the Gemini moment there you go the Gemini moment hasn’t occurred yet The Bard what is it The Bard moment maybe I don’t know but it’s coming and um you know I’m involved with some companies that are training enormous models to help uh synthesize and design proteins there are a few great efforts out there to do this and um the capabilities that are popping out of these things are incredible the ability to you describe a Target describe a structure and just have it produce a sequence of amino acids that you can
[00:44:01] then synthesize and test in the lab for safety and efficacy it can short circuit a huge part of this uh sort of cognitive work and experimental work that’s been necessary historically for drug design and so I think that’s one thing I think another thing is is um there will be a surge of new discoveries that happen as we as AI digests all the existing scientific literature and so there’s there’s a whole area of study which is metaanalysis where people will study across papers to find connections and correlations across existing research that haven’t been noticed I so I mean I’m bubbling with excitement on that notion that idea yeah so imagine you know I I have a friend uh shaa Swan she’s a a scientist at Berkeley and does a bunch of uh research on environmental toxins she spent two years running one metaanalysis that you know I’m now working with her and trying to support her and the effort to use AI to automate this and it’ll I mean in theory with
[00:45:01] these long context Windows this could potentially happen in in minutes and so I think new discoveries will pop out of that immediately and just because we’re short on time I’m going to say you found Mount Vesuvius oh yeah after the eruption buried a number of uh parchment Scrolls that were if you imagine buried under all of the ashes and so and so forth you took some of those Scrolls you x-rayed them gathered the X-ray d from those Scrolls and then you ran a order an order of a million dooll uh vus challenge like an X prize yeah and your the winner solved it it worked yeah no it’s true so yeah 2,000 years ago Mount vuia erupts it buried the town of herculanum under 65 ft of Ash and mud and then in the 1700s some Farmers digging a well found the Villa 65 ft down and then later during these tunneling excursions people kept running into these little chunks of char Co they didn’t know what they were they turned out to be carbonized Papyrus Scrolls
[00:46:00] that were not openable physically they just sort of turn to dust in your hands when you open them they’ve been stored in a library in Naples for years and we used a particle accelerator to scan them at super high resolution but then we needed to use Ai and machine learning to to unroll them they’re so badly distorted by how cool is that being a volcano [Applause] yeah um want you close us out here with what you’re most excited about um going forward what’s a vision of the world in the next 1 to three years that you want people to take away from here I I think this AI is augmenting not replacing and so you see what kind of nat said and I look how creatives use it AI can’t do art it can do content right now but you can use it to riff and jam with so you’re in flow more often so the thing I’m most excited about is its impact upon education science over the coming years is because everyone will have access to all the knowledge that amplifies them I think things like
[00:47:02] creativity are also great because it’s great to create but realistically again every single person in this room will just have access this is the worst it will ever be that’s so important this is the worst AI will ever be and view it and the lowest Bitcoin will ever be too yeah and then view it amplifying yourself and there’s no there’s nothing you can’t achieve with this I think um people are sitting down here with goals for the year uh objectives they want to achieve what is your top piece of advice for the CEO entrepreneur philanthropist here that has like oh man I I’m trying to do more and more what should they what should they think about well I think um what’s happening or what’s about to happen it’s sort of like we’ve just discovered a new continent with 100 billion people on it and there willing to work for free for us and you should
[00:48:02] probably factor that into your plans over the next few years because uh your competitors will and U because it’ll benefit you enormously at home at work and your family we just discovered a new content with 100 million brilliant workers willing to work 100 billion 100 bil they’re going to outnumber us yeah yeah 100 billion okay and they’ll work for a few watts of power and um so this is the good scenario and I think it’s very very likely and uh so I would and I think you know this sort of like being an internet native you want to be an AI native and you want to spend time using the stuff and not looking for the problems but looking for the looking for the value and how to what’s it good at what’s it not good at how do you interact with it it’s amazing to see people use stable diffusion for example um you’ve improved uh it so much over time but there are there’s a skill to being good at interacting with these models and partnering with them and and so I think we all have an advantage just to get that hands- on ourselves you know
[00:49:01] imagine being a CEO of a company when electrification was happening and um you know obviously every company should Electrify uh and then not doing it in your own home like that would be strange amazing imod you’re going to be with us for the next few days so thank you so much for that uh Nate it’s a pleasure to get to know you thank you for joining us this morning let’s give it up for Nate and mod [Music]