Inside OpenAI [Entire Talk]

who you are defines how you build welcome YouTube and Stanford communities to the entrepreneurial thought leaders seminar um brought to you by stvp the entrepreneurship Center in the School of Engineering at Stanford and basis The Business Association of Stanford entrepreneurial students today we are so honored to have Ilia suitskiver here at ETL Ilya is the co-founder and chief scientist of open AI which aims to build artificial general intelligence for the benefit of all Humanity Elon Musk and others have cited that Ilya is the foundational mind behind the large language model generative pre-trained Transformer 3 or gpt3 and its public-facing product chat gbt a few product releases have created as much excitement Intrigue and fear as the release of chat gbt in November of 2022. Ilia was Ilia is another example of how the U.S and the world has been the beneficiary of amazing talent from Israel and Russia is Elia was born in Russia and then when he was five he moved to Israel where he grew up and he spent um at the first half of undergrad even in Israel and then he transferred and went to the University of Toronto to complete his bachelor's degree in mathematics he went on to get a master's in PhD in computer science from the University of Toronto and then came over here to the farm and did a short stint with Andrew ing before returning back to Toronto to work under his advisor Jeffrey Hintz research company DNN research Google then acquired DNN research shortly thereafter in 2013 and Ilya became a research scientist as part of Google brain and in 2015 he left Google to become a director of the then newly formed open AI it's hard to overestimate the impact that chat gbt has had on the world since its release in November of last year and while it feels like chat gbt came out of nowhere to turn the world on its head the truth is there's a deep history of innovation that has led to that moment and as profound as chat gbt is Ilia is No Stranger in uttering in discontinuous leaps of innovation and AI Jeff Hinton has said that Ilya was the main impetus for Alex net which was the convolutional neural network in 2012 that is attributed to setting off the deep learning Revolution that has led to the moment that we are now in and of course it was seven years since the founding of open AI that chat GB T was finally Unleashed to the world Ilyas was elected a fellow of the Royal Society in 2022 he's been named to the MIT tech review 35 under 35 list in 2015. he's received the University of Toronto's innovator of the Year award in 2014 and the Google graduate Fellowship from 2010 to 2012. so with that everybody please give a virtual warm Round of Applause and welcome for Ilia to the entrepreneurial thought leader seminar so Ilya imagine lots of Applause and you're always invited back onto the farm physically whenever you are able so Ilya there's so much to discuss and I know we're gonna have solo time and we have quite a broad range of fluency around the audience in terms of chat gbt and lot large language models I wanted to start off with just a quick question on the technology which is just the key technology underlying open Ai and generative AI more broadly is large language models can you describe the technology in simple terms and now that you're at the Forefront of the tech can you share would have surprised you the most about what the tech can do that you didn't anticipate yeah I I can't explain well this technology is and why it works I think the explanation for why it works is both simple and extremely beautiful and it works for the following reason so you know how the human brain is our best example of intelligence in in the world and we know that the human brain is made out of a large number of neurons a very very large number of neurons neuroscientists have studied neurons for many decades to try to understand how they work precisely and while the operation of our biological neurons are still mysterious there's been a pretty bold conjecture made by the earliest deep learning researchers in the 40s the idea that an artificial neuron the ones that we have in our artificial neural networks kind of sort of similar to a biological neuron if you squint so that's there's an assumption there and we can just run with this assumption now one of the nice things about these artificial neurons is that you can they are much simpler and you can study them mathematically and a very important breakthrough that was done by the very very early deep learning Pioneers before it was known as deep learning was the discovery of the back propagation algorithm which is a mathematical equation for how these artificial neural networks should learn it provides us with a way of taking a large computer and implementing this neural network in code and then there would be there is an equation that we can code up that tells us how this neural network should adapt its connections to learn from experience now a lot of additional further progress had to do with understanding just how good and how capable this learning procedure is and what are the exact conditions under which this learning procedure works well it's although this is although we do with computers it was a little bit of an experimental science a little bit like biology where you have something that is you know like like like a local biological experiment a little bit and so then a lot of the progress with deep learning basically boils down to this we can build these neural networks in our large computers and we can train them on some data we can train those large neural networks to do whatever it is that the data asks them to do now the idea of a large language model is that if you have a very large neural network perhaps one that's now not that far from like these neural networks are pretty large and we train them on the task to guess the next word from a bunch of previous words in text so this is the idea of a large language model you train a big neural network to guess the next word from a previous from the previous words in text and you want the neural network to guess the next word as accurately as possible now the thing that happens here is we need to come back to our original assumption that maybe biological neurons aren't that different from artificial neurons and so if you have a large neural network like this that guesses the next word really well maybe it will be not that different from what people do when they speak and that's what you get so now when you talk to a neural network like this it's because it has such a great such an excellent sense of what comes next what word comes next it can narrow down it can't see the future but it can narrow down the possibilities correctly from its understanding being able to guess what comes next very very accurately requires prediction which is the way you operationalize understanding what does it mean for a neural network to understand it's hard to come up with a clean answer but it is very easy to measure and optimize the Network's prediction error of the next word so we say we want understanding but we can optimize prediction and that's what we do and that's how you get this current large language models these are neural networks which are large they are trained with the back propagation algorithm which is very capable and if you allow yourself to imagine that an artificial neuron is not that different from a biological neuron then yeah like our brains are doing are capable of doing a pretty good job at guessing the next word if you pay if you pay very close attention so so if I let I love that and I just want to make this more concrete so just to push that analogy further between the biological brain and these neural um uh analog physical networks digital networks um if the human if if we consider you know before it was considered untenable for these machines to learn now it's a given that they can learn or do this um uh do predictive outcomes of what's going to come next if a human is at 1X learning and you have the visibility into the most recent chat gbt models what would you put the most recent chat gbt model as a ratio of where the humans are at so if humans are at 1X what's chat gpdn you know it's a bit hard to make direct comparisons between our artificial neural networks and people because at present people are able to learn more from a lot less data this is why these neural networks like Chad GPT are trained on so much data to compensate for their initial slow learning ability you know as we train these neural networks and we make them better faster learning abilities start to emerge but overall overall it is the case that we are we are quite different the way people learn is quite different from the way these neural networks learn like one example might be you know these neural networks they are you know solidly good at math or programming but like the amount of math books they needed to get let's say good at something like calculus is very high or as a person would need a fairly you know two textbooks and maybe 200 exercises and you're pretty pretty much good to go so there is just to get an order of magnitude sense if you relax the data constraint so if you let the machine consume as much data as it needs do you think it's operating at like one-tenth of a human right now or you know it's quite hard to answer that question still and let me tell you why I hesitate to like I think that any figure like this will be misleading and I want to explain why like because right now any such neural network is obviously very superhuman when it comes to the breadth of its knowledge and to the very large number of skills that these neural networks have for example they're very good at poetry and they're very good you know like they can talk eloquently about any topic pretty much and they can talk about historical events and lots of things like this on the other hand on the other hand people can go deep and they do go deep so you may have an expert like someone who understands something very deeply despite having read only a small amount of documents let's say on the topic so because of this difference I really hesitated to answer the question in terms of oh yeah it's like some some number between zero do you think there is a singularity point where the machines will surpass the humans in terms of the pace of learning and adaption yeah and when do you think that point will occur I don't know I don't know when it will occur I think some additional advances will need to do will happen but you know I absolutely would not bet against this point occurring at some at some point can you give me a range is it at some point next month is it next year you know I think it's like the the uncertainty on this thing is quite High because these advances I can imagine it can take in quite a while I can imagine it can take any disappoint in a long time I can also imagine it's taking you know some number of years but it's just very it's very hard to give a Cali braided answer and I I know there's lots of push forward so I'm going to ask one more question then move on to some of the other issues but um I know I read that when you were a child you were disturbed by the notion of Consciousness and I wasn't sure what that what that word meant Disturbed but I'm curious do you view Consciousness or sentience or self-awareness as an extenuation of learning do you think that that is something that also is an inevitability that will happen or not yeah I mean on the Consciousness questions like yeah I was as a child that would like you know look into my in my hand and I would be like how can it be that this is my hand that I get to see like I something of this nature I don't know how to explain it much better so that's been something I was curious about you know it's It's Tricky with Consciousness because how do you define it it's something that the looted definition for a long time and how can you test it in a system maybe there is a system which acts perfectly right but um perfectly the way you'd expect um a conscious system would act yet maybe it won't be conscious for some reason I do think there is a very simple way to there's there is an experiment which we could run on an AI system which we can't run on which we can't run just yet but maybe in like the Future Point when the AI learns very very quickly from less from less data we could do the following experiment very carefully with very carefully curate the data such that we never ever mention anything about consciousness it would only say you know here is here's a ball and here's a castle and here is like a little toy like you would imagine imagine you'd have data of this sort it would be very controlled maybe we'd have some number of years worth of this kind of training data maybe it would be maybe such an AI system would be interacting with a lot of different teachers learning from them but all very carefully you never ever mentioned Consciousness you don't talk about people don't talk about anything except for the most surface level Notions of their experience and then at some point you sit down this Ai and you say Okay I want to tell you about Consciousness it's the stain that's a little bit not well understood people disagree about it but that's how they describe it and imagine if the AI then goes and says oh my god I've been feeling the same thing but I didn't know how to articulate it that would be okay that would be definitely something to think about it's like if the AI was just trained on very mundane data around objects and going from place to place or maybe you know something like this from a very narrow set of Concepts we would never ever mention that and if it could somehow eloquently correctly talk about it in a way that we would recognize that would be convincing and do you think of it as a some as Consciousness as something of degree or is it something more binary uh I think it's something that's more a matter of degree I think that I think that like you know let's say if a person is very tired extremely tired and maybe drunk then perhaps if that's when when someone is in that state and maybe their Consciousness is already reduced to some degree I can imagine that animals have a more reduced form of Consciousness if you imagine going from you know large primates maybe dogs cats and then eventually you get mice you might get an insect like feels like I would say it's pretty continuous yeah okay I want to move on even though I could I would love to keep asking more questions along the lines of the technology but I want to move on to talking about the mission of openai and how you perceive or any issues around ethics and your role as Chief science officer how ethics informs if at all how you think about your role and so let me just lay a couple Foundation points out and then have you speak um as you know open ai's mission is to ensure the art of that artificial general intelligence benefits all of humanity and it started off as a non-profit and open source and it is now a for-profit and closed-sourced and with a close relationship with Microsoft and Elon Musk who I believe recruited you to originally join open Ai and gave 100 million dollars when it was a non-profit has says that the original Vision was to create a counterweight to Google and the corporate world and he didn't want to have a world in which AI which is has which he perceives and others can have an existential threat to humanity to be solely in the holds of of corporate of a for-profit um and now open AI is neither open nor exclusively a non-profit it's also a for-profit with close ties to Microsoft and it looks like the world may be headed towards um a private duopoly between Microsoft and Google can you shed light on the calculus to shift from a for-profit to a non-profit and did you weigh in the ethics of that decision and do ethics play a role in how you conceive of your role as the chief science officer or do you view it more as something that somebody else should handle and you are mainly just tasked with pushing the technology forward yeah so this question is many parts let me yeah let me think about the best way to to approach it so there are several parts there is the there is the question around open source versus closed source there is a question around non-profit versus for-profit and the connection with Microsoft and how to see that in the context of Elon musk's recent comments and then the question about how I see my role in this maybe I'll start with that because I think that's easier Okay so I feel yeah the way I see my role I feel a lot I I feel direct responsibility for whatever open AI does even though I my role is primarily around advancing the science it is still the case I'm one of the founders of the company and ultimately I care a lot about open ai's overall impact now I want to go so with this context I want to go and talk about the open source versus closed source and the non-profit versus for-profit and I want to start with open source which is closed source because I think that you know the challenge with AI is that AI is so all encompassing encompassing and it comes with many different challenges it comes with many many different dentures which come into conflict with each other and I think the open source versus closed source is a great example of that why is it desirable well let me put it this way what are some reasons for which it is desirable to open source AI the answer there would be to prevent concentration of power in the hands of those who are building the AI so if you are in a world where let's say there is only a small number of companies you might that control this very powerful technology you might say this is an undesirable world and that AI should be open and that anyone could use the AI this is the argument for open source but this argument you know of course you know to State the obvious there are near-term commercial incentives against open source but there is another longer term argument against open sourcing as well which is if we believe if one believes that eventually AI is going to be unbelievably powerful if we get to a point where your AI is so powerful where you can just tell it hey can you autonomously create a biological I don't know a biological research lab autonomously do all the paperwork render space hire the technicians aggregate experiments do all this autonomously like that starts to get incredible that starts to get like mind-bandingly powerful should this be open sourced also so my position on the open source question is that I think that I think that there is a maybe a level of capability you can think about these neural networks in terms of capability how capable they are how smart they are how much how many how much how much can they do when the capability is on the lower end I think open sourcing is a great thing but at some point and you know there can be debate about where the pointer is but I would say that at some point the capability will become so vast that it will be obviously irresponsible to open source models and was that the driver Behind Closed sourcing it or was it driven by a a devil's compact or business necessity to get cash in uh from Microsoft or others to support the viability of the business was the decision making to close it down actually driven by that line of reasoning or was it driven by more so it's so so the way I'd articulate it you know my view is that the current level of capability is still not that high where it will be the safety consideration it will drive the close closed Source in the model this kind of this kind of research so in other words a claim that it goes in phases right now it is indeed the competitive phase but I claim that as the capabilities of these models keep increasing there will come a day where it will be the safety consideration that will be the obvious and immediate driver to not open source these models so this is the open source versus closed Source but your question had enough but your question in another part which is non-profit versus for-profit and we can talk about that also you know indeed it would be preferable in a certain meaningful sense if open AI could just be a for a non-profit from now until the mission of open AI is complete however one of the things that's worth pointing out is the very significant cost of these data centers I'm sure you're reading about various AI startups and the amount of money they are raising the great majority of which goes to the cloud providers why is that well the reason so much money is needed is because this is the nature of these large neural networks they need the compute end of story you can see something like this that's all you can see a divide that's now happening between Academia and the AI companies so for a long time for many decades Cutting Edge research in AI took place in academic departments in universities that cap being the case up until the mid-2010s but at some point when the complexity and the cost of this project started to get very large it no longer remained possible for universities to be competitive and now universities need a University Research in AI needs to find some other way in which to contribute those ways exist they're just different from the way they're used to and different from the way the companies are contributing right now now with this context you're saying okay the thing about non-profit a non-profit is the people who give money to a non-profit never get to see any any of it back it is a real donation and believe it or not it is quite a bit harder to convince people to give money to a non-profit and so we so so we think what's what's the solution there or what is a good course of action so we came up with an idea that to my knowledge is unique in all corporate structures in the world the open air corporate structure is absolutely unique open AI is not a for-profit company it is a capped profit company and I'd like to explain what that means what that means is that equity in open AI can be better seen as Bond rather than equity in a normal company the main feature of a bond is that once it's paid out it's gone so in other words open AI has a finite obligation to its investors as opposed to an infinite obligation to that normal companies have and does that include the founders do the founders have equity in open AI so Sam Altman does not have equity but the other Founders do and is it capped or is it unlimited it is capped and how does that cap is that capped at because the the founders I presume didn't buy in unless it's capped at the nominal Share value um I'm not sure I understand the question precisely but what I can say like what what I can answer the part which I do understand which is like there is certainly like it isn't there are it is a different it is different from normal startup Equity but there are some similarities as well where the earlier you join the company the higher the cap is because then the larger cap is needed to attract the initial investors as the company continues to succeed the cap decreases and why is that important it's important because it means that the company one once when once all the obligation to investors and employees are paid out open AI becomes a non-profit again and you can say this is totally crazy what are you talking about like it's not going to change anything but it's worth considering what we expect like it's worth looking at what we think AI will be I mean we can look at what AI is today and I think it is not at all inconceivable for open AI tool achieve its to pay out its obligation to the investors and employees become a non-profit at around the time when perhaps the computers will become so capable where the economic destruction will be very big where this transition will be very beneficial so this is the answer on the cap profit versus non-profit there was a last part to your question I know I'm speaking for a while but the question had many parts the last part of your question is the Microsoft relationship and so here the thing that's very fortunate is that Microsoft is a there thinking about these questions the right way they understand the potential and the gravity of AGI and so for example on the on all the investor documents that any investor in open AI has signed and by the way Microsoft is an investor into open AI which is a very different relationship from the deepmind any anyone who signed any document any investment document there is a a purple rectangle at the top of the investment document which says that the fiduciary duty of open AI is to the open AI mission which means that you run the risk of potentially losing all your money if the mission comes in conflict so this is something that all the investors have signed and let me just make this clear for everybody because Google Google acquired deepmind so deepmind was just an asset inside of Google but beholden to Google you're making the distinction that with openai Microsoft is an investor and so beholden to this fiduciary duty for the mission of openai which is held by the non-profit which is a is is a a GP or an LP in the um in in the for-profit um okay understood yeah so it's not something like this you know I am you know there are people I can't tell you the precise details yeah but so but this is the general picture and you know some have claimed though now especially it uh um Steve Wozniak the co-founder of apple and Elon Muska famously signed this very public petition saying that the point of no return is already passed or we're approaching it where it's going to be impossible to reign in Ai and it's and it's it's repercussions if we don't halt it now and they've called for halting AI um I'm curious on you are a world citizen Ilia you were born in Russia you were raised in Israel you're Canadian um and I'm and it's open ai's response to that public petition was um I know Sam basically said that you know this wasn't the right way to go about doing that but also in parallel Sam is on a world tour with many countries that also can be antagonistic towards the West are there any citizen obligations ethical obligations that you think also overweigh your your technological obligations when it comes to spreading the technology around the world right now through open AI do you think that should be beholden to a regulation or some oversight let me think once again the question had a number of Parts did I apologize I'm trying to give you the so you can respond however you want to on that I know we're going to come out of off of time so I just want to give you the mic and just share everything that's on my mind and you can decide how you want to handle it yeah thank you I mean you know it is true that AI is going to become truly extremely powerful and truly extremely transformative and I do think that we will want to move to a world with sensible government regulations and there you know there are several Dimensions to it we want to be in a world where there are clear rules about for example training more powerful neural networks we want there to be some kind of careful evaluation careful prediction of these of what we expect these neural networks of what they can do today and on what we expect them to be able to do let's say in a year from now or by the time they finish training I think all these things will be very necessary in order to like rational like rationally I wouldn't use the word slow down the progress I would use the term you want to make it so that the progress is sensible so that with each step we've done the homework and indeed we can make a credible story that okay the neural network the system that we've trained it has we are doing this and here all the steps and it's been verified or certified I think that is the world that we are headed to which I think is correct and as for the citizen obligation I feel like I mean 15 what I'll answer it like this like I think I think like there are there are two answers to it so obviously you know I live I live in the United States and I really like it here and I want and I want this place to flourish as much as possible I care about that I think that of course there will be lots of but the world is much more than just the US and I think that these are the kind of questions which I feel a little bit let's say outside of my expertise how these between country relationships work out but I'm sure there will be lots of discussions there as well yeah um Julia can I turn a little bit towards strategy um I'm curious for you guys internally what metrics do you track as your North Star what are the most sacred kpis that you use to measure open ai's success right now the most sacred kpis you know I think this is also the kind of question where maybe different people will give you different answers but I would say I would say that there are if I were to really narrow it down I would say that there are there is a couple of really important kpi of a really important dimensions of progress one is undeniably the technical progress are we doing good research do we understand our systems better are we able to train them better can we control them better I is our is ours is our research plan being executed well is our safety plan being executed well how happy are we with it I would say this would be my description of the primary kpi which is do a good job of the technology then there is of course stuff around the product but which I think is cool but I would say that it is really the core technology which is the heart of openai the technology its development and on end its control it's steering and and do you view um right now chat gbt is a destination do you view open AI in the future being a destination that people go to like Google or will it be powering other applications and be the back end or be be you know used as part of the back end infrastructure um is it a destination or is it going to be more behind the scenes um in in five to ten years yeah well I mean things change so fast I I cannot make any claims about five to ten years in terms of the correct shape of the product I imagine a little bit of both perhaps but this kind of question I mean I think it remains to be seen I think there are I think this stuff is still so new okay I'm gonna ask one more question I'm gonna jump to the student questions if you're a student at Stanford today interested in AI if you were you know somebody who wants to be Ilia um what would you focus your time and another second question on this if you're also interested in entrepreneurship um where would you what would you what advice would you give for a Stanford undergrad engineer that's interested in Ai and Entrepreneurship so I think on the first one it's always hard to give generic advice like this but I can still provide some generic advice nonetheless and I think it's something like it it is generally a good idea to lean into one's unique predispositions you know every you know why if you think if you look if you think about the set of let's say inclinations or skills or talents that the person might have the combination is pretty rare so leaning into that is a very good idea no matter which direction you choose to go look to going and then on the AI research like I would say I would say that there you know I could say something but even but there especially you want to lean into your own ideas and really ask yourself what can you is is there something that's totally obvious to you that makes you go why is everyone else not getting it if you feel like this that's a good sign it means that you might be able that that you you want to lean into that and explore it and see if your instinct is true or not it may not be true but you know my my advisor Jeff Hinton says this thing which I really like he says you should trust your intuition because if your intuition is good you go really far and if it's not good then there's nothing you can do hmm and as far as entrepreneurship is concerned I feel like this is a place where the unique perspective is even more valuable or maybe equally it's because it's maybe maybe I'll I'll explain why I think it's more valuable than in research well in research it's very valuable too but in entrepreneurship like you need to like almost pull from your unique life experience where you say okay I see this thing I see this technology I see something like take a very very Broad View and see if you can hone in on something and then actually just go for it so that would that would be the conclusion of my generic advice okay which is great that's also great I'm going to move on to the student question so one of the most upvoted question is how do you see the field of deep learning evolving in the next five to ten years let's see you know I expect deep learning to continue to make progress I I expect that you know there was a period of time where a lot of progress came from scaling and you you saw that most in the most pronounced way in going from GPT 1 to gpd3 but things will change a little bit the reason the reason that the reason that progress in scaling was so rapid is because people had all these data centers which they weren't using for a single training run so by simply reallocating existing resources you could make a lot of progress and it doesn't take that long necessarily to reallocate existing resources you just need to you know someone just needs to decide to do so it is different now because the training runs are very big and the scaling is not going to be progressing as fast as it used to be because building data center takes time but at the same time I expect deep learning to continue to make progress in uh from other places the Deep learning stack is quite deep and I expect that there will be improvements in many layers of the stack and together they will still lead to progress being very robust and so if I had to guess I'd imagine that there would be maybe I'm certain we will discover new properties which are currently unknown of deep learning and those properties will be utilized and I fully expect that the systems of five to ten years from now will be much much better than once they are we have right now but exactly how it's going to look like I think I think it's a bit harder to answer it's a bit like it's because the improvements that there is there will be maybe a small number of big improvements and also a large number of small improvements all integrated into a large complex engineering artifact and can I ask your you know your co-founder Sam Altman has said that we've reached the limits of what we can achieve by scaling to larger language models is do you agree um and if so you know what then what is the next Innovation Frontier that you're focusing on if that's the case yeah so I think maybe I don't remember I don't know exactly what he said but maybe he meant something like that the age of easy scaling has ended or something like this like of course of course the larger neural Nets will be better but it will be a lot of effort and cost to do them but I think there will be lots of different Frontiers and actually into the question of how can one contribute in deep learning identifying such a frontier perhaps one that's been missed by others is very fruitful and is it can I go even just deeper on that because I think there is this debate about vertical Focus versus General um uh General's training you know is it better do you think there's better performance that can be achieved in particular domains such as law or Medicine by training with special data sets or is it likely that generalist training with all available data will be more beneficial so like at some point we should absolutely expect Specialists training to make a huge impact but the reason we do the generalist training is just so that we can even reach the point where just so that we can reach the point where the neural network can even understand the questions that we are asking and only when it has a very robust understanding only then we can go into specialist training and really benefit from it so yeah I mean I think all these I think these are all fruitful directions but you don't think when do you think we'll be at that point when specialist training is the thing to focus on I mean you know like if you look at people who do open source work people who work with open source models they do a fair bit of this kind of specialist training because they have a fairly underpowered model and they try to get any ounce of performance they can out of it so I would say that this is an example I'd say that this is an example of it happening like it's already happening to some degree it's not a binary it's you might want to think of it as of like a continual Spectrum but do you think that the competitor do you think that the winning Advantage is going to be having these proprietary data sets or is it going to be having a much higher performance large language model when it comes to these applications of AI into verticals so I think it may be productive to think about about an AI like this as a combination of multiple factors where each factor makes a contribution and is it better to have a special data which helps you make your AI better in a particular set of tasks of course is it better to have a more capable base model of course from the perspective of the task so maybe this is the the answer it's not an either or I'm going to move down to the other questions um there's a question on what was the cost of training and developing GPT T3 slash four yeah so you know for for obvious reasons I can't comment on that um but there I think there is a you know I think even from our research Community there's a strong desire to be able to get access to um uh to different aspects of open ai's technology and are there any plans for releasing it to researchers or to other startups to encourage more competition and Innovation some of the requests that I've heard are unfettered interactions without safeguards to understand the model's performance model specifications including details on how it was trained and access to the model itself I.E the trained parameters do you want to comment on any of that yeah I mean I think like it's related to our earlier question about open versus closed I think that there are some intermediate approaches which can be very fruitful for example model access and various combinations of that can be very very productive because these mineral networks already have such a large and complicated surface area of behavior and and studying that alone can be extremely interesting look if you have an academic access problem we provide various forms of access to the models and in fact plenty of academic research Labs do study them in this way so I think this kind of approach is viable and it's something that we could that we are doing I know we're coming up on time I want to end with just one final question which is can you just share any unintuitive but compelling use cases for how you love to use chat gbt that others may not know about um so I mean I don't I wouldn't say that it's unknown but I I really enjoy its poem writing ability it can write poems it can rap it can it can be it can be it can be pretty amusing and do you guys use it is it is it an integrated part of the um of teamwork at open I assume it is but I'm curious do you have any insights on how it changes Dynamics with teams when you have ai deeply integrated into you know a human team and how they're working and any insights into to what we may not know but that will come I would say I would say to today the best way to describe the impact is that everyone is a little bit more productive people are a little bit more on top of things I wouldn't say that right now there is a dramatic impact on Dynamics which I can say oh yeah the Dynamics have shifted in this pronounced way okay I'm curious if it depersonalizes conversations because it's the AI bot or maybe it but maybe we're not at that point yet where it's specifically that I definitely I I don't think that's the case and I predict that will not be the case but we'll see well thank you Ilya for a fascinating discussion time is always too short you're always invited back to the farm um we'd love to have you either virtually or in person um so thank you thank you thank you um to our audience thank you for tuning in for this session of the entrepreneurial thought leader series next week where we're going to be joined by the executive chairman and co-founder of OCTA Frederick karist and you can find that event and other future events in this ETL series on our Stanford e-corner YouTube channel and you'll find even more of the videos podcasts and articles about entrepreneurship and Innovation at Stanford e-corner that's ecorner.stanford.edu and as always thank you for tuning in to ETL thank you

Channel: Stanford eCorner

Views: 114,029

Keywords: Challenges, competition, culture, decisions, ethics, funding, government, growth, ideas, innovation, investors, metrics, mission, open-source, regulation, research, scale, strategy, technology

Id: Wmo2vR7U9ck

Length: 50min 23sec (3023 seconds)

Published: Wed Apr 26 2023