No Priors Ep. 35 | With Sarah Guo and Elad Gil

Key Takeaways

[Music] hi no pryors listeners time for a host only episode this week a lot and I talk about the path to better model quality from here the potential of fine-tuning rhf rlaif rag and retrieval systems generally met a sponsorship of the open source model ecosystem and finally the beginning of a new set of consumer applications and social networks thanks for tuning in so one thing everybody is thinking about is what it takes to get to 10x or 100x better AI systems like I think be useful just sort of enumerate the the elements to sort of Step function better A lot what do you think yeah you know it's interesting because there's there's a few different aspects of that that people always talk about their scalability of data sets and compute and parameters and all these things but the reality is I think a lot of people believe that in order to 10x or even 100x use cases and usages for AI outside of that there's things that could just be done on existing models today so you don't need to wait for gpt7 or whatever you could start with gpt4 or GPT 3.5 and add these things and I think they are kind of bucketed into five or six areas number one is multi-modality so that means being able to use text or Voice or images or video is both input and output so you should be able to talk to a model type to it upload an image and ask about the image and then it could output anything from code to a short video for you um second is long context windows so basically when you prompt a model you basically are feeding a data or commands or other things and everybody realizes that you need longer and longer and longer context windows so magic for example is doing that for code you should be able to dump it entire code repo into a coding model instead of having to do a piecemeal third which we're going to talk about today is model customization so that's things like fine-tuning something known as rag there's data cleaning there's labeling there's a bunch of stuff that just makes models work better for you fourth is some form of memory so the AI actually remembers what it's doing fifth is some form of recursion so looping back and reusing models and then six which is related is potentially a bunch of small models that are very specialized being orchestrated by a central model or sort of AI router this as well for this for this specific tasker use case I'm going to route The Prompt or the data or the output into this other model that's doing this other thing which is basically how the human brain works right you process visual information through your visual cortex but then you use other parts of your brain to make decisions right and so it's very similar to what Evolutions where I decided was an optimal approach but I think it's really interesting because I think many people in the field know that these five or six things are absolutely coming and they can dramatically improve the performance on existing systems again 10x 100x better for certain things and so it's more just a matter of when right it's not really an if anymore a bunch of people are working on different aspects of this and you know I think it's all coming really fast and so what you know there's sort of two things that came out in the last week or two that are really relevant to this it'd be great to get your thoughts on one is open AI announcing that they're not going to allow people to fine-tune models and then second is Google where they looked at human generated feedback versus AI generated feedback for models and sort of fine-tuning models that way so and if you want to tell people a bit more about what happened with openai and why that's important yeah so fine-tuning as a capability has been offered by openai for several years right but they've made like a a specific investment in allowing people to do that with more sophisticated models in particular like three five and also making it possible for more Enterprise use cases right and if you think about sort of like why that matters at all as you said like you know you have a bunch of these Labs who are working on General capability and working on this sort of direction of scaling laws like Transformers predictably improve with scale data and compute but I think what's really interesting is like the way every the way these models end up being used in many business or even consumer application contacts is against a specific task right and so we've talked a lot about like where research effort is being work put or compute is being spent in the industry right now and there's a really I think there's really interesting question of we don't even know how good models can be at certain scale right at 70 or 30 or 100 billion parameters or more but not a gpt4 Scale based on really high quality data and curation of that data because it hasn't hasn't been explored and so I think we should talk about some of the different ways you get these models to actually operate against a specific task uh with either fine-tuning with rohf uh against you know the reward for your task or with with rag as you said in terms of retrieving from a data set that you've specified right and there's reasons you would do all three of these but I think it's actually a pretty big step for openai to enable this because I think there was at certain points in the in the research world there's been a narrative that like fine-tuning doesn't really matter right the general model matters and I'd be curious if you think that's a change in research point of view or just a commercial decision in terms of labs wanting to make money or that being more important than ever yeah I think everybody realized that fine-tuning works really well when Chachi PT came out because what Chachi PT is is they took this model GPT 3.5 which existed at the time and that wasn't seeing as much usage at least from you know people just going in and querying it unless they were really good at prompts and they basically hired a bunch of people and the people ranked the output of the model and they effectively fine-tune the model against that feedback from the people who are assessing is this the answer that I wanted based on the prompt that I put in right and so fine-tuning really just means you create a lot of feedback usually at least today through people responding to output and saying is it good or bad and it created a dramatic step function and the utility of GPT 3.5 for end consumers or end users or students or lawyers or all sorts of different types of people and it really helped it was kind of the starting gun for this whole AI Revolution right now because everybody suddenly realized how powerful these models were and the model underlying it fundamentally hadn't really changed that much what they've done is they fine-tuned it without with reinforcement learning through human feedback or rhf and so I think that created this uh Viewpoint that these these types of fine tunings or you know we can talk about rag in a minute love to get your thoughts on that can fundamentally change the user affinity for a product and so you could imagine in an Enterprise you say well I really want to fine-tune this model so that it reflects medical data that I have this proprietary that could help make a better doctor assistant or I want to fine-tune it against this you know set of HR responses that are unique to my company so that if I have a uh an employee who really wants to get answered a question that can get a really good answer back and so it really gets into those sorts of things where you can dramatically improve the output of a model against something that you specialize do you want to talk about how rag ties into that because I think that's a really key component of it too I think the sort of basic premise with rag that everybody should understand is you want to retrieve against a specific Corpus right and so you can you're still going to reason you might have a generation or an answer based on that Corpus but if you pick a set of documents it could be legal cases it could be internal company documents it could be medical information as you said right so you still want the reasoning capabilities of the model right a diagnosis requires reasoning but you want it to come from a specific set of data versus like let's say all of the pre-training data of you know random information on the internet about whether or not you have this disease right and every piece of forum conversation about this disease and ever happen so you know I think of the um the core driver as like trustworthiness right citation control of information source and and so now you have this architecture where people are using um think of it as like traditional information retrieval techniques and search in combination with these models I think the other sort of driver besides trustworthiness on these rag approaches is two things one is cost and the other is like freshness right so every time you uh retrain a model or even fine-tuna model like there is compute involved see the idea that you know being able to incorporate new information without retraining and just using the reasoning capabilities of the models I think it's very attractive to people and very that's also related to the freshness point of view which is like you actually want the most recent medical research or the cases from this past year I think that's that's sort of a set of the drivers behind people being excited to take this approach and use it against their private data sets yeah and that actually helps a lot with uh hallucinations right and so I think it's important to sort of explicitly point that out because one of the knocks on the current set of AI Technologies is while it may hallucinate or say and you know say things that aren't necessarily true or cite a legal case that doesn't exist and by using rag you can actually help say okay I'm only going to use things that that I know exist or I'm going to filter for things that are going to be um answers that fit well with you know the the current set of knowledge that people have relative with these sets of issu use so to your point on trustworthiness I think it's really important to call out hallucinations explicitly since that's something people people keep bringing up is sort of naysayers oh my gosh what if it hallucinates and some terrible misinterpretation happens and therefore we need to regulate this thing right so uh it's kind of interesting you know I guess related to that there's this reinforcement learning through human feedback versus AI feedback and Google just came out with a really interesting paper on that where you know they showed that you can have an AI similarly provide feedback to whether the AI itself is generating good output and for certain use cases that works as well as people and so suddenly instead of having to hire an army of people to go and help fine-tune these models you can actually have an AI help fine-tune this model and I think the the early signs that that was going to be true was actually medpalm 2 where Google showed that they trained a model specifically on medical data and the output from the model tended to be more correct than human physician experts and so for certain use cases we are already seeing AI provide more accurate answers than Specialists experts right and in our aif you're trying to sort of generalize that and say what are all the different ways that instead of using expensive people to do this we can use really cheap AI models to provide that same feedback and sort of train things and so there's all these techniques and technologies that are coming now as part of this sort of list of six six big innovations that are part of the future AI 10x or 100x redmap that are starting to fall into place I think it's a very exciting time and I think you know in the next year we'll keep seeing stuff like that so there's a few other announcements that have come out related to this in terms of using different data sets or different models but coming from social networks so for example Twitter or I guess now we should call it X said it will train ml models off of Twitter data and that may have really interesting consumer applications or outcomes and then meta is really now emerging as a primary sponsor for open source models llama and llama2 have really taken off and sort of the developer and Enterprise ecosystem around the llms so it'd be great to hear what you think in terms of why are they doing this you know why why are they becoming the primary sponsor for open source Ai and how do you think they're going to apply it within their own company I really draw a analogy from the current sponsorship of meta and Zuck of you know llama and the open source model ecosystem to like MySQL right so for those of us who remember like what happened with these open source database companies moscale ended up being originally made by this guy Monty wardenius and some Swedish company became partisan became part of Oracle and in the early days like MySQL would crash and corrupt data and there were some early internet scale companies like Facebook who wanted to use it wanted to not be beholden to commercial database vendors made at scale made it more robust and contributed back right and I think like it's a reasonable analogy in terms of like some core technology to your company where you don't want to have a vendor uh you don't see it as part of your core business model but you want there to be open source options right and so I have a lot of admiration for what meta is doing and I think like I think it is very likely to be a big mover in the ecosystem because if they sponsor some baseline of models that are big enough to be valuable high quality enough to be valuable with Facebook AI research and then enough people find these models useful and strategic and they create a developer ecosystem it's hard for me to picture them not being sustained as an important ecosystem an alternative to these you know research Labs that in many ways compete with Facebook or meta in different ways and are very expensive to maintain but if you look at the history of Open Source is that really true so say for example you look at Linux right and Linux in part was very much sponsored by IBM throughout the late 90s to the tune of in some in some years a billion dollars a year and so even these external ecosystems tend to get quite expensive you know and the the reason that IBM sponsored Linux was to provide a real offset to Microsoft right they basically said Microsoft is dominant on the desktop they're really getting aggressive on sort of the server and infrastructure world and so therefore let's fund this offset for open source how do you think that analog applies it to meta or does it or do you just think it's a different reason in terms of why they're pursuing it um well I think they're pursuing it because they want to use it and they don't want to be trapped right oh sure but they don't have to open source it right they could just continue to develop it like they have been and so why open source it one piece of it is like wanting to offset the development costs and the compute costs at some point right and and just like that's sort of one of the core premises of Open Source they've also done like other really related things like the open compute project but you know if you think about why that analogy does or doesn't apply right like one is does meta want to make money off of this in some sort of like B2B way if they keep open sourcing it the answer is no right they want to use it in their core consumer businesses and then two like for for this to work I think one of the ways the analogy breaks down is very much um like the need for centralized training today right it's a complicating Factor like can you really coordinate that with the politics and slow decision making of Open Source communities I don't know I think that's challenging they're um there are interesting folks working on at least these sort of like technical coordination of of this as well right like Foundry and together um but if you just to like make explicit like why might they care my guess is like the ability to use these models it applies in sort of thing more traditional ways like we can use them to make the data center like more energy efficient we can and there's been publishing about this we can use these models to improve um like ad serving right like lots of things that matter to the core meta business but it's also just one of the most interesting things to happen in consumer in a long time right you have things like character inflection mid-journey Pica experiments like can of soup like these things they have caught the attention of consumers in a way that few things have over the last few years and so I think it's known that their Instagram chat Bots being tested right and so if this is a path to Consumer engagement and then therefore ads and it's going to be a really important element I think they just want to have access to it without being to hold into a sponsor what's your view yeah I mean I think it's amazing that metas decided to make this move and I think it's really beneficial to the ecosystem overall so you know at this point I think llama 2 is really emerging as a model that a lot of people are rallying around and obviously that may change over time but for now I think it's one of the primary models people are using on the open source side and the people view is quite high quality um so I think it's super impressive I think more broadly in Social and AI it's kind of striking that the last large social network in some sense was Tick Tock which was launched seven years ago now so it's been a while since we've seen a major shift and part of that is because large-scale social products have already been established and so now you need to sort of pry users away from existing products which is much harder than just filling time otherwise I remember talking once with Jack the founder of wikiHow which was like a how to you know Community Driven website and he said that the main way that they lost people who were contributing to wikiHow was they went to social gaming they were just playing games instead right so it was sort of this time and attention shift 10 years ago and when you mentioned this to me right and so number one is you have to despise other people um number two you know a lot of the Innovation and social kind of stagnated a little bit for startups right it became a lot more let's do Twitter but more woke or more right wing um or let's do early Facebook again as a mobile app versus hey we're going to reinvent the modality or we're going to reinvent the use case or the communication Channel whatever maybe and it feels like generative AI is the first thing in many years to sort of create that new window or opening and I think the big social networks like meta and Twitter and others may actually be the biggest beneficiaries of this new way but there also should be room for startups and there's some new things you know can of soup was in the recent YC batch and they're doing kind of interesting things and I think it's almost like asking what's the Gen AI native modality and use case and typically when you look at Social products you used to have this two by two or some people had like a two by three of you know is it broadcast versus Mutual follows in terms of network structure what's the modality is it images as a video Etc and then what's the length and Persistence of it is it long form is it ephemeral et cetera so for example Snapchat started off as um you know short form uh broadcast and one-on-one that was ephemeral right and so uh you could kind of map out the whole social World against those dimensions and now there's this new interesting thing of you know new forms of content creation potentially upending one or two of those quadrants so it seems like a very exciting time overall yeah yeah I had a a you know long time obsession with hotel and Tick Tock and some of the Chinese social companies that really started as like AI native content aggregators right if you think about what they did um they really figured out this like cold start problem in terms of they like total originally they aggregated um news content from other places and then bootstrapped your preferences they didn't require explicit user input to say like I am interested in these topics they analyzed your social profile for your interests they collected like location and demo and analyzed articles for like quality and topics so they had these like Rich per user models of Engagement it based on interaction data and then you have this magical experience of like a better content feed that then drove the iteration around better labeling and I think exactly as you said if those companies figured out like the cold start on relevance um maybe the opportunity I think one of the potential opportunities in in this generation of social is like cold start on the content itself right like you've seen um other amazing companies like like the Instagrams of the worlds right they they create tools for Content creation for like magically compelling assets that are much easier and then like turn it into a social network and so generation feels like a um a really compelling answer in terms of like how to have a Content feed that is both like really engaging for you and then giving people creation superpowers yeah and I think uh mid journey and PK are two great examples of that to the point earlier and then character is sort of a form of that if you decide to create your own character or sort of interact with something that's more customized there so it does seem like there are these really interesting uh shifts that are happening and then the question is is it more for creation and sharing or does it become a new social product or a new communication product in other words is it giphy or is it you know uh Facebook right and the lenza was a good example of giphy right it was used to basically create content that you share on other social networks and the question is what are going to be the big consumer apps that sort of emerge on top of that and again it may just be meta again right but I think it's a super interesting question and uh and probably the most exciting time in Social for a very long time and it's kind of this oddly almost ignored area from uh entrepreneurship and founder perspective right now everybody's rushing at the Enterprise staff and the infrastructure and you know that whole stack and it's almost like the generation of people who are going to start social products all did them five years ago and did the you know let's do Twitter again and the generation that's really focused now kind of grew up where SAS was sort of opportunity or SAS and Dev tools were the opportunities that everybody was mining against so it'll be interesting to see whether or not that shifts back in any meaningful way um the one other thing I think is kind of interesting just related to entrepreneurship and AI right now and I was talking to a Founder about this where they were trying to do something really hard and um by really hard I mean addressing a really hard Market but using gen AI and early in markets like when a new technology shifts and disrupts the whole Market you actually want to just do the easy stuff right why do the hard stuff there's so much low-hanging fruit why don't you just go after this stuff is super easy and my my sort of advice to Founders generically on this stuff is like don't do the hard stuff right now or if it's hard do something that's technically hard that enables a giant breakthrough in terms of use case but don't actually do the hard Market because there's so many easy markets right now you should just you should just go for the easy stuff and if you're grinding and grinding and grinding and not getting customer attention don't spend more time on it it's just not worth it right now now five years from now when the use of these Technologies are a bit more saturated that's when you have to go do the hard stuff right but you know it's kind of interesting to to think about you know prior technology waves and when should you do the easy versus hard yeah it's actually just talking to some of the founders that are in our accelerator right now that come from like really great Technical and research backgrounds and they were reaching for a problem broadly in the engineering and code generation space that was very ambitious right and I could see kind of a a solve it all type problem um and it's not that it's not valuable it's just that there is so much you could do that is as you point out easier and valuable today um and like requires pushing the balance of research but you have far higher likelihood of having something that's useful to give to customers this year with far less risk and I don't mean to constrain people's Ambitions but the ability to give yourself multiple at bats with the wind at your your back in terms of the entire field progressing versus trying to get out in front of everyone else with a a multi-year research goal when there's like it's just gold hang out everywhere you know my my orientation is is I think similar here yeah it's no GPU before product Market fit I think that's the takeaway the loud slogan of the Year okay awesome uh fun to hang out and talk about the news of the week find us on Twitter at no Pryor's pod subscribe to our YouTube channel if you want to see our faces follow the show on Apple podcast Spotify or wherever you listen that way you get a new episode every week and sign up for emails or find transcripts for every episode at no dashbriars.com