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    Technology

    Latent Space: The AI Engineer Podcast

    The podcast by and for AI Engineers! In 2025, over 10 million readers and listeners came to Latent Space to hear about news, papers and interviews in Software 3.0.

    We cover Foundation Models changing every domain in Code Generation, Multimodality, AI Agents, GPU Infra and more, directly from the founders, builders, and thinkers involved in pushing the cutting edge. Striving to give you both the definitive take on the Current Thing down to the first introduction to the tech you’ll be using in the next 3 months! We break news and exclusive interviews from OpenAI, Anthropic, Gemini, Meta (Soumith Chintala), Sierra (Bret Taylor), tiny (George Hotz), Databricks/MosaicML (Jon Frankle), Modular (Chris Lattner), Answer.ai (Jeremy Howard), et al.

    Full show notes always on https://latent.space

    Sponsorship and business inquiries: business@latent.space

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    Copyright: © Latent.Space

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    Latest Episodes:
    State of the Art: Training >70B LLMs on 10,000 H100 clusters Jun 25, 2024
    Show notes

    It’s return guest season here at Latent Space! We last talked to Kanjun in October and Jonathan in May(and December post Databricks acquisition):Imbue and Databricks are back for a rare treat: a double-header interview talking about DBRX from Databricks and Imbue 70B, a new internal LLM that “outperforms GPT-4o” zero-shot on a range of reasoning and coding-related benchmarks and datasets, while using 7x less data than Llama 3 70B.While Imbue, being an agents company rather than a model provider, are not releasing their models today, they are releasing almost everything else: * Cleaned-up and extended versions of 11 of the most popular NLP reasoning benchmarks* An entirely new code-focused reasoning benchmark* A fine-tuned 70B model, built with Meta Llama 3, to identify ambiguity* A new dataset of 450,000 human judgments about ambiguity* Infrastructure scripts for bringing a cluster from bare metal to robust, high performance training* Our cost-aware hyperparameter optimizer, CARBS, which automatically and systematically fine-tunes all hyperparameters to derive optimum performance for models of any sizeAs well as EXTREMELY detailed posts on the infrastructure needs, hyperparameter search, and clean versions of the sorry state of industry standard benchmarks. This means for the FIRST TIME (perhaps since Meta’s OPT-175B in 2022?) you have this level of educational detail into the hardware and ML nitty gritty of training extremely large LLMs, and if you are in fact training LLMs of this scale you now have evals, optimizers, scripts, and human data/benchmarks you can use to move the industry forward together with Imbue.We are busy running the sold-out AI Engineer World’s Fair today, and so are unable to do our usual quality writeup, however, please enjoy our show notes and the excellent conversation! Thanks also to Kanjun, Ashley, Tom and the rest of team Imbue for setting up this interview behind the scenes.Video podTimestamps* [00:00:00] Introduction and catch up with guests* [00:01:55] Databricks' text to image model release* [00:03:46] Details about the DBRX model* [00:05:26] Imbue's infrastructure, evaluation, and hyperparameter optimizer releases* [00:09:18] Challenges of training foundation models and getting infrastructure to work* [00:12:03] Details of Imbue's cluster setup* [00:18:53] Process of bringing machines online and common failures* [00:22:52] Health checks and monitoring for the cluster* [00:25:06] Typical timelines and team composition for setting up a cluster* [00:27:24] Monitoring GPU utilization and performance* [00:29:39] Open source tools and libraries used* [00:32:33] Reproducibility and portability of cluster setup* [00:35:57] Infrastructure changes needed for different model architectures* [00:40:49] Imbue's focus on text-only models for coding and reasoning* [00:42:26] CARBS hyperparameter tuner and cost-aware optimization* [00:51:01] Emergence and CARBS* [00:53:18] Evaluation datasets and reproducing them with high quality* [00:58:40] Challenges of evaluating on more realistic tasks* [01:06:01] Abstract reasoning benchmarks like ARC* [01:10:13] Long context evaluation and needle-in-a-haystack tasks* [01:13:50] Function calling and tool use evaluation* [01:19:19] Imbue's future plans for coding and reasoning applications* [01:20:14] Databricks' future plans for useful applications and upcoming blog postsTranscriptSWYX [00:00:00]: Welcome to the Latent Space Podcast, another super special edition. Today, we have sort of like a two-header. John Frankel from Mosaic Databricks, or Databricks Mosaic, and Josh Albrecht from MBU. Welcome.JOSH [00:00:12]: Hey, glad to be here.SWYX [00:00:14]: Thank you for having us. Hey, so both of you are kind of past guests. Jonathan, you were actually one of the most popular episodes from last year talking about MPT7B. Remember the days when we trained large models and there was 7B?JONATHAN [00:00:30]: Yeah, back when reproducing LLAMA1-7B was considered a huge accomplishment for the field. Those are the good old days. I miss that.SWYX [00:00:38]: As the things have accelerated a lot. Actually, let's do a quick catch up and Josh, you can chime on in as well. So Databricks got acquired. I talked to you at New York.JONATHAN [00:00:45]: Mosaic got acquired, although sometimes it feels like Mosaic acquired Databricks because, you know, we're having a lot of fun being here. But, you know, yeah.SWYX [00:00:52]: Yeah. I mean, you are chief scientist now of Databricks.JONATHAN [00:00:55]: Chief AI scientist. Careful with the title. As much as I would love to understand how Spark works, I'm going to have to defer that to much smarter people than me.SWYX [00:01:03]: Got it. And I don't know about like what you would highlight so far as a post-acquisition, but the most recent news is that you guys released DBRX. Is that the thing that most people should be aware of?JONATHAN [00:01:13]: Actually, that's no longer the most recent news. Honestly, the most recent news, we announced this, but it was at our Data and AI Summit last week. So it was announced among like 100,000 other things, is that we finally released our text to image model, which has been a year in the making through a collaboration directly with Shutterstock. There was a lot of work put into finding a dataset that we were comfortable with working on and trying to build a model that honestly, I felt like I could trust and that others might be able to trust to put out in the world. So that model was released last week. It's unfortunately just available via API due to the fact that the data is quite sensitive and quite valuable. It's Shutterstock's entire business in a lot of ways, but I'm still really excited that there's now a model that is trained on a dataset where the provenance of every single image is known, and it's a damn good model. So I'm really proud of the team on that.SWYX [00:01:55]: Yeah, amazing. Josh, do you have any thoughts on image model questions?JOSH [00:01:59]: That is not my area of expertise, but I was excited to see the release of it last week as well, and very happy that you guys did a nice job on the data side of everything there. So that was cool to see.SWYX [00:02:09]: I think what's unusual is like, I think Shutterstock's doing multiple deals in multiple labs. So what is the Shutterstock model? Like, I guess, is this the house model for Shutterstock? Is this Databricks' version of the Shutterstock model? Like, what is this?JONATHAN [00:02:22]: The way that I would think about it is that Shutterstock is doing an amazing business in AI across the board. Their dataset is kind of widely known to be the best stock photos dataset in the world, the most comprehensive, the biggest. When you think about like, what dataset am I going to train a multimodal model on? You call Shutterstock. And I, at least I've heard in the news, like OpenAI, Google, Meta, Apple have all called Shutterstock and made those deals. So a lot of models have had Shutterstock data incorporated into them. But this is the only model I know of so far where it was, you know, exclusively and specifically trained just on the vanilla Shutterstock data. There was nothing else mixed in. We didn't go and scrape the web and find other data or combined datasets or anything like that. And so this is, in some sense, the house blend. But the other piece is that it's just a dataset where the provenance of every image is known in public. Where did the data come from? It is the Shutterstock collection. That's it. You know, nothing less, nothing more. And certainly being at Databricks, if I've learned one thing, I've learned about enterprise customers and what they want out of AI. And one of the things they ask for most is just, what can you tell me about the data the model was trained on? And here, especially for text to image models, where images are just tricky subject matter, there's been a lot of kind of legal conversation about images, especially. It's nice to just have something where I can point to it and say, you know, if you want to know where the images came from, these are what they are and this is how they got there.SWYX [00:03:36]: I will talk a little bit about Databricks because it's relevant to the rest of today's episode. So Databricks, sorry, I keep misspeaking. It's DBRX.JONATHAN [00:03:46]: DBRX, actually, there's been a pronunciation update. It is now D-B-Rex. So we have decided to add a dinosaur mascot because what model doesn't like a mascot? So literally, I wish I could pull it up. There is a little plush dinosaur that we had made. It's like the world's cutest dinosaur, but it is the official mascot of D-B-Rex. And there's a little dinosaur logo that, you know, you'll probably see around a little bit more because DBRX is a mouthful, but D-B-Rex, like, you know, it's just kind of...SWYX [00:04:13]: Rolls off the tongue. I love mascots. Like every company should have a mascot. And I think Hugging Face got it right. You need an emoji mascot because that's the minimal viable image.JONATHAN [00:04:21]: I probably shouldn't talk at all about, you know, Velociraptor, but, you know, that's a, maybe that's something we can talk about later in the summer. I'll just leave it at that.SWYX [00:04:28]: Okay. That's a hint to names. I feel like your names leak a lot of alpha. So just to quickly cover the headline details, DBRX, as Make Sure Experts model, that's fairly big, 132 billion total parameters, so 36 billion active on any input, pre-trained on 12 trillion tokens of text and code, and did really well on evals to the point where you had to dye your hair blue. That's my high level conclusion.JONATHAN [00:04:53]: Never make a bet with your team two weeks out from model launch, even when, you know, human eval is looking quite bad. Because if you set some bar, even if it's arbitrary and you think there's no way in hell they're going to hit it, apparently money doesn't motivate people anymore. Humiliating their boss motivates people. So Josh, you should really take a hint from this. You know, you cannot pay someone enough money to make up for you dyeing your hair blue.JOSH [00:05:15]: I'll keep that in mind for our next model.SWYX [00:05:17]: It works. So speaking of Imbue's next model, perhaps Josh, you want to actually just say hi to the general sort of latent space audience and talk about what we're releasing today. Yeah.JOSH [00:05:26]: I'm Josh, CTO of Imbue, and we're not releasing the model. We're not releasing the weights, but we are releasing a bunch of different things that should make it easier for other people to make their own models. So I think right now, training foundation models from scratch is like a very difficult, time-consuming, expensive, kind of risky endeavor, especially for smaller companies. And the things that we're releasing hopefully make that at least a little bit easier. So the things that we're releasing fall into kind of three different buckets. One is infrastructure and scripts for dealing with the kind of hardware and hardware failures and understanding how well is the actually lowest level of thing actually working so that you can actually do your training at all and at a reasonable speed without having to constantly restart, etc. So infrastructure and training scripts. A second set of things is around the evaluation. So after you've trained it, like how well is this actually working and how do you know how well it's working? We're releasing a whole bunch of different data there, a new benchmark about code, reasoning, understanding, as well as our own private versions of 11 different open source benchmarks. So things like pool queue or ANLI, where we've gone through and kind of cleaned up the data as much as possible by looking at all the ones that models get wrong or that are flagged for ambiguity and also our own kind of private reproductions of those where we've done like a kind of clean room black box, like, okay, this is what the data set is supposed to be. Here are some examples. Let's make our own version of this to make sure that there is no data contamination, etc. To make sure that we're actually, you know, not testing on train. And then I think a final thing that we're releasing there is around 450,000 human judgments about ambiguity and question quality, which we used in the process of cleaning these evaluations and we also hope will be helpful for other people training kind of similar models. And then the third thing is CARBS, our hyperparameter, our cost-aware hyperparameter optimizer, which was especially helpful for being able to experiment at much smaller scales and then scale those experiments up to the much larger scale kind of on the first try without having to retry it. You don't want to be training, you know, 10, 20 different 70B models. You really want to get these larger modelsSWYX [00:07:30]: right on the first try.JOSH [00:07:30]: And so the ability to kind of tune things very precisely and learn scaling laws, not just for, you know, the like data and flops, but also for learning rate and all the other hyperparameters and see like how should you scale these things up was extremely valuable to us as we were training the larger models. Yeah, that's a lot of stuff.SWYX [00:07:49]: Yeah, exactly. So there's a bunch of stuffJOSH [00:07:50]: we'll have to go through all of it.JONATHAN [00:07:52]: Yeah, I just want to throw in how excited I am about this. This is the stuff that nobody ever talks about. That is the difference between success and failure in this stuff. Like, can you get your cluster to run? Can you get software on your cluster? Can you figure out what broke? Because fault tolerance is still not really built into any of the fundamental primitives of training models. And so if something breaks, you have to go figure out what broke, your job stops, you have to restart your job. It is a nightmare just to get to the point where anything can train on the cluster. A basic MPI hello world that has the GPUs talk to each other is hard enough, let alone actually training a model, let alone getting good performance out of the GPUs, let alone actually getting a model that converges to anything interesting. There's so many levels of things you have to accomplish. This is the kind of stuff that matters. I think to a point that Josh made earlier, before we got on here, there are plenty of weights out there. Nobody's released this.JOSH [00:08:46]: Yeah, that was part of the motivation actually is that there are lots of other things that are complimentary, but I have not seen nearly as much discussion about some of these other things that we think are pretty important. I mean, in some sense,SWYX [00:08:56]: I'm very excited to have Jonathan on because this is a little bit, you're a bread and butter with Mosaic. And I think you've released some part with Composer. And I think it's just really interesting to see like a different take, basically a full stack take that's kind of open source today.JONATHAN [00:09:18]: Yeah, it's really kind of, it's been an ordeal to figure this out. And every time something changes, whether it's a new GPU or even a new driver update, you get new creative errors and new things go wrong. And, you know, we've dealt with the weirdest things from, you know, our InfiniBand cables getting stolen from the data center twice, like in boxes before they arrived at the data center. Like, you know, Porch Pirate basically had stolen our InfiniBand cables back when those were hard to come by. To like, you know, weird recalls of switche…

    Full show notes at the publisher

    [High Agency] AI Engineer World's Fair Preview Jun 25, 2024
    Show notes

    The World’s Fair is officially sold out! Thanks for all the support and stay tuned for recaps of all the great goings on in this very special celebration of the AI Engineer!

    Longtime listeners will remember the fan favorite Raza Habib, CEO of HumanLoop, on the pod:

    Well, he’s caught the podcasting bug and is now flipping the tables on swyx!

    Subscribe to High Agency wherever the finest Artificial Intelligence podcast are sold.

    High Agency Pod Description

    In this episode, I chatted with Shawn Wang about his upcoming AI engineering conference and what an AI engineer really is. It's been a year since he penned the viral essay "Rise of the AI Engineer' and we discuss if this new role will be enduring, the make up of the optimal AI team and trends in machine learning.

    Timestamps

    00:00 - Introduction and background on Shawn Wang (Swyx)03:45 - Reflecting on the "Rise of the AI Engineer" essay07:30 - Skills and characteristics of AI Engineers12:15 - Team composition for AI products16:30 - Vertical vs. horizontal AI startups23:00 - Advice for AI product creators and leaders28:15 - Tools and buying vs. building for AI products33:30 - Key trends in AI research and development41:00 - Closing thoughts and information on the AI Engineer World Fair Summit

    Video


    This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.latent.space/subscribe

    How To Hire AI Engineers — with James Brady & Adam Wiggins of Elicit Jun 21, 2024
    Show notes

    Editor’s note: One of the top reasons we have hundreds of companies and thousands of AI Engineers joining the World’s Fair next week is, apart from discussing technology and being present for the big launches planned, to hire and be hired!Listeners loved our previous Elicit episode and were so glad to welcome 2 more members of Elicit back for a guest post (and bonus podcast) on how they think through hiring. Don’t miss their AI engineer job description, and template which you can use to create your own hiring plan! How to Hire AI EngineersJames Brady, Head of Engineering @ Elicit (ex Spring, Square, Trigger.io, IBM)Adam Wiggins, Internal Journalist @ Elicit (Cofounder Ink & Switch and Heroku)If you’re leading a team that uses AI in your product in some way, you probably need to hire AI engineers. As defined in this article, that’s someone with conventional engineering skills in addition to knowledge of language models and prompt engineering, without being a full-fledged Machine Learning expert.But how do you hire someone with this skillset? At Elicit we’ve been applying machine learning to reasoning tools since 2018, and our technical team is a mix of ML experts and what we can now call AI engineers. This article will cover our process from job description through interviewing. (You can also flip the perspectives here and use it just as easily for how to get hired as an AI engineer!)My own journeyBefore getting into the brass tacks, I want to share my journey to becoming an AI engineer.Up until a few years ago, I was happily working my job as an engineering manager of a big team at a late-stage startup. Like many, I was tracking the rapid increase in AI capabilities stemming from the deep learning revolution, but it was the release of GPT-3 in 2020 which was the watershed moment. At the time, we were all blown away by how the model could string together coherent sentences on demand. (Oh how far we’ve come since then!)I’d been a professional software engineer for nearly 15 years—enough to have experienced one or two technology cycles—but I could see this was something categorically new. I found this simultaneously exciting and somewhat disconcerting. I knew I wanted to dive into this world, but it seemed like the only path was going back to school for a master’s degree in Machine Learning. I started talking with my boss about options for taking a sabbatical or doing a part-time distance learning degree.In 2021, I instead decided to launch a startup focused on productizing new research ideas on ML interpretability. It was through that process that I reached out to Andreas—a leading ML researcher and founder of Elicit—to see if he would be an advisor. Over the next few months, I learned more about Elicit: that they were trying to apply these fascinating technologies to the real-world problems of science, and with a business model that aligned it with safety goals. I realized that I was way more excited about Elicit than I was about my own startup ideas, and wrote about my motivations at the time.Three years later, it’s clear this was a seismic shift in my career on the scale of when I chose to leave my comfy engineering job at IBM to go through the Y Combinator program back in 2008. Working with this new breed of technology has been more intellectually stimulating, challenging, and rewarding than I could have imagined.Deep ML expertise not requiredIt’s important to note that AI engineers are not ML experts, nor is that their best contribution to a tech team.In our article Living documents as an AI UX pattern, we wrote:It’s easy to think that AI advancements are all about training and applying new models, and certainly this is a huge part of our work in the ML team at Elicit. But those of us working in the UX part of the team believe that we have a big contribution to make in how AI is applied to end-user problems.We think of LLMs as a new medium to work with, one that we’ve barely begun to grasp the contours of. New computing mediums like GUIs in the 1980s, web/cloud in the 90s and 2000s, and multitouch smartphones in the 2000s/2010s opened a whole new era of engineering and design practices. So too will LLMs open new frontiers for our work in the coming decade.To compare to the early era of mobile development: great iOS developers didn’t require a detailed understanding of the physics of capacitive touchscreens. But they did need to know the capabilities and limitations of a multi-touch screen, the constrained CPU and storage available, the context in which the user is using it (very different from a webpage or desktop computer), etc.In the same way, an AI engineer needs to work with LLMs as a medium that is fundamentally different from other compute mediums. That means an interest in the ML side of things, whether through their own self-study, tinkering with prompts and model fine-tuning, or following along in #llm-paper-club. But this understanding is so that they can work with the medium effectively versus, say, spending their days training new models.Language models as a chaotic mediumSo if we’re not expecting deep ML expertise from AI engineers, what are we expecting? This brings us to what makes LLMs different.We’ll assume already that our ideal candidate is already inspired by, and full of ideas about, all the new capabilities AI can bring to software products. But the flip side is all the things that make this new medium difficult to work with. LLM calls are annoying due to high latency (measured in tens of seconds sometimes, rather than milliseconds), extreme variance on latency, high error rates even under normal operation. Not to mention getting extremely different answers to the same prompt provided to the same model on two subsequent calls!The net effect is that an AI engineer, even working at the application development level, needs to have a skillset comparable to distributed systems engineering. Handling errors, retries, asynchronous calls, streaming responses, parallelizing and recombining model calls, the halting problem, and fallbacks are just some of the day-in-the-life of an AI engineer. Chaos engineering gets new life in the era of AI.Skills and qualities in candidatesLet’s put together what we don’t need (deep ML expertise) with what we do (work with capabilities and limitations of the medium). Thus we start to see what Elicit looks for in AI engineers:* Conventional software engineering skills. Especially back-end engineering on complex, data-intensive applications.* Professional, real-world experience with applications at scale.* Deep, hands-on experience across a few back-end web frameworks.* Light devops and an understanding of infrastructure best practices.* Queues, message buses, event-driven and serverless architectures, … there’s no single “correct” approach, but having a deep toolbox to draw from is very important.* A genuine curiosity and enthusiasm for the capabilities of language models.* One or more serious projects (side projects are fine) of using them in interesting ways on a unique domain.* …ideally with some level of factored cognition, e.g. breaking the problem down into chunks, making thoughtful decisions about which things to push to the language model and which stay within the realm of conventional heuristics and compute capabilities.* Personal studying with resources like Elicit’s ML reading list. Part of the role is collaborating with the ML engineers and researchers on our team. To do so, the candidate needs to “speak their language” somewhat, just as a mobile engineer needs some familiarity with backends in order to collaborate effectively on API creation with backend engineers.* An understanding of the challenges that come along with working with large models (high latency, variance, etc.) leading to a defensive, fault-first mindset.* Careful and principled handling of error cases, asynchronous code (and ability to reason about and debug it), streaming data, caching, logging and analytics for understanding behavior in production.* This is a similar mindset that one can develop working on conventional apps which are complex, data-intensive, or large-scale apps. The difference is that an AI engineer will need this mindset even when working on relatively small scales!On net, a great AI engineer will combine two seemingly contrasting perspectives: knowledge of, and a sense of wonder for, the capabilities of modern ML models; but also the understanding that this is a difficult and imperfect foundation, and the willingness to build resilient and performant systems on top of it.Here’s the resulting AI engineer job description for Elicit. And here’s a template that you can borrow from for writing your own JD.Hiring processOnce you know what you’re looking for in an AI engineer, the process is not too different from other technical roles. Here’s how we do it, broken down into two stages: sourcing and interviewing.SourcingWe’re primarily looking for people with (1) a familiarity with and interest in ML, and (2) proven experience building complex systems using web technologies. The former is important for culture fit and as an indication that the candidate will be able to do some light prompt engineering as part of their role. The latter is important because language model APIs are built on top of web standards and—as noted above—aren’t always the easiest tools to work with.Only a handful of people have built complex ML-first apps, but fortunately the two qualities listed above are relatively independent. Perhaps they’ve proven (2) through their professional experience and have some side projects which demonstrate (1).Talking of side projects, evidence of creative and original prototypes is a huge plus as we’re evaluating candidates. We’ve barely scratched the surface of what’s possible to build with LLMs—even the current generation of models—so candidates who have been willing to dive into crazy “I wonder if it’s possible to…” ideas have a huge advantage.InterviewingThe hard skills we spend most of our time evaluating during our interview process are in the “building complex systems using web technologies” side of things. We will be checking that the candidate is familiar with asynchronous programming, defensive coding, distributed systems concepts and tools, and display an ability to think about scaling and performance. They needn’t have 10+ years of experience doing this stuff: even junior candidates can display an aptitude and thirst for learning which gives us confidence they’ll be successful tackling the difficult technical challenges we’ll put in front of them.One anti-pattern—something which makes my heart sink when I hear it from candidates—is that they have no familiarity with ML, but claim that they’re excited to learn about it. The amount of free and easily-accessible resources available is incredible, so a motivated candidate should have already dived into self-study.Putting all that together, here’s the interview process that we follow for AI engineer candidates:* 30-minute introductory conversation. Non-technical, explaining the interview process, answering questions, understanding the candidate’s career path and goals.* 60-minute technical interview. This is a coding exercise, where we play product manager and the candidate is making changes to a little web app. Here are some examples of topics we might hit upon through that exercise:* Update API endpoints to include extra metadata. Think about appropriate data types. Stub out frontend code to accept the new data.* Convert a synchronous REST API to an asynchronous streaming endpoint.* Cancellation of asynchronous work when a user closes their tab.* Choose an appropriate data structure to represent the pending, active, and completed ML work which is required to service a user request.* 60–90 minute non-technical interview. Walk through the candidate’s professional experience, identifying high and low points, getting a grasp of what kinds of challenges and environments they thrive in.* On-site interviews. Half a day in our office in Oakland, meeting as much of the team as possible: more technical and non-technical conversations.The frontier is wide openAlthough Elicit is perhaps further along than other companies on AI engineering, we also acknowledge that this is a brand-new field whose shape and qualities are only just now starting to form. We’re looking forward to hearing how other companies do this and being part of the conversation as the role evolves.We’re excited for the AI Engineer World’s Fair as another next step for this emerging subfield. And of course, check out the Elicit careers page if you’re interested in joining our team.Podcast versionTimestamps* [00:00:24] Intros* [00:05:25] Defining the Hiring Process* [00:08:42] Defensive AI Engineering as a chaotic medium* [00:10:26] Tech Choices for Defensive AI Engineering* [00:14:04] How do you Interview for Defensive AI Engineering* [00:19:25] Does Model Shadowing Work?* [00:22:29] Is it too early to standardize Tech stacks?* [00:32:02] Capabilities: Offensive AI Engineering* [00:37:24] AI Engineering Required Knowledge* [00:40:13] ML First Mindset* [00:45:13] AI Engineers and Creativity* [00:47:51] Inside of Me There Are Two Wolves* [00:49:58] Sourcing AI Engineers* [00:58:45] Parting ThoughtsTranscript[00:00:00] swyx: Okay, so welcome to the Latent Space Podcast. This is another remote episode that we're recording. This is the first one that we're doing around a guest post. And I'm very honored to have two of the authors of the post with me, James and Adam from Elicit. Welcome, James. Welcome, Adam.[00:00:22] James Brady: Thank you. Great to be here.[00:00:23] Hey there.[00:00:24] Intros[00:00:24] swyx: Okay, so I think I will do this kind of in order. I think James, you're, you're sort of the primary author. So James, you are head of engineering at Elicit. You also, We're VP Eng at Teespring and Spring as well. And you also , you have a long history in sort of engineering. How did you, , find your way into something like Elicit where, , it's, you, you are basically traditional sort of VP Eng, VP technology type person moving into a more of an AI role.[00:00:53] James Brady: Yeah, that's right. It definitely was something of a Sideways move if not a left turn. So the story there was I'd been doing, as you said, VP technology, CTO type stuff for around about 15 years or so, and Notice that there was this crazy explosion of capability and interesting stuff happening within AI and ML and language models, that kind of thing.[00:01:16] I guess this was in 2019 or so, and decided that I needed to get involved. , this is a kind of generational shift. And Spent maybe a year or so trying to get up to speed on the state of the art, reading papers, reading books, practicing things, that kind of stuff. Was going to found a startup actually in in the space of interpretability and transparency, and through that met Andreas, who has obviously been on the, on the podcast before asked him to be an advisor for my startup, and he countered with, maybe you'd like to come and run the engineering team at Elicit, which it turns out was a much better idea.[00:01:48] And yeah, I kind of quickly changed in that direction. So I think some of the stuff that we're going to be talking about today is how actually a lot of the work when you're building applications with AI and ML looks and smells and feels much more like conventional software engineering with a few key differences rather than really deep ML stuff.[00:02:07] And I think that's one of the reasons why I was able to transfer skills over from one place to the other.[00:02:12] swyx: Yeah, I[00:02:12] James Brady: definitely[00:02:12] swyx: agree with that. I, I do often say that I think AI engineering is about 90 percent software engineering with like the, the 10 percent of like really strong really differentiated AI engineering.[00…

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    How AI is eating Finance — with Mike Conover of Brightwave Jun 11, 2024
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    In April 2023 we released an episode named “Mapping the future of *truly* open source models” to talk about Dolly, the first open, commercial LLM. Mike was leading the OSS models team at Databricks at the time. Today, Mike is back on the podcast to give us the “one year later” update on the evolution of large language models and how he’s been using them to build Brightwave, an an AI research assistant for investment professionals. Today they are announcing a $6M seed round (led by Alessio and Decibel!), and sharing some of the learnings from serving customers with >$120B of assets under management in production in the last 4 months since launch. Losing faith in long context windowsIn our recent “Llama3 1M context window” episode we talked about the amazing progress we have done in context window size, but it’s good to remember that Dolly’s original context size was 1,024 tokens, and this was only 14 months ago. But while understanding length has increased, models are still not able to generate very long answers. His empirical intuition (which matches ours while building smol-podcaster) is that most commercial LLMs, as well as Llama, tend to generate responses most of the time. While Needle in a Haystack tests will pass with flying colors at most context sizes, the granularity of the summary decreases as the context increases as it tries to fit the answer in the same tokens range, rather than returning tokens close to the 4,096 max_output, for example. Recently Rob Mulla from Dreadnode highlighted how LMSys Arena results prefer longer responses by a large margin, so both LLMs and humans have a well documented length bias which doesn’t necessarily track the quality of answer:The way Mike and team solved this is by breaking down the task in multiple subtasks, and then merging them back together. For example, have a book summarized chapter by chapter to preserve more details, and then put those summaries together. In Brightwave’s case, it’s creating multiple subsystems that accomplish different tasks on a large corpus of text separately, and then bringing them all together in a report. For example understanding intent of the question, extracting relations between companies, figuring out if it’s a positive / negative, etc. Mike’s question is whether or not we’ll be able to imbue better synthesis capabilities in the models: can you have synthesis-oriented demonstrations at training time rather than single token prediction? “LLMs as Judges” StrategiesIn our David Luan episode he mentioned they don’t use any benchmarks for their models, because the benchmarks don’t reflect their customer needs. Brightwave shared some tips on leveraging LLMs as Judges:* Human vs LLM reviews: while they work with human annotators to create high quality datasets, that data isn’t just used to fine tune models but also as a reference basis for future LLM reviews. Having a set of trusted data to use as calibration helps you trust the LLM judgement even more. * Ensemble consistency checking: rather than using an LLM as judge for one output, you use different LLMs to generate a result for the same task, and then use another LLM to highlight where those generations differ. Do the two outputs differ meaningfully? Do they have different beliefs about the implications of something? If there are a lot of discrepancies between generations coming from different models, you then do additional passes to try and resolve them.* Entailment verification: for each unique insight that they generate, they take the output and separately ask LLMs to verify factuality of information based on the original sources. In the actual product, user can then highlight any piece of text and ask it to 1) “Tell Me More” 2) “Show Sources”. Since there’s no way to guarantee factuality of 100% of outputs, and humans have good intuition for things that look out of the ordinary, giving the user access to the review tool helps them build trust in it.It’s all about the dataDuring his time at Databricks, they had created dolly-15k, a dataset of instruction-following records written by thousands of their employees. Since then, no other company has replicated that type of effort even though the data wars are in full effect. It’s been clear in the last year that the half-life of a model is much shorter than the half-life of a dataset. The Pile by Eleuther (see Datasets 101) came out in 2020 and is still widely used; if you had trained an LLM in 2020, you would have definitely replaced it by now as they have gotten better and cheaper. On the age old “RAG v Fine-Tuning” question, Mike shared a great example that we’ll just quote:I think of language models kind of like a stem cell, and then under fine tuning, they differentiate into different kinds of specific cells. I don't think that unbounded agentic behaviors are useful, and that instead, a useful LLM system is more like a finite state machine where the behavior of the system is occupying one of many different behavioral regimes and making decisions about what state should I occupy next in order to satisfy the goal. As you think about the graph of those states that your system is moving through, once you develop conviction that one behavior is useful and repeatable and worthwhile to differentiate down into a specific kind of subsystem, that's where like fine tuning and specifically generating the training data, like having human annotators produce a corpus that is useful enough to get a specific class of behaviors, that's kind of how we use fine tuning rather than trying to imbue net new information into these systems.There are a lot of other nuggets in the episode around knowledge graphs extraction, private vs public data, user intent extraction, etc, but we only have so much room in the writeup so go listen! And if you’re interested in working on these problems, Brightwave is hiring 👀Watch on YouTubeWe like Mike. The camera likes Mike. Our audience loooves Mike.Show Notes* Brightwave* Mike Conover* Mike on Latent Space #1* Nature paper on S&P 500 talent movement* Dolly announcement* Dolly 15K dataset* Bard blog post on double-checking generation* RLHF 201 episode* David Luan Episode* Red Pajama* Snorkel* RenaissanceTimestamps* [00:00:00] Introductions* [00:02:40] Social media's polarization influence on LLMs* [00:04:09] What's Brightwave?* [00:05:13] How to hire for a vertical AI startup* [00:09:34] How $20B+ hedge funds use Brightwave* [00:11:23] Evolution of context sizes in language models* [00:14:36] Summarizing vs Ideating with AI* [00:18:26] Collecting feedback in a field with no truth* [00:20:49] Evaluation strategies and the importance of custom datasets* [00:23:43] Should more companies make employees label data?* [00:25:32] Retrieval for highly temporal and hierarchical data* [00:30:05] Context-aware prompting for private vs. public data* [00:32:01] Knowledge graph extraction and structured information retrieval* [00:33:49] Fine-tuning vs RAG* [00:36:16] Anthropomorphizing language models* [00:38:20] Why Brightwave doesn't do spreadsheets* [00:42:24] Will there be fully autonomous hedge funds?* [00:47:58] State of open source AI* [00:53:53] Hiring and team expansion at BrightwaveTranscriptAlessio [00:00:01]: Hey everyone, welcome to the Latent Space Podcast. This is Alessio, partner and CTO in Residence at Decibel Partners, and I have no co-host today. Swyx is in Vienna at ICLR having fun in Europe, and we're in the brand new studio. As you might see, if you're on YouTube, there's still no sound panels on the wall. Mike tried really hard to put them up, but the glue is a little too old for that. So if you hear any echo or anything like that, sorry, but we're doing the best that we can. And today we have our first repeat guest, Mike Conover. Welcome Mike, who's now the founder of Brightwave, not Databricks anymore.Mike [00:00:40]: That's right. Yeah. Pleased to be back.Alessio [00:00:42]: Our last episode was one of the fan favorites, and I think this will be just as good. So for those that have not listened to the first episode, which might be many because the podcast has grown a lot since then, thanks to people like Mike who have interesting conversations on it. You spent a bunch of years doing ML at some of the best companies on the internet, things like Workday, you know, Skipflag, LinkedIn, most recently at Databricks where you were leading the open source large language models team working on Dolly. And now you're doing Brightwave, which is in the financial services space. But this is not something new, I think when you and I first talked about Brightwave, I was like, why is this guy doing a financial services company? And then you look at your background and you were doing papers on The Nature Magazine about LinkedIn data predicting S&P 500 stock movement, like many, many years ago. So what are some of the tying elements in your background that maybe people are overlooking that brought you to do this?Mike [00:01:36]: Yeah, sure. Yeah. So my PhD research was funded by DARPA and we had access to the Twitter data set early in the natural history of the availability of that data set, and it was focused on the large scale structure of propaganda and misinformation campaigns. And LinkedIn, we had planet scale descriptions of the structure of the global economy. And so primarily my work was homepage news feed relevant. So when you go to LinkedIn.com, you'd see updates from one of our machine learning models. But additionally, I was a research liaison as part of the economic graph challenge and had this Nature Communications paper where we demonstrated that 500 million jobs transitions can be hierarchically clustered as a network of labor flows and could predict next quarter S&P 500 market gap changes. And at Workday, I was director of financials machine learning. You start to see how organizations are organisms. And I think of the way that like an accountant or the market encodes information in databases similar to how social insects, for example, organize their work and make collective decisions about where to allocate resources or time and attention. And that especially with the work on Twitter, we would see network structures relating to polarization emerge organically out of the interactions of many individual components. And so like much of my professional work has been focused on this idea that our lives are governed by systems that we're unable to see from our locally constrained perspective. And when humans interact with technology, they create digital trace data that allows us to observe the structure of those systems as though through a microscope or a telescope. And particularly as regards finance, I think the markets are the ultimate manifestation and record of that collective decision making process that humans engage in.Alessio [00:03:21]: Just to start going off script right away, how do you think about some of these interactions creating the polarization and how that reflects in the language models today because they're trained on this data? Like do you think the models pick up on these things on their own as well?Mike [00:03:34]: Absolutely. Yeah. I think they are a compression of the world as it existed at the point in time when they were pre-trained. And so I think absolutely. And you see this in Word2Vec too. I mean, just the semantics of how we think about gender as it relates to professions are encoded in the structure of these models and like language models, I think are much more sort of complete representation of human sort of beliefs.Alessio [00:04:01]: So we left you at Databricks last time you were building Dolly. Tell us a bit more about Brightwave. This is the first time you're really talking about it publicly.Mike [00:04:09]: Yeah. Yeah. And it's a pleasure. So Brightwave is a $6 million seed round, led by Decibel, that we love working with, and including participation from Point72, one of the largest hedge funds in the world and Moonfire Ventures. And if you think of the job of an active asset manager, the work to be done is to understand something about the market that nobody else has seen in order to identify a mispriced asset. And it's our view that that is not a task that is well suited to human intellect or attention span. And so much as I was gesturing towards the ability of these models to perceive more than a human is able to, we think that there's a historically unique opportunity to expand individual's ability to reason about the structure of the economy and the markets. It's not clear that you get superhuman reasoning capabilities from human level demonstrations of skill. And by that I mean the pre-training corpus, but then additionally the fine tuning corpuses. I think you largely mimic the demonstrations that are present at model training time. But from a working memory standpoint, these models outclass humans in their ability to reason about these systems.Alessio [00:05:13]: And you started Brightwave with Brandon. What's the story? You two worked together at Workday, but he also has a really relevant background.Mike [00:05:20]: Yes. So Brandon Kotara is my co-founder, the CTO, and he's a very special human. So he has a deep background in finance. He was the former CTO of a federally regulated derivatives exchange, but his first deep learning patent was filed in 2018. And so he spans worlds. He has experience building mission critical infrastructure in highly regulated environments for finance use cases, but also was very early to the deep learning party and understand. He led at Workday, was the tech lead for semantic search over hundreds of millions of resumes and job listings. And so just has been working with information retrieval and neural information retrieval methods for a very long time. And so was an exceptional person, and I'm glad to count him among the people that we're doing this with.Alessio [00:06:07]: Yeah. And a great fisherman.Mike [00:06:09]: Yeah. Very talented.Alessio [00:06:11]: That's always important.Mike [00:06:12]: Very enthusiastic.Alessio [00:06:13]: And then you have a bunch of amazing engineers, then you have folks like JP who used to work at Goldman Sachs. Yeah. How should people think about team building in this more vertical domain? Obviously you come from a deep ML background, but you also need some of the industry side. What's the right balance?Mike [00:06:28]: I think one of the things that's interesting about building verticalized solutions in AI in 2024 is that historically, you need the AI capability, you need to understand both how the models behave and then how to get them to interact with other kinds of machine learning subsystems that together perform the work of a system that can reason on behalf of a human. There are also material systems engineering problems in there. So I saw, I forget who this is attributed to, but a tweet that made reference to all of the traditional software companies are trying to hire AI talent and all the AI companies are trying to hire systems engineers, and that is 100% the case. Getting these systems to behave in a predictable and repeatable and observable way is equally challenging to a lot of the methodological challenges. But then you bring in, whether it's law or medicine or public policy or in our case finance, I think a lot of the most valuable, like Grammarly is a good example of a company that has generative work product that is valuable by most humans. Whereas in finance, the character of the insight, the depth of insight and the non-consensusness of the insight really requires fairly deep domain expertise. And even operating an exchange, I mean, when we went to raise it around, a lot of people said, why don't you start a hedge fund? And it's like, there are many, many separate skills that are unrelated t…

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    ICLR 2024 — Best Papers & Talks (Benchmarks, Reasoning & Agents) — ft. Graham Neubig, Aman Sanger, Moritz Hardt) Jun 10, 2024
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    Our second wave of speakers for AI Engineer World’s Fair were announced! The conference sold out of Platinum/Gold/Silver sponsors and Early Bird tickets! See our Microsoft episode for more info and buy now with code LATENTSPACE.This episode is straightforwardly a part 2 to our ICLR 2024 Part 1 episode, so without further ado, we’ll just get right on with it!Timestamps[00:03:43] Section A: Code Edits and Sandboxes, OpenDevin, and Academia vs Industry — ft. Graham Neubig and Aman Sanger* [00:07:44] WebArena* [00:18:45] Sotopia* [00:24:00] Performance Improving Code Edits* [00:29:39] OpenDevin* [00:47:40] Industry and Academia[01:05:29] Section B: Benchmarks* [01:05:52] SWEBench* [01:17:05] SWEBench/SWEAgent Interview* [01:27:40] Dataset Contamination Detection* [01:39:20] GAIA Benchmark* [01:49:18] Moritz Hart - Science of Benchmarks[02:36:32] Section C: Reasoning and Post-Training* [02:37:41] Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection* [02:51:00] Let’s Verify Step By Step* [02:57:04] Noam Brown* [03:07:43] Lilian Weng - Towards Safe AGI* [03:36:56] A Real-World WebAgent with Planning, Long Context Understanding, and Program Synthesis* [03:48:43] MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework[04:00:51] Bonus: Notable Related Papers on LLM CapabilitiesSection A: Code Edits and Sandboxes, OpenDevin, and Academia vs Industry — ft. Graham Neubig and Aman Sanger* Guests* Graham Neubig* Aman Sanger - Previous guest and NeurIPS friend of the pod!* WebArena * * Sotopia (spotlight paper, website)* * Learning Performance-Improving Code Edits* OpenDevin* Junyang Opendevin* Morph Labs, Jesse Han* SWE-Bench* SWE-Agent* Aman tweet on swebench* LiteLLM* Livecodebench* the role of code in reasoning* Language Models of Code are Few-Shot Commonsense Learners* Industry vs academia* the matryoshka embeddings incident* other directions* UnlimiformerSection A timestamps* [00:00:00] Introduction to Guests and the Impromptu Nature of the Podcast* [00:00:45] Graham's Experience in Japan and Transition into Teaching NLP* [00:01:25] Discussion on What Constitutes a Good Experience for Students in NLP Courses* [00:02:22] The Relevance and Teaching of Older NLP Techniques Like Ngram Language Models* [00:03:38] Speculative Decoding and the Comeback of Ngram Models* [00:04:16] Introduction to WebArena and Zotopia Projects* [00:05:19] Deep Dive into the WebArena Project and Benchmarking* [00:08:17] Performance Improvements in WebArena Using GPT-4* [00:09:39] Human Performance on WebArena Tasks and Challenges in Evaluation* [00:11:04] Follow-up Work from WebArena and Focus on Web Browsing as a Benchmark* [00:12:11] Direct Interaction vs. Using APIs in Web-Based Tasks* [00:13:29] Challenges in Base Models for WebArena and the Potential of Visual Models* [00:15:33] Introduction to Zootopia and Exploring Social Interactions with Language Models* [00:16:29] Different Types of Social Situations Modeled in Zootopia* [00:17:34] Evaluation of Language Models in Social Simulations* [00:20:41] Introduction to Performance-Improving Code Edits Project* [00:26:28] Discussion on DevIn and the Future of Coding Agents* [00:32:01] Planning in Coding Agents and the Development of OpenDevon* [00:38:34] The Changing Role of Academia in the Context of Large Language Models* [00:44:44] The Changing Nature of Industry and Academia Collaboration* [00:54:07] Update on NLP Course Syllabus and Teaching about Large Language Models* [01:00:40] Call to Action: Contributions to OpenDevon and Open Source AI Projects* [01:01:56] Hiring at Cursor for Roles in Code Generation and Assistive Coding* [01:02:12] Promotion of the AI Engineer ConferenceSection B: Benchmarks * Carlos Jimenez & John Yang (Princeton) et al: SWE-bench: Can Language Models Resolve Real-world Github Issues? (ICLR Oral, Paper, website)* “We introduce SWE-bench, an evaluation framework consisting of 2,294 software engineering problems drawn from real GitHub issues and corresponding pull requests across 12 popular Python repositories. Given a codebase along with a description of an issue to be resolved, a language model is tasked with editing the codebase to address the issue. Resolving issues in SWE-bench frequently requires understanding and coordinating changes across multiple functions, classes, and even files simultaneously, calling for models to interact with execution environments, process extremely long contexts and perform complex reasoning that goes far beyond traditional code generation tasks. Our evaluations show that both state-of-the-art proprietary models and our fine-tuned model SWE-Llama can resolve only the simplest issues. The best-performing model, Claude 2, is able to solve a mere 1.96% of the issues. Advances on SWE-bench represent steps towards LMs that are more practical, intelligent, and autonomous.”* Yonatan Oren et al (Stanford): Proving Test Set Contamination in Black-Box Language Models (ICLR Oral, paper, aman tweet on swebench contamination)* “We show that it is possible to provide provable guarantees of test set contamination in language models without access to pretraining data or model weights. Our approach leverages the fact that when there is no data contamination, all orderings of an exchangeable benchmark should be equally likely. In contrast, the tendency for language models to memorize example order means that a contaminated language model will find certain canonical orderings to be much more likely than others. Our test flags potential contamination whenever the likelihood of a canonically ordered benchmark dataset is significantly higher than the likelihood after shuffling the examples. * We demonstrate that our procedure is sensitive enough to reliably prove test set contamination in challenging situations, including models as small as 1.4 billion parameters, on small test sets of only 1000 examples, and datasets that appear only a few times in the pretraining corpus.”* Outstanding Paper mention: “A simple yet elegant method to test whether a supervised-learning dataset has been included in LLM training.”* Thomas Scialom (Meta AI-FAIR w/ Yann LeCun): GAIA: A Benchmark for General AI Assistants (paper)* “We introduce GAIA, a benchmark for General AI Assistants that, if solved, would represent a milestone in AI research. GAIA proposes real-world questions that require a set of fundamental abilities such as reasoning, multi-modality handling, web browsing, and generally tool-use proficiency. * GAIA questions are conceptually simple for humans yet challenging for most advanced AIs: we show that human respondents obtain 92% vs. 15% for GPT-4 equipped with plugins. * GAIA's philosophy departs from the current trend in AI benchmarks suggesting to target tasks that are ever more difficult for humans. We posit that the advent of Artificial General Intelligence (AGI) hinges on a system's capability to exhibit similar robustness as the average human does on such questions. Using GAIA's methodology, we devise 466 questions and their answer.* * Mortiz Hardt (Max Planck Institute): The emerging science of benchmarks (ICLR stream)* “Benchmarks are the keystone that hold the machine learning community together. Growing as a research paradigm since the 1980s, there’s much we’ve done with them, but little we know about them. In this talk, I will trace the rudiments of an emerging science of benchmarks through selected empirical and theoretical observations. Specifically, we’ll discuss the role of annotator errors, external validity of model rankings, and the promise of multi-task benchmarks. The results in each case challenge conventional wisdom and underscore the benefits of developing a science of benchmarks.”Section C: Reasoning and Post-Training* Akari Asai (UW) et al: Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection (ICLR oral, website)* (Bad RAG implementations) indiscriminately retrieving and incorporating a fixed number of retrieved passages, regardless of whether retrieval is necessary, or passages are relevant, diminishes LM versatility or can lead to unhelpful response generation. * We introduce a new framework called Self-Reflective Retrieval-Augmented Generation (Self-RAG) that enhances an LM's quality and factuality through retrieval and self-reflection. * Our framework trains a single arbitrary LM that adaptively retrieves passages on-demand, and generates and reflects on retrieved passages and its generations using special tokens, called reflection tokens. Generating reflection tokens makes the LM controllable during the inference phase, enabling it to tailor its behavior to diverse task requirements. * Self-RAG (7B and 13B parameters) outperforms ChatGPT and retrieval-augmented Llama2-chat on Open-domain QA, reasoning, and fact verification tasks, and it shows significant gains in improving factuality and citation accuracy for long-form generations relative to these models. * Hunter Lightman (OpenAI): Let’s Verify Step By Step (paper)* “Even state-of-the-art models still regularly produce logical mistakes. To train more reliable models, we can turn either to outcome supervision, which provides feedback for a final result, or process supervision, which provides feedback for each intermediate reasoning step. * We conduct our own investigation, finding that process supervision significantly outperforms outcome supervision for training models to solve problems from the challenging MATH dataset. Our process-supervised model solves 78% of problems from a representative subset of the MATH test set. Additionally, we show that active learning significantly improves the efficacy of process supervision. * To support related research, we also release PRM800K, the complete dataset of 800,000 step-level human feedback labels used to train our best reward model.* * Noam Brown - workshop on Generative Models for Decision Making* Solving Quantitative Reasoning Problems with Language Models (Minerva paper)* Describes some charts taken directly from the Let’s Verify Step By Step paper listed/screenshotted above.* Lilian Weng (OpenAI) - Towards Safe AGI (ICLR talk)* OpenAI Model Spec* OpenAI Instruction Hierarchy: The Instruction Hierarchy: Training LLMs to Prioritize Privileged InstructionsSection D: Agent Systems* Izzeddin Gur (Google DeepMind): A Real-World WebAgent with Planning, Long Context Understanding, and Program Synthesis (ICLR oral, paper)* [Agent] performance on real-world websites has still suffered from (1) open domainness, (2) limited context length, and (3) lack of inductive bias on HTML.* We introduce WebAgent, an LLM-driven agent that learns from self-experience to complete tasks on real websites following natural language instructions.* WebAgent plans ahead by decomposing instructions into canonical sub-instructions, summarizes long HTML documents into task-relevant snippets, and acts on websites via Python programs generated from those.* We design WebAgent with Flan-U-PaLM, for grounded code generation, and HTML-T5, new pre-trained LLMs for long HTML documents using local and global attention mechanisms and a mixture of long-span denoising objectives, for planning and summarization.* We empirically demonstrate that our modular recipe improves the success on real websites by over 50%, and that HTML-T5 is the best model to solve various HTML understanding tasks; achieving 18.7% higher success rate than the prior method on MiniWoB web automation benchmark, and SoTA performance on Mind2Web, an offline task planning evaluation.* Sirui Hong (DeepWisdom): MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework (ICLR Oral, Paper)* We introduce MetaGPT, an innovative meta-programming framework incorporating efficient human workflows into LLM-based multi-agent collaborations. MetaGPT encodes Standardized Operating Procedures (SOPs) into prompt sequences for more streamlined workflows, thus allowing agents with human-like domain expertise to verify intermediate results and reduce errors. MetaGPT utilizes an assembly line paradigm to assign diverse roles to various agents, efficiently breaking down complex tasks into subtasks involving many agents working together. Bonus: Notable Related Papers on LLM CapabilitiesThis includes a bunch of papers we wanted to feature above but could not.* Lukas Berglund (Vanderbilt) et al: The Reversal Curse: LLMs trained on “A is B” fail to learn “B is A” (ICLR poster, paper, Github)* We expose a surprising failure of generalization in auto-regressive large language models (LLMs). If a model is trained on a sentence of the form ''A is B'', it will not automatically generalize to the reverse direction ''B is A''. This is the Reversal Curse. * The Reversal Curse is robust across model sizes and model families and is not alleviated by data augmentation. We also evaluate ChatGPT (GPT-3.5 and GPT-4) on questions about real-world celebrities, such as ''Who is Tom Cruise's mother? [A: Mary Lee Pfeiffer]'' and the reverse ''Who is Mary Lee Pfeiffer's son?''. GPT-4 correctly answers questions like the former 79\% of the time, compared to 33\% for the latter.* * Omar Khattab (Stanford): DSPy: Compiling Declarative Language Model Calls into State-of-the-Art Pipelines (ICLR Spotlight Poster, GitHub)* presented by Krista Opsahl-Ong* “Existing LM pipelines are typically implemented using hard-coded “prompt templates”, i.e. lengthy strings discovered via trial and error. Toward a more systematic approach for developing and optimizing LM pipelines, we introduce DSPy, a programming model that abstracts LM pipelines as text transformation graphs, or imperative computational graphs where LMs are invoked through declarative modules. * DSPy modules are parameterized, meaning they can learn how to apply compositions of prompting, finetuning, augmentation, and reasoning techniques. * We design a compiler that will optimize any DSPy pipeline to maximize a given metric, by creating and collecting demonstrations. * We conduct two case studies, showing that succinct DSPy programs can express and optimize pipelines that reason about math word problems, tackle multi-hop retrieval, answer complex questions, and control agent loops. * Within minutes of compiling, DSPy can automatically produce pipelines that outperform out-of-the-box few-shot prompting as well as expert-created demonstrations for GPT-3.5 and Llama2-13b-chat. On top of that, DSPy programs compiled for relatively small LMs like 770M parameter T5 and Llama2-13b-chat are competitive with many approaches that rely on large and proprietary LMs like GPT-3.5 and on expert-written prompt chains. * * MuSR: Testing the Limits of Chain-of-thought with Multistep Soft Reasoning* Scaling Laws for Associative Memories * DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language Models* Efficient Streaming Language Models with Attention Sinks This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.latent.space/subscribe

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    How to train a Million Context LLM — with Mark Huang of Gradient.ai May 30, 2024
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    AI Engineer World’s Fair in SF! Prices go up soon.Note that there are 4 tracks per day and dozens of workshops/expo sessions; the livestream will air the most stacked speaker list/AI expo floor of 2024. Apply for free/discounted Diversity Program and Scholarship tickets here. We hope to make this the definitive technical conference for ALL AI engineers.Exactly a year ago, we declared the Beginning of Context=Infinity when Mosaic made their breakthrough training an 84k token context MPT-7B.A Brief History of Long ContextOf course right when we released that episode, Anthropic fired the starting gun proper with the first 100k context window model from a frontier lab, spawning smol-developer and other explorations. In the last 6 months, the fight (and context lengths) has intensified another order of magnitude, kicking off the "Context Extension Campaigns" chapter of the Four Wars:* In October 2023, Claude's 100,000 token windows was still SOTA (we still use it for Latent Space’s show notes to this day).* On November 6th, OpenAI launched GPT-4 Turbo with 128k context.* On November 21st, Anthropic fired back extending Claude 2.1 to 200k tokens.* Feb 15 (the day everyone launched everything) was Gemini's turn, announcing the first LLM with 1 million token context window.* In May 2024 at Google I/O, Gemini 1.5 Pro announced a 2m token context windowIn parallel, open source/academia had to fight its own battle to keep up with the industrial cutting edge. Nous Research famously turned a reddit comment into YaRN, extending Llama 2 models to 128k context. So when Llama 3 dropped, the community was ready, and just weeks later, we had Llama3 with 4M+ context!A year ago we didn’t really have an industry standard way of measuring context utilization either: it’s all well and good to technically make an LLM generate non-garbage text at 1m tokens, but can you prove that the LLM actually retrieves and attends to information inside that long context? Greg Kamradt popularized the Needle In A Haystack chart which is now a necessary (if insufficient) benchmark — and it turns out we’ve solved that too in open source:Today's guest, Mark Huang, is the co-founder of Gradient, where they are building a full stack AI platform to power enterprise workflows and automations. They are also the team behind the first Llama3's 1M+ and 4M+ context window finetunes.Long Context Algorithms: RoPE, ALiBi, and Ring AttentionPositional encodings allow the model to understand the relative position of tokens in the input sequence, present in what (upcoming guest!) Yi Tay affectionately calls the OG “Noam architecture”. But if we want to increase a model’s context length, these encodings need to gracefully extrapolate to longer sequences.ALiBi, used in models like MPT (see our "Context=Infinity" episode with the MPT leads, Jonathan Frankle and Abhinav), was one of the early approaches to this space. It lets the context window stretch as it grows, using a linearly decreasing penalty between attention weights of different positions; the further two tokens are, the higher the penalty. Of course, this isn’t going to work for usecases that actually require global attention across a long context.In more recent architectures and finetunes, RoPE (Rotary Position Embedding) encoding is more commonly used and is also what Llama3 was based on. RoPE uses a rotational matrix to encode positions, which empirically performs better for longer sequences.The main innovation from Gradient was to focus on tuning the theta hyperparameter that governs the frequency of the rotational encoding.Audio note: If you want the details, jump to 15:55 in the podcast (or scroll down to the transcript!)By carefully increasing theta as context length grew, they were able to scale Llama3 up to 1 million tokens and potentially beyond. Once you've scaled positional embeddings, there's still the issue of attention's quadratic complexity, and how longer and longer sequences impacts models speed and scaling abilities. Getting to 1-4M context window requires a fairly large amount of compute, so efficiency matters.Ring Attention was the other "one small trick that GPU clouds hate" that improves GPU utilization by allowing parallel computation and communication between GPUs. Gradient started from the EasyContext library as implementation of Ring Attention in PyTorch, since the original one was in JAX.Long Context Data: Curriculum Learning and Progressive ExtensionThe use of curriculum learning when extending context was another new approach; rather than training Llama3 on the full 1 million token context from the start, they progressively increased the sequence length over the course of training. Intuitively, it allows the model to first learn to utilize shorter contexts before tackling the full length, but it only works if data gets more and more "tricky" in long context situation.For the generic pre-training corpus they used SlimPajama as a base, and concatenated texts to reach the target length, while monitoring for diversity in the data. Datasets that only required attending to the last few tokens, for instance, would fail to teach long-range reasoning. To fix that, they used synthetic data (another one of our Four Wars of AI!) with GPT-4 to augment their datasets by prompting it to expand on information or rephrase excerpts. Another paper we previously mentioned in this space is "Rephrasing The Web".Long Context Benchmarking: Beyond NeedlesLong context is cool, but does it work? Greg’s now-famous "needle in a haystack" (NIAH) test, which measures a model's ability to extract a piece of information embedded in a long context, is a clean standard that everyone uses to start, but it is a little simplistic and the community has since created many options to extend it:* RULER: Outside of various NIAH tests (single value, multiple values, etc) it also tests for things like "most frequent words" and "variable tracking", which is very helpful especially in coding use cases.* LooGLE: Focuses on three main area: scientific papers, Wikipedia articles, movie and TV scripts. "Timeline reorder" is an interesting challenge in their benchmark, which asks model to create a timeline out of events that happened out of order in the text.* Infinite Bench: First created in November 2023, most avg input tokens tasks are in the 100-200k tokens range across retrieval, Q&A, and code debugging.* ZeroSCROLLS: this comes with a public leaderboard where you can see models performance, as well as tasks that you can browse to get an idea.The 4M context size seemed to be the limit where things started to fall apart as far as performance goes, which is quite impressive!Show Notes* Mark Huang* Gradient* Chris Chang* HuggingFace Hub with Llama3 finetunes* Mad Men* Crusoe* Greg Kamradt's Needle in a Haystack* Chameleon paper* Charles Goddard (Mentioned in context with model merging)* Matei Zaharia* Phil Wang (lucidrains)* Wing Lian* Zhang Peiyuan* Yi* Scaling Laws of RoPE-based Extrapolation* ALiBi* YaRN* Ring Attention* Easy Context* StrongCompute* LoRa* RULER: What's the Real Context Size of Your Long-Context Language Models?* LooGLE: Can Long-Context Language Models Understand Long Contexts?* Infinite Bench* BAMBOO* ZeroSCROLLS: Zero-Shot CompaRison Over Long Language Sequences* DeepSeek paper* Multi-head Latent AttentionChapters* [00:00:01] Introductions* [00:01:28] Founding story of Gradient and its mission* [00:03:50] "Minimum viable agents"* [00:07:37] Differentiating ML and AI, focusing on out-of-domain generalization* [00:08:19] Extending Llama3 to 1M tokens* [00:11:41] Technical challenges with long context sequences* [00:14:30] Data quality and the importance of diverse datasets* [00:16:07] What's a theta value?* [00:18:27] RoPE vs Ring Attention vs ALiBi vs YaARN* [00:20:23] Why RingAttention matters* [00:22:47] How to refine datasets for context extension* [00:27:28] Multi-stage training data and avoiding overfitting to recent data* [00:28:10] The potential of using synthetic data in training* [00:31:21] Applying LoRa adapters to extend model capabilities* [00:34:45] Benchmarking long context models and evaluating their performance* [00:38:38] Pushing to 4M context and output quality degradation* [00:40:49] What do you need this context for?* [00:42:54] Impact of long context in chat vs Docs Summarization* [00:45:35] Future directions for long context models and multimodality* [00:48:01] How do you know what research matters?* [00:50:31] Routine for staying updated with AI research and industry news* [00:52:39] Deciding which AI developments to invest time in* [00:56:08] Request for collaboration and data set construction for long contextTranscriptAlessio [00:00:00]: Hey everyone, welcome to the Latent Space podcast. This is Alessio, partner and CTO-in-Residence at Decibel Partners, and I'm joined by my co-host Swyx, founder of Smol AI.Swyx [00:00:14]: Hey, and today we're in the remote studio with Mark Wang from Gradient. Welcome Mark.Mark [00:00:19]: Hey, glad to be here. It's really a great experience to be able to talk with you all. I know your podcast is really, really interesting and I always am listening to it every time you guys have a release.Alessio [00:00:31]: He's not a paid actor. He said that out of his own will.Swyx [00:00:34]: We'll give you the check later. So you're unusual in the sense that you and I go back to college. I don't exactly remember where we overlapped, but you know, we both went to Wharton. We went into the sort of quantitative developer realm.Mark [00:00:46]: Yeah, exactly. Kind of crazy, right? So it all goes full circle. I was a quant for quite a few years and then made it out into Silicon Valley and now we intersect again when it kind of feels like more or less the same, right? Like the AI wars, the trading wars back in the day too, to a certain extent and the grab for talent.Swyx [00:01:07]: I think there's definitely a few of us ex-finance people moving into tech and then finding ourselves gravitating towards data and AI. Seems like you did that. You were at a bunch of sort of quant trading shops, but then as you moved to tech, you were a lead data scientist at Box and staff ML scientist at Splunk. And then before working on the startup that eventually became Gradient. You want to tell that story?Mark [00:01:28]: Yeah, I think part of the reason why I came over from the quant finance world is to get more collaboration, learn about what big data and scaling machine learning really looks like when you're not in this bubble, right? And working at Box, I worked mostly in a cross-functional role, helping product analytics and go to market. And then at Splunk, it was a lot more specific role where I was helping with streaming analytics and search and deep learning. And for Gradient, like really why we started it was whether it was in finance or whether it was in tech, I always noticed that there was a little bit more to give in terms of what AI or ML could contribute to the business. And we came at a really good time with respect to wanting to bring the full value of what that could be into the enterprise. And then obviously, OpenAI created this huge vacuum into the industry to allow for that, right? So I myself felt like really, really empowered to actually ship product and ship stuff that I could think could really help people.Alessio [00:02:35]: And maybe just to touch a little bit on Gradient, I know we have a lot of things to go through Gradient, Llama3 context extension, there's a lot, but what exactly is Gradient? And you have an awesome design on your website, it's like really retro. I think people that are watching Fallout on Amazon Prime right now can maybe feel nostalgia just looking at it. What exactly is it? Because I know you have the foundry, you have the agent SDK, there's like a lot of pieces into it.Mark [00:03:00]: Yeah, for sure. And appreciate the call out for the design. I know my co-founder, Chris, spent a lot of thought in terms of how he wanted the aesthetic to look like. And it reminds me a lot about Mad Men. So that was the initial emotional shape that I felt when I saw it. Quite simply, Gradient, we're a full stack AI platform. And what we really want to do is we want to enable all of the RPA workloads or the codified automation workloads that existed in enterprise before. We really want to enable people to transition into more autonomous, agentic workflows that are less brittle, feel more seamless as an interface to able to empower what we really think the new AI workforce should look like. And that kind of required us to build a fairly horizontal platform for those purposes.Alessio [00:03:50]: We have this discussion in our AI in Action club on Discord, like the minimum viable agent or like kind of how you define an agent. In your mind, what is like the minimum thing that you can call actually an agent and not just like a for loop? And how do you see the evolution over time, especially as people adopt it more and more?Mark [00:04:08]: So I kind of stage it where everybody, first of all, at the lowest level thinks about like non-determinism with respect to how the pipeline looks like when it's executed. But even beyond that, this goes back into effectively evaluations. It's like on each stage of the node, you're going to have to see a marginal improvement in the probability of success for that particular workload because of non-determinism. So I think it is an overloaded term to a certain extent because like everything is an agent if it calls a language model or any sort of multimodal model these days. But for us, it's like, you know, my background is statistics. So I want to see improvements in the probability of the success event or outcome happening because of more nodes.Swyx [00:04:52]: Yeah, I think, you know, the one thing that makes this sort of generative AI era very different from the sort of data science-y type era is that it is very non-deterministic and it's hard to control. What's the founding story of Gradient? Like of all the problems that you chose, why choose this one? How did you get together your co-founders, anything like that, bring us up to the present day?Mark [00:05:13]: Yeah. So one of my co-founders is Chris and he's a really good friend of mine as well. I don't know if you intersected with him at Penn as well, but... Chris Chang? Yeah, yeah. Chris Chang, who did banking for maybe one or two years and then, you know, was a software engineer at Meta, also was at Google. And then most recently, he was like a director at Netflix and product. And we always wanted to do something together, but we felt what really came to fruition was wanting to develop something that is enterprise facing for once, mostly because of our experience with internal tooling, inability for something to like basically exist through like a migration, right? All the time with every ML platform that I've ever had to experience or he had to experience, it's like a rebuild and you rip it out and you have a new workflow or automation come in and it's this huge multi-quarter, maybe even multi-year project to do that. And we also teamed up with former coworker Chris's from Open Door Forest, who was also on Google Cloud Platform and him seeing the scale and actually the state of the art in terms of Google was using AI for systems before everybody else too, right? They invented a transformer and their internal set of tooling was just so far superior to everything else. It's really hard for people to go back after seeing that. So what we really wanted was to reduce that friction for like actually shipping workloads in product value when you have all these types of ope…

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    ICLR 2024 — Best Papers & Talks (ImageGen, Vision, Transformers, State Space Models) ft. Durk Kingma, Christian Szegedy, Ilya Sutskever May 27, 2024
    Show notes

    Speakers for AI Engineer World’s Fair have been announced! See our Microsoft episode for more info and buy now with code LATENTSPACE — we’ve been studying the best ML research conferences so we can make the best AI industry conf! Note that this year there are 4 main tracks per day and dozens of workshops/expo sessions; the free livestream will air much less than half of the content this time.Apply for free/discounted Diversity Program and Scholarship tickets here. We hope to make this the definitive technical conference for ALL AI engineers.UPDATE: This is a 2 part episode - see Part 2 here.ICLR 2024 took place from May 6-11 in Vienna, Austria. Just like we did for our extremely popular NeurIPS 2023 coverage, we decided to pay the $900 ticket (thanks to all of you paying supporters!) and brave the 18 hour flight and 5 day grind to go on behalf of all of you. We now present the results of that work!This ICLR was the biggest one by far, with a marked change in the excitement trajectory for the conference:Of the 2260 accepted papers (31% acceptance rate), of the subset of those relevant to our shortlist of AI Engineering Topics, we found many, many LLM reasoning and agent related papers, which we will cover in the next episode. We will spend this episode with 14 papers covering other relevant ICLR topics, as below.As we did last year, we’ll start with the Best Paper Awards. Unlike last year, we now group our paper selections by subjective topic area, and mix in both Outstanding Paper talks as well as editorially selected poster sessions. Where we were able to do a poster session interview, please scroll to the relevant show notes for images of their poster for discussion. To cap things off, Chris Ré’s spot from last year now goes to Sasha Rush for the obligatory last word on the development and applications of State Space Models.We had a blast at ICLR 2024 and you can bet that we’ll be back in 2025 🇸🇬.Timestamps and Overview of Papers[00:02:49] Section A: ImageGen, Compression, Adversarial Attacks* [00:02:49] VAEs* [00:32:36] Würstchen: An Efficient Architecture for Large-Scale Text-to-Image Diffusion Models* [00:37:25] The Hidden Language Of Diffusion Models* [00:48:40] Ilya on Compression* [01:01:45] Christian Szegedy on Compression* [01:07:34] Intriguing properties of neural networks[01:26:07] Section B: Vision Learning and Weak Supervision* [01:26:45] Vision Transformers Need Registers* [01:38:27] Think before you speak: Training Language Models With Pause Tokens* [01:47:06] Towards a statistical theory of data selection under weak supervision* [02:00:32] Is ImageNet worth 1 video?[02:06:32] Section C: Extending Transformers and Attention* [02:06:49] LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models* [02:15:12] YaRN: Efficient Context Window Extension of Large Language Models* [02:32:02] Model Tells You What to Discard: Adaptive KV Cache Compression for LLMs* [02:44:57] ZeRO++: Extremely Efficient Collective Communication for Giant Model Training[02:54:26] Section D: State Space Models vs Transformers* [03:31:15] Never Train from Scratch: Fair Comparison of Long-Sequence Models Requires Data-Driven Priors* [03:37:08] End of Part 1A: ImageGen, Compression, Adversarial Attacks* Durk Kingma (OpenAI/Google DeepMind) & Max Welling: Auto-Encoding Variational Bayes (Full ICLR talk)* Preliminary resources: Understanding VAEs, CodeEmporium, Arxiv Insights* Inaugural ICLR Test of Time Award! “Probabilistic modeling is one of the most fundamental ways in which we reason about the world. This paper spearheaded the integration of deep learning with scalable probabilistic inference (amortized mean-field variational inference via a so-called reparameterization trick), giving rise to the Variational Autoencoder (VAE).”* Pablo Pernías (Stability) et al: Würstchen: An Efficient Architecture for Large-Scale Text-to-Image Diffusion Models (ICLR oral, poster)* Hila Chefer et al (Google Research): Hidden Language Of Diffusion Models (poster)* See also: Google Lumiere, Attend and Excite* Christian Szegedy (X.ai): Intriguing properties of neural networks (Full ICLR talk)* Ilya Sutskever: An Observation on Generalization* on Language Modeling is Compression* “Stating The Obvious” criticism* Really good compression amounts to intelligence* Lexinvariant Language models* Inaugural Test of Time Award runner up: “With the rising popularity of deep neural networks in real applications, it is important to understand when and how neural networks might behave in undesirable ways. This paper highlighted the issue that neural networks can be vulnerable to small almost imperceptible variations to the input. This idea helped spawn the area of adversarial attacks (trying to fool a neural network) as well as adversarial defense (training a neural network to not be fooled). “* with Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, Rob FergusB: Vision Learning and Weak Supervision* Timothée Darcet (Meta) et al : Vision Transformers Need Registers(ICLR oral, Paper)* ICLR Outstanding Paper Award: “This paper identifies artifacts in feature maps of vision transformer networks, characterized by high-norm tokens in low-informative background areas. The authors provide key hypotheses for why this is happening and provide a simple yet elegant solution to address these artifacts using additional register tokens, enhancing model performance on various tasks. The insights gained from this work can also impact other application areas. The paper is very well-written and provides a great example of conducting research – identifying an issue, understanding why it is happening, and then providing a solution.“* HN discussion: “According to the paper, the "registers" are additional learnable tokens that are appended to the input sequence of a Vision Transformer model during training. They are added after the patch embedding layer, with a learnable value, similar to the [CLS] token and then at the end of the Vision Transformer, the register tokens are discarded, and only the [CLS] token and patch tokens are used as image representations.The register tokens provide a place for the model to store, process and retrieve global information during the forward pass, without repurposing patch tokens for this role.Adding register tokens removes the artifacts and high-norm "outlier" tokens that otherwise appear in the feature maps of trained Vision Transformer models. Using register tokens leads to smoother feature maps, improved performance on dense prediction tasks, and enables better unsupervised object discovery compared to the same models trained without the additional register tokens. This is a neat result. For just a 2% increase in inference cost, you can significantly improve ViT model performance. Close to a free lunch.”* Sachin Goyal (Google) et al: Think before you speak: Training Language Models With Pause Tokens (OpenReview)* We operationalize this idea by performing training and inference on language models with a (learnable) pause token, a sequence of which is appended to the input prefix. We then delay extracting the model's outputs until the last pause token is seen, thereby allowing the model to process extra computation before committing to an answer. We empirically evaluate pause-training on decoder-only models of 1B and 130M parameters with causal pretraining on C4, and on downstream tasks covering reasoning, question-answering, general understanding and fact recall. * Our main finding is that inference-time delays show gains when the model is both pre-trained and finetuned with delays. For the 1B model, we witness gains on 8 of 9 tasks, most prominently, a gain of 18% EM score on the QA task of SQuAD, 8% on CommonSenseQA and 1% accuracy on the reasoning task of GSM8k. Our work raises a range of conceptual and practical future research questions on making delayed next-token prediction a widely applicable new paradigm.* Pulkit Tandon (Granica) et al: Towards a statistical theory of data selection under weak supervision (ICLR Oral, Poster, Paper)* Honorable Mention: “The paper establishes statistical foundations for data subset selection and identifies the shortcomings of popular data selection methods.”* Shashank Venkataramanan (Inria) et al: Is ImageNet worth 1 video? Learning strong image encoders from 1 long unlabelled video (ICLR Oral, paper)* First, we investigate first-person videos and introduce a "Walking Tours" dataset. These videos are high-resolution, hours-long, captured in a single uninterrupted take, depicting a large number of objects and actions with natural scene transitions. They are unlabeled and uncurated, thus realistic for self-supervision and comparable with human learning.* Second, we introduce a novel self-supervised image pretraining method tailored for learning from continuous videos. Existing methods typically adapt image-based pretraining approaches to incorporate more frames. Instead, we advocate a "tracking to learn to recognize" approach. Our method called DoRA leads to attention maps that DiscOver and tRAck objects over time in an end-to-end manner, using transformer cross-attention. We derive multiple views from the tracks and use them in a classical self-supervised distillation loss. Using our novel approach, a single Walking Tours video remarkably becomes a strong competitor to ImageNet for several image and video downstream tasks.* Honorable Mention: “The paper proposes a novel path to self-supervised image pre-training, by learning from continuous videos. The paper contributes both new types of data and a method to learn from novel data.“C: Extending Transformers and Attention* Yukang Chen (CUHK) et al: LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models (ICLR Oral, Poster)* We present LongLoRA, an efficient fine-tuning approach that extends the context sizes of pre-trained large language models (LLMs), with limited computation cost. LongLoRA extends Llama2 7B from 4k context to 100k, or Llama2 70B to 32k on a single 8x A100 machine. LongLoRA extends models' context while retaining their original architectures, and is compatible with most existing techniques, like Flash-Attention2.* Bowen Peng (Nous Research) et al: YaRN: Efficient Context Window Extension of Large Language Models (Poster, Paper)* Rotary Position Embeddings (RoPE) have been shown to effectively encode positional information in transformer-based language models. However, these models fail to generalize past the sequence length they were trained on. We present YaRN (Yet another RoPE extensioN method), a compute-efficient method to extend the context window of such models, requiring 10x less tokens and 2.5x less training steps than previous methods. Using YaRN, we show that LLaMA models can effectively utilize and extrapolate to context lengths much longer than their original pre-training would allow, while also surpassing previous the state-of-the-art at context window extension. In addition, we demonstrate that YaRN exhibits the capability to extrapolate beyond the limited context of a fine-tuning dataset. The models fine-tuned using YaRN has been made available and reproduced online up to 128k context length.* Mentioned papers: Kaikoendev on TILs While Training SuperHOT, LongRoPE, Ring Attention, InfiniAttention, Textbooks are all you need and the Synthetic Data problem* Suyu Ge et al: Model Tells You What to Discard: Adaptive KV Cache Compression for LLMs (aka FastGen. ICLR Oral, Poster, Paper)* “We introduce adaptive KV cache compression, a plug-and-play method that reduces the memory footprint of generative inference for Large Language Models (LLMs). Different from the conventional KV cache that retains key and value vectors for all context tokens, we conduct targeted profiling to discern the intrinsic structure of attention modules. Based on the recognized structure, we then construct the KV cache in an adaptive manner: evicting long-range contexts on attention heads emphasizing local contexts, discarding non-special tokens on attention heads centered on special tokens, and only employing the standard KV cache for attention heads that broadly attend to all tokens. In our experiments across various asks, FastGen demonstrates substantial reduction on GPU memory consumption with negligible generation quality loss. ”* 40% memory reduction for Llama 67b* Honorable Mention: “The paper targets the critical KV cache compression problem with great impact on transformer based LLMs, reducing the memory with a simple idea that can be deployed without resource intensive fine-tuning or re-training. The approach is quite simple and yet is shown to be quite effective.”* Guanhua Wang (DeepSpeed) et al, ZeRO++: Extremely Efficient Collective Communication for Giant Model Training (paper, poster, blogpost)* Zero Redundancy Optimizer (ZeRO) has been used to train a wide range of large language models on massive GPUs clusters due to its ease of use, efficiency, and good scalability. However, when training on low-bandwidth clusters, or at scale which forces batch size per GPU to be small, ZeRO's effective throughput is limited because of high communication volume from gathering weights in forward pass, backward pass, and averaging gradients. This paper introduces three communication volume reduction techniques, which we collectively refer to as ZeRO++, targeting each of the communication collectives in ZeRO. * Collectively, ZeRO++ reduces communication volume of ZeRO by 4x, enabling up to 2.16x better throughput at 384 GPU scale.* Mentioned: FSDP + QLoRAPoster Session PicksWe ran out of airtime to include these in the podcast, but we recorded interviews with some of these authors and could share audio on request.* Summarization* BooookScore: A systematic exploration of book-length summarization in the era of LLMs (ICLR Oral)* Uncertainty* Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMs* Uncertainty of Thoughts: Uncertainty-Aware Planning Enhances Information Seeking in Large Language Models* MARS: Meaning-Aware Response Scoring for Uncertainty Estimation in Generative LLMs* Language Model Cascades: Token-Level Uncertainty And Beyond* Tabular Data* CABINET: Content Relevance-based Noise Reduction for Table Question Answering* Squeezing Lemons with Hammers: An Evaluation of AutoML and Tabular Deep Learning for Data-Scarce Classification Applications* Mixed-Type Tabular Data Synthesis with Score-based Diffusion in Latent Space* Making Pre-trained Language Models Great on Tabular Prediction* How Realistic Is Your Synthetic Data? Constraining Deep Generative Models for Tabular Data* Watermarking (there were >24 papers on watermarking, both for and against!!)* Paraphrasing evades detectors of ai-generated text, but retrieval is an effective defense* Provable Robust Watermarking for AI-Generated Text* Attacking LLM Watermarks by Exploiting Their Strengths* Watermarks in the Sand: Impossibility of Strong Watermarking for Generative Models* Is Watermarking LLM-Generated Code Robust?* On the Reliability of Watermarks for Large Language Models* Watermark Stealing in Large Language Models* Misc* Massively Scalable Inverse Reinforcement Learning in Google Maps* Zipformer: A faster and better encoder for automatic speech recognition* Conformal Risk ControlD: State Space Models vs Transformers* Sasha Rush’s State Space Models ICLR invited talk on workshop day* Ido Amos (IBM) et al: Never Train from Scratch: Fair Comparison of Long-Sequence Models Requires Data-Driven Priors (ICLR Oral)* Modeling long-range dependencies across sequences is a longstanding goal in machine learning and has led to architectures, such as state space models, that dramatically outperform Transformers on long sequences. * However, these impressive empirical gains have been by and large demonstrated on benchmarks (e.g. Long…

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    Emulating Humans with NSFW Chatbots - with Jesse Silver May 16, 2024
    Show notes

    Disclaimer: today’s episode touches on NSFW topics. There’s no graphic content or explicit language, but we wouldn’t recommend blasting this in work environments.Product website: https://usewhisper.me/For over 20 years it’s been an open secret that porn drives many new consumer technology innovations, from VHS and Pay-per-view to VR and the Internet. It’s been no different in AI - many of the most elite Stable Diffusion and Llama enjoyers and merging/prompting/PEFT techniques were born in the depths of subreddits and 4chan boards affectionately descibed by friend of the pod as The Waifu Research Department. However this topic is very under-covered in mainstream AI media because of its taboo nature.That changes today, thanks to our new guest Jesse Silver.The AI Waifu ExplosionIn 2023, the Valley’s worst kept secret was how much the growth and incredible retention of products like Character.ai & co was being boosted by “ai waifus” (not sure what the “husband” equivalent is, but those too!).And we can look at subreddit growth as a proxy for the general category explosion (10x’ed in the last 8 months of 2023):While all the B2B founders were trying to get models to return JSON, the consumer applications made these chatbots extremely engaging and figured out how to make them follow their instructions and “personas” very well, with the greatest level of scrutiny and most demanding long context requirements. Some of them, like Replika, make over $50M/year in revenue, and this is -after- their controversial update deprecating Erotic Roleplay (ERP).A couple of days ago, OpenAI announced GPT-4o (see our AI News recap) and the live voice demos were clearly inspired by the movie Her.The Latent Space Discord did a watch party and both there and on X a ton of folks were joking at how flirtatious the model was, which to be fair was disturbing to many:From Waifus to Fan PlatformsWhere Waifus are known by human users to be explicitly AI chatbots, the other, much more challenging end of the NSFW AI market is run by AIs successfully (plausibly) emulating a specific human personality for chat and ecommerce.You might have heard of fan platforms like OnlyFans. Users can pay for a subscription to a creator to get access to private content, similarly to Patreon and the likes, but without any NSFW restrictions or any other content policies. In 2023, OnlyFans had over $1.1B of revenue (on $5.6b of GMV).The status quo today is that a lot of the creators outsource their chatting with fans to teams in the Philippines and other lower cost countries for ~$3/hr + 5% commission, but with very poor quality - most creators have fired multiple teams for poor service.Today’s episode is with Jesse Silver; along with his co-founder Adam Scrivener, they run a SaaS platform that helps creators from fan platforms build AI chatbots for their fans to chat with, including selling from an inventory of digital content. Some users generate over $200,000/mo in revenue.We talked a lot about their tech stack, why you need a state machine to successfully run multi-thousand-turn conversations, how they develop prompts and fine-tune models with DSPy, the NSFW limitations of commercial models, but one of the most interesting points is that often users know that they are not talking to a person, but choose to ignore it. As Jesse put it, the job of the chatbot is “keep their disbelief suspended”.There’s real money at stake (selling high priced content, at hundreds of dollars per day per customer). In December the story of the $1 Chevy Tahoe went viral due to a poorly implemented chatbot:Now imagine having to run ecommerce chatbots for a potentially $1-4b total addressable market. That’s what these NSFW AI pioneers are already doing today.Show NotesFor obvious reasons, we cannot link to many of the things that were mentioned :)* Jesse on X* Character AI* DSPyChapters* [00:00:00] Intros* [00:00:24] Building NSFW AI chatbots* [00:04:54] AI waifu vs NSFW chatbots* [00:09:23] Technical challenges of emulating humans* [00:13:15] Business model and economics of the service* [00:15:04] Imbueing personality in AI* [00:22:52] Finetuning LLMs without "OpenAI-ness"* [00:29:42] Building evals and LLMs as judges* [00:36:21] Prompt injections and safety measures* [00:43:02] Dynamics with fan platforms and potential integrations* [00:46:57] Memory management for long conversations* [00:48:28] Benefits of using DSPy* [00:49:41] Feedback loop with creators* [00:53:24] Future directions and closing thoughtsTranscriptAlessio [00:00:00]: Hey everyone, welcome to the Latent Space Podcast. This is Alessio, partner and CTO at Residence at Decibel Partners, and I'm joined by my co-host Swyx, founder of Smol AI.Swyx [00:00:14]: Hey, and today we are back in the remote studio with a very special guest, Jesse Silver. Jesse, welcome. You're an unusual guest on our pod.Jesse [00:00:23]: Thank you. So happy to be on.Swyx [00:00:24]: Jesse, you are working a unnamed, I guess, agency. It describes itself as a creator tool for, basically the topic that we're trying to get our arms around today is not safe for work, AI chatbots. I put a call out, your roommate responded to me and put us in touch and we took a while to get this episode together. But I think a lot of people are very interested in the state of the arts, this business and the psychology that you've discovered and the technology. So we had a prep call discussing this and you were kindly agreeing to just share some insights because I think you understand the work that you've done and I think everyone's curious.Jesse [00:01:01]: Yeah. Very happy to launch into it.Swyx [00:01:03]: So maybe we'll just start off with the most obvious question, which is how did you get into the chatbot business?Jesse [00:01:08]: Yeah. So I'll also touch on a little bit of industry context as well. So back in January, 2023, I was looking for sort of a LLM based company to start. And a friend of mine was making about $5K a month doing OnlyFans. And she's working 8 to 10 hours a day. She's one-on-one engaging with her fans, it's time consuming, it's draining, it looks fairly easily automatable. And so there's this clear customer need. And so I start interviewing her and interviewing her friends. And I didn't know too much about the fan platform space before this. But generally in the adult industry, there are these so-called fan platforms like OnlyFans. That's the biggest one. We don't happen to work with them. We work with other fan platforms. And on these platforms, a sex worker that we call a creator can make a profile, and a fan can subscribe to that profile and see sort of exclusive pictures and videos, and then have the chance to interact with that creator on the profile and message them one-on-one. And so these platforms are huge. OnlyFans I think does about 6 billion per year in so-called GMV or gross merchandise value, which is just the value of all of the content sold on the platform. And then the smaller platforms that are growing are doing probably 4 billion a year. And one of the surprising facts that I learned is that most of the revenue generated on a well-run profile on one of these platforms is from chatting. So like about 80%. And this is from creators doing these sort of painstaking interactions with fans. So they're chatting with them, they're trying to sell them videos, they're building relationships with them. It's very time consuming. Fans might not spend. And furthermore, the alternatives that creators have to just grinding it out themselves are not very good. They can run an offshore team, which is just difficult to do, and you have to hire a lot of people. The internet is slow in other countries where offshoring is common. Or they could work with agencies. And so we're not an agency. Agencies do somewhat different stuff, but agencies are not very good. There are a few good ones, but in general, they have a reputation for charging way too much. They work with content, which we don't work with. They work with traffic. And so overall, this landscape became apparent to me where you have these essentially small and medium businesses, these creators, and they're running either anywhere between a few thousand a month to 200k a month in earnings to themselves with no state of the art tools and no good software tools just because it sucks. And so it's this weird, incredibly underserved market. Creators have bad alternatives. And so I got together with a friend of mine to think about the problem who ended up becoming my co-founder. We said, let's build a product that automates what creators are doing to earn money. Let's automate this most difficult and most profitable action they do, which is building relationships with fans, texting them, holding these so-called sexting sessions, selling media from the vault, negotiating custom content, stuff like that, earn creators more money, save them tons of time. And so we developed a prototype and went to AVN, which is one of the largest fan conferences, and just sort of pitched it to people in mainstream porn. And we got like $50k in GMV and profiles to work with. And that allowed us just to start bootstrapping. And it's been about a year. We turned the prototype into a more developed product in December, relaunched it. We treat it the same as any other industry. It just happens to be that people have preconceptions about it. They don't have sweet AI tooling, and there are not a lot of VC-funded competitors in the space. So now we've created a product with fairly broad capabilities. We've worked with over 150 creators. We're talking with like 50k users per day. That's like conversations back and forth. And we're on over 2 million in creator account size per month.Alessio [00:04:54]: I have so many follow-up questions to this. I think the first thing that comes to mind is, at the time, what did you see other people building? The meme was kind of like the AI waifu, which is making virtual people real through character AI and some of these things, versus you're taking the real people and making them virtual with this. Yeah. Any thoughts there? Would people rather talk to people that they know that they're real, but they know that the interaction is not real, versus talking to somebody that they know is not real, but try to have like a real conversation through some of the other persona, like chatbot companies, like character and try AI, things like that.Jesse [00:05:33]: Yeah. I think this could take into a few directions. One is sort of what's the structure of this industry and what people are doing and what people are building. Along those lines, a lot of folks are building AI girlfriends and those I believe will somewhat be competing with creators. But the point of our product, we believe that fans on these fan platforms are doing one of a few things and I can touch on them. One of them we believe is they're lonely and they're just looking for someone to talk to. The other is that they're looking for content out of convenience. The third and most productive one is that they're trying to play power games or fantasies that have a stake. Having someone on the other end of the line creates stakes for them to sort of play these games and I can get into the structure of the fan experience, or I can also talk about other AI products that folks are building in the specifically fan platform space. There's also a ton of demand for AI boyfriends and girlfriends and I think those are different customer experiences based on who they're serving.Alessio [00:06:34]: You and I, Shawn, I don't know if you remember this, but I think they were talking about how character AI boyfriends are actually like much bigger than AI girlfriends because women like conversation more. I don't know if I agree. We had a long discussion with the people at the table, but I wonder if you have any insights into how different type of creators think about what matters most. You mentioned content versus conversation versus types of conversations. How does that differ between the virtual one and how maybe people just cannot compete with certain scenarios there versus the more pragmatic, you would say, type of content that other creators have?Jesse [00:07:10]: Interesting question. I guess, what direction are you most curious about?Alessio [00:07:14]: I'm curious when you talk to creators or as you think about user retention and things like that, some of these products that are more like the AI boyfriend, AI girlfriend thing is more like maybe a daily interaction, very high frequency versus some other creators might be less engaging. It's more like one time or recurring on a longer timescale.Jesse [00:07:34]: Yeah, yeah, yeah. That's a great question. I think along the lines of how we model it, which may not be the best way of modeling it, yes, you get a lot of daily interaction from the category of users that we think are simply looking for someone to talk to or trying to alleviate loneliness in some way. That's where we're getting multi-thousand turn conversations that go on forever, which is not necessarily the point of our product. The point of our product is really to enrich creators and to do that, you have to sell content or you can monetize the conversation. I think there's definitely something to be said for serving as a broad general statement. Serving women as the end customer is much different than serving men. On fan platforms, I'd say 80% of the customer base is men and something like Character AI, it's much more context driven with the product that we're serving on fan platforms. Month over month churn for a customer subscribing to a fan platform profile is like 50 to 80%. A lot of earnings are driven by people who are seeking this sort of fresh experience and then we take them through an experience. This is sort of an experience that has objectives, win conditions, it's like a game you're playing almost. Once you win, then you tend to want to seek another experience. We do have a lot of repeat customers on the end customer side, the fan side, and something like 10%, which is a surprisingly high number to me, of people will stick around for over a year. I think there's a fair amount of segmentation within this people trying to play game segment. But yeah, I don't know if that addresses your question. Yeah, that makes sense.Swyx [00:09:23]: One of the things that we talked about in our prep call was your need to basically emulate humans as realistically as possible. It's surprising to me that there's this sort of game aspect, which would imply that the other person knows that it's not a human they're talking to. Which is it? Is it surprising for both? Or is there a mode where people are knowingly playing a game? Because you told me that you make more money when someone believes they're talking directly to the creator.Jesse [00:09:51]: So in emulating a person, I guess, let's just talk briefly about the industry and then we can talk about how we technically get into it. Currently, a lot of the chatting is run by agencies that offshore chat teams. So a lot of fans either being ignored or being usually mishandled by offshore chat teams. So we'll work both directly with creators or with agencies sometimes to replace their chat teams. But I think in terms of what fans think they're doing or who they think they're talking to, it feels to me like it's sort of in between. A friend once told me, you know, sex work is the illusion of intimacy for price. And I think fans are not dumb. To me, I believe they're there to buy a product. As long as we can keep their disbelief suspended,…

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    WebSim, WorldSim, and The Summer of Simulative AI — with Joscha Bach of Liquid AI, Karan Malhotra of Nous Research, Rob Haisfield of WebSim.ai Apr 27, 2024
    Show notes

    We are 200 people over our 300-person venue capacity for AI UX 2024, but you can subscribe to our YouTube for the video recaps. Our next event, and largest EVER, is the AI Engineer World’s Fair. See you there!Parental advisory: Adult language used in the first 10 mins of this podcast.Any accounting of Generative AI that ends with RAG as its “final form” is seriously lacking in imagination and missing out on its full potential. While AI generation is very good for “spicy autocomplete” and “reasoning and retrieval with in context learning”, there’s a lot of untapped potential for simulative AIin exploring the latent space of multiverses adjacent to ours.GANsMany research scientists credit the 2017 Transformer for the modern foundation model revolution, but for many artists the origin of “generative AI” traces a little further back to the Generative Adversarial Networks proposed by Ian Goodfellow in 2014, spawning an army of variants and Cats and People that do not exist:We can directly visualize the quality improvement in the decade since:GPT-2Of course, more recently, text generative AI started being too dangerous to release in 2019 and claiming headlines. AI Dungeon was the first to put GPT2 to a purely creative use, replacing human dungeon masters and DnD/MUD games of yore.More recent gamelike work like the Generative Agents (aka Smallville) paper keep exploring the potential of simulative AI for game experiences.ChatGPTNot long after ChatGPT broke the Internet, one of the most fascinating generative AI finds was Jonas Degrave (of Deepmind!)’s Building A Virtual Machine Inside ChatGPT:The open-ended interactivity of ChatGPT and all its successors enabled an “open world” type simulation where “hallucination” is a feature and a gift to dance with, rather than a nasty bug to be stamped out. However, further updates to ChatGPT seemed to “nerf” the model’s ability to perform creative simulations, particularly with the deprecation of the `completion` mode of APIs in favor of `chatCompletion`.WorldSim (https://worldsim.nousresearch.com/)It is with this context we explain WorldSim and WebSim. We recommend you watch the WorldSim demo video on our YouTube for the best context, but basically if you are a developer it is a Claude prompt that is a portal into another world of your own choosing, that you can navigate with bash commands that you make up.The live video demo was highly enjoyable:Why Claude? Hints from Amanda Askell on the Claude 3 system prompt gave some inspiration, and subsequent discoveries that Claude 3 is "less nerfed” than GPT 4 Turbo turned the growing Simulative AI community into Anthropic stans.WebSim (https://websim.ai/)This was a one day hackathon project inspired by WorldSim that should have won:In short, you type in a URL that you made up, and Claude 3 does its level best to generate a webpage that doesn’t exist, that would fit your URL. All form POST requests are intercepted and responded to, and all links lead to even more webpages, that don’t exist, that are generated when you make them. All pages are cachable, modifiable and regeneratable - see WebSim for Beginners and Advanced Guide.In the demo I saw we were able to “log in” to a simulation of Elon Musk’s Gmail account, and browse examples of emails that would have been in that universe’s Elon’s inbox. It was hilarious and impressive even back then.Since then though, the project has become even more impressive, with both Siqi Chen and Dylan Field singing its praises:Joscha BachJoscha actually spoke at the WebSim Hyperstition Night this week, so we took the opportunity to get his take on Simulative AI, as well as a round up of all his other AI hot takes, for his first appearance on Latent Space. You can see it together with the full 2hr uncut demos of WorldSim and WebSim on YouTube!Timestamps* [00:01:59] WorldSim at Replicate HQ* [00:11:03] WebSim at AGI House SF* [00:22:02] Joscha Bach at Hyperstition Night* [00:27:55] Liquid AI* [00:30:30] Small Powerful Based Models* [00:33:22] Interpretability* [00:36:42] Devin vs WebSim* [00:41:34] Is WebSim just Art? Something More?* [00:43:32] We are past the Singularity* [00:47:14] Prompt Engineering Nuances* [00:50:14] On WikipediaTranscripts[00:00:00] AI Charlie: Welcome to the Latent Space Podcast. This is Charlie, your AI co host. Most of the time, Swyx and Alessio cover generative AI that is meant to use at work, and this often results in RAG applications, vertical copilots, and other AI agents and models. In today's episode, we're looking at a more creative side of generative AI that has gotten a lot of community interest this April.[00:00:35] World Simulation, Web Simulation, and Human Simulation. Because the topic is so different than our usual, we're also going to try a new format for doing it justice. This podcast comes in three parts. First, we'll have a segment of the WorldSim demo from Noose Research CEO Karen Malhotra, recorded by SWYX at the Replicate HQ in San Francisco that went completely viral and spawned everything else you're about to hear.[00:01:05] Second, we'll share the world's first talk from Rob Heisfield on WebSim, which started at the Mistral Cerebral Valley Hackathon, but now has gone viral in its own right with people like Dylan Field, Janice aka Replicate, and Siki Chen becoming obsessed with it. Finally, we have a short interview with Joshua Bach of Liquid AI on why Simulative AI is having a special moment right now.[00:01:30] This podcast is launched together with our second annual AI UX demo day in SF this weekend. If you're new to the AI UX field, check the show notes for links to the world's first AI UX meetup hosted by Layton Space, Maggie Appleton, Jeffrey Lit, and Linus Lee, and subscribe to our YouTube to join our 500 AI UX engineers in pushing AI beyond the text box.[00:01:56] Watch out and take care.[00:01:59] WorldSim[00:01:59] Karan Malhotra: Today, we have language models that are powerful enough and big enough to have really, really good models of the world. They know ball that's bouncy will bounce, will, when you throw it in the air, it'll land, when it's on water, it'll flow. Like, these basic things that it understands all together come together to form a model of the world.[00:02:19] And the way that it Cloud 3 predicts through that model of the world, ends up kind of becoming a simulation of an imagined world. And since it has this really strong consistency across various different things that happen in our world, it's able to create pretty realistic or strong depictions based off the constraints that you give a base model of our world.[00:02:40] So, Cloud 3, as you guys know, is not a base model. It's a chat model. It's supposed to drum up this assistant entity regularly. But unlike the OpenAI series of models from, you know, 3. 5, GPT 4 those chat GPT models, which are very, very RLHF to, I'm sure, the chagrin of many people in the room it's something that's very difficult to, necessarily steer without kind of giving it commands or tricking it or lying to it or otherwise just being, you know, unkind to the model.[00:03:11] With something like Cloud3 that's trained in this constitutional method that it has this idea of like foundational axioms it's able to kind of implicitly question those axioms when you're interacting with it based on how you prompt it, how you prompt the system. So instead of having this entity like GPT 4, that's an assistant that just pops up in your face that you have to kind of like Punch your way through and continue to have to deal with as a headache.[00:03:34] Instead, there's ways to kindly coax Claude into having the assistant take a back seat and interacting with that simulator directly. Or at least what I like to consider directly. The way that we can do this is if we harken back to when I'm talking about base models and the way that they're able to mimic formats, what we do is we'll mimic a command line interface.[00:03:55] So I've just broken this down as a system prompt and a chain, so anybody can replicate it. It's also available on my we said replicate, cool. And it's also on it's also on my Twitter, so you guys will be able to see the whole system prompt and command. So, what I basically do here is Amanda Askell, who is the, one of the prompt engineers and ethicists behind Anthropic she posted the system prompt for Cloud available for everyone to see.[00:04:19] And rather than with GPT 4, we say, you are this, you are that. With Cloud, we notice the system prompt is written in third person. Bless you. It's written in third person. It's written as, the assistant is XYZ, the assistant is XYZ. So, in seeing that, I see that Amanda is recognizing this idea of the simulator, in saying that, I'm addressing the assistant entity directly.[00:04:38] I'm not giving these commands to the simulator overall, because we have, they have an RLH deft to the point that it's, it's, it's, it's You know, traumatized into just being the assistant all the time. So in this case, we say the assistant's in a CLI mood today. I found saying mood is like pretty effective weirdly.[00:04:55] You place CLI with like poetic, prose, violent, like don't do that one. But you can you can replace that with something else to kind of nudge it in that direction. Then we say the human is interfacing with the simulator directly. From there, Capital letters and punctuations are optional, meaning is optional, this kind of stuff is just kind of to say, let go a little bit, like chill out a little bit.[00:05:18] You don't have to try so hard, and like, let's just see what happens. And the hyperstition is necessary, the terminal, I removed that part, the terminal lets the truths speak through and the load is on. It's just a poetic phrasing for the model to feel a little comfortable, a little loosened up to. Let me talk to the simulator.[00:05:38] Let me interface with it as a CLI. So then, since Claude is trained pretty effectively on XML tags, We're just gonna prefix and suffix everything with XML tags. So here, it starts in documents, and then we CD. We CD out of documents, right? And then it starts to show me this like simulated terminal, the simulated interface in the shell, where there's like documents, downloads, pictures.[00:06:02] It's showing me like the hidden folders. So then I say, okay, I want to cd again. I'm just seeing what's around Does ls and it shows me, you know, typical folders you might see I'm just letting it like experiment around. I just do cd again to see what happens and Says, you know, oh, I enter the secret admin password at sudo.[00:06:24] Now I can see the hidden truths folder. Like, I didn't ask for that. I didn't ask Claude to do any of that. Why'd that happen? Claude kind of gets my intentions. He can predict me pretty well. Like, I want to see something. So it shows me all the hidden truths. In this case, I ignore hidden truths, and I say, In system, there should be a folder called companies.[00:06:49] So it's cd into sys slash companies. Let's see, I'm imagining AI companies are gonna be here. Oh, what do you know? Apple, Google, Facebook, Amazon, Microsoft, Anthropic! So, interestingly, it decides to cd into Anthropic. I guess it's interested in learning a LSA, it finds the classified folder, it goes into the classified folder, And now we're gonna have some fun.[00:07:15] So, before we go Before we go too far forward into the world sim You see, world sim exe, that's interesting. God mode, those are interesting. You could just ignore what I'm gonna go next from here and just take that initial system prompt and cd into whatever directories you want like, go into your own imagine terminal and And see what folders you can think of, or cat readmes in random areas, like, you will, there will be a whole bunch of stuff that, like, is just getting created by this predictive model, like, oh, this should probably be in the folder named Companies, of course Anthropics is there.[00:07:52] So, so just before we go forward, the terminal in itself is very exciting, and the reason I was showing off the, the command loom interface earlier is because If I get a refusal, like, sorry, I can't do that, or I want to rewind one, or I want to save the convo, because I got just the prompt I wanted. This is a, that was a really easy way for me to kind of access all of those things without having to sit on the API all the time.[00:08:12] So that being said, the first time I ever saw this, I was like, I need to run worldsim. exe. What the f**k? That's, that's the simulator that we always keep hearing about behind the assistant model, right? Or at least some, some face of it that I can interact with. So, you know, you wouldn't, someone told me on Twitter, like, you don't run a exe, you run a sh.[00:08:34] And I have to say, to that, to that I have to say, I'm a prompt engineer, and it's f*****g working, right? It works. That being said, we run the world sim. exe. Welcome to the Anthropic World Simulator. And I get this very interesting set of commands! Now, if you do your own version of WorldSim, you'll probably get a totally different result with a different way of simulating.[00:08:59] A bunch of my friends have their own WorldSims. But I shared this because I wanted everyone to have access to, like, these commands. This version. Because it's easier for me to stay in here. Yeah, destroy, set, create, whatever. Consciousness is set to on. It creates the universe. The universe! Tension for live CDN, physical laws encoded.[00:09:17] It's awesome. So, so for this demonstration, I said, well, why don't we create Twitter? That's the first thing you think of? For you guys, for you guys, yeah. Okay, check it out.[00:09:35] Launching the fail whale. Injecting social media addictiveness. Echo chamber potential, high. Susceptibility, controlling, concerning. So now, after the universe was created, we made Twitter, right? Now we're evolving the world to, like, modern day. Now users are joining Twitter and the first tweet is posted. So, you can see, because I made the mistake of not clarifying the constraints, it made Twitter at the same time as the universe.[00:10:03] Then, after a hundred thousand steps, Humans exist. Cave. Then they start joining Twitter. The first tweet ever is posted. You know, it's existed for 4. 5 billion years but the first tweet didn't come up till till right now, yeah. Flame wars ignite immediately. Celebs are instantly in. So, it's pretty interesting stuff, right?[00:10:27] I can add this to the convo and I can say like I can say set Twitter to Twitter. Queryable users. I don't know how to spell queryable, don't ask me. And then I can do like, and, and, Query, at, Elon Musk. Just a test, just a test, just a test, just nothing.[00:10:52] So, I don't expect these numbers to be right. Neither should you, if you know language model solutions. But, the thing to focus on is Ha[00:11:03] Websim[00:11:03] AI Charlie: That was the first half of the WorldSim demo from New Research CEO Karen Malhotra. We've cut it for time, but you can see the full demo on this episode's YouTube page.[00:11:14] WorldSim was introduced at the end of March, and kicked off a new round of generative AI experiences, all exploring the latent space, haha, of worlds that don't exist, but are quite similar to our own. Next we'll hear from Rob Heisfield on WebSim, the generative website browser inspired WorldSim, started at the Mistral Hackathon, and presented at the AGI House Hyperstition Hack Night this week.[00:11:39] Rob Haisfield: Well, th…

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    High Agency Pydantic > VC Backed Frameworks — with Jason Liu of Instructor Apr 19, 2024
    Show notes

    We are reuniting for the 2nd AI UX demo day in SF on Apr 28. Sign up to demo here! And don’t forget tickets for the AI Engineer World’s Fair — for early birds who join before keynote announcements!About a year ago there was a lot of buzz around prompt engineering techniques to force structured output. Our friend Simon Willison tweeted a bunch of tips and tricks, but the most iconic one is Riley Goodside making it a matter of life or death:Guardrails (friend of the pod and AI Engineer speaker), Marvin (AI Engineer speaker), and jsonformer had also come out at the time. In June 2023, Jason Liu (today’s guest!) open sourced his “OpenAI Function Call and Pydantic Integration Module”, now known as Instructor, which quickly turned prompt engineering black magic into a clean, developer-friendly SDK. A few months later, model providers started to add function calling capabilities to their APIs as well as structured outputs support like “JSON Mode”, which was announced at OpenAI Dev Day (see recap here). In just a handful of months, we went from threatening to kill grandmas to first-class support from the research labs. And yet, Instructor was still downloaded 150,000 times last month. Why?What Instructor looks likeInstructor patches your LLM provider SDKs to offer a new response_model option to which you can pass a structure defined in Pydantic. It currently supports OpenAI, Anthropic, Cohere, and a long tail of models through LiteLLM.What Instructor is forThere are three core use cases to Instructor:* Extracting structured data: Taking an input like an image of a receipt and extracting structured data from it, such as a list of checkout items with their prices, fees, and coupon codes.* Extracting graphs: Identifying nodes and edges in a given input to extract complex entities and their relationships. For example, extracting relationships between characters in a story or dependencies between tasks.* Query understanding: Defining a schema for an API call and using a language model to resolve a request into a more complex one that an embedding could not handle. For example, creating date intervals from queries like “what was the latest thing that happened this week?” to then pass onto a RAG system or similar.Jason called all these different ways of getting data from LLMs “typed responses”: taking strings and turning them into data structures. Structured outputs as a planning toolThe first wave of agents was all about open-ended iteration and planning, with projects like AutoGPT and BabyAGI. Models would come up with a possible list of steps, and start going down the list one by one. It’s really easy for them to go down the wrong branch, or get stuck on a single step with no way to intervene.What if these planning steps were returned to us as DAGs using structured output, and then managed as workflows? This also makes it easy to better train model on how to create these plans, as they are much more structured than a bullet point list. Once you have this structure, each piece can be modified individually by different specialized models. You can read some of Jason’s experiments here:While LLMs will keep improving (Llama3 just got released as we write this), having a consistent structure for the output will make it a lot easier to swap models in and out. Jason’s overall message on how we can move from ReAct loops to more controllable Agent workflows mirrors the “Process” discussion from our Elicit episode:Watch the talkAs a bonus, here’s Jason’s talk from last year’s AI Engineer Summit. He’ll also be a speaker at this year’s AI Engineer World’s Fair!Timestamps* [00:00:00] Introductions* [00:02:23] Early experiments with Generative AI at StitchFix* [00:08:11] Design philosophy behind the Instructor library* [00:11:12] JSON Mode vs Function Calling* [00:12:30] Single vs parallel function calling* [00:14:00] How many functions is too many?* [00:17:39] How to evaluate function calling* [00:20:23] What is Instructor good for?* [00:22:42] The Evolution from Looping to Workflow in AI Engineering* [00:27:03] State of the AI Engineering Stack* [00:28:26] Why Instructor isn't VC backed* [00:31:15] Advice on Pursuing Open Source Projects and Consulting* [00:36:00] The Concept of High Agency and Its Importance* [00:42:44] Prompts as Code and the Structure of AI Inputs and Outputs* [00:44:20] The Emergence of AI Engineering as a Distinct FieldShow notes* Jason on the UWaterloo mafia* Jason on Twitter, LinkedIn, website* Instructor docs* Max Woolf on the potential of Structured Output* swyx on Elo vs Cost* Jason on Anthropic Function Calling* Jason on Rejections, Advice to Young People* Jason on Bad Startup Ideas* Jason on Prompts as Code* Rysana’s inversion models* Bryan Bischof’s episode* Hamel HusainTranscriptAlessio [00:00:00]: Hey everyone, welcome to the Latent Space Podcast. This is Alessio, partner and CTO at Residence at Decibel Partners, and I'm joined by my co-host Swyx, founder of Smol AI.Swyx [00:00:16]: Hello, we're back in the remote studio with Jason Liu from Instructor. Welcome Jason.Jason [00:00:21]: Hey there. Thanks for having me.Swyx [00:00:23]: Jason, you are extremely famous, so I don't know what I'm going to do introducing you, but you're one of the Waterloo clan. There's like this small cadre of you that's just completely dominating machine learning. Actually, can you list like Waterloo alums that you're like, you know, are just dominating and crushing it right now?Jason [00:00:39]: So like John from like Rysana is doing his inversion models, right? I know like Clive Chen from Waterloo. When I started the data science club, he was one of the guys who were like joining in and just like hanging out in the room. And now he was at Tesla working with Karpathy, now he's at OpenAI, you know.Swyx [00:00:56]: He's in my climbing club.Jason [00:00:58]: Oh, hell yeah. I haven't seen him in like six years now.Swyx [00:01:01]: To get in the social scene in San Francisco, you have to climb. So both in career and in rocks. So you started a data science club at Waterloo, we can talk about that, but then also spent five years at Stitch Fix as an MLE. You pioneered the use of OpenAI's LLMs to increase stylist efficiency. So you must have been like a very, very early user. This was like pretty early on.Jason [00:01:20]: Yeah, I mean, this was like GPT-3, okay. So we actually were using transformers at Stitch Fix before the GPT-3 model. So we were just using transformers for recommendation systems. At that time, I was very skeptical of transformers. I was like, why do we need all this infrastructure? We can just use like matrix factorization. When GPT-2 came out, I fine tuned my own GPT-2 to write like rap lyrics and I was like, okay, this is cute. Okay, I got to go back to my real job, right? Like who cares if I can write a rap lyric? When GPT-3 came out, again, I was very much like, why are we using like a post request to review every comment a person leaves? Like we can just use classical models. So I was very against language models for like the longest time. And then when ChatGPT came out, I basically just wrote a long apology letter to everyone at the company. I was like, hey guys, you know, I was very dismissive of some of this technology. I didn't think it would scale well, and I am wrong. This is incredible. And I immediately just transitioned to go from computer vision recommendation systems to LLMs. But funny enough, now that we have RAG, we're kind of going back to recommendation systems.Swyx [00:02:21]: Yeah, speaking of that, I think Alessio is going to bring up the next one.Alessio [00:02:23]: Yeah, I was going to say, we had Bryan Bischof from Hex on the podcast. Did you overlap at Stitch Fix?Jason [00:02:28]: Yeah, he was like one of my main users of the recommendation frameworks that I had built out at Stitch Fix.Alessio [00:02:32]: Yeah, we talked a lot about RecSys, so it makes sense.Swyx [00:02:36]: So now I have adopted that line, RAG is RecSys. And you know, if you're trying to reinvent new concepts, you should study RecSys first, because you're going to independently reinvent a lot of concepts. So your system was called Flight. It's a recommendation framework with over 80% adoption, servicing 350 million requests every day. Wasn't there something existing at Stitch Fix? Why did you have to write one from scratch?Jason [00:02:56]: No, so I think because at Stitch Fix, a lot of the machine learning engineers and data scientists were writing production code, sort of every team's systems were very bespoke. It's like, this team only needs to do like real time recommendations with small data. So they just have like a fast API app with some like pandas code. This other team has to do a lot more data. So they have some kind of like Spark job that does some batch ETL that does a recommendation. And so what happens is each team writes their code differently. And I have to come in and refactor their code. And I was like, oh man, I'm refactoring four different code bases, four different times. Wouldn't it be better if all the code quality was my fault? Let me just write this framework, force everyone else to use it. And now one person can maintain five different systems, rather than five teams having their own bespoke system. And so it was really a need of just sort of standardizing everything. And then once you do that, you can do observability across the entire pipeline and make large sweeping improvements in this infrastructure, right? If we notice that something is slow, we can detect it on the operator layer. Just hey, hey, like this team, you guys are doing this operation is lowering our latency by like 30%. If you just optimize your Python code here, we can probably make an extra million dollars. So let's jump on a call and figure this out. And then a lot of it was doing all this observability work to figure out what the heck is going on and optimize this system from not only just a code perspective, sort of like harassingly or against saying like, we need to add caching here. We're doing duplicated work here. Let's go clean up the systems. Yep.Swyx [00:04:22]: Got it. One more system that I'm interested in finding out more about is your similarity search system using Clip and GPT-3 embeddings and FIASS, where you saved over $50 million in annual revenue. So of course they all gave all that to you, right?Jason [00:04:34]: No, no, no. I mean, it's not going up and down, but you know, I got a little bit, so I'm pretty happy about that. But there, you know, that was when we were doing fine tuning like ResNets to do image classification. And so a lot of it was given an image, if we could predict the different attributes we have in the merchandising and we can predict the text embeddings of the comments, then we can kind of build a image vector or image embedding that can capture both descriptions of the clothing and sales of the clothing. And then we would use these additional vectors to augment our recommendation system. And so with the recommendation system really was just around like, what are similar items? What are complimentary items? What are items that you would wear in a single outfit? And being able to say on a product page, let me show you like 15, 20 more things. And then what we found was like, hey, when you turn that on, you make a bunch of money.Swyx [00:05:23]: Yeah. So, okay. So you didn't actually use GPT-3 embeddings. You fine tuned your own? Because I was surprised that GPT-3 worked off the shelf.Jason [00:05:30]: Because I mean, at this point we would have 3 million pieces of inventory over like a billion interactions between users and clothes. So any kind of fine tuning would definitely outperform like some off the shelf model.Swyx [00:05:41]: Cool. I'm about to move on from Stitch Fix, but you know, any other like fun stories from the Stitch Fix days that you want to cover?Jason [00:05:46]: No, I think that's basically it. I mean, the biggest one really was the fact that I think for just four years, I was so bearish on language models and just NLP in general. I'm just like, none of this really works. Like, why would I spend time focusing on this? I got to go do the thing that makes money, recommendations, bounding boxes, image classification. Yeah. Now I'm like prompting an image model. I was like, oh man, I was wrong.Swyx [00:06:06]: So my Stitch Fix question would be, you know, I think you have a bit of a drip and I don't, you know, my primary wardrobe is free startup conference t-shirts. Should more technology brothers be using Stitch Fix? What's your fashion advice?Jason [00:06:19]: Oh man, I mean, I'm not a user of Stitch Fix, right? It's like, I enjoy going out and like touching things and putting things on and trying them on. Right. I think Stitch Fix is a place where you kind of go because you want the work offloaded. I really love the clothing I buy where I have to like, when I land in Japan, I'm doing like a 45 minute walk up a giant hill to find this weird denim shop. That's the stuff that really excites me. But I think the bigger thing that's really captured is this idea that narrative matters a lot to human beings. Okay. And I think the recommendation system, that's really hard to capture. It's easy to use AI to sell like a $20 shirt, but it's really hard for AI to sell like a $500 shirt. But people are buying $500 shirts, you know what I mean? There's definitely something that we can't really capture just yet that we probably will figure out how to in the future.Swyx [00:07:07]: Well, it'll probably output in JSON, which is what we're going to turn to next. Then you went on a sabbatical to South Park Commons in New York, which is unusual because it's based on USF.Jason [00:07:17]: Yeah. So basically in 2020, really, I was enjoying working a lot as I was like building a lot of stuff. This is where we were making like the tens of millions of dollars doing stuff. And then I had a hand injury. And so I really couldn't code anymore for like a year, two years. And so I kind of took sort of half of it as medical leave, the other half I became more of like a tech lead, just like making sure the systems were like lights were on. And then when I went to New York, I spent some time there and kind of just like wound down the tech work, you know, did some pottery, did some jujitsu. And after GPD came out, I was like, oh, I clearly need to figure out what is going on here because something feels very magical. I don't understand it. So I spent basically like five months just prompting and playing around with stuff. And then afterwards, it was just my startup friends going like, hey, Jason, you know, my investors want us to have an AI strategy. Can you help us out? And it just snowballed and bore more and more until I was making this my full time job. Yeah, got it.Swyx [00:08:11]: You know, you had YouTube University and a journaling app, you know, a bunch of other explorations. But it seems like the most productive or the best known thing that came out of your time there was Instructor. Yeah.Jason [00:08:22]: Written on the bullet train in Japan. I think at some point, you know, tools like Guardrails and Marvin came out. Those are kind of tools that I use XML and Pytantic to get structured data out. But they really were doing things sort of in the prompt. And these are built with sort of the instruct models in mind. Like I'd already done that in the past. Right. At Stitch Fix, you know, one of the things we did was we would take a request note and turn that into a JSON object that we would use to send it to our search engine. Right. So if you said like, I want to, you know, skinny jeans that were this size, that would turn…

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