Adam Oliner

AI memory is fundamental for conversational AI and AI agents. It is critical for building personalized experiences and improving reasoning. Check out Letta’s course to understand how to use AI memory to enhance the customization and quality of your AI app.

15

Thrilled that mine and Qian Li's paper was just published in the Communications of the ACM! The big challenge we've been working on since grad school is how to make serverless computing work well for stateful applications. This is tricky because it's hard to make long-lived stateful workflows reliable in ephemeral serverless execution environments. The solution, which we talk about in this paper and are building at DBOS, Inc., is using database transactions to make programs durable. The key idea is to store the execution state of a serverless workflow in a database. That way, if the workflow is interrupted, it can automatically recover from where it left off by looking up its execution state in the database. If you know that stateful programs are durable, it becomes a lot easier to make them serverless. You can deploy them to ephemeral executors and leverage their durability to autoscale them and move them between executors as needed. That way, you can build incredibly reliable stateful programs that also benefit from the scalability and convenience of serverless.

109

9 Comments

This is just incredible. Neal Ghosh's demonstration of how #VentureIQ takes deeply technical and complicated content across a range of industrial use cases and ages/stages through an AI-generated conversational podcast (a la #notebookLM) is really mind-blowing. Check out his post, below, and if you haven't seen VentureIQ, yet, let's get you set up to learn more. You can visit VentureIQ.ai or DM any of us over here to book a demo!

2

Today, I found a fascinating way to think about “Tokens” in LLMs. It came from a conversation between Jensen Huang and Patrick Collison at a recent Stripe event. When asked about the future of compute capacity in five years, Jensen smartly dodged that question a bit, but then gave an excellent explanation and analogy about why tokens are the new forces that will power the next decades of humanity. He explained that we're now producing something unprecedented (and at scale): floating point numbers that possess “value”, which we now call tokens. These tokens are valuable because they encapsulate “intelligence”. People are now taking these tokens and transforming them into English, French, images, videos, chemicals, proteins, robotic movements, etc. And many are working hard to expand the range of concepts and ideas we can create with these tokens. He then goes on to made a compelling comparison between tokens and electricity. In the past industrial revolution, we successfully found a way to convert “atoms” into “electrons” (by boiling water to power electricity turbines). And now, we've discovered a way to convert "electrons" into "tokens" (by using energy to power data centers that train and run LLMs). When electricity was first introduced, few people understood its value. Today, paying for kilowatts is routine. The same will happen with tokens. Right now, only early adopters and builders are paying tokens. Soon, everyone will be paying for tokens on a daily basis to supercharge productivity and power new products and services. Many new industries will be born from and built on top of tokens. When I first heard about this way of thinking, I got goosebumps. Perhaps it’s because Jensen has a good storytelling skill that helps him sell what he builds, but I have to admit this way of thinking gave me a brief surge of pride. I'm proud of humanity's collective effort and creativity, which have taken us from living under rocks to building machines that can "think." That's nothing short but a miracle.

18

5 Comments

In the race to advance AI, we often focus on algorithms and data. But what if the most fundamental bottleneck lies in the design of GPUs? I recently spoke with Sean Lie, co-founder and CTO of Cerebras Systems, about their audacious approach to AI computing. Instead of iterating on existing chip designs, they're reimagining the entire system from the ground up. This end-to-end approach is pushing Cerebras to innovate in unexpected areas. Take server design, a field long thought to be settled. When you're working with a chip the size of a dinner plate—57x larger than NVIDIA’s H100—even servers need an overhaul. It's a reminder that breakthrough innovation in AI will not come from algorithms alone, but from rethinking every layer of the stack, from bare metal to high-level software. Our full conversation here: https://lnkd.in/gWZCi4zE

37

Please media types writing about the cost of AI: recognize there is a difference between huge GPU/TPU clusters needed to train foundation models vs. inference costs to run most apps on top of foundation models. The two are not synonymous, so you can dial back some of the doom and gloom about AI startups and your commingling of the two different AI business types.

11

Batch processing for LLM inference activity: "One sign of that is the rise of batch processing. Today, popular consumer AI products like ChatGPT and Perplexity provide users with near instantaneous responses, otherwise known as real-time inference. However, businesses don't always need immediate responses and are often willing to wait hours, days, or even weeks for responses, as long as they don't have to shell out as much money. Founders of cloud and inference providers have told me there's growing pressure from business customers for this kind of flexibility, otherwise known as batch processing." -- Nathaniel Whittemore on "The AI Daily Brief" (with much deserved shout-out to his inspo from Stephanie Palazzolo at The Information) I fall into this bucket as we have a lot of backfilling to do as we're building out the underlying data in our service, and some of that could just as well be done in the wee hours of the night. The new stuff flowing in I want processed ASAFP, but these backfills—I'd mos def take more cost-efficient delayed batch processing. There's also use cases like needing (near) real-time inference for the first inference pass for timeliness / contemporaneousness value, but then a second pass slow-time inference for wider classification exercises over an ever-growing data corpus. I could see us mixing Sonnet & Haiku models in both cases. Just a heads up to Anthropic Mike Krieger as you contemplate this, along with Amazon Web Services (AWS) Jeff Barr Mark Roy Swami Sivasubramanian on the Bedrock side of things...and I don't mean the "batch" mode that doesn't seem to have any real value other than helping AWS manage its own rate limit challenges. (DeepCast is using Bedrock.)

11

2 Comments

Having worked at Cruise and Waymo on self-driving taught me many things. I hope to bring some of these learnings to healthcare so it too can soon have the magical, "no-driver-at-the-wheel" moment. Here's a short conversation I had with Louie Cai, MD on this topic!

16

1 Comment

Lin Guan and Yifan Zhou have led a very interesting study investigating the effectiveness of Vision Language Models in critiquing robot behaviors in terms of the desirability of their "style." If you are wondering how our work on LLM's inability to verify correctness squares with this study on using them as style critics, see my earlier post https://lnkd.in/gBYAr5qq ! This is the reason why being a style critic is one of the roles LLMs can play in LLM-Modulo framework: https://lnkd.in/gyAyKx4E Check out the details of the work @ https://lnkd.in/gfVZ4EYW ( Lin Guan also has a nice twitter thread on the work here: https://lnkd.in/gnieU-qM )

89

2 Comments

If you think compute scaling will plateau, think again. We are in the baby states of inference time compute. VLLMs coupled with computer tools, reward learning and RL can leverage orders of magnitude more inference time compute than what we have seen with foundation models. Here’s a concrete training architecture (replace \pi with a large pre-trained vLLM and add it to the reward learner too, \R is the space of all computer tools)

22

1 Comment

This week, I'm interviewing extraordinary Bradley Voytek — neuroscience prof at UC San Diego, founding member of UCSD's Data Science Institute, and he was Uber's first data scientist. Got questions for him? Questions at the intersection of neuroscience and data science would be particularly interesting, given that Prof. Voytek's research specialty is combining large-scale data science and machine learning to study how brain regions communicate with one another, and how that communication changes with aging and disease. (UCSD is arguably the world's best neuroscience research institute, so this is quite a special opportunity for ya!) More detail on Brad: • Professor in UC San Diego's Department of Cognitive Science, the Halıcıoğlu Data Science Institute, and the Neurosciences Graduate Program. • Alfred P. Sloan Neuroscience Research Fellow. • Kavli Fellow of the National Academies of Sciences. • Joined Uber as their first data scientist, when it was a 10-person startup, helping build their data science strategy and team. • Outreach work has appeared in Scientific American, NPR... and Comic-Con! • Co-authored the amusing book "Do Zombies Dream of Undead Sheep?" The "Super Data Science Podcast with Jon Krohn" is available on all major podcasting platforms and a video version is on YouTube. Brad's episode will likely be #829 and be released on October 22nd. #superdatascience #machinelearning #ai #datascience #neuroscience

86

1 Comment

Today we’re releasing Chai-1r, an updated version of Chai-1 with full support for restraints. Prompting Chai-1r with prior structural knowledge can boost accuracy by >2x. https://lnkd.in/euP7frVQ Custom restraints can refine predictions with knowledge obtained from scientific literature or lab assays. For example, a “contact” restraint specifies that two tokens are within a certain distance. A “pocket restraint” indicates a token should be in contact with another chain. Chai-1r excels in antibody-antigen interaction prediction with mean DockQ % of - Chai-1r (+4 epitope residues): 43.7 - Chai-1: 35.6 - Boltz (an AlphaFold3 reproduction from MIT & Genesis): 26.4 - AlphaFold2: 20.6 Restraints are available immediately both in our public codebase (https://lnkd.in/e3SGx_dj) and on our web server (https://lnkd.in/e9YfR6GV).

572

13 Comments

🚀 Exciting News! With Med, we just applied for the SPC Founder Fellowship in Fall 2024, and we highly recommend that other early-stage startups do the same. The SPC Fellowship offers an incredible opportunity to connect with a vibrant community of founders, gain valuable mentorship, and accelerate your startup’s growth. If you’re an early-stage founder looking to take your venture to the next level, don’t miss out on this chance. Check out their blog in the comments to learn more about the fellowship and how to apply. #startup #entrepreneurship #innovation #fellowship #SPC #earlystage

1

4 Comments

ThermoMPNN rapidly predicts point mutations to proteins for changes in stability. This neural network achieves state-of-the art performance on the Fireprot benchmark, and returns predictions in a less than a minute. Try it out on Tamarind Bio: https://lnkd.in/gSCbrpDH Paper:https://lnkd.in/gCXQwUTS

43

Catch Thomas Smoker of WhyHow.AI talking with Demetrios Brinkmann of MLOps Community about "Managing Small Knowledge Graphs for Multi-agent Systems" Key takeaways: 1. Knowledge Graphs and Deterministic Systems: Tom Smoker discusses the utilization of knowledge graphs in multi-agent systems to create deterministic systems that enhance reliability. Knowledge graphs help in distinguishing complex relationships within data, such as different perceptions of "rice" by a farmer versus a chef. 2. Importance of Being "Exactly Wrong" & Practical Implementation of Graphs in systems: A notable concept from Tom is the strategy of being "exactly wrong" to create a framework for determinism. This approach allows for the systematic improvement of systems by identifying and correcting precise inaccuracies, thus moving towards being "exactly right." Tom provides insights into practical applications and the implementation of knowledge graphs in systems, emphasizing their role in structuring data to support specific, reliable outcomes in complex systems like legal and healthcare sectors. 3. Challenges and Solutions in Real-World AI Applications: There is a discussion on the challenges faced when integrating AI into real-world applications. Tom illustrates how maintaining small, manageable knowledge graphs can be more beneficial than larger, more complex ones, particularly when accuracy and reliability are crucial. Link to podcast: https://lnkd.in/ejh7F2KG

142

13 Comments

Ethernet always wins. It has already adopted most of the features of Infiniband. Now, Meta reports Ethernet is more reliable.

12

I'm currently putting together a newsletter (vivianchan.substack.com) to share the latest startup and VC jobs in the Deeptech space. Quite a few people have asked me for intros or to tap into my network, so I thought I’d collate everything and share it with everyone. If you or your portfolio companies are hiring, let me know and I'll include it in the next edition! 🙌 Examples of available roles: AI Research Engineer, FutureHouse (AI, Non-profit research org backed by Eric Schmidt), San Francisco, US Head of Operations - AimHi Earth (Sustainability, Seed), Remote Clinical Laboratory Scientist - Juno Bio (Bioinformatics / Microbiomes, Seed), Oakland, California, US Deeptech/Climate Investor - Founders Factory (VC), London, UK or NY, US Bioinformatics Consultant - Deep Science Ventures (VC), London, UK VP Finance - Space Capital (VC), NY, US 📩 More in the first edition: vivianchan.substack.com #Deeptech #Startups #VentureCapital #JobOpportunities #Networking #TechJobs #Innovation #Entrepreneurship #Hiring #Jobs #CareerGrowth

23

5 Comments

We're shipping a lot here at Schematic, take a look at what went live this month. https://lnkd.in/ejmxiyi2 Highlights:  💲 Stripe integration: Sync billing data from Stripe and map it to company profiles in Schematic 🔌 SDK update: Caching, event buffering, and offline support for Go, Python, and Node.js ✨ UX improvements: Feature usage sidebar and bulk entitlements adding 🪵 Audit log: View, search, and filter updates to a company's features access, troubleshoot issues, and track changes for compliance 👥 Team permissions: Invite teammates and specify what permissions they have within Schematic

29