『Interconnects』のカバーアート

Interconnects

Interconnects

著者： Nathan Lambert
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 Audio essays about the latest developments in AI and interviews with leading scientists in the field. Breaking the hype, understanding what's under the hood, and telling stories.

www.interconnects.aiInterconnects AI, LLC 科学
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  • The American DeepSeek Project

    The American DeepSeek Project
    2025/07/04
    https://www.interconnects.ai/p/the-american-deepseek-projectWhile America has the best AI models in Gemini, Claude, o3, etc. and the best infrastructure with Nvidia it’s rapidly losing its influence over the future directions of AI that unfold in the open-source and academic communities. Chinese organizations are releasing the most notable open models and datasets across all modalities, from text to robotics or video, and at the same time it’s common for researchers worldwide to read far more new research papers from Chinese organizations rather than their Western counterparts.This balance of power has been shifting rapidly in the last 12 months and reflects shifting, structural advantages that Chinese companies have with open-source AI — China has more AI researchers, data, and an open-source default.On the other hand, America’s open technological champions for AI, like Meta, are “reconsidering their open approach” after yet another expensive re-org and the political environment is dramatically reducing the interest of the world’s best scientists in coming to our country.It’s famous lore of the AI industry that much of the flourishing of progress around ChatGPT is downstream from Google Research’s, and the industry’s writ-large, practice of openly sharing the science of AI until approximately 2022. Stopping this practice, and the resulting power shifts mean it will be likely that the next “Transformer”-style breakthrough will be built on or related to Chinese AI models, AI chips, ideas, or companies. Countless Chinese individuals are some of the best people I’ve worked with, both at a technical and personal level, but this direction for the ecosystem points to AI models being less accountable, auditable, and trustworthy due to inevitable ties to the Chinese Government.The goal for my next few years of work is what I’m calling The American DeepSeek Project — a fully open-source model at the scale and performance of current (publicly available) frontier models, within 2 years. A fully open model, as opposed to just an “open weights” model, comes with data, training code, logs, and decision making — on top of the weights to run inference — in order to distribute the knowledge and access for how to train AI models fully.This project serves two goals, where balancing the scales with the pace of the Chinese ecosystem is only one piece:* Reclaim the AI research default home being on top of American (or Western) technologies and tools, and* Reduce the risk that the only viable AI ecosystem for cutting edge products in built atop of proprietary, closed, for-profit AI models.More people should be focused on this happening. A lot of people talk about how nice it would be to have “open-source AGI for all,” but very few people are investing in making it reality. With the right focus, I estimate this will take ~$100M-500M over the next two years.Within the context of recent trends, this is a future that has a diminishing, minute probability. I want to do this at Ai2, but it takes far more than just us to make it happen. We need advocates, peers, advisors, and compute.The time to do this is now, if we wait then the future will be in the balance of extremely powerful, closed American models counterbalancing a sea of strong, ubiquitous, open Chinese models. This is a world where the most available models are the hardest to trust. The West historically has better systems to create AI models that are trustworthy and fair across society. Consider how:* Practically speaking, there will never be proof that Chinese models cannot leave vulnerabilities in code or execute tools in malicious ways, even though it’s very unlikely in the near future.* Chinese companies will not engage as completely in the U.S. legal system on topics from fair use or non-consensual deepfakes.* Chinese models will over time shift to support a competitive software ecosystem that weakens many of America and the West’s strongest companies due to in-place compute restrictions.Many of these practical problems cannot be fixed by simply fine-tuning the model, such as Perplexity’s R1-1776 model. These are deep, structural realities that can only be avoided with different incentives and pretrained models.My goal is to make a fully open-source model at the scale of DeepSeek V3/R1 in the next two years. I’ve been starting to champion this vision in multiple places that summarizes the next frontier for performance on open-source language models, so I needed this document to pin it down.I use scale and not performance as a reference point for the goal because the models we’re collectively using as consumers of the AI industry haven’t really been getting much bigger. This “frontier scale” is a ballpark for where you’ve crossed into a very serious model, and, by the time a few years has gone by, the efficiency gains that would’ve accumulated by then will mean this model will far outperform DeepSeek V3. ...
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    11 分
  • Some ideas for what comes next

    Some ideas for what comes next
    2025/06/23
    https://www.interconnects.ai/p/summertime-outlook-o3s-novelty-comingSummer is always a slow time for the tech industry. OpenAI seems fully in line with this, with their open model “[taking] a little more time” and GPT-5 seemingly always delayed a bit more. These will obviously be major news items, but I’m not sure we see them until August.I’m going to take this brief reprieve in the bombardment of AI releases to reflect on where we’ve been and where we’re going. Here’s what you should know.1. o3 as a technical breakthrough beyond scalingThe default story around OpenAI’s o3 model is that they “scaled compute for reinforcement learning training,” which caused some weird, entirely new over-optimization issues. This is true, and the plot from the livestream of the release still represents a certain type of breakthrough — namely scaling up data and training infrastructure for reinforcement learning with verifiable rewards (RLVR).The part of o3 that isn’t talked about enough is how different its search feels. For a normal query, o3 can look at 10s of websites. The best description I’ve heard of its relentlessness en route to finding a niche piece of information is akin to a “trained hunting dog on the scent.” o3 just feels like a model that can find information in a totally different way than anything out there.The kicker with this is that we’re multiple months out from its release in April of 2025 and no other leading lab has a model remotely like it. In a world where releases between labs, especially OpenAI and Google, seem totally mirrored, this relentless search capability in o3 still stands out to me.The core question is when will another laboratory release a model that feels qualitatively similar? If this trend goes on through the end of the summer it’ll be a confirmation that OpenAI had some technical breakthrough to increase the reliability of search and other tool-use within reasoning models.For a contrast, consider basic questions we are facing in the open and academic community on how to build a model inspired by o3 (so something more like a GPT-4o or Claude 4 in its actual search abilities):* Finding RL data where the model is incentivized to search is critical. It’s easy in an RL experiment to tell the model to try searching in the system prompt, but as training goes on if the tool isn’t useful the model will learn to stop using it (very rapidly). It is likely that OpenAI, particularly combined with lessons from Deep Research’s RL training (which, I know, is built on o3), has serious expertise here. A research paper showing a DeepSeek R1 style scaled RL training along with consistent tool use rates across certain data subsets will be very impressive to me.* The underlying search index is crucial. OpenAI’s models operate on a Bing backend. Anthropic uses Brave’s API and it struggles for it (lots of SEO spam). Spinning up an academic baseline with these APIs is a moderate additive cost on top compute.Once solid open baselines exist, we could do fun science such as studying which model can generalize to unseen data-stores best — a crucial feature for spinning up a model on local sensitive data, e.g. in healthcare or banking.If you haven’t been using o3 for search, you really should give it a go.Interconnects is a reader-supported publication. Consider becoming a subscriber.2. Progress on agents will be higher variance than modeling was, but often still extremely rapidClaude Code’s product market fit, especially with Claude 4, is phenomenal. It’s the full package for a product — works quite often and well, a beautiful UX that mirrors the domain, good timing, etc. It’s just a joy to use.With this context, I really have been looking for more ways to write about it. The problem with Claude Code, and other coding agents such as Codex and Jules, is that I’m not in the core audience. I’m not regularly building in complex codebases — I’m more of a research manager and fixer across the organization than someone that is building in one repository all the time — so, I don’t have practical guides on how to get the most out of Claude Code or a deep connection with it that can help you “feel the AGI.”What I do know about is models and systems, and there are some very basic facts of frontier models that make the trajectory for the capabilities of these agents quite optimistic.The new part of LLM-based agents is that they involve many model calls, sometimes with multiple models and multiple prompt configurations. Previously, the models everyone was using in chat windows were designed to make progress on linear tasks and return that to the user — there wasn’t a complex memory or environment to manage.Adding a real environment for the models has made it so the models need to do more things and often a wider breadth of tasks. When building these agentic systems, there are two types of bottlenecks:* The models cannot solve any of the task we ...
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    10 分
  • Crafting a good (reasoning) model

    Crafting a good (reasoning) model
    2025/06/18
    Why are some models that are totally exceptional on every benchmark a total flop in normal use? This is a question I was hinting at in my post on GPT-4o’s sycophancy, where I described it as “The Art of The Model”:RLHF is where the art of the model is crafted and requires a qualitative eye, deep intuition, and bold stances to achieve the best outcomes. In many ways, it takes restraint to land a great model. It takes saying no to researchers who want to include their complex methods that may degrade the overall experience (even if the evaluation scores are better). It takes saying yes to someone advocating for something that is harder to measure.In many ways, it seems that frontier labs ride a fine line between rapid progress and usability. Quoting the same article:While pushing so hard to reach the frontier of models, it appears that the best models are also the ones that are closest to going too far.Once labs are in sight of a true breakthrough model, new types of failure modes and oddities come into play. This phase won’t last forever, but seeing into it is a great opportunity to understanding how the sausage is made and what trade-offs labs are making explicitly or implicitly when they release a model (or in their org chart).This talk expands on the idea and goes into some of the central grey areas and difficulties in getting a good model out the door. Overall, this serves as a great recap to a lot of my writing on Interconnects in 2025, so I wanted to share it along with a reading list for where people can find more.The talk took place at an AI Agents Summit local to me in Seattle. It was hosted by the folks at OpenPipe who I’ve been crossing paths with many times in recent months — they’re trying to take similar RL tools I’m using for research and make them into agents and products (surely, they’re also one of many companies).Slides for the talk are available here and you can watch on YouTube (or listen wherever you get your podcasts).Reading listIn order (2025 unless otherwise noted):* Setting the stage (June 12): The rise of reasoning machines * Reward over-optimization* (Feb. 24) Claude 3.7 Thonks and What’s Next for Inference-time Scaling* (Apr. 19) OpenAI's o3: Over-optimization is back and weirder than ever* RLHF Book on over optimization* Technical bottlenecks* (Feb. 28) GPT-4.5: "Not a frontier model"?* Sycophancy and giving users what they want* (May 4) Sycophancy and the art of the model* (Apr. 7) Llama 4: Did Meta just push the panic button?* RLHF Book on preference data* Crafting models, past and future* (July 3 2024) Switched to Claude 3.5* (June 4) A taxonomy for next-generation reasoning models* (June 9) What comes next with reinforcement learning* (Mar. 19) Managing frontier model training organizations (or teams)Timestamps00:00 Introduction & the state of reasoning05:50 Hillclimbing imperfect evals09:18 Technical bottlenecks13:02 Sycophancy18:08 The Goldilocks Zone19:28 What comes next? (hint, planning)26:40 Q&ATranscriptTranscript produced with DeepGram Nova v3 with some edits by AI.Hopefully, this is interesting. I could sense from some of the talks, it'll be a bit of a change of pace than some of the talks that have come before. I think I was prompted to talk about kind of a half theme of one of the blog posts I wrote about sycophancy and try to expand on it. There's definitely some overlap with things I'm trying to reason through that I spoke about at AI Engineer World Fair, but largely a different through line. But mostly, it's just about modeling and what's happening today at that low level of the AI space.So for the state of affairs, everybody knows that pretty much everyone has released a reasoning model now. These things like inference time scaling. And most of the interesting questions at my level and probably when you're trying to figure out where these are gonna go is things like what are we getting out of them besides high benchmarks? Where are people gonna take training for them? Now that reasoning and inference time scaling is a thing, like how do we think about different types of training data we need for these multi model systems and agents that people are talking about today?And it's just a extremely different approach and roadmap than what was on the agenda if a AI modeling team were gonna talk about a year ago today, like, what do we wanna add to our model in the next year? Most of the things that we're talking about now were not on the road map of any of these organizations, and that's why all these rumors about Q Star and and all this stuff attracted so much attention. So to start with anecdotes, I I really see reasoning as unlocking new ways that I interact with language models on a regular basis. I've been using this example for a few talks, which is me asking O3, I can read it, is like, can you find me the GIF of a motorboat over optimizing a game that was used by RL researchers for a long time? I've used this GIF in a lot of talks, but...
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    30 分

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