Milad M

PyTorch @Meta Superintelligence Labs - Ex: @Google, @Stanford, @Nvidia, @Apple

[Milad M @_miladm_]

@sh_reya Indeed this is a special time - very glad to see you kick off your academic journey at CMU - well deserved!

[a16z @a16z]

Right place, right time: many neoclouds spent years mining crypto, then AI showed up and turned their power rights, data centers, and GPUs into some of the hottest assets in tech.

25 quarters in, CoreWeave is pulling in more quarterly revenue than Azure, AWS, or Google Cloud were at 30.

Charts of the Week: https://t.co/2D6Avy4f2T

[Brett Winton @wintonARK]

Frontier lab ARR crossed combined Windows + Office revenue in late June of this year (on our estimates).

They are toppling the Titans.

[Dario Amodei @DarioAmodei]

2/2 Second, on the messaging around AI.  I do not agree that my messaging has been disproportionately negative.  In fact it has been about equally balanced between risks and benefits: I’ve written one major essay about each, and even in interviews where I discuss the risks, I make sure to frequently mention the incredible benefits as well as proposing possible solutions to the risks (short clips from my interviews that end up on social media tend to be disproportionately negative, as that gets clicks).  In fact, I wrote Machines of Loving Grace because I didn’t feel the AI industry was painting an inspiring enough picture of how the technology could radically transform the world for the better.  The bulk of the essay is devoted to refuting skepticism of AI’s potential in health and biology, and showing why I think it will actually be possible to cure most human disease in ~5-10 years, as crazy as it may sound to ordinary people and frankly to biologists as well (I used to be one!).  And, if you read my most recent essay (Policy on the AI Exponential), I discuss concrete proposals for how to streamline the FDA process to make sure the deluge of AI-accelerated drugs isn’t slowed down by the regulatory process.  I feel the urgency here: I lost my father to Hepatitis C only a few years before the development of direct-acting antivirals (sofosbuvir), which cure 95% of patients and probably would have cured him.

I do agree that the public has a negative view of AI (and that this is a big problem), but I don’t think it is primarily caused by me or any other AI leader warning about AI’s risks.  I think it is fundamentally a crisis of trust.  I think that ordinary people don’t trust companies, governments, or the tech industry and always suspect that we are cooking up some new way to screw them over.  The causes of this go back decades and AI is just the latest iteration of it.  I don’t think that a glitzy marketing campaign with a positive spin (which some have advocated that Anthropic do) is the way to win back that trust — at this point, saying that AI will cure cancer is more a cliche than it is inspiring, and most people think it is deceptive.  The thing that will work is *actually curing cancer*.  I think by far the most accurate criticism of AI companies including Anthropic is that we haven’t yet delivered on our big promises to benefit the world.  That is totally on us, and I think it’s the criticism you should be making, instead of all this stuff about messaging and marketing.

We are however doing our best to fix this: Anthropic is ramping up its efforts very quickly in biology and medicine, and we hope to have incredible results in the coming years and some early glimmers in the coming months.  When we’ve actually accomplished something real, the whole world will hear about it, as loudly as possible, you have my word on that.  But until then I don’t want to make empty promises, and in the meantime I feel compelled to speak honestly about the very real risks of AI and how to address them.  Honesty is the right thing on the merits, and in terms of public credibility and trust it is no worse than, and may in fact be better than, an approach that ignores or distracts from risks which people instinctively understand are real.

[Milad M @_miladm_]

Open Source is the cornerstone of technology innovation. Happy to see @JensenHuang giving this discussion the attention it deserves.

[Jensen Huang @JensenHuang]

For my first post, I’m sharing a letter @NVIDIA signed on why open models matter.

AI will transform every industry, power every company, and be built by every country.

Open models strengthen safety and cybersecurity, accelerate innovation and diffusion, and enable sovereignty.

The world needs both frontier closed models and frontier open models.

https://t.co/AUKzoQ5Ikb

[Alexandr Wang @alexandr_wang]

Muse Spark 1.1 is SOTA on the Radiologists Last Exam Handover Readiness Index (RadLE-H), nearing human expert performance.

[Mark Zuckerberg @finkd]

(1) Today we're releasing Muse Spark 1.1 -- a strong agentic and coding model at a very low price. It's available through our new Meta Model API and in Meta AI.

[Palantir @PalantirTech]

Palantir CEO Alex Karp on what customers actually want, the real business of frontier labs, and the importance of open source models:

“What the technical customers want is control over their compute, their models, their data stack, and their alpha. They want to know they own the means of production, and it's not being transferred to someone else.”

"Who owns the data? Are the prompts secure? Is this being transferred to you?"

"If it was so valuable, and I can make you a billion dollars, wouldn't I say I'll make you a billion dollars and I want 30%? Why are they charging for tokens if it's so valuable?"

[Alexandr Wang @alexandr_wang]

First, Mark was clearly talking about the industry’s progress on agentic capabilities on the whole.

But, while we’re on the topic: Our next Muse Spark update is coming soon. Big improvements in coding and agentic capabilities to be more competitive with other leading models.

Excited to get these into your hands—will be rolling out to Meta AI and our new API!

[Milad M @_miladm_]

"frontier ecosystem" - an interesting term to protect and compound the data IP of industries in the age of AI.

[Satya Nadella @satyanadella]

https://t.co/vLmiBKTtX3

[Alexandr Wang @alexandr_wang]

the muse spark API will be coming soon!

we have been thrilled with the amount of excitement amongst developers who want to try muse spark inside their agentic harnesses

stay tuned!

[elvis @omarsar0]

Nice paper from Google on automated AI research paper writing.

[Alexandr Wang @alexandr_wang]

Meta AI is up to #6 in the App Store overnight, and still growing :)

Also who knew the 7-Eleven app was so popular

[Alexandr Wang @alexandr_wang]

1/ today we're releasing muse spark, the first model from MSL. nine months ago we rebuilt our ai stack from scratch. new infrastructure, new architecture, new data pipelines. muse spark is the result of that work, and now it powers meta ai. 🧵

[Andrej Karpathy @karpathy]

Three days ago I left autoresearch tuning nanochat for ~2 days on depth=12 model. It found ~20 changes that improved the validation loss. I tested these changes yesterday and all of them were additive and transferred to larger (depth=24) models. Stacking up all of these changes, today I measured that the leaderboard's "Time to GPT-2" drops from 2.02 hours to 1.80 hours (~11% improvement), this will be the new leaderboard entry. So yes, these are real improvements and they make an actual difference. I am mildly surprised that my very first naive attempt already worked this well on top of what I thought was already a fairly manually well-tuned project.

This is a first for me because I am very used to doing the iterative optimization of neural network training manually. You come up with ideas, you implement them, you check if they work (better validation loss), you come up with new ideas based on that, you read some papers for inspiration, etc etc. This is the bread and butter of what I do daily for 2 decades. Seeing the agent do this entire workflow end-to-end and all by itself as it worked through approx. 700 changes autonomously is wild. It really looked at the sequence of results of experiments and used that to plan the next ones. It's not novel, ground-breaking "research" (yet), but all the adjustments are "real", I didn't find them manually previously, and they stack up and actually improved nanochat. Among the bigger things e.g.:

- It noticed an oversight that my parameterless QKnorm didn't have a scaler multiplier attached, so my attention was too diffuse. The agent found multipliers to sharpen it, pointing to future work.
- It found that the Value Embeddings really like regularization and I wasn't applying any (oops).
- It found that my banded attention was too conservative (i forgot to tune it).
- It found that AdamW betas were all messed up.
- It tuned the weight decay schedule.
- It tuned the network initialization.

This is on top of all the tuning I've already done over a good amount of time. The exact commit is here, from this "round 1" of autoresearch. I am going to kick off "round 2", and in parallel I am looking at how multiple agents can collaborate to unlock parallelism.
https://t.co/WAz8aIztKT

All LLM frontier labs will do this. It's the final boss battle. It's a lot more complex at scale of course - you don't just have a single train. py file to tune. But doing it is "just engineering" and it's going to work. You spin up a swarm of agents, you have them collaborate to tune smaller models, you promote the most promising ideas to increasingly larger scales, and humans (optionally) contribute on the edges.

And more generally, *any* metric you care about that is reasonably efficient to evaluate (or that has more efficient proxy metrics such as training a smaller network) can be autoresearched by an agent swarm. It's worth thinking about whether your problem falls into this bucket too.

[Milad M @_miladm_]

@drfeifei Congratulations @drfeifei

[TIME @TIME]

2025 was the year when artificial intelligence’s full potential roared into view, and when it became clear that there will be no turning back.

For delivering the age of thinking machines, for wowing and worrying humanity, for transforming the present and transcending the possible, the Architects of AI are TIME’s 2025 Person of the Year. https://t.co/NiTkWet1cB

[Fei-Fei Li @drfeifei]

This came as a total surprise this morning. Very humbled… 🙏 AI is built by generations of technologists, starting with the daring question of “can machines think?” by Alan Turing. It will be further developed, used and governed by many and all of us! Let’s keep our AI mission human-centered for the benefit of humanity! And I can’t wait to see where AI’s next frontier - spatial intelligence - will be taking us!

[PyTorch @PyTorch]

Today’s "Inside Helion Live Q&A" brought the #PyTorch community together with Jason Ansel, @oguz_ulgen, @weifengpy, and Jongsok Choi from @Meta’s PyTorch Compiler and Helion teams. The discussion explored how Helion approaches kernel authoring, #AIInfrastructure performance, and autotuning at scale.

🖇️ Watch the full recording: https://t.co/uQNKhzpSWF
More from Jason Ansel at #PyTorch Conference 2025: https://t.co/wsqPsCHpsK
Read the Helion blog: https://t.co/KqlsxOciIY