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Professor at Stanford working on robot learning and meta-learning; co-founded Physical Intelligence.

From one piece Chelsea Finn: This is the State of the Art in Robotics 2 beliefs, in the piece's order there

• Our readingkorrents.com

  Hand-tuning a robot's data set cannot reach very high reliability, because people get tired; the system has to seek out its own missing data.

  Their words

  Um, and while this generally improves the reliability of the model, uh, people eventually get tired and it's hard to get really, really high reliability with a person that's manually tuning this. And so what would be even better is if the AI system itself can iterate on the scenario in which you want it to have higher reliability where it on its own automatically seeks out places where it needs more data, where it needs more supervision.

  youtube.com

• Our readingkorrents.com

  A small team should start from an open-source generalist policy and fine-tune it on day one, rather than scale up its own specialist models first.

  Their words

  I mean at the very least I actually think that just starting with a generalist policy and then fine-tuning it even like right off the bat uh can be really effective.

  youtube.com

Software developer and entrepreneur; co-founder of Heroku, author of The Twelve-Factor App, and a researcher at Ink & Switch.

• Our readingkorrents.com

  Explicit signals like follows and subscriptions are as brittle and hard to maintain as manually tuned filter rules.

  Their words

  Following and subscribing are heavyweight signals, and thus suffer from the same problem of manually tuning rules and filters.

  A Bluesky feed for oneadamwiggins.com

Founding member of OpenAI and former director of AI at Tesla; creator of nanoGPT and the term "vibe coding".

• Our readingkorrents.com

  An overnight automated search found tunings that two decades of hand-tuning experience had missed on an already well-tuned repository.

  Their words

  And I've gotten to a certain point and I thought it was like fairly well tuned and then I let auto research go for like overnight and it came back with like tunings that I didn't see.

  youtube.com

Computer performance engineer working on datacenter performance at OpenAI, previously Netflix and Sun/Joyent. Author of Systems Performance and BPF Performance Tools, and creator of flame graphs.

• Our readingkorrents.com

  Widespread AI-driven performance tuning agents will meaningfully reduce global energy waste.

  energy

  Their words

  In future there will be AI performance agents that can be run on everything, helping to save the planet by reducing energy usage.

  On "AI Brendans" or "Virtual Brendans"brendangregg.com

Computer performance engineer working on datacenter performance at OpenAI, previously Netflix and Sun/Joyent. Author of Systems Performance and BPF Performance Tools, and creator of flame graphs.

From one piece Third Stage Engineering 2 beliefs, in the piece's order there

• Our readingkorrents.com

  Fair hardware benchmarks must use the best software and tuning for each hardware option, not hardware specs alone.

  benchmarks

  Their words

  The most accurate and realistic evaluation for HW involves selecting the best software and then tuning it, and doing this for all HW options.

  Third Stage Engineeringbrendangregg.com

• Our readingkorrents.com

  Computer hardware performance depends on hardware, software, and tuning together, not hardware alone.

  Their words

  You need all three stages to reach the highest, and most competitive, performance.

  Third Stage Engineeringbrendangregg.com

American travel writer and broadcaster. Runs Rick Steves' Europe and writes the guidebooks, packing advice and travel-tip articles published on ricksteves.com.

• Usesrcmnd.app

  Bose noise-canceling headphones

  Their words

  These are a must for listening to music, tuning in to audio tours, or simply drowning out whiny kids on the plane. (I never travel without my noise-canceling Bose headphones.)

  ricksteves.com

Machine-learning researcher on open language models; writes the Interconnects newsletter and the RLHF Book, after leading post-training at Ai2.

From one piece DeepSeek, China, OpenAI, NVIDIA, xAI, TSMC, Stargate, and AI Megaclusters | Lex Fridman Podcast #459 3 beliefs, in the piece's order there

• Our readingkorrents.com

  Humans keep their place in AI as judges rather than authors, because telling which of two answers is better is far easier than writing a good one.

  Their words

  And humans are actually very good at reading or judging between two things versus... This goes back to the core of what RLHF and preference tuning is that it's hard to generate a good answer for a lot of problems, but it's easy to see which one is better.

  youtube.com

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  Reasoning will generalize the way instruction tuning did: add enough verifiable domains and, at some point nobody can yet locate, the rest start working on their own.

  Their words

  And the history of NLP and language processing instruction, tuning and tasks per language model used to be like one language model did one task, and then in the instruction tuning literature, there's this point where you start adding more and more tasks together where it just starts to generalize to every task. And we don't know where on this curve we are.

  youtube.com

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  RLHF is not a tax on capability: preference tuning also raises maths and code scores, which is why the labs keep reaching for it.

  Their words

  And the important thing to say is that no matter how you want the model to behave, these RLHF and preference-tuning techniques also improve performance. So, on things like math evals and code evals, there is something innate to these, what is called contrastive loss functions.

  youtube.com

Machine-learning researcher; has written the Lil'Log survey posts on how a model technique works since 2017, and worked at OpenAI from 2018 to 2024, latterly leading its safety systems team.

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  Using supervised fine-tuning to teach a language model new knowledge risks increasing its hallucination rate.

  LLMs

  Their words

  These empirical results from Gekhman et al. (2024) point out the risk of using supervised fine-tuning for updating LLMs' knowledge.

  Extrinsic Hallucinations in LLMslilianweng.github.io

Research scientist at Meta in Berlin working on multilingual models and evaluation; led the multilingual team at Cohere and was a research scientist at Google DeepMind before that.

• Our readingkorrents.com

  Full fine-tuning of large language models has become too costly, making parameter-efficient fine-tuning methods necessary.

  LLMs

  Their words

  Full fine-tuning of LLMs has become prohibitive and requires parameter-efficient methods instead.

  EMNLP 2023 Primerruder.io

Research scientist at Meta in Berlin working on multilingual models and evaluation; led the multilingual team at Cohere and was a research scientist at Google DeepMind before that.

• Our readingkorrents.com

  In instruction tuning, a smaller set of high-quality training examples outperforms a larger, noisier dataset.

  Their words

  training on a small set of high-quality data outperforms instruction-tuning on larger, noisier data.

  An Overview of Instruction Tuning Dataruder.io