Theoretical physicist and philosopher, Homewood Professor of Natural Philosophy at Johns Hopkins and fractal faculty at the Santa Fe Institute. Writes the Preposterous Universe blog and hosts the Mindscape podcast.

• Our readingkorrents.com

  A language model's apparent mind is mostly our own bias: it predicts text, and leverages our evolved habit of attributing intentionality to anything that acts human.

  LLMs

  Their words

  What a large language model is trying to do is to predict text. That’s what it does. And it is leveraging the fact that we human beings for very good evolutionary biology reasons, attribute intentionality and intelligence and agency to things that act like human beings.

  youtube.com

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

  Retrieval-augmented generation is the most practical approach for reducing LLM hallucinations.

  LLMs

  Their words

  Retrieval-augmented generation (RAG; Lewis et al., 2020), which conditions on the LLM's generation on retrieved documents is the most practical paradigm IMO.

  Command R+ruder.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

  Available online text data is becoming a limiting factor for AI training, and repeating it yields diminishing returns.

  LLMs

  Their words

  recent LLMs are reaching the limits of text data online and repeating data eventually leads to diminishing returns

  NLP Research in the Era of LLMsruder.io

Founder of Amazon in 1994 and of the space company Blue Origin in 2000.

• Our readingkorrents.com

  Large language models are discoveries rather than inventions, closer to looking through a telescope than to engineering an aircraft.

  LLMs

  Their words

  It's interesting to me that large language models in their current form are not inventions, they're discoveries. The telescope was an invention, but looking through it at Jupiter, knowing that it had moons, was a discovery.

  youtube.com

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.

From one piece EMNLP 2023 Primer 3 beliefs, in the piece's order there

• Our readingkorrents.com

  The rise of closed-source large language models makes preserving an open culture of sharing knowledge and software important for NLP.

  LLMs

  Their words

  In light of the increasing number of closed-source LLMs, it is important to continue to promote an open culture of sharing knowledge, data, and software, from which the NLP community has benefited greatly.

  EMNLP 2023 Primerruder.io

• Our readingkorrents.com

  Large language models remain limited in non-English settings, making further multilingual development an important research direction.

  LLMs

  Their words

  LLMs are still limited in non-English settings and that making LLMs more multilingual is an important direction.

  EMNLP 2023 Primerruder.io

• 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

Theoretical physicist and cosmologist, Frank B. Baird, Jr. Professor of Science at Harvard University, working on particle physics, extra dimensions and dark matter.

• Our readingkorrents.com

  Large language models are generalisations of what we already have; the breakthrough that would let AI discover things is still waiting to happen.

  LLMs

  Their words

  Large language models are more or less generalizations of stuff that we have. There’s still breakthroughs in AI waiting to happen, and maybe they are happening and maybe they’ll be good, maybe not, but that’s not quite the same.

  youtube.com

Programmer; founded comma.ai and tinygrad, and was the first to unlock the iPhone.

From one piece George Hotz: Tiny Corp, Twitter, AI Safety, Self-Driving, GPT, AGI & God | Lex Fridman Podcast #387 2 beliefs, in the piece's order there

• Our readingkorrents.com

  AlphaGo and MuZero are far more impressive than large language models, because they play beyond the best humans while the models write middle-school essays.

  LLMs

  Their words

  I think things like MuZero and AlphaGo are so much more impressive because these things are playing beyond the highest human level. The language models are writing middle school level essays and people are like, wow, it’s a great essay.

  youtube.com

• Our readingkorrents.com

  Future language models will be smaller and loop on themselves with a retrieval system, which is what will finally let them cite their sources.

  LLMs

  Their words

  I think future LLMs are going to be smaller, but are going to run looping on themselves and are going to have retrieval systems. And the thing about using a retrieval system is you can cite sources, explicitly.

  youtube.com

Designer and engineer; formerly principal research engineer at GitHub Next; writes at wattenberger.com.

• Our readingkorrents.com

  Chatbots are a terrible interface for large language models, because a text box has no affordances.

  LLMs

  Their words

  Hopefully I've convinced you that chatbots are a terrible interface for LLMs. Or, at the very least, that we can add controls, information, and affordances to our chatbot interfaces to make them more usable.

  Why Chatbots Are Not the Futurewattenberger.com

Alignment researcher at Anthropic; co-led OpenAI's superalignment team until 2024, and writes Musings on the Alignment Problem.

• Our readingkorrents.com

  Large language models are not aligned with their users.

  LLMs

  Their words

  For example, large language models can generate outputs that are untruthful, toxic, or simply not helpful to the user. In other words, these models are not aligned with their users.

  arxiv.org