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Writes Don't Worry About the Vase, a near-daily account of what is happening in AI and what they think it means. Former Magic: the Gathering pro and trader; unusually willing to put a number on a belief and to grade their own past calls.

• Our readingkorrents.com

  Anthropomorphizing AI systems is necessary for reasoning about, explaining, and predicting their behavior accurately.

  Their words

  Anthropomorphizing the AIs is the only way to reason about, explain to civilians about, or make good predictions about current AIs.

  thezvi.substack.com

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

• Our readingkorrents.com

  Most state-of-the-art robot foundation models have no memory at all, and that is what stops them doing long multi-step tasks.

  Their words

  So, you might be surprised to hear that most state-of-the-art foundation models for robotics have no memory or no context. They're just operating on the current sensor observations, the current camera readings, uh, and predicting actions based off of that.

  youtube.com

Professor of economics at George Mason University and Bartley J. Madden Chair at the Mercatus Center. Co-writes the blog Marginal Revolution and co-authors the textbook Modern Principles of Economics with Tyler Cowen.

• Likedrcmnd.app

  Pressure

  Their words

  Who would have thought that someone could make a gripping movie about predicting the weather? Nevertheless, Pressure delivers.

  marginalrevolution.com

Founder of HumanLayer; wrote 12-Factor Agents, on how to build AI agents that hold up in production.

• Our readingkorrents.com

  An agent repeats its own history: whatever it did on the last change — ran the tests or skipped them — is what it will do on the next one, because it is predicting the next message in the conversation.

  Their words

  And so if I say, "Hey, make this change." and the agent makes the change and then it runs the test and then they're broken and then it fixes the test. I have very high confidence the next change I asked it to make, it's going to follow that path again

  Context engineering with Dex Horthyyoutube.com

Geneticist at Harvard; reads ancient DNA to work out how populations moved and mixed, and wrote Who We Are and How We Got Here.

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  The selection signal on the genetics of schooling is not a European artefact: the same variants predict years of school in China, a population disconnected from Europe for the whole period.

  EuropeChina

  Their words

  So we just could not see a way this could happen by chance and once we saw that we really felt quite convinced that this was a real signal and that really somehow there has been natural selection to increase the genetic changes that today manifest themselves as more years of school at predicting more years of schooling.

  youtube.com

Senior economist at the Foundation for American Innovation; writes Second Best, and was the Niskanen Center's director of social policy before that.

From one piece Empiricists vs. Extrapolators 2 beliefs, in the piece's order there

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  Forecasting AI capability progress is more like solving a thermodynamics equation than predicting chaotic, short-term dynamics.

  Their words

  Forecasting AI capabilities is closer in kind to solving a thermodynamics equation than solving the three-body problem.

  Empiricists vs. Extrapolatorssecondbest.ca

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  People who extrapolate from first-principles models have a stronger track record predicting AI progress than those who wait for empirical proof.

  Their words

  Extrapolators have a remarkable track record in the AI field, being repeatedly early to trends and capabilities that empiricists believed were still decades away.

  Empiricists vs. Extrapolatorssecondbest.ca

Co-founder and CEO of Anthropic; previously VP of research at OpenAI.

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  Predicting that AI would write 90% of the code was never a prediction that we would need 90% fewer software engineers.

  Their words

  But but that's actually a very weak criterion, right? People thought I was saying like we won't need 90% of the software engineers. Those things are worlds apart, right?

  youtube.com

Founder of WP Engine and Smart Bear; writes A Smart Bear, long essays on strategy, pricing and how startups actually fail.

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  An LLM is an averaging machine, so it is good at themes and bad at exactly the un-average details that make customer research actionable.

  LLMs

  Their words

  if you think about it, it sort of makes sense because the LLM is an averaging machine, right? It's predicting the most likely that's an averaging kind of a thing. And so when what you're looking for is a kind of average, it's usually pretty good. So, summarization, topics, themes. But when you're asking for like what is interesting and not average, it's actually pretty bad at it.

  youtube.com

Member of technical staff at Anthropic. He has led ML/AI teams at Amazon, Alibaba and Lazada, and writes about LLMs, recommender systems and engineering at eugeneyan.com.

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  Traditional recommender systems predict user behavior well but cannot be steered via natural language or explain their own reasoning.

  Their words

  They excel at predicting what a user will click or buy next, but can't be steered via natural language or reason on their choices.

  eugeneyan.com

Economist specialising in China's economy, international macroeconomics and global trade, and author of The New China Playbook.

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  China's economy will not collapse, whatever the West has been predicting on and off since the 1980s.

  China

  Their words

  Collapse is such a strong word, and the West has been using this word repeatedly. If I remember correctly, maybe four to five times, maybe even six times since 1980s during the period of China’s fastest growth. I tend to not think that Chinese economy will collapse.

  youtube.com

Founded CD Baby in 1998 and sold it in 2008, giving the proceeds to a charitable trust for music education. Since then a writer and speaker: five books and 550+ articles, published first at sive.rs, which they hand-code and keep free of advertising.

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  As much as possible, one should avoid predicting the future and instead wait for it to become the present and reveal what is actually needed.

  Their words

  As much as possible, try to not predict what the future may hold, but just wait as long as possible for that future to become the present and show you what it actually needs.

  sive.rs

Writes about the biotech industry, drug development and forecasting — “I write to learn, mostly about biotech.”

• Our readingkorrents.com

  Deep learning is not yet ready for the task that would actually add value in drug development: predicting safety and efficacy in humans.

  Their words

  it will take time for deep learning systems and training data to mature enough to be useful for the truly value-adding task of predicting safety and efficacy in humans

  atelfo.github.io

Economist at George Mason University, co-author of Marginal Revolution, host of Conversations with Tyler. Writes in short, dense claims about economics, culture and AI, and has moved position on AI in public more than once.

• Our readingkorrents.com

  Neither monetarist money-supply-focused models nor Keynesian macro models have outperformed simple random-walk models at forecasting the macroeconomy.

  Their words

  Friedman’s focus on the money supply has not held up, as Samuelson suggested, but the alternative Keynesian macro models recommended by Samuelson in the same interview have not done better and they were not outperforming simple random walk models of predicting the macroeconomic future.

  tylercowen.com

Co-founded GiveWell and led Open Philanthropy; writes Cold Takes, on the most important century and how to think about it.

• Our readingkorrents.com

  The track record of thoughtful people predicting the long-run future is mediocre rather than hopeless, which is enough to take such predictions seriously.

  Their words

  They weren't infallible oracles, but they weren't blindly casting about either.

  The Track Record of Futurists Seems ... Finecold-takes.com

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

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  The next-token language modeling objective is misaligned with following user instructions helpfully and safely.

  AI alignment

  Their words

  This is because the language modeling objective used for many recent large LMs-predicting the next token on a webpage from the internet-is different from the objective "follow the user's instructions helpfully and safely" (Radford et al.,, 2019; Brown et al.,, 2020; Fedus et al.,, 2021; Rae et al.,, 2021; Thoppilan et al.,, 2022). Thus, we say that the language modeling objective is misaligned.

  arxiv.org

Writes Nintil, long researched essays on metascience, biology, economics and whatever he has decided to read the literature on.

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  Pre-grant peer review predicts which research will have impact substantially better than chance does.

  Their words

  I have reviewed before the effectiveness of peer review at figuring out "what's good" in the context of grant awards, finding that peer review as currently practiced does substantially better than chance at predicting future impact, especially for the most impactful of the papers; but at the same time is is far from perfect, leaving plenty of variance unexplained.

  nintil.com