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Back to Inside the black box of AI: How AI models actually think | Emmanuel Ameisen (Anthropic), with Emmanuel Ameisen.

Why Is Emmanuel Ameisen Optimistic About Model Alignment?

Emmanuel Ameisen is personally optimistic they will figure it out and keep models aligned with our interests. Right now a lot of people are paying attention, and he thinks that attention is a great thing. The technology is powerful. It has benefits and it has risks. As it progresses, the response is to adapt and shift resources from benefits to risks and back again, and to keep studying the models.

As the models get better, they get easier to study in some ways. The world model inside them is clearer, and people can understand some parts of them better. They also get more capable, and more able to evade basic security measures. Ameisen treats the push and pull as the reason to put more or less effort into monitoring and into understanding their circuits. Progress also depends on people understanding what kind of systems the models are, how they work, and how they are made, so they can form good mental models of the present and of the future.

Transcript

Inside the black box of AI: How AI models actually think | Emmanuel Ameisen (Anthropic) ·

Lenny's Podcast: Product | Career | Growth

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  1. How Does an AI Model Produce the Next Word?Emmanuel Ameisen says a model takes a bunch of words, turns the words into numbers, does math on the numbers, and gets intermediate results.
  2. How Do Language Models Rhyme Without Saying the Plan?Emmanuel Ameisen says a language model can write a coherent report or code even though it outputs only one word at a time.
  3. Why Does More Compute Keep Improving Word Prediction?Emmanuel Ameisen says pouring more compute and more data into the task of predicting the next word makes the model's skill at predicting the next word keep…
  4. Is Watching an AI Model the Solution to Alignment?Emmanuel Ameisen says that watching a model and catching it if it does bad stuff is, in one sense, totally the solution to alignment.
  5. Why Did an Anthropic Model Say a Demon Possessed It?Emmanuel Ameisen says a model his team was training would go crazy on math problems.
  6. How Is Next-token Prediction Breaking New Ground in Math?Emmanuel Ameisen says an incredibly simple process, next-token prediction on text, is already breaking new ground in mathematics.
  7. How Similar Are AI Models Trained Years Apart?Emmanuel Ameisen says the structure inside artificial intelligence models is surprisingly similar even when the models were trained years apart, on different…
  8. How Do Language Models Spot Bad Math Without Calculating?Emmanuel Ameisen says language models answer a question one way and check it another way. They can do math, and they can say the color of a banana.
  9. Should I Aim for Levity in a Serious Meeting?Emmanuel Ameisen says that even in serious situations you should aim to have some levity.

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