glial

I think of System 1 as a hash map. If you have a map, and see a new state/key whose action/value is not defined in the map, you have to go with System 2.

System 1/2 is the pop version, but the fast/slow distinction is prevalent in both RL and computational cognitive science, sometimes going under different names: procedural/deliberative, model-free/model-based, automatic/controlled, associative/rule-based, autonomous/algorithmic, etc.

'fast' means executing a policy, that is, a state-action mapping. A trained RL model does this.

'slow' means making one or several action-dependent forecasts, evaluating the expected value of the outcomes, and making a decision based on that.

Neither map exactly to the situation with LLMs, but very roughly, the first is analogous to trained classifiers and the second to reasoning models.

The analogy breaks down, since each instance of token being produced is an example of a policy execution (system 1), and reasoning is just stringing lots of these together. But there are those who argued, before LLMs, that system 2 is just "policy composition" anyway...

> it suddenly looks a lot more plausible that “new” “ideas” can emerge from that process

This is not intuitive to me. It seems like a "new idea" is something that (almost by definition) isn't in the training set. Can you elaborate a bit?

Edit: but perhaps a good model could arise from training, which would be a good idea in the sense that parsimonious ideas are good scientific ideas.

Yes, that's why psychology is the hardest science.

This makes sense to me.

I get a similar ick when I hear someone say "I enjoy consuming this type of content." They're not selling anything, but they are still adopting the soulless language. Like, if it still exists after you interact with it, you haven't consumed anything. Second, do you really see yourself this way? Why not just say "I love to read travel blogs"?

I appreciate this push-back. I keep a running list of words like this...content, product, device, experience, space, vehicle, resource, etc. I can't put my finger on why, but all of these make me distrust the speaker.

The key operation of Bayesian inference is integrating information. This can be from the same source (an additional coin flip, for example) or from different sources (coin flips, plus auxiliary knowledge about where the coin came from, or the person's motives).

Calculated frequency is a point-estimate of bias. A Bayesian estimate is a distribution of belief over possible values of the bias, integrating all available information.

Someone walks out of a magic store holding a coin.

They propose a bet. If they flip it 100 times and the proportion of heads is within [0.4, 0.6], you win $100. If it's not, you pay $100. Do you take that bet?

Explanation: absent the magic store scenario, a `rational' person would take the bet. Your prior belief is that most coins are roughly unbiased. Given that they walked out of a magic store, you now have additional information. Maybe the coin is a trick coin. In that case, your belief that the coin is unbiased should be weaker, even if you don't know which direction the coin is biased in.

This illustrates two things: one, additional information (magic store) can update your beliefs. Two, a strong prior and a weak prior, in this case about the coin's bias, can lead to materially different decisions.

Paper reviews are traditionally blinded, so the reviewer doesn't know the authorship of the paper they're reading.

That faith and ethics can be so easily conflated points to a failure of Western post-enlightenment intellectual leadership.

On the other hand, perhaps this is just responsibility laundering on behalf of the tech orgs.

I think this agrees with the parent's point. How do you know when to refactor?

I'm not convinced. There is already an 'Auto' and 'P/A/S' setting option on most of these cameras. P/A/S is great for those who want control. I wish Auto was better for those who don't care and just want good photos.

In other words, no camera exists that has great computational photography and a lens bigger than your thumbnail. Why?