Thread

1. creativeSlumber · 1790580636 · focus · HN ↗

   How relevant is this fast/slow thinking thing with regards to current frontier models?

   I know a large organization who's built their AI framework completely around this concept, and I feel that it's not really meaningful concept with the capabilities of current models.

   1. Retric · 1790582220 · focus · HN ↗

      You can ask a model for output directly and stop, or you can recursively ask it to keep refining the output.

      That seems to fit the fast vs slow model of human thought reasonably well.

      1. usernametaken29 · 1790587118 · focus · HN ↗

         > You can ask a model for output directly and stop

         That’s still several orders of magnitudes too slow to fit fast vs slow. Think of 30ms vs 3-4 seconds to get an idea of what we’re talking about here

         1. Retric · 1790587655 · focus · HN ↗

            That’s a function of the amount of processing power involved not the underlying architecture of decision making.

            1. usernametaken29 · 1790591813 · focus · HN ↗

               In terms of making an LLM faster but not in terms of meta-cognition. System 1 thinking as defined by Kahneman doesn’t have 100000x more compute than System 2, it is actually the opposite. That completely contradicts your claim

               1. pixl97 · 1790606318 · focus · HN ↗

                  >System 1 thinking as defined by Kahneman doesn’t have 100000x more compute than System 2, it is actually the opposite.

                  When are you measuring?

                  Systems 1 thinking is closer to precomputed tables in some ways. That is by evolution or massive amounts of training your neural network has a narrow fast path it can execute with as little compute at execution as needed.

                  1. Retric · 1790616381 · focus · HN ↗

                     Slower paths means loops here for humans where the output of a neuron gets feed back into itself. The fastest path = a feed forward neural network without loops.

                     LLM’s operate strictly feed forward neural networks.

                     1. usernametaken29 · 1790645438 · focus · HN ↗

                        Your assumption is wrong. Unlike artificial neurons, real neurons process massively and concurrently, let’s say 10-20 thousand inputs at once in less than 1 millisecond. In Kahnemans theory system 2 always involves the anterior cingulate cortex and arises when there are multiple conflicting streams of information. That’s the defining characteristic. Both system 1 and system two process in the same way, including looping back to previous areas. So really, no, stopping an LLM early vs letting it run has nothing to do with Kahnemans theory. In general LLMs are so far removed from what we consider natural cognition that it’s very hard to apply neuroscience concepts to LLMs simply because they share no real world similarity. At best, you’re simulating something in a very inefficient way.

                        1. Retric · 1790650165 · focus · HN ↗

                           We’re talking about biological processes which have physical limitations on how frequently a neuron can fire as well as a signaling system that based on how rapidly neurons fire. Loops anywhere in the system enforce a minimum 5ms latency and more realistically several times that. A typical neuron is firing closer to 0.1 to 2 times a second. Add to that the physical transmission speed and rapid response doesn’t allow for loops.

                           There’s a reason reflexes for the feet are handled by the spine that’s got nothing to do with total processing power.

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