31–40 of 156 posts
The feedback loop on novel/genuine breakthroughs is too long and the training data is too small.
Another reason is that there's plenty of incentive to go after the majority of the economy which relies on routine knowledge and maybe judgement, a narrow slice actually requires novel/genuine breakthroughs.
The OP's proposed solution is a constant "daydreaming loop" in which an LLM is does the following on its own, "unconsciously," as a background task, without human intervention:
1) The LLM retrieves random facts.
2) The LLM "thinks" (runs a chain-of-thought) on those retrieved facts to see if they are any interesting connections between them.
3) If the LLM finds interesting connections, it promotes them to "consciousness" (a permanent store) and possibly adds them to a dataset used for ongoing incremental training.
It could work.
The question is: How do we get LLMs to have "Eureka!" moments, on their own, when their minds are "at rest," so to speak? The OP's proposed solution is a constant "daydreaming loop" in which an LLM is does the following on its own, "unconsciously," as a background task, without human intervention: 1) The LLM retrieves random facts. 2) The LLM "thinks" (runs a chain-of-thought) on those retrieved facts to see if they…
I also hope we have something like this. But sadly, this is not going to work. The reason is this line from the article, which is so much harder that it looks: > and a critic model filters the results for genuinely valuable ideas. In fact, people have tryied this idea. And if you use a LLM or anything similar as the critic, the performance of the model actually degrades in this process. As the LLM tries too hard to s…
Exactly. This not only affects a potential critic model, but the entire concept of a "reasoning" model is based on the same flawed idea—that the model can generate intermediate context to improve its final output. If that self-generated context contains hallucinations, baseless assumptions or doubt, the final output can only be an amalgamation of that. I've seen the "thinking" output arrive at a correct solution in t…
Except that we can try the exact same pre-trained model with reasoning enabled vs. disabled and empirically observe that reasoning produces better, more accurate results.
The question is: How do we get LLMs to have "Eureka!" moments, on their own, when their minds are "at rest," so to speak? The OP's proposed solution is a constant "daydreaming loop" in which an LLM is does the following on its own, "unconsciously," as a background task, without human intervention: 1) The LLM retrieves random facts. 2) The LLM "thinks" (runs a chain-of-thought) on those retrieved facts to see if they…
The breakthrough isn't in their datasets.
I also hope we have something like this. But sadly, this is not going to work. The reason is this line from the article, which is so much harder that it looks: > and a critic model filters the results for genuinely valuable ideas. In fact, people have tryied this idea. And if you use a LLM or anything similar as the critic, the performance of the model actually degrades in this process. As the LLM tries too hard to s…
I also hope we have something like this. But sadly, this is not going to work. The reason is this line from the article, which is so much harder that it looks: > and a critic model filters the results for genuinely valuable ideas. In fact, people have tryied this idea. And if you use a LLM or anything similar as the critic, the performance of the model actually degrades in this process. As the LLM tries too hard to s…
But what if the critic is just hard reality? If you ask an LLM to write a computer program, instead of criticizing it, you can run it and test it. If you ask an LLM to prove a theorem, let it write the proof in a formal logic language so it can be verified. Etcetera.
In examining the possibility of genuinely creative computing, Gelernter discovers and defends a model of cognition that explains so much about the human experience of creativity, including daydreaming, dreaming, everyday “aha” moments, and the evolution of human approaches to spirituality.
https://uranos.ch/research/references/Gelernter_1994/Muse%20...