I asked ChatGPT 

Have there been any great discoveries made by someone who wasn't particularly smart? (i.e. average or below)

and it's difficult to get examples out of it. Even with additional drilling down and accusing it of being not inclusive of people with cognitive impairments, most of its examples are either pretty smart anyway, savants or only from poor backgrounds. The only ones I could verify that fit are:

• Richard Jones accidentally created the Slinky
• Frank Epperson, as a child, Epperson invented the popsicle
• George Crum inadvertently invented potato chips

I asked ChatGPT (in a separate chat) to estimate the IQ of all the inventors is listed and it is clearly biased to estimate them high, precisely because of their inventions. It is difficult to estimate the IQ of people retroactively. There is also selection and availability bias.

Testosterone influences brain function but not so much general IQ. It may influence to which areas your attention and thus most of your learning goes. For example, Lower testosterone increases attention to happy faces while higher to angry faces. 

I think it is often worth for multiple presentations of the same subject to exist. One may be more accessible for some of the audience.

Interesting to see this just weeks after Generalized Stat Mech: The Boltzmann Approach

there's a mental move of going up and down the ladder of abstraction, where you zoom in on some particularly difficult and/or confusing part of the problem, solve it, and then use what you learned from that to zoom back out and fill in a gap in the larger problem you were trying to solve. For an LLM, that seems like it's harder, and indeed it's one of the reasons I inside-view suspect LLMs as-currently-trained might not actually scale to AGI. [bold by me]

But that might already no longer be true with model that have short term memory and may might make moves like you. See my Leave No Context Behind - A Comment.

If I haven't overlooked the explanation (I have read only part of it and skimmed the rest), my guess for the non-membership definition of the empty string would be all the SQL and programming queries where "" stands for matching all elements (or sometimes matching none). The small round things are a riddle for me too. 

Toward Self-Improvement of LLMs via Imagination, Searching, and Criticizing
Abstract:
 

Despite the impressive capabilities of Large Language Models (LLMs) on various tasks, they still struggle with scenarios that involves complex reasoning and planning. Recent work proposed advanced prompting techniques and the necessity of fine-tuning with high-quality data to augment LLMs’ reasoning abilities. However, these approaches are inherently constrained by data availability and quality. In light of this, self-correction and self-learning emerge as viable solutions, employing strategies that allow LLMs to refine their outputs and learn from self-assessed rewards. Yet, the efficacy of LLMs in self-refining its response, particularly in complex reasoning and planning task, remains dubious. In this paper, we introduce ALPHALLM for the self-improvements of LLMs, which integrates Monte Carlo Tree Search (MCTS) with LLMs to establish a self-improving loop, thereby enhancing the capabilities of LLMs without additional annotations. Drawing inspiration from the success of AlphaGo, ALPHALLM addresses the unique challenges of combining MCTS with LLM for self-improvement, including data scarcity, the vastness search spaces of language tasks, and the subjective nature of feedback in language tasks. ALPHALLM is comprised of prompt synthesis component, an efficient MCTS approach tailored for language tasks, and a trio of critic models for precise feedback. Our experimental results in mathematical reasoning tasks demonstrate that ALPHALLM significantly enhances the performance of LLMs without additional annotations, showing the potential for self-improvement in LLMs

https://arxiv.org/pdf/2404.12253.pdf

This looks suspiciously like using the LLM as a Thought Generator, the MCTS roll-out as the Thought Assessor, and the reward model R as the Steering System.This would be the first LLM model that I have seen that would be amenable to brain-like steering interventions.

Examples of blessed information that I have seen in the context of logging:

• Stacktraces logged by a library that elide all the superfluous parts of the stacktraces. 
• A log message that says exactly what the problem is, why it is caused (e.g., which parameters lead to it), and where to find more information about it (ticket number, documentation page).
• The presence of a Correlation IDs (also called Transaction ID, Request ID, Session ID, Trace ID).
  • What is a correlation ID? It is an ID that is created at the start of a request/session and available in all logs related to that request/session. See here or here, implementations here or here. There are even hierarchical correlation IDs 
  • Esp. useful: A correlation ID that is accessible from the client.
  • Even more useful: If there is a single place to search all the logs of a system for the ID.
•  Aggregation of logs, such that only the first, ten's, 100s... of a log message is escalated. 

That's a nice graphical illustration of what you do. Thanks.

Guys, social reality is one if not the cause of the self:

Robin Hanson:

And the part of our minds we most fear losing control of is: our deep values.

PubMed: The essential moral self

folk notions of personal identity are largely informed by the mental faculties affecting social relationships, with a particularly keen focus on moral traits.