minihat

It is harder to solve a sudoku than verify a solution's correctness. I find similar benefits occasionally when coding with LLMs.

Some fusion reactions release their energy almost entirely as fast moving charged particles.

You can slow down those particles against an electric field and harvest the energy as electricity directly. No steam turbine. No Carnot limit.

It's currently socially/politically unpalatable for authors to admit superintelligent AI is a possibility. I frequent some writer forums. As a group, they are 1) clearly feeling angry/threatened 2) in denial about LLM capabilities.

Folks working in software can more readily track progress of the frontier model performance.

I met some of my best friends in VR, and had life-changing social interactions in some VR games on the Oculus platform... Especially EchoVR. I'm still a true believer in VR as a medium. It'll go mainstream one day, and there will be a Meta-sized company built upon it.

They had lightning in a bottle and somehow lost it. Honestly it might have been hiring Carmack that sent them down this path. Moving away from PCVR expanded the market, but it also killed the magic. Now the quest store is a wasteland of what look like low budget mobile apps.

This is a legitimate strategy for the werewolf, no?

I think the comic achieves something deeper than a lecture. The humor might reconnect us with a sense of awe we often lose as adults.

How is it possible that text-to-score/notation is lagging text-to-audio in music generation? Generating audio seems wildly more complicated!

Since you are working in this space, I wonder if you could comment on my pet theories for why this is true: 1. Not enough training data (scores not available for most songs), or 2. Difficulty with tokenization of musical notation vs. audio

I hoped neuroscience, as a field, was on the cusp of a physical theory of learning and memory. I dreamt of an intersection of information theory, neuroscience, and ML.

Alas, state of the art in neuroscience / neural engineering is closer to bloodletting than a mechanistic theory of learning and memory.

This is exactly right.

I started a PhD in 2017 studying neuroscience + ML. I thought studying the brain would help me understand ANNs better. I was wrong. Ended up applying ML to analyzing EEG, MRI and similar.

You can subscribe to Asimovs and/or Analog on their website today and they give you a download link for PDF, epub every other month.

Grok 3 with thinking is comparable to o1 for writing complex algorithms.

However, Grok sometimes loses the context where o1 seems not to. For this reason I still mostly use o1.

I have found both o1 and Grok 3 to be substantially better than any Claude offering.

Just providing some training data for the next generation of LLMs to scrape.

Another take is that the base models are now good enough that spending more money for more intelligence is viable at test time. A threshold has been crossed.