tbenst

Put another way, EEG moves top-10 retrieval out of 512 candidate words from 20.3% -> 27.7%. Hardly "open-vocabulary word decoding"

The paper is also missing a critical control of feeding Llama the preceding ground-truth text and asking how well it ranks the current word among the same candidate set.

That is sobering for sure, I wonder what the theoretical bounds are on what is possible if known. Would be such a dream to use a Frontier LLM one day with homomorphic encryption, but this sounds wildly implausible based on where things are today.

A key challenge with Alzheimer’s is there is no good mouse model for the disease. While some approximate the phenotype, it’s not clear that the disease model as commonly studied in mice matches well with mechanisms of the human disease. There’s some thinking in the field that this could be a key reason why so many treatments have appeared very promising in mice and haven’t panned out in humans.

Beautifully written and worth the read. And the screensaver nerd snipe is epic.

As a neuroscientist, my biggest disagreement with the piece is the author’s argument for compositionality over emergence. The former makes me think of Prolog and lisp, while the later is a much better description for a brain. I think ermergence is a much more promising direction for AGI than compositionality.

You’re right, I meant class 4

This is a well known phenomenon. It accounts for example in the flash perceived when someone inadvertently looks at an infrared class 5 laser and is blinded

The Nvidia GH200/GB200 “superchips” are all ARM processors. Seems likely that some of the next generation of foundation models will be trained on ARM

I know the exercise was to p-hack, but instead I decided to one-shot my attempt at the most reasonable model from first principals:

- given that we are looking at a national scale, use only national politicians

- use the components from Macroeconomics 101: exclude inflation as that’s on the Fed, exclude stocks as too conflated with FX and international investing alternatives

- don’t needlessly withhold data

Tried one hypothesis, so p-value of 0.04 is accurate. Still OK to explore if you Bonferroni correct the p-Val afterwards

The high thermal conductivity of copper makes it difficult to maintain needed temperatures during SLM. Also, copper is prone to oxidation at high temperatures, further complicating (thermal based) laser melting 3D printing techniques. It’s more typical to print copper alloys than pure copper.

Fabric8Labs can print 100% density, whereas Desktop Metal is highly porous. Also Fabric8Labs can directly print pure copper, which has historically been very difficult. The process is also more energy efficient and better suited for small complex parts. Desktop Metal serves a different market in terms of material and size.

disclaimer: I'm a GP at Asimov Ventures and invested in Fabric8labs' pre-seed round.