Piezoid

As re reminder, In the UK Palantir holds extensive contracts across defense (multi-billion MoD deals for AI-driven battlefield and intelligence systems) and healthcare (7y £330m+ NHS Data Platform). In France, its involvement is narrower but concentrated on *domestic* intelligence.

What you're describing is likely saccadic masking, where the brain suppresses visual input during eye movements. It "freezes" perception just before a saccade and masks the blur, extending the perception of a "frame" up to the point in time of the sharp onset of masking. That's how you get a still of a partially illuminated frame instead of the blended together colors.

I’m no expert in this, but if you're curious, check out the Wikipedia pages on interstimulus interval, saccadic masking, chronostasis, and related research.

What does recall means in this context? De-energizing the superconductor and shipping it back? Seems like a waste and a planning nightmare.

Given that large language models perform some kind of implicit gradient descent during in-context learning, it raises the question of whether they are also doing some form of predictive coding. If so, could this provide insights on how to better leverage stochasticity in language models?

I'm not particularly knowledgeable in the area of probabilistic (variational) inference, I realize that attempting to draw connections to this topic might be a bit of a stretch.

[1] The free-energy principle: a unified brain theory: <https://www.fil.ion.ucl.ac.uk/~karl/The%20free-energy%20prin...>

[2] Predictive Coding: Towards a Future of Deep Learning beyond Backpropagation?: https://arxiv.org/abs/2202.09467

    We downloaded libgen sql and zlib sql and exported the necessary data from them.

I do agree that generics are required for modern programming, but for some, the cost of complexity of modern languages (compared to C) and the importance of compatibility seem to outweigh the benefits.

[1]: http://attractivechaos.github.io/klib

Do you know if there is available methods for shrinking a fine-tuned derivative of such big models?

Beside generating a specialized corpora using the big model and then train a smaller model on it, is there a more direct way to reduce the matrices dimensions while optimizing for a more specific inference problem? How far can we scale down before the need of a different network topology?

SDF modeling is great for organic shapes.

On the surface, it feels similar to OpenSCAD since CSG operations are natural primitives (min/max/...). Fillets / chamfers are easier to produce, compared to OpenSCAD: http://mercury.sexy/hg_sdf/#snippet

Libfive is one implementation geared towards CAD work. One issue with SDFs for CAD is that it can be difficult to work on complex models. The representation is not minimal: two SDFs can represent the same volume, but act differently when you combine them with other bodies.

Libfive's "stdlib" is quite minimal. For anything fancy, you have to build your own "DOM" on top of it, in order to organize your parametric models. I have not enough experience for that, but I think that it should be possible to build a DSL that render to an SDF expression, while supporting introspection, constraint solving, AD for gradients, etc, with goals similar to CadQuery (I don't like the stack API either). This might also help with the normalization issue above.

I also have been wondering how this would play out with some kind of decentralized indexes. The nodes could automatically cluster with other nodes of users sharing the same interests, using some notion of distances between query distributions. The caching and crawling tasks could then be distributed between neighbors.

Indeed, it seems it's an hybrid of your fellow's tech, using magnesium both as a substrate for H2 and as a reducing agent for water: Mg + 2H2O -> Mg(OH)2(aq) + H2(g)

Less explosive than Na, hopefully :)

I would mostly be concerned about the sub-products of peracids reacting with foods. I don't know if there is any studies on this subject.