newpavlov

Open weight models challenge the "winner takes all" hypothesis, which motivates the astronomical valuations and acts as a justification for trains of cash dumped into AI development.

In other words, if AI models become a commodity, the "AI leaders" will not be able to burn the cash like they do today, thus resulting in a "slower" (one could say instead "more sustainable") development of AI models.

I wonder if NNs could be trained well enough with 1 bit weights (i.e. 0 and 1) with some layers doing addition, while others substraction (i.e. weight sign would be "hardcoded" into the network architecture). Or with zero-less weights (e.g. -1, -0.5, 0.5, 1).

Some people use cryptocurrency miners to heat their homes. It's certainly better than dumb resistive heating, but depending on various conditions it can cost more than installing a heat pump.

Because it's not economical, the required hardware is unlikely to pay for itself during its lifetime. The gradient is too small (~50C), which means low Carnot efficiency. Additionally, extraction of low-enthalpy energy involves obstruction of heat transfer, meaning lower cooling efficiency. It may have been a different story if we had computer hardware able to efficiently operate at 200-300C.

Even steel plants which deal with significantly higher waste heat gradients rarely bother with recovering energy.

>Its capabilities all the way down.

IIUC one problem with such layering of capability processing is that each passed layer results in a context switch (i.e. switch of memory mappings, thrashing of caches, etc.) and its on top of the cost of passing through the kernel. In other words, you may need to pay cost of N syscalls for one multi-layered capability-based operation.

>Are we going to run out of space?

In a certain sense, we do. Pumping thousands satellites to LEO increases probability of triggering the Kessler syndrome. Luckily, LEO orbits are also self-cleaning on reasonable time scales (decades), so I think that some day we will trigger it (potentially, with some "help" from anti-satellite weapons) after which some kind of international regulation will be introduced to prevent repeating it in future.

Isn't it just a lawmakers' version of diff? :) You just can't conveniently apply it automatically to compile the resulting text.

>Why the hell you not just rewrite the old law and bump the revision?

Because it's aimed at lawyers and judges who have to be up-to-date with all changes and its easier for them to remember "section 123 was amended in 2026", than to recall a whole new revision and mentally compute the difference.

It's also why you often can see skipped items (e.g. 1, 2, 4, 8, 9, 10). Because humans often think in terms of "<..> code, section 123", not "<..> code, revision 24, section 123, which was section 130 in revision 20", so when you remove a part, it's more efficient to leave an empty space and to not reuse it later.

>it requires creating logical qubits with error rates that we are only now seeing companies report

And yet 21 was not factored on a real hardware.

>There is linear engineering progress for getting from 32 bits to 256 bits being factored is my claim.

IMO it's a very bold claim until linear progress is demonstrated between 8, 16, and 32 bits. Not in theoretical papers. On a real hardware. With honest experiments using arbitrary integers.

It's easy to claim "QC will repeat Moore's law!" especially when your salary depends on it, but the practical evidence is quite lacking at the moment.

>The underlying scaling needed to go to 32 bit requires only linear progress to get to 256

Nope. Firstly, for RSA you need to scale from 32 to 4096. Secondly, Shor requires N^2*log(N) quantum gates where N is number of bits in the integer, so the scaling is superquadratic. And it's very much an open question whether QEC protocols will continue to work with the same efficiency on the required scales.