orbifold

At some point we will be so tired of distinguishing between AI generated content and human content that we will stop using the Internet and it will be left to bots.

It took cerebras less than a billion to get to where they are now, CPUs are not that hard. You would probably be able to reverse engineer them for ~100 million

I mean in a normal math curriculum you would define only the multiplicative inverse and then there is a separate way to define fraction, if you start out with certain rings. It is kind of surprising to me that they did a lazy definition of division.

One other thing I've observed is that Claude fares much better in a well engineered pre-existing codebase. It adopts to most of the style and has plenty of "positive" examples to follow. It also benefits from the existing test infrastructure. It will still tend to go in infinite loops or introduce bugs and then oscillate between them, but I've found it to be scarily efficient at implement medium sized features in complicated codebases.

200 is a lot of monkeys

A relative of mine worked for the UN and interfaced with the UN after they left for a non-profit. Anyone that knows anything about them and also just simply observing what and how they are doing things should have no doubt that it is filled with people that got there by using their connections. And you absolutely constantly run into people that have no business being there other than through nepotism. Btw. I am sure that US staff is less likely to be a total nepo baby, but because the UN "has" to hire from all over the world, most roles are not filled like that.

It will all make sense once you realize who works at the UN, basically nepo babies of all colors and variety, including second cousins of Saudi royalty etc.

what is striking to me is how far reasoning by analogy and generalization can get you. some of the deepest theorems are about relating disparate things by analogy.

It would be great if we got "kernel independent" Nvidia drivers. I have some experience with bare-metal development and it really seems like most of what an operating system provides could be provided in a much better way as a set of libraries that make specific pieces of hardware work, plus a very good "build" system.

This is a terrible idea and direction but it will not stop people from pursuing it and as soon as they have a critical mass of people reviewing each other it will go on for quite a while. Transformers for time series is one of those things that seems to make sense but not really.

computing correlations between 50.000 financial instruments (X^T X) and doing linear regression ;).

Airbus

I think it is a given that they are aiming for a fully custom training cluster with custom training chips and inference hardware. That would align well with their abilities and actually isn't too hard to pull off for them given that they have very decent processors, GPUs and NPUs already.

In this case the hardware is a nightmare to program.

Writing style and length of paragraphs strongly suggest that this is AI generated in full.

I think what current agent's are missing is taste or the ability to tune taste. So capturing taste across many users might be really valuable.

It is possibly to create synthetic fuel from coal. For usecases that absolutely require fuel we would be able to synthetically create it.

That isn’t actually true. I‘ve personally worked on an embedded POWER processor https://github.com/electronicvisions/nux. It is used as a dual core embedded micro-controller in a neuromorphic chip. Each core has just 16kB of memory on chip and 4kB instruction cache. POWER has subsets of the instruction set which have a gcc and llvm target and are well suited for embedded use cases.

Main advantage of RISCV at this point is that it has a larger community. But at the time it didn’t even have a vector instruction set, whereas we could easily modify the POWER one for our purposes.

I don’t think that is true, there is only so much heavy water on the planet and doing hydrogen fusion is not all that economical.

Not every vector field has a potential. So not every weight update can be written as a gradient.

Personally I am almost certain that the current framing of RL and its relationship to animal behavior is deeply misguided. It proves close to impossible to train animals using this paradigm (not for a lack of trying), i.e. animals such as mice only make any progress when water deprived and under conditions that exploit their natural instincts. Nevertheless they are capable of far more complex natural behaviors. There is a non-zero chance that RL as an explanation of animal behavior is just plain wrong or not applicable.

Mustafa Suleyman is building a team at Microsoft just for that purpose.

> “Although the study found inadequate sleep duration was not an issue in brain atrophy in this study, we cannot say there is no association,” she said, noting that a previous CARDIA study showed that shorter sleep was associated with worse white matter integrity, indicating lower cognitive functioning.

That quote seems to directly contradict the headline.

There are tasks, which are implemented as part of the runtime and they appear to plan to integrate libuv in the future. Some of the runtime seems to be fairly easy to hack and have somewhat nice ways of interoperating with both C, C++ and Rust.

This reminded me of another project: https://nodes.io. Apparently it was inspired by cables.gl

Classifying MNIST digits with 93% accuracy can also be accomplished using a linear classifier. So it isn't clear to me what the advantage would be.

It cost 6 billion to build the current collider (in an existing tunnel) and CERN has a budget of 1-2 billion per year. In terms of data they are easily producing 100x of previous colliders.

Compare the number of issues here: https://wiki.archlinux.org/title/Vulkan. The only issue with the Nvidia driver is that there might be another (open-source) driver installed. AMD has several drivers which fail in different situations. The same has been true for OpenGL implementations, the only truly good implementation is the one by Nvidia, this is even well known in the PC game development industry.

I tried AMD exactly once for Linux graphics, it was so unstable that I bought a NVIDIA card within a month and have never looked back. Potentially NVidias approach of staying as far away as possible from the Linux kernel abstractions is much saner than to play ball with them.