Because he was ceo of a company in a critical position to define the future of economy. Currently the tech field is the biggest and most influential of all economic fields. And by tech here we talk about digital world. There’s absolutely no comparable sector at the moment for importance, not even pharma.

It literally defines the modern economy. In the field, openai is an incredibly important company for future relative success and power of big tech companies.

This is why it is so important for world economy

I used to dual boot linux with windows Vista on an old laptop. I had only installed there the first assassin’s creed and Rome total war. Nothing else, never really connected to internet. After 1 year of not using it a part than few total war sessions, vista was so slow that was unusable. It spontaneously became slow for no reason. I completely removed it, left only linux, and that laptop survived 7 years of intensive use, and was still working 10 years later (just too old).

Vista was a scam

How is it useful? Dessert vs food?

“At the end of the lunch nowadays everyone want to have a dessert, but this is wrong because they should have food”…

The sentence AI vs algorithms sounds pretty much like this

AI is a broad family of statistical and simulation algorithms.

They don’t replace algorithms, they are algorithms very powerful for some cases. For other cases they are less powerful, or overkill and they shouldn’t be used. But there is no dichotomy, as one (AI) is part of the other (algorithms)

It’s not an average machine though. It’s a non-linear predictive system. Averages suck in non-linear predictions

Wow!

What about the flaming and jelly windows? That was cool stuff

You don’t really need to tune the operating system, you just need a good one. The hpc crowd has a pretty unique perspective. You won’t find anyone doing any real hpc on windows, not even Microsoft

When I talk about hpc, I don’t talk about a script in Matlab. I talk about the work you do on supercomputers, real computing intensive jobs that takes weeks or months on hundreds or thousands of processors. I guess you don’t find benchmarks simply because no one uses windows, same reason you probably don’t find a fiat panda in the Nürburgring rankings.

Tbf, on Linux performances for hpc are better even on standard desktop distros, no need to clean it up. It manages resources and jobs much better. For performances that is the critical part, once the code is optimized.

No no, it is better. Take a real hpc library, install debian and test it yourself. No tweak needed. Linux as kernel and the overall OS manages resources much better. Linux is a better kernel than windows kernel.

I’ve been doing hpc for over 15 years now. People install standard distros on their workstations and clusters. No tweak needed

One comment to add to your post, Linux is better on performances not just because of the less overhead, but because manages resources much effectively. You could have a bloated linux, it still would perform better because resources are properly managed

Could be both. Who knows. For high performance computing Linux is the de facto standard because it has better performances than windows, and Linux distros are usually better, stabler OSes overall when one needs raw performances. In this case, who knows, someone should investigate further

For research labs, dell workstations used to be great. Put debian on it and you could forget about problems. I don’t know if it is the case anymore.

The selling point of xps is that they are light. Many of us just need light laptops nowadays, as almost any hardware is more than capable of any task with the exception of gaming. But I have never gamed on laptops

Xps developer edition has been a thing since almost a decade. I bought a xps13 with ubuntu in Europe. I replaced it as soon as it arrived though. The built in OS was not “standard”.

I still use it almost daily. Battery has gone, but everything else works

Light ThinkPads are not cheap either

Main issue of mine was the wifi card. Awful. Everything else very nice.

In the easiest example of a neuron in a artificial neural network, you take an image, you multiply every pixel by some weight, and you apply a very simple non linear transformation at the end. Any transformation is fine, but usually they are pretty trivial. Then you mix and match these neurons to create a neural network. The more complex the task, the more additional operations are added.

In our brain, a neuron binds some neurotransmitters that trigger a electrical signal, this electrical signal is modulated and finally triggers the release of a certain quantity of certain neurotransmitters on the other extreme of the neuron. Detailed, quantitative mechanisms are still not known. These neurons are put together in an extremely complex neural network, details of which are still unknown.

Artificial neural network started as an extremely coarse simulation of real neural networks. Just toy models to explain the concept. Since then, they diverged, evolving in a direction completely unrelated to real neural network, becoming their own thing.

No, what you describe is a basic decision tree. Let’s say the simplest possible ML algorithm, but it is not used as is in practice anywhere. Usually you find “forests” of more complex trees, and they cannot be used for generation, but are very powerful for labeling or regression (eli5 predict some number).

Generative models are based on multiple transformations of images or sentences in extremely complex, nested chains of vector functions, that can extract relevant information (such as concepts, conceptual similarities, and so on).

In practice (eli5), input is transformed in a vector and passed to a complex chain of vector multiplications and simple mathematical transformations until you get an output that in the vast majority of cases is original, i.e. not present in the training data. Non original outputs are possible in case of few “issues” in the training dataset or training process (unless explicitly asked).

In our brain there are no if/else, but electrical signals modulated and transformed, which is conceptually more similar to the generative models than to a decision tree.

In practice however our brain works very differently than generative models

There is not a single if/else in a neural network. You are confusing it with decision trees that are used for classification