How do you get the subdivisions on the right given the category labels on the left?

How can 10% of optimists say AI will be bad or extremely bad long term?

There's already research on this. For example, see "DIRECT: A Transformer Model for Decompiled Variable Name Recovery" by Nitin et al.

Yes. This ship sailed a couple years ago.

I generally agree with you. But it is useful to have a term for training methods that use clever tricks to bypass manual data labeling, usually with some secondary objective in mind (that the model should do something that is not strictly the same as the SSL objective). In that sense, I think of it as a subset of supervised learning. In ML, literally every innovation gets its own catchy name. This is in contrast to, say, statistics, where major innovations often aren't named until years later. I suspect this has to do with the hotness and competitiveness of ML - you need a catchy name to stand out in a crowd of thousands of papers doing very similar things.

I know what they are. What are they missing natively that would make them "ML languages"? A "maths language" as you call it sounds an awful lot like an ML language to me...

Well, sort of. There's CUDA and Julia and Scala, right?

The weights are part of the model, not the algorithm. Whether these can be copyrighted is (a) unclear and (b) should have no bearing on the status of the algorithm itself.

Edit: The output of an algorithm has been ruled by courts to not be copyrightable. A Transformer is, itself, the "output" of an algorithm (e.g., SGD). Therefore, IMHO (IANAL), a Transformer cannot be copyrighted. We'll see if the judges who start taking these cases are savvy enough to rule correctly. Similarly, recipes cannot be copyrighted and they're quite similar to algorithms.

It basically means you read the paper and write the code to do what the paper describes yourself.

If you start with their code base, then your work is derivative of that copyrighted work and the question becomes a bit more complicated.

Yes, the line is fuzzy. However, it's typically very easy to stay on the "not copyright or license infringing" side of the line if you make an honest effort to rewrite the code from scratch and simply use their code base to check your understanding of the algorithm.

Again, IANAL but changing a for loop to a while loop is probably not sufficient to distinguish between their work and yours. Rewriting the code in another language may be. Rewriting it in the same language but making substantial changes to (for example) user interface, data preprocessing, training data, hyperparameters, etc... may be.

Edit: courts and lawyers usually aren't too concerned with technical details. Think of it like a book. The same story gets told over and over again by different authors who use different words to tell it. Your implementation needs to tell the same story but in different words, basically.

IANAL but the copyright protects the paper's text, data, and the code. Algorithms themselves can't be copyrighted. If you reimplement the algorithm, you can do whatever you want with it.

Edit to add: licenses on (trained) models haven't been tested in court as far as I'm aware. I can imagine this being very complicated. Can you copyright and license a linear regression fit to simple economic data? For example: log(gdp) = alpha + beta×population? That seems silly. So why would a Transformer (e.g.) be any different? If you add Gaussian noise to every weight in a Transformer, is the license still valid?

The BBC made every single Poirot mystery in series/miniseries format, starring David Suchet. They're really excellent. Slow and methodical, the acting is usually great and all of the clues are there for you to solve the mystery as you watch (if you're observant enough).

You should be a better scientist. Best papers shouldn't be awarded for performance, that would be bad science. Best papers are awarded for innovation and quality. Fixing your results is the responsible thing to do.

... Except that you don't know what the situation would be like if Epic wasn't able to open an app store -- because steam didn't prevent them from doing so. I get what you're saying, but the fact that steam has better deals than Epic is fairly weak evidence against a competitive market.

Furthermore, steam never had close to a monopoly like the phone app stores have. Steam has always competed with direct sales, publisher stores, brick and mortar, and the windows store, among others. The fact that one more store didn't make a huge difference isn't surprising because the market was already operating properly.

This is something of a misunderstanding of how monopoly power is abused according to US law, at least. Monopolies aren't illegal. Using your power as a monopoly to maintain your monopoly is illegal. It simply doesn't matter if the other side (Epic, for example) accepts the payout to not open an app store. According to antitrust and monopoly laws, Epic isn't the victim, consumers are. You (the victim) prefer a single app store because it's all you know -- you haven't observed the counterfactual competitive app market and so are unlikely to be able to assess the impact it will have on your experience as a user. Historically, competitive markets have lead to lower prices and greater options for consumers. The alternative (that can also be good for consumers, in certain circumstances) is a single regulated monopoly. Think, for example, power companies. However, what we have now is an unregulated monopoly which exclusively benefits the monopoly holder.

Also, monopolies are market specific - so a market over OSes is different from a market within a given OS (e.g., an app store).

The reality in which there's not a monopoly abusing it's monopoly status to prevent competitors?

I don't know if they're available in podcast form but Machine Learning Street Talk and Yannic Kilcher's videos are exactly what you want.