Learning English is not simply memorizing a billion sample sentences.

The problem is that we want it to learn to string words together for itself, not regurgitate words which already appear in the training set in that order.

This paper attempts to solve the difficult dilemma of detecting how much of the success of an llm is due to rote memorization.

Maybe more importantly: how much parameter space/ training resources are wasted on this?

Noop (Language)

AngularJS (Framework)

The latter was quite popular as a JavaScript web framework. There may be more examples, I’m not an expert at hating google.

TLDR: No, they are not officially planning to deprecate TF. Yes they are still actively developing TF. No, that doesn’t fill me with much confidence, coming from Google, especially while they are also developing Jax.

Just searched this again and kudos, I can’t find anything but official Google statements that they are continuing support for TF in the foreseeable future. For a while people were doom-saying so confidently that Google is completely dropping TF for JAX that I kinda just took it on blind faith.

All that said: #TF REALLY COULD GET DEPRECATED SOON Despite their insistence that this won’t happen, Google is known for deprecating strong projects with bright futures with little/no warning. Do not take the size of Tensorflow as evidence that the Goog is going to stand by it. Especially when they are actively developing a competing product in the niche.

fwiw, it is also the current fad in tech to make high level decisions abruptly without proper warning to engineers. It really does mean almost nothing when a company’s engineers are enthusiastically continuing their support of a product.

TF is just not on solid ground.

Biggest ‘advantage’ i can see is that, since Google is deprecating tf soon, JAX is the only googly deep learning lib left. It fills a niche, insofar a that is a definable niche. I’m sticking with pytorch for now.

No clue about things like speed/efficiency, which may be a factor.