It is not intelligent.

Hiding the training data behind gradient descent and then making attributions to the program that responds using this model is certainly artificial though.

This analogy just isn't holding water.

Can't you judge on the results though rather than saying AI isn't intelligent because it uses gradient descent and biology is intelligent because it uses wet neurons?

I am and that is also a problem.

I strongly believe that our concept of intelligence is like the „god of the gaps“ [0]. Intelligent is only what we haven’t yet explained.

Chess computers surely must be intelligent, but then deep blue was „just search“.

Go computers surely must have intelligence because it requires intuition and search is intractable, but then it’s „just CNN based pattern matching“.

Writing essays surely requires intelligence, because of the creativity, but then it‘s actually just a „stochastic parrot“.

We keep attributing intelligence to what is currently out of reach even as this set is rapidly shrinking before out eyes.

It would be better to say that intelligence is an emergent phenomenon and that behavior that seems intelligent is intelligent.

[0] https://en.m.wikipedia.org/wiki/God_of_the_gaps

In-context learning is proof that LLMs are not stochastic parrots.

I don't believe they have made it public.

I worked this wreck with RIMAP and had to sign an NDA before boating out, but that was back in 2020

Steganography tool called ez-steg. It supports least significant bit steganography as well as emoji/unicode encoding via variation selectors. It grew from a set of scripts I had written to test out data loss prevention systems.

Includes some nice-to-haves like payload encryption, carrier image creation

https://github.com/a-bissell/ez-steg