"Explainable AI as a consequence of target system ignorance in Machine Learning" by Clément Arlotti

[Personal opinion: this is one of my main problems with generators: they cannot explain why they said what they said.]

#UndoneCS

"epistemic agent with graduate physics knowledge" (aka a student) 🙂

#UndoneCS

Unlike traditional modelling, in deep learning, model, data and algorithms are intertwined. Explainability requires separability and we don't have it.

(Example is of course a cat image recognition system.)

No structuring hypothesis, no prior knowledge, no explainability.

#UndoneCS

"Radical alternative for AI" by François Levin

Limitations of the AGI (Artificial General Intelligence): some technical (it is irrealist), some practical (specialized IA would be more interesting), some political.

Proposal: attempt instead Alien Intelligence, an intelligence different from ours. Human are not the reference.

#UndoneCS

"Memory Undone: Between Knowing and Not Knowing in Data Systems" by Viktoriia Makovska,

Not obvious how to implement real deletion of data: data is always stored in many places (think of logs).

Also, if you delete a nasty account in a social network, AI trained from data will retain his nastyness ("memory ghosts").

Erasure is possible, Unlearning more difficult.

#UndoneCS

There is now a scientific field called "machine unlearning" (implementing real deletion).

It is not just for individual privacy, it is also to fix the training data.

Unlearning may mean to *add* information to counterbalance the ghost of the deleted information.

#UndoneCS #unlearning