Description du poste
This brief post is meant to inspire and to start an open dialogue. I am genuinely curious to listen and incorporate your opinions, challenges, and insights.
The ContextI have been pondering for some time about the fact that Multi-Layer Perceptrons are known to be universal approximators and good learners, and yet, they are clearly not as useful as Transformers. The general consensus is that it's mostly due to performance limits: that is definitely true, but the explanation never felt satisfactory to me.
Intuition suggests that self-attention enables contextual-based learning, as opposed to memorization-based learning. Until some days ago, I couldn't point my finger to what "contextual" really meant in practice.
The eye opener was this paper, The Bayesian Geometry of Transformer Attention: I am not going to discuss it since that is beyond my competence, but one of its merits is in posing the correct question: can pure in-context learning be identified at all? The authors succeeded in exactly that: they brainstormed a tool called Bayesian wind tunnels, in which learning is possible, but memorization is prohibitively expensive (at fixed capacity) and doesn't improve the learning process.
And they proved exactly what is anecdotically well-known in the ML community: MLPs memorize, while Transformers contextualize.
The paper's findings go well beyond this major insight; however I am going to stop here and, instead, I'll follow a tangential path and pose the next correct question: is attention, in the form of automatic contextualization, something that can be formally defined regardless of the chosen ML architecture?
Let me explain the reason why I am interested into this specific question:
• we humans know very well what attention means. Most animals can clearly show when they attend to something. That seems to indicate that it may be an emergent phenomenon in the biological world• if attention can be defined, then it will be possible to ask if it is present or not in som