Self-Consuming Generative Models Go MAD https://arxiv.org/abs/2307.01850

CERN Ukrainian REMOTE Student Program:
скинули в Slack щойно цю пропозицію, запостили на сайті сьогодні, спробуйте якнайшвидше податися, за 4 тижні будуть починати відбирати людей. платять гарно, задачі цікаві, все за посиланням, це дуже гарна пропозиція (і потрапити реяльніше, ніж ви могли б подумати)

https://jobs.smartrecruiters.com/CERN/743999917982762-ukrainian-remote-student-program

https://www.youtube.com/watch?v=MqZgoNRERY8
old, but somwhat related

The paradox of the efficient code and the neural Tower of Babel

«A pervasive metaphor in neuroscience is the idea that neurons “encode” stuff: some neurons encode pain; others encode the location of a sound; maybe a population of neurons encode some other property of objects. What does this mean? In essence, that there is a correspondence between some objective property and neural activity: when I feel pain, this neuron spikes; or, the image I see is “represented” in the firing of visual cortical neurons. The mapping between the objective properties and neural activity is the “code”. How insightful is this metaphor?

An encoded message is understandable to the extent that the reader knows the code. But the problem with applying this metaphor to the brain is only the encoded message is communicated, not the code, and not the original message. Mathematically, original message = encoded message + code, but only one term is communicated. This could still work if there were a universal code that we could assume all neurons can read, the “language of neurons”, or if somehow some information about the code could be gathered from the encoded messages themselves.

Unfortunately, this is in contradiction with the main paradigm in neural coding theory, “efficient coding”.
The efficient coding hypothesis stipulates that neurons encode signals into spike trains in an efficient way, that is, it uses a code such that all redundancy is removed from the original message while preserving information, in the sense that the encoded message can be mapped back to the original message (Barlow, 1961; Simoncelli, 2003). This implies that with a perfectly efficient code, encoded messages are undistinguishable from random. Since the code is determined on the statistics of the inputs and only the encoded messages are communicated, a code is efficient to the extent that it is not understandable by the receiver. This is the paradox of the efficient code.
In the neural coding metaphor, the code is private and specific to each neuron. If we follow this metaphor, this means that all neurons speak a different language, a language that allows expressing concepts very concisely but that no one else can understand. Thus, according to the coding metaphor, the brain is a Tower of Babel.»