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visarga t1_iqsob64 wrote

Reply to comment by Kolinnor in Self-Programming Artificial Intelligence Using Code-Generating: a self-programming AI implemented using a code generation model can successfully modify its own source code to improve performance and program sub-models to perform auxiliary tasks. by Schneller-als-Licht

Cool down. It's not that revolutionary as it sounds.

First of all, they reuse a code model.

> Our model is initialized with a standard encoder-decoder transformer model based on T5 (Raffel et al., 2020).

They use this model to randomly perturb the code of the proposed model.

> Given an initial source code snippet, the model is trained to generate a modified version of that code snippet. The specific modification applied is arbitrary

Then they use evolutionary methods - a population of candidates and a genetic mutation and selection process.

> Source code candidates that produce errors are discarded entirely, and the source code candidate with the lowest average training loss in extended few-shot evaluation is kept as the new query code

A few years ago we had black box optimisation papers using sophisticated probability estimation to pick the next candidate. It was an interesting subfield. This paper just takes random attempts.

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ThroawayBecauseIsuck t1_iqt8f30 wrote

If we had infinite computational power random evolution would probably be good enough to create things smarter than us. Unfortunately I believe we have to find something more focused

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GenoHuman t1_ir038nj wrote

That's assuming these NNs have the capability to be truly smart in the first place.

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magistrate101 t1_iqt48ez wrote

So it's an unconscious evolutionary code generator, guided by an internal response to an external assessment. I suppose you could try to use it to generate a better version of itself and maybe come across something that thinks... After years... You'd really have to stress it out with a ton of different domains of problems to make something that flexible though

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