ANN-decompiler VS intelligent-trading-bot

I can understand to why they would say that, as to what they refer as "woke" has massively invaded everything, but you are probably right that it was not done on purpose. Its not that easy to filter things like that out, especially not if humans have to do that manually. Also a friendly reminder that this is not real artificial intelligence, there is a good resource here https://github.com/Shamar/ANN-decompiler that explains this a bit better to why its mostly a magic trick, we have done models like that on paper in the 70's, not as large as those but its not exactly a new trick.

> Does this somehow smuggle the training dataset back into the VM?

Turns out you were right about this: http://www.tesio.it/2021/09/01/a_decompiler_for_artificial_n...

Obviously I was not aware of this, so the whole decompilation process was a waste of computation time, but it doesn't prove nor disprove anything about the "model"'s relation with the source dataset.

But I would be very happy to learn from you how to compute the whole source dataset from the output produced by compile.py without considering the "model".

I agree that the conventional (numeric) forecasting can hardly benefit from the newest approaches like transformers and LLMs. I made such a conclusion while working on the intelligent trading bot [0] by experimenting with many ML algorithms. Yet, there exist some cases where transformers might provide significant advantages. They could be useful where the (numeric) forecasting is augmented with discrete event analysis and where sequences of events are important. Another use case is where certain patterns are important like those detected in technical analysis. Yet, for these cases much more data is needed.

[0] https://github.com/asavinov/intelligent-trading-bot Intelligent Trading Bot: Automatically generating signals and trading based on machine learning and feature engineering