Hi,
I try to motivate a Biology Teacher already for a while to
replicate the below grokking experiment. But I have my
own worries, why bother with the blackbox of what a
machine learning method has learnt?
Simple PyTorch Implementation of "Grokking"
https://github.com/teddykoker/grokking
Well its not correct to say that the learnt model is a black box.
The training data was somehow a black box, but the resulting
model is a white box, you can inspect it.
This gives rise to a totally new scientific profession of
full time artificial intelligence model gazers. And it is
aprils fools day all year long:
Language Models Use Trigonometry to Do Addition
https://arxiv.org/abs/2502.00873
Have Fun!
Bye
Post by Mild ShockHi,
Because of the wide availability of Machine Learning
via Python libraries , the whole world (at least China)
has become a big Petri Dish that is experimenting with
new strategies to evolve brains on the computer.
Recent discovery seems to be Group Preference Optimization.
This is when you make the chat bot, detect and react
differently to different groups of people. It seems to
work on the "policy level". I don't understand it yet
completely. But chat bots can then evolve and use
Group Preference Optimization
https://arxiv.org/abs/2310.11523
DeepSeekMath: Pushing the Limits
https://arxiv.org/abs/2402.03300
Now it seems that it is also at the core of DeepSeekMath,
what is possibly detected is not group of people, but
mathematical topics, so that in the end it excells.
When unsupervised learning is used groups or math
topics might be found from data, through a form of
abduction.
Bye
Post by Mild ShockHi,
Wait till USA figures out there is a second
Yi-Lightning Technical Report
https://arxiv.org/abs/2412.01253
Eric Schmidt DROPS BOMBSHELL: China DOMINATES AI!
http://youtu.be/ddWuEUjo4u4
Bye
Post by Mild ShockHi,
https://www.instagram.com/p/Cump3losObg
https://9gag.com/gag/azx28eK
Bye