While fundamentals are important to learn, there is also a huge benefit in learning specific tools and frameworks. There is no ”one size fits all” when it comes to software and more often than not, you need very customized solution for optimal performance. Moreover, learning to master a tool often means you are also able to improve it, which is part of the reason open-source tools usually improve with the loyal contributers!
> This ‘goal drift’ means that agents, or tasks done in a sequence with iteration, get less reliable. It ‘forgets’ where to focus, because its attention is not selective nor dynamic.
I don't know if I agree with this. The attention module is specifically designed to be selective and dynamic, otherwise it would not be much different than a word embedding (look up "soft" weights vs "hard" weights [1]).
I think deep learning should not be confused with deep RL. LLMs are autoregressive models which means that they are trained to predict the next token and that is all they do. The next token is not necessarily the most reasonable (this is why datasets are super important for better performance). Deep RL models on the other hand, seem to be excellent at agency and decision making (although in restricted environment), because they are trained to do so.
> The facilities will reportedly consume as much as 13 percent of the plant's output.
Why are AI products being shipped so aggressively despite being so inefficient? Is code autocompletion and generating random images really worth so much electricity? Shouldn’t we wait until the research has created an efficient architecture that is easily scalable first?
Isn’t that what the softmax layer is doing? The token with highest probability among all the available tokens in the model dictionary is chosen as the next token!
I think in the long run, it is in the interest of AI companies to incentivize creators to create high quality data! Not paying them their fair share will likely decrease the volume of high quality data available (or make it much less accessible). Unless these companies already have developed another architecture that can learn much more from the same dataset, the lack of new high quality data will be a problem for future larger models!
Merry Christmas everyone! Thanks everyone for you great contributions to the community and thanks to the dear moderators for the great job they are doing!
It is the first time, to my knowledge, that there is strong evidence that indicates it could actually happen! (The evidence being the rapidly changing climate and our inability to adapt quickly enough)