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It's fairly clear that they understand that much. The objection is that the word 'learn' is generally used in reference to how humans learn. There is a lot imp

by hexaga 3y ago

It's fairly clear that they understand that much. The objection is that the word 'learn' is generally used in reference to how humans learn. There is a lot implied in this usage, and a long history of use by the general populace. By overloading it as 'things that are kinda like human learning, in some ways' you must ignore swathes of that implicit meaning to remain correct while using it. It is no longer a phenomena, but a class of phenomena. The problem they pose is that people aren't going to do that - they'll continue using 'learn' as a stand-in for 'human learning' even when discussing ml adjacent topics, and that is damaging to effective discourse. Practically speaking, that ship has sailed. Regardless, it's a reasonable take. You can literally see their implied scenario play out in this thread - they claim that llms are doing machine-like learning rather than human-like learning (an entirely uncontroversial claim, humans are almost certainly not transformers in disguise) and the conditioned response is along the lines of "you're wrong, they do learn! obviously you don't understand the most trivial aspects of machine learning!" This exchange merely proves their point (which is really the observation): overloading this word in particular causes grief. Misunderstandings abound and arguments arise from no actual disagreement in object level reality.

xcv123 3y ago
> they'll continue using 'learn' as a stand-in for 'human learning' even when discussing ml adjacent topics, and that is damaging to effective discourse. Whoever doesn't understand the term are non-experts. It's not an issue among people who studied and research the topic.
xcv123 3y ago
> It's fairly clear that they understand that much. No they thought humans are creating and tweaking the models manually. Fundamental misunderstanding.
- hexaga 3y ago
  Speaking frankly, this seems like a misread to me. Ascribing an author's intent to the output of a program they wrote is not the same as believing they manually performed the actions of the program.
  - xcv123 3y ago
    https://news.ycombinator.com/item?id=37776217 https://news.ycombinator.com/item?id=37776217
    - hexaga 3y ago
      > [...] modulated the logic behind the Weighting methods. I fail to see how this is not literally the case. The 'logic behind the weighting methods' refers to something like cross-entropy loss minimization via AdamW (or insert optimizer / loss function of choice) over some dataset. I read that as 'the process that decides the weights'. If the model is unaligned ("too racist"), fine tuning with rlhf or alternative of choice is a modulation of the aforementioned 'logic behind the weighting methods'. It is a modification to the process that is deciding weights. Judging by your response, you read 'logic behind the weighting methods' as 'the weights'. I don't think this is a reasonable interpretation unless you're trying to construct a strawman to argue against. Sanity check via chatgpt: > In the context of generative pretrained transformers (GPT), "logic behind the weighting methods" refers to the rationale or reasoning behind the techniques used to assign different weights to words or tokens during the model's training process. - https://chat.openai.com/share/8af4ce45-2c4a-485a-ae8a-e1e016220f90 https://chat.openai.com/share/8af4ce45-2c4a-485a-ae8a-e1e016...
      - xcv123 3y ago
        > Judging by your response, you read 'logic behind the weighting methods' as 'the weights'. I don't think this is a reasonable interpretation unless you're trying to construct a strawman to argue against. So now we are talking about two separate things. The algorithm used to train the model, and the model itself. They did not alter the algorithm that generated the model and they also did not directly alter the model itself. The neural net was modified indirectly through training. That's machine learning 101. When fine tuning a model we do not alter the training algorithm that generates the model. As far as we know, no one is manually tweaking weights or manually editing nodes in its neural network (yet). That is far beyond current knowledge and abilities but is an active field of research in the very early stages. The training process is a search in model space where bad answers are penalized and good answers are rewarded. The algorithm itself doesn't need altering to find a "less racist" model. There's nothing special about racism that requires a fundamental change in the transformer architecture. It's just language and semantics. They trained the shit out of it by RLHF (Reinforcement Learning from Human Feedback) until it was less racist.

by hexaga 3y ago

xcv123 3y ago

> they'll continue using 'learn' as a stand-in for 'human learning' even when discussing ml adjacent topics, and that is damaging to effective discourse. Whoever doesn't understand the term are non-experts. It's not an issue among people who studied and research the topic.

xcv123 3y ago

> It's fairly clear that they understand that much. No they thought humans are creating and tweaking the models manually. Fundamental misunderstanding.

hexaga 3y ago
Speaking frankly, this seems like a misread to me. Ascribing an author's intent to the output of a program they wrote is not the same as believing they manually performed the actions of the program.
- xcv123 3y ago
  https://news.ycombinator.com/item?id=37776217 https://news.ycombinator.com/item?id=37776217
  - hexaga 3y ago
    > [...] modulated the logic behind the Weighting methods. I fail to see how this is not literally the case. The 'logic behind the weighting methods' refers to something like cross-entropy loss minimization via AdamW (or insert optimizer / loss function of choice) over some dataset. I read that as 'the process that decides the weights'. If the model is unaligned ("too racist"), fine tuning with rlhf or alternative of choice is a modulation of the aforementioned 'logic behind the weighting methods'. It is a modification to the process that is deciding weights. Judging by your response, you read 'logic behind the weighting methods' as 'the weights'. I don't think this is a reasonable interpretation unless you're trying to construct a strawman to argue against. Sanity check via chatgpt: > In the context of generative pretrained transformers (GPT), "logic behind the weighting methods" refers to the rationale or reasoning behind the techniques used to assign different weights to words or tokens during the model's training process. - https://chat.openai.com/share/8af4ce45-2c4a-485a-ae8a-e1e016220f90 https://chat.openai.com/share/8af4ce45-2c4a-485a-ae8a-e1e016...
    - xcv123 3y ago
      > Judging by your response, you read 'logic behind the weighting methods' as 'the weights'. I don't think this is a reasonable interpretation unless you're trying to construct a strawman to argue against. So now we are talking about two separate things. The algorithm used to train the model, and the model itself. They did not alter the algorithm that generated the model and they also did not directly alter the model itself. The neural net was modified indirectly through training. That's machine learning 101. When fine tuning a model we do not alter the training algorithm that generates the model. As far as we know, no one is manually tweaking weights or manually editing nodes in its neural network (yet). That is far beyond current knowledge and abilities but is an active field of research in the very early stages. The training process is a search in model space where bad answers are penalized and good answers are rewarded. The algorithm itself doesn't need altering to find a "less racist" model. There's nothing special about racism that requires a fundamental change in the transformer architecture. It's just language and semantics. They trained the shit out of it by RLHF (Reinforcement Learning from Human Feedback) until it was less racist.