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Mechanical Turks will be the first to go. More seriously, the output quality of LLMs for code is pretty inconsistent. I think there's an analogy to be made wit

Mechanical Turks will be the first to go. More seriously, the output quality of LLMs for code is pretty inconsistent. I think there's an analogy to be made with literature. For instance, a short story generated by an LLM can't really hold a candle to the work of a human author. LLM-generated code can be a good starting point for avoiding tedious aspects of software development, like boilerplate or repetitive tasks. When it works, it saves a lot of time. For example, if I need to generate a bunch of similar functions, an LLM can sometimes act like an ad-hoc code generator, helping to skip the manual labor. I’ve also gotten some helpful suggestions on code style, though mostly for small snippets. It’s especially useful for refreshing things you already know—like quickly recalling "How do I do this with TypeScript?" without needing to search for documentation. Anyway, literature writers and software engineers aren't going to be replaced anytime soon.

visarga 2y ago
> Mechanical Turks will be the first to go. On the contrary, human annotation work is stepping up now because we create so many more prompts and want to test them.
torginus 2y ago
Those jobs are already outsourced if possible (remember RLHF for ChatGPT outsourced to Kenya, Facebook content moderation to India?) And if they aren't, that's usually for regulatory reasons.
razodactyl 2y ago
Yes, but don't dismiss the laws of scaling and the inability of ourselves to predict the future. Wait But Why did a great take on the AI leap. Imagine that current AI is already curating and generating datasets for the next generation. Also consider that what we have now is only possible because hardware capability increased.
- satvikpendem 2y ago
  Isn't scaling already a big problem in generative AI? Apparently there is not enough data to actually appreciably increase the quality of outputs, and lots of data is also being polluted by AI generations. This is why these companies are now shifting to more ancillary features like in this article (which Claude already has to some extent, as well as Copilot in VSCode and other editors).
  - Bloedcoins 2y ago
    We got a jump start with 'a ton of data' and discovering now that less but better data is actually better. So yes we will see manual labor to finetune the data lair but this will only be necessary for a certain amount of time. And in parallel we also help by just using it: With the feedback we give these systems. A feedback loop mechanism is fundamental part of AI ecosystems.
  - NitpickLawyer 2y ago
    > Apparently there is not enough data This was pretty much refuted by Meta with their LLama3 release. Two key points I got from a podcast with the lead data person, right after release: a) Internet data is generally shit anyway. Previous generations of models are used to sift through, classify and clean up the data b) post-processing (aka finetuning) uses mostly synthetic datasets. Reward models based on human annotators from previous runs were already outperforming said human annotators, so they just went with it. This also invalidates a lot of the early "model collapse" findings when feeding the model's output to itself. It seems that many of the initial papers were either wrong, used toy models, or otherwise didn't use the proper techniques to avoid model collapse (or, perhaps they wanted to reach it...)
Hard_Space 2y ago
Human studies participants have a safer job than firemen. The trouble is that it is already woefully underpaid, and unsettled industries will increase supply. There are crucial quality issues with Mechanical Turk, though, and when these really start damaging AI in obvious ways, the system (and the compensation, vetting procedures and oversight) seems likely to change.

visarga 2y ago

> Mechanical Turks will be the first to go. On the contrary, human annotation work is stepping up now because we create so many more prompts and want to test them.

torginus 2y ago

Those jobs are already outsourced if possible (remember RLHF for ChatGPT outsourced to Kenya, Facebook content moderation to India?) And if they aren't, that's usually for regulatory reasons.

razodactyl 2y ago

Yes, but don't dismiss the laws of scaling and the inability of ourselves to predict the future. Wait But Why did a great take on the AI leap. Imagine that current AI is already curating and generating datasets for the next generation. Also consider that what we have now is only possible because hardware capability increased.

satvikpendem 2y ago
Isn't scaling already a big problem in generative AI? Apparently there is not enough data to actually appreciably increase the quality of outputs, and lots of data is also being polluted by AI generations. This is why these companies are now shifting to more ancillary features like in this article (which Claude already has to some extent, as well as Copilot in VSCode and other editors).
- Bloedcoins 2y ago
  We got a jump start with 'a ton of data' and discovering now that less but better data is actually better. So yes we will see manual labor to finetune the data lair but this will only be necessary for a certain amount of time. And in parallel we also help by just using it: With the feedback we give these systems. A feedback loop mechanism is fundamental part of AI ecosystems.
- NitpickLawyer 2y ago
  > Apparently there is not enough data This was pretty much refuted by Meta with their LLama3 release. Two key points I got from a podcast with the lead data person, right after release: a) Internet data is generally shit anyway. Previous generations of models are used to sift through, classify and clean up the data b) post-processing (aka finetuning) uses mostly synthetic datasets. Reward models based on human annotators from previous runs were already outperforming said human annotators, so they just went with it. This also invalidates a lot of the early "model collapse" findings when feeding the model's output to itself. It seems that many of the initial papers were either wrong, used toy models, or otherwise didn't use the proper techniques to avoid model collapse (or, perhaps they wanted to reach it...)

Hard_Space 2y ago

Human studies participants have a safer job than firemen. The trouble is that it is already woefully underpaid, and unsettled industries will increase supply. There are crucial quality issues with Mechanical Turk, though, and when these really start damaging AI in obvious ways, the system (and the compensation, vetting procedures and oversight) seems likely to change.