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The work seems to be exclusively about using openAI apis and hacking response pipelines together, but the hiring processes are focused on getting ML specialists

by herval 3y ago

The work seems to be exclusively about using openAI apis and hacking response pipelines together, but the hiring processes are focused on getting ML specialists. So yea, it’s weird

12_throw_away 3y ago
> The work seems to be exclusively about using openAI apis and hacking response pipelines together And ironically, this is not going to be a marketable skill for long, in any scenario. If the hype train crashes and burns, this is no longer a useful skill. But if the hype is real, an ML model will be able to do this sort of thing for you.
- kingkongjaffa 3y ago
  I mean the baseline value is already there in removing writers block and helping you get started with writing, listing obvious things from whatever topic you ask about, and basic and boilerplate code. What's missing is any novel insight and real high quality writing. I guess what I'm trying to say is if LLM development totally stopped tomorrow I would still use it for generating a bunch of ideas and volume of writing to then pick from. It's like being an editor vs being an author, and tweaking text output is easier than starting from scratch.
- ianbicking 3y ago
  LLMs produce useful work right now, it doesn't need a hype train to keep going. OTOH ML models seldom "do" much of anything on their own, it needs integration work to make it matter.
- herval 3y ago
  I think a very large number of people have already experienced significant results with LLMs. I don’t see how it’d be hype tbh (unless for the fringe ppl expecting AGI)
ilaksh 3y ago
I think at least part of it is that many people creating the job postings literally don't know the difference between a machine learning specialist and an "AI Engineer" who is just calling an API. One thing that is confusing is that with the powerful models that have come out in the last few years, someone using the right API and maybe a good prompt can actually handle many tasks that three years ago would have taken an ML engineer months to train a custom model for. I have been saying I'm a "software engineer with a recent focus on generative AI". Half of my clients are convinced that they need to train a custom model in order to solve their problems at all. Because they typed a request into ChatGPT and it didn't do exactly what they wanted. But usually if I just do an API call with an actual system message and temperature 0 then I can demonstrate that they don't need a custom model. There are good reasons to want custom models, but those are probably phase two or three of most projects. And 100 X as expensive and complex. Phase one for many projects will work fine as a proof of concept using an API call or two. I think there will be jobs for ML specialists that know how to do fine-tuning of common models. But you will be competing with people like me who don't have an ML PhD. But beyond that, it seems that you have to build specialized architectures that can compete with the general ML architectures built by leading edge researchers at huge companies. And it's becoming increasingly difficult to invent a new architecture that is truly better in a niche.

by herval 3y ago

The work seems to be exclusively about using openAI apis and hacking response pipelines together, but the hiring processes are focused on getting ML specialists. So yea, it’s weird

12_throw_away 3y ago

> The work seems to be exclusively about using openAI apis and hacking response pipelines together And ironically, this is not going to be a marketable skill for long, in any scenario. If the hype train crashes and burns, this is no longer a useful skill. But if the hype is real, an ML model will be able to do this sort of thing for you.

kingkongjaffa 3y ago
I mean the baseline value is already there in removing writers block and helping you get started with writing, listing obvious things from whatever topic you ask about, and basic and boilerplate code. What's missing is any novel insight and real high quality writing. I guess what I'm trying to say is if LLM development totally stopped tomorrow I would still use it for generating a bunch of ideas and volume of writing to then pick from. It's like being an editor vs being an author, and tweaking text output is easier than starting from scratch.
ianbicking 3y ago
LLMs produce useful work right now, it doesn't need a hype train to keep going. OTOH ML models seldom "do" much of anything on their own, it needs integration work to make it matter.
herval 3y ago
I think a very large number of people have already experienced significant results with LLMs. I don’t see how it’d be hype tbh (unless for the fringe ppl expecting AGI)

ilaksh 3y ago

I think at least part of it is that many people creating the job postings literally don't know the difference between a machine learning specialist and an "AI Engineer" who is just calling an API. One thing that is confusing is that with the powerful models that have come out in the last few years, someone using the right API and maybe a good prompt can actually handle many tasks that three years ago would have taken an ML engineer months to train a custom model for. I have been saying I'm a "software engineer with a recent focus on generative AI". Half of my clients are convinced that they need to train a custom model in order to solve their problems at all. Because they typed a request into ChatGPT and it didn't do exactly what they wanted. But usually if I just do an API call with an actual system message and temperature 0 then I can demonstrate that they don't need a custom model. There are good reasons to want custom models, but those are probably phase two or three of most projects. And 100 X as expensive and complex. Phase one for many projects will work fine as a proof of concept using an API call or two. I think there will be jobs for ML specialists that know how to do fine-tuning of common models. But you will be competing with people like me who don't have an ML PhD. But beyond that, it seems that you have to build specialized architectures that can compete with the general ML architectures built by leading edge researchers at huge companies. And it's becoming increasingly difficult to invent a new architecture that is truly better in a niche.