3 ms·

I would point you to tow pieces of data. 1. Drive a new Tesla with the latest Supervised FSD and measure how often you have to intervene to stop a crash. 2. G

I would point you to tow pieces of data. 1. Drive a new Tesla with the latest Supervised FSD and measure how often you have to intervene to stop a crash. 2. Go back and look at your own expectations around AI two years ago. Did thing progress the way you expected or did they progress further?

oska 2y ago
1. You are comparing 2 very different things. GPT6 is a generative LLM. Tesla's FSD is machine learning. 2. I have no expectations for 'AI' because the term is a nonsense label. I have followed and been excited by machine learning for a good number of years, and my expectations of progress were pretty much on par. The progress with LLMs has taken me a little by surprise, but I am also cognisant that their progress is being massively over-hyped presently, not least by ppl who call them 'AI' and then, even more foolishly, go on to talk about 'AGI' (a nonsense upon a nonsense).
- gitfan86 2y ago
  I agree that AGI is a meaningless term. I'm intentionally including FSD and LLMs under the same category of technologies that will have a huge impact. The point of this thread is that the demand for inference is going to skyrocket because AI is going to get a lot more useful.
  - oska 2y ago
    Putting aside my (trenchant) philosophical issues with the term 'AI', I also don't think just pragmatically that it's a good categorical label. We both appear to agree that Machine Learning is a very powerful technology that will have huge impacts. Machine Learning requires (and will continue to require) a lot of compute and thus large costs but will also, almost certainly, produce great profits in some domains (FSD being one). It's a lot less clear to me that LLMs will 1) continue to require lots of compute beyond the short term (languages can get close to being 'solved') or 2) that LLMs will generate substantial profits because a) the model can escape capture from a monopoly player far more easily and b) while useful for translation, pulling summarised data from a corpus, recognition of voice commands, etc, none of these applications actually make for the kind of profound impacts that ML is capable of, because none of them transcend human ability like ML has the power to do.
    - gitfan86 2y ago
      Reasoning and MultiModal are emerging out of the larger LLMs. That opens up more use cases, which then drive demand for inference. And that also drives demand for more research. It is hard to say exactly which use cases are going to be huge in a year but it seems very likely that more use cases will open up given how widely you could apply even a small amount of visual reasoning with robotics.
noirbot 2y ago
1. The friend I know with FSD has had it nearly kill him twice in the last year, but it does seem notably better, but in the sort of incremental way I'd expect. They keep it more as a novelty than a functional service. 2. If anything, GPT4 has turned out to be less of an advancement over 3.5 than either OpenAI was claiming and what I'd expected. 2 years ago, people were all but promising AGI by now. Even the folks I know working in the GenAI space are telling me they're using Copilot/ChatGPT less now than a year or so ago. My work has actively cut back on spending in the area and investors have been asking our board questions to make sure we're not overinvesting in it. I want to be clear, I'm not a doomer at all about this. I use these tools a fair bit and find value in them. But the value that GPT3 and 3.5 brought to me versus what GPT 4 has brought certainly isn't 100x. GPT4 isn't even 100x better than me using Google Search most of the time.

oska 2y ago

1. You are comparing 2 very different things. GPT6 is a generative LLM. Tesla's FSD is machine learning. 2. I have no expectations for 'AI' because the term is a nonsense label. I have followed and been excited by machine learning for a good number of years, and my expectations of progress were pretty much on par. The progress with LLMs has taken me a little by surprise, but I am also cognisant that their progress is being massively over-hyped presently, not least by ppl who call them 'AI' and then, even more foolishly, go on to talk about 'AGI' (a nonsense upon a nonsense).

gitfan86 2y ago
I agree that AGI is a meaningless term. I'm intentionally including FSD and LLMs under the same category of technologies that will have a huge impact. The point of this thread is that the demand for inference is going to skyrocket because AI is going to get a lot more useful.
- oska 2y ago
  Putting aside my (trenchant) philosophical issues with the term 'AI', I also don't think just pragmatically that it's a good categorical label. We both appear to agree that Machine Learning is a very powerful technology that will have huge impacts. Machine Learning requires (and will continue to require) a lot of compute and thus large costs but will also, almost certainly, produce great profits in some domains (FSD being one). It's a lot less clear to me that LLMs will 1) continue to require lots of compute beyond the short term (languages can get close to being 'solved') or 2) that LLMs will generate substantial profits because a) the model can escape capture from a monopoly player far more easily and b) while useful for translation, pulling summarised data from a corpus, recognition of voice commands, etc, none of these applications actually make for the kind of profound impacts that ML is capable of, because none of them transcend human ability like ML has the power to do.
  - gitfan86 2y ago
    Reasoning and MultiModal are emerging out of the larger LLMs. That opens up more use cases, which then drive demand for inference. And that also drives demand for more research. It is hard to say exactly which use cases are going to be huge in a year but it seems very likely that more use cases will open up given how widely you could apply even a small amount of visual reasoning with robotics.

noirbot 2y ago

1. The friend I know with FSD has had it nearly kill him twice in the last year, but it does seem notably better, but in the sort of incremental way I'd expect. They keep it more as a novelty than a functional service. 2. If anything, GPT4 has turned out to be less of an advancement over 3.5 than either OpenAI was claiming and what I'd expected. 2 years ago, people were all but promising AGI by now. Even the folks I know working in the GenAI space are telling me they're using Copilot/ChatGPT less now than a year or so ago. My work has actively cut back on spending in the area and investors have been asking our board questions to make sure we're not overinvesting in it. I want to be clear, I'm not a doomer at all about this. I use these tools a fair bit and find value in them. But the value that GPT3 and 3.5 brought to me versus what GPT 4 has brought certainly isn't 100x. GPT4 isn't even 100x better than me using Google Search most of the time.