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>Now that we're a decade into this hype cycle and investors are getting antsy, they're doubling down by anthropomorphizing the technology and selling us "chain

>Now that we're a decade into this hype cycle and investors are getting antsy, they're doubling down by anthropomorphizing the technology and selling us "chain of thought" and "reasoning", as if making everyone say these things will somehow magically produce intelligence. The Transformer, nevermind GPT-3 did not exist a decade ago. I guess LLMs aren't the only things that hallucinate and spout confident nonsense. >I also care about the results, but I judge it based on direct experience. IME the current SOTA models can't be relied on to accurately produce working code most of the time, yet I'm expected to trust they can produce accurate healthcare diagnosis and advice? Give me a break. You don't see the fallacy of forcing your experience in one domain onto a completely unrelated one regardless of any evidence to the contrary (even if you don't trust open ai, this is hardly the only paper trialing SOTA LLMS for diagnosis)? What does code have to do with diagnosis ? And while the current SOTA is by no means perfect, if you can't get them to produce working code, that's a you problem. At the very least, many users would disagree. Yeah benchmarks aren't perfect. Doesn't mean they aren't useful. Certainly a lot more useful than your approach.

imiric 1y ago
> The Transformer, nevermind GPT-3 did not exist a decade ago. I'm attributing the start of the current AI hype cycle to the resurgence of CNNs using GPUs, roughly around the time of AlexNet and AlphaGo, not to LLMs and the Transformer architecture. Though if we're being really pedantic, the original Transformer paper is from 2017, so almost a decade ago. But this is beside my point. > You don't see the fallacy of forcing your experience in one domain onto a completely unrelated one The machine has no concept of a "domain". Whether it's outputting code, poetry, images, or video, it's all data generated by probabilistic pattern matching and pseudo-randomness. The structure and accuracy of the generated data is meaningful only to humans, and it's the most important factor that is measured in all these benchmarks. We might find it collectively amusing when an AI produces funny text and weird looking pictures and video. Some might find it acceptable when it produces buggy code that humans need to fix, or when it mimics an expert by confidently spouting nonsense, which is where I personally draw the line. But we should all be concerned when the same models are used in industries where human lives depend on critical thinking by experts. We have been promised fully autonomous vehicles for more than a decade now, and only in the last couple of years have some parts of that promise begun to come true in very limited scenarios. We're all understandably reluctant to give control of a speeding 2-ton object to a machine, for obvious reasons. The process has been very gradual, with a lot of oversight and regulation, as there should be. All I'm saying is that there should be an equal amount of oversight in other industries as well, particularly healthcare. Arbitrary benchmarks don't make me trust these systems more, regardless of who produces them.
- famouswaffles 1y ago
  >I'm attributing the start of the current AI hype cycle to the resurgence of CNNs using GPUs, roughly around the time of AlexNet and AlphaGo, not to LLMs and the Transformer architecture. The current hype cycle, the one fueling hundreds of billions in investment by multiple of the biggest tech companies in the world has little to do with Alex Net and AlphaGo and everything to do with LLMs and generative AI in general. >Though if we're being really pedantic, the original Transformer paper is from 2017, so almost a decade ago. But this is beside my point. The transformer paper did not start the generative AI craze. GPT-3 in 2020 did it for the research world, and the release of ChatGPT in Nov 22 did it for the general public. >The machine has no concept of a "domain". Whether it's outputting code, poetry, images, or video, it's all data generated by probabilistic pattern matching and pseudo-randomness. The structure and accuracy of the generated data is meaningful only to humans, and it's the most important factor that is measured in all these benchmarks. What are you on about? Of course it does. This conversations are getting tiring. Yes, LLMs model concepts directly independent of the text it is trained on. This has been demonstrated multiple times including very recently again by anthropic. There is nothing random about the predictions they make. And even if the machine didn't model these things directly, the concept of domains would still be relevant to the humans testing it as 'data' is not equal. SOTA LLMs are good for diagnosis. That was evident even before this benchmark. I'm not talking about some imagined future. I'm talking about right now. Sticking your head in the sand because of your 'experience' with coding is nonsensical. The benchmarks aren't arbitrary - In some cases they are directly testing the ability in question. I'm not advocating for removing doctors from the picture entirely. It wouldn't even be possible even if I was, at least at the moment.

imiric 1y ago

> The Transformer, nevermind GPT-3 did not exist a decade ago. I'm attributing the start of the current AI hype cycle to the resurgence of CNNs using GPUs, roughly around the time of AlexNet and AlphaGo, not to LLMs and the Transformer architecture. Though if we're being really pedantic, the original Transformer paper is from 2017, so almost a decade ago. But this is beside my point. > You don't see the fallacy of forcing your experience in one domain onto a completely unrelated one The machine has no concept of a "domain". Whether it's outputting code, poetry, images, or video, it's all data generated by probabilistic pattern matching and pseudo-randomness. The structure and accuracy of the generated data is meaningful only to humans, and it's the most important factor that is measured in all these benchmarks. We might find it collectively amusing when an AI produces funny text and weird looking pictures and video. Some might find it acceptable when it produces buggy code that humans need to fix, or when it mimics an expert by confidently spouting nonsense, which is where I personally draw the line. But we should all be concerned when the same models are used in industries where human lives depend on critical thinking by experts. We have been promised fully autonomous vehicles for more than a decade now, and only in the last couple of years have some parts of that promise begun to come true in very limited scenarios. We're all understandably reluctant to give control of a speeding 2-ton object to a machine, for obvious reasons. The process has been very gradual, with a lot of oversight and regulation, as there should be. All I'm saying is that there should be an equal amount of oversight in other industries as well, particularly healthcare. Arbitrary benchmarks don't make me trust these systems more, regardless of who produces them.

famouswaffles 1y ago
>I'm attributing the start of the current AI hype cycle to the resurgence of CNNs using GPUs, roughly around the time of AlexNet and AlphaGo, not to LLMs and the Transformer architecture. The current hype cycle, the one fueling hundreds of billions in investment by multiple of the biggest tech companies in the world has little to do with Alex Net and AlphaGo and everything to do with LLMs and generative AI in general. >Though if we're being really pedantic, the original Transformer paper is from 2017, so almost a decade ago. But this is beside my point. The transformer paper did not start the generative AI craze. GPT-3 in 2020 did it for the research world, and the release of ChatGPT in Nov 22 did it for the general public. >The machine has no concept of a "domain". Whether it's outputting code, poetry, images, or video, it's all data generated by probabilistic pattern matching and pseudo-randomness. The structure and accuracy of the generated data is meaningful only to humans, and it's the most important factor that is measured in all these benchmarks. What are you on about? Of course it does. This conversations are getting tiring. Yes, LLMs model concepts directly independent of the text it is trained on. This has been demonstrated multiple times including very recently again by anthropic. There is nothing random about the predictions they make. And even if the machine didn't model these things directly, the concept of domains would still be relevant to the humans testing it as 'data' is not equal. SOTA LLMs are good for diagnosis. That was evident even before this benchmark. I'm not talking about some imagined future. I'm talking about right now. Sticking your head in the sand because of your 'experience' with coding is nonsensical. The benchmarks aren't arbitrary - In some cases they are directly testing the ability in question. I'm not advocating for removing doctors from the picture entirely. It wouldn't even be possible even if I was, at least at the moment.