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A disappointment in the hype cycle isn't equivalent to AI winter. The hype cycle always, eventually, hits a trough, even with wildly successful products. It's

A disappointment in the hype cycle isn't equivalent to AI winter. The hype cycle always, eventually, hits a trough, even with wildly successful products. It's very likely we'll be disappointed with AI in many oversold contexts (I share the sentiment about self-driving cars), but it can't be denied that ChatGPT is a product that's being used _right now_ to massive success and still has quite a ways to go.

RandomLensman 4y ago
It doesn't go into large value decisions and projects in a meaningful way (pretty much like all AI) - that is the critical thing AI needs to get to. Where was AI when guaranteeing SVB's deposits was decided, for example?
- Hermitian909 4y ago
  > It doesn't go into large value decisions and projects in a meaningful way (pretty much like all AI) - that is the critical thing AI needs to get to Large decisions such as SVB deposits are literally the last thing that will be given to AI, it's the most important part of the business. That doesn't mean AI is useless or is not being incorporated into valuable parts of businesses. I'm at a large tech company, LLMs are already entering some of our key product workflows. It massively lowers the floor for a number of product features e.g. recommendation systems. In a more general sense, many features that were previously too difficult to expose (e.g. simple coding commands) to business users are now on the table as long as we give them limited access to an LLM. The current hype train means that we don't even need to do much user education.
  - RandomLensman 4y ago
    I am not claiming AI is useless. Rather that pushing AI too hard will create disappointment as it isn't just going into every part of businesses with the same speed (or at all for still some time on some things).
kenjackson 4y ago
Also can I add that I hate the comparison to self-driving cars. The issue with self-driving cars is the penalty for a mis-step can be huge, even if doing something mundane, e.g., driving home from the neighborhood grocery store. If crashing a self-driving car required me to spend 5 minutes rejiggering it, I'd probably use the feature a lot. With generative models there's usually very little cost to it making a mistake -- and even better, I can determine when the cost is likely to be high and modify my behavior. Self-driving cars do seem much better than they used to be. But I have no interest in using them until they get better still. I don't have this same bar with LLMs or DALL-Es. And I think this will contribute to more continuous improvement in the technology.

RandomLensman 4y ago

It doesn't go into large value decisions and projects in a meaningful way (pretty much like all AI) - that is the critical thing AI needs to get to. Where was AI when guaranteeing SVB's deposits was decided, for example?

Hermitian909 4y ago
> It doesn't go into large value decisions and projects in a meaningful way (pretty much like all AI) - that is the critical thing AI needs to get to Large decisions such as SVB deposits are literally the last thing that will be given to AI, it's the most important part of the business. That doesn't mean AI is useless or is not being incorporated into valuable parts of businesses. I'm at a large tech company, LLMs are already entering some of our key product workflows. It massively lowers the floor for a number of product features e.g. recommendation systems. In a more general sense, many features that were previously too difficult to expose (e.g. simple coding commands) to business users are now on the table as long as we give them limited access to an LLM. The current hype train means that we don't even need to do much user education.
- RandomLensman 4y ago
  I am not claiming AI is useless. Rather that pushing AI too hard will create disappointment as it isn't just going into every part of businesses with the same speed (or at all for still some time on some things).

kenjackson 4y ago

Also can I add that I hate the comparison to self-driving cars. The issue with self-driving cars is the penalty for a mis-step can be huge, even if doing something mundane, e.g., driving home from the neighborhood grocery store. If crashing a self-driving car required me to spend 5 minutes rejiggering it, I'd probably use the feature a lot. With generative models there's usually very little cost to it making a mistake -- and even better, I can determine when the cost is likely to be high and modify my behavior. Self-driving cars do seem much better than they used to be. But I have no interest in using them until they get better still. I don't have this same bar with LLMs or DALL-Es. And I think this will contribute to more continuous improvement in the technology.