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I think what you are describing is the AI effect: https://en.wikipedia.org/wiki/AI_effect https://en.wikipedia.org/wiki/AI_effect 'AI' has always almost been a

by fault1 5y ago

I think what you are describing is the AI effect: https://en.wikipedia.org/wiki/AI_effect https://en.wikipedia.org/wiki/AI_effect 'AI' has always almost been a marketing term, anyways. I would call all of this pattern detection btw. But so would i call the kernel perceptron (1964): https://en.wikipedia.org/wiki/Kernel_perceptron https://en.wikipedia.org/wiki/Kernel_perceptron

Jensson 5y ago
I think the AI effect is reversed. It should be "any time a program can compete with humans at a new task it will get marketed as AI". I don't really get the original argument. I never thought "todays AI isn't smart, but if it could play Go, that would be an intelligent agent!". So "the AI effect" is just a strawman, I have seen no evidence that anyone actually made such a change of heart. AI research is important, but nothing so far has been anywhere remotely intelligent and I never thought "if it could do X it would be intelligent" for any of the X AI can do today. When an AI can pretty reliably hold a remote job and get paid without getting fired for like a year, that is roughly the point I'd agree we have an intelligent agent. Edit: That wouldn't require "much". If the AI can read and understand text, then it can read about backend programming and what makes a good server and build a mental model for stuff like code quality. Based on what it learned it can also device a strategy for how to get a job, so it creates a github account, writes some example projects and puts them on github and writes a CV based on those, then go look for jobs. Of course, this would only be simple if the agent was intelligent. Not like todays agents which are coded for extremely specific scenarios. Even the general gameplay AI they talked about is for very specific scenarios compared to what a human deals with. In order to move towards creating an intelligent agent we need to make progress in this direction. But nothing that has came out of AI research recently really do make any progress in that direction.
- treesprite82 5y ago
  > I don't really get the original argument. I never thought "todays AI isn't smart, but if it could play Go, that would be an intelligent agent!" I think the "AI effect" primarily refers to how practical successes in the field of AI get taken for granted and no longer considered AI - leaving AI with the unsolved problems and bleeding-edge research. If you ordered something for Christmas recently, 'AI' may have been used to understand your search query, rank relevant pages, allow the websites to determine you're not a DDoS attack, allow your bank to determine that your transaction is legitimate, let the confirmation email through your spam filter, then work out routes and other logistics to get it delivered. Before you started your order, 'AI' may have also been involved in the product's design (like optimization of circuits), used to detect defects during manufacturing, or for monitoring and maintenance of rail/road surfaces used for delivery. But all this has been working for a while, so fades into the background rather than being what people think of as AI. There is also that more philosophical element (rather than just about perception of AI as a field) involving our boundary for what "intelligence" is. People won't usually phrase it as "once AI can do X I'll be happy calling it intelligent", but sometimes "doing X requires intelligence" or "an unintelligent machine could never do X", with similar implications. Like, going way back, Descartes' claims that machines will be incapable of responding appropriately in conversation. With AI becoming increasingly capable, even though the timeframes are often far longer than AI-optimists predict, a prevalent view seems to be that AI could behave identically to humans and still not be intelligent. Possibly even be physically identical to humans and still not be intelligent, if you buy into the P-zombie argument (and additionally their nomological possibility, which most people don't). Holding a remote job (including the inverview and portfolio process) is an interesting benchmark, but I can't help but feel your reponse when that gets achieved may be "oh but that's basically just patchwork of language models - clearly in retrospect my criteria must not have been strict enough".

by fault1 5y ago

Jensson 5y ago

I think the AI effect is reversed. It should be "any time a program can compete with humans at a new task it will get marketed as AI". I don't really get the original argument. I never thought "todays AI isn't smart, but if it could play Go, that would be an intelligent agent!". So "the AI effect" is just a strawman, I have seen no evidence that anyone actually made such a change of heart. AI research is important, but nothing so far has been anywhere remotely intelligent and I never thought "if it could do X it would be intelligent" for any of the X AI can do today. When an AI can pretty reliably hold a remote job and get paid without getting fired for like a year, that is roughly the point I'd agree we have an intelligent agent. Edit: That wouldn't require "much". If the AI can read and understand text, then it can read about backend programming and what makes a good server and build a mental model for stuff like code quality. Based on what it learned it can also device a strategy for how to get a job, so it creates a github account, writes some example projects and puts them on github and writes a CV based on those, then go look for jobs. Of course, this would only be simple if the agent was intelligent. Not like todays agents which are coded for extremely specific scenarios. Even the general gameplay AI they talked about is for very specific scenarios compared to what a human deals with. In order to move towards creating an intelligent agent we need to make progress in this direction. But nothing that has came out of AI research recently really do make any progress in that direction.

treesprite82 5y ago
> I don't really get the original argument. I never thought "todays AI isn't smart, but if it could play Go, that would be an intelligent agent!" I think the "AI effect" primarily refers to how practical successes in the field of AI get taken for granted and no longer considered AI - leaving AI with the unsolved problems and bleeding-edge research. If you ordered something for Christmas recently, 'AI' may have been used to understand your search query, rank relevant pages, allow the websites to determine you're not a DDoS attack, allow your bank to determine that your transaction is legitimate, let the confirmation email through your spam filter, then work out routes and other logistics to get it delivered. Before you started your order, 'AI' may have also been involved in the product's design (like optimization of circuits), used to detect defects during manufacturing, or for monitoring and maintenance of rail/road surfaces used for delivery. But all this has been working for a while, so fades into the background rather than being what people think of as AI. There is also that more philosophical element (rather than just about perception of AI as a field) involving our boundary for what "intelligence" is. People won't usually phrase it as "once AI can do X I'll be happy calling it intelligent", but sometimes "doing X requires intelligence" or "an unintelligent machine could never do X", with similar implications. Like, going way back, Descartes' claims that machines will be incapable of responding appropriately in conversation. With AI becoming increasingly capable, even though the timeframes are often far longer than AI-optimists predict, a prevalent view seems to be that AI could behave identically to humans and still not be intelligent. Possibly even be physically identical to humans and still not be intelligent, if you buy into the P-zombie argument (and additionally their nomological possibility, which most people don't). Holding a remote job (including the inverview and portfolio process) is an interesting benchmark, but I can't help but feel your reponse when that gets achieved may be "oh but that's basically just patchwork of language models - clearly in retrospect my criteria must not have been strict enough".