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I think the term GAI/AGI isn't meaningless. It's just imprecise. But the phrase has value since there is no better term for "human-level cognitive ability".

I think the term GAI/AGI isn't meaningless. It's just imprecise. But the phrase has value since there is no better term for "human-level cognitive ability". I'm pretty sure Siri/Alexa/GoogleNow don't use rules. Like all modern Q&A systems like IBM Watson, they use a variety of statistical word match/associations and phrase/sentence response templates. Like Watson, the algorithm identifies the most probable word match to fill the void in your query and then inserts it into the most appropriate answer template. Yes, it's a bit hit or miss (like Watson answering "Toronto" for the US city with an airport named after a WWII hero). But if Apple/Google/Amazon adds some simple sequential disambiguation and refinement (clarification Q&A), I suspect this approach should answer most casual questions quite nicely. IMHO, calling it "AI" means that the problem is not amenable to a closed form solution, so some form of iterative search and refinement is required. In venerable GOFAI parlance, AI problems are inherently 'scruffy'. Once the problem can be solved efficiently, it becomes 'neat' and it's no longer considered to be AI. IMHO, most of today's progress in AI is scruffy (probabilistic word matches and deep learning), since they're driven not by formal internally-curated information but by smart search against presumed-relevant external referents, requiring secondary refinement -- a process that is largely inefficient and open-ended and error-prone, and will remain so indefinitely, I think, since we're not trying to improve the quality of the referent data, which is the weakest link in the cognitive chain.

dpandey 10y ago
I was just thinking about that. Once you can solve a problem in a way that's currently comprehensible and doesn't feel like 'magic', it's no longer AI. Having said that, I still see abuse of the phrase (more in industry than academia) where standard machine learning applied to a workflow is marketed as some big AI in a product. But I understand why they do it - it usually helps in the long drawn out enterprise sales process to be perceived as an AI powered product.
- ktta 10y ago
  >I was just thinking about that. Once you can solve a problem in a way that's currently comprehensible and doesn't feel like 'magic', it's no longer AI. https://en.wikipedia.org/wiki/AI_effect https://en.wikipedia.org/wiki/AI_effect
dpandey 10y ago
Conceptually speaking, 'statistical word match/associations' and rules engines are not very far apart in their level of 'artificial intelligence' or 'magical ability to solve a currrnt hard problem' which was my point. You're probably right about how it's done.
ankurdhama 10y ago
This idea that "we are not using the old rules based system" is nothing but self appreciation, a way to justify that we are doing something new. The reality is that the 'statistical word match/associations' is nothing but described by a set of rules. People need to understand that the only way we can build something is by designing the rules of the thing. A probabilistic system has its own set of rules different from other type of systems. Yes, there is something new and that is we are representing the problems in new and different ways that has allowed us to solve problems that were not feasible with the old representations. The solution, of whatever problem representation you are using, is still rules based.

dpandey 10y ago

I was just thinking about that. Once you can solve a problem in a way that's currently comprehensible and doesn't feel like 'magic', it's no longer AI. Having said that, I still see abuse of the phrase (more in industry than academia) where standard machine learning applied to a workflow is marketed as some big AI in a product. But I understand why they do it - it usually helps in the long drawn out enterprise sales process to be perceived as an AI powered product.

ktta 10y ago
>I was just thinking about that. Once you can solve a problem in a way that's currently comprehensible and doesn't feel like 'magic', it's no longer AI. https://en.wikipedia.org/wiki/AI_effect https://en.wikipedia.org/wiki/AI_effect

dpandey 10y ago

Conceptually speaking, 'statistical word match/associations' and rules engines are not very far apart in their level of 'artificial intelligence' or 'magical ability to solve a currrnt hard problem' which was my point. You're probably right about how it's done.

ankurdhama 10y ago

This idea that "we are not using the old rules based system" is nothing but self appreciation, a way to justify that we are doing something new. The reality is that the 'statistical word match/associations' is nothing but described by a set of rules. People need to understand that the only way we can build something is by designing the rules of the thing. A probabilistic system has its own set of rules different from other type of systems. Yes, there is something new and that is we are representing the problems in new and different ways that has allowed us to solve problems that were not feasible with the old representations. The solution, of whatever problem representation you are using, is still rules based.