The only brand new definition of AI is the one that came in full marketing speed shortly after ChatGPT, to have us believe that "AI has been solved and is a commodity, now" while all we got were more chatbots.
In the academic fields where this taxonomy matters, nothing much has changed with LLMs, or not more than with DNNs, SVMs, etc. Nobody that's been involved in ML research for more than 5 years seriously thinks "job's done pals, we got to pack it up, after 70 or so years of effort, we've finally figured it out, and that AI we were looking for, we got it".
In 1968 Marvin Minsky defined “artificial intelligence” as “the science of making machines do things that would require intelligence if done by men”
I suspect Alan Turing would also have disagreed with you, but to my knowledge he didn't actually use the precise words "artificial intelligence" so maybe we can disregard him.
But hey, your definition is much better established, right?
What you're proposing is in fact a brand new definition of AI. There are terms in use to describe what you want, AGI and ASI for example are more in that direction.
> But hey, your definition is much better established, right?
I don't like this manner of baselessly singling me out. Once again, there is no "my definition" of AI. Here is the one from wikipedia, that matches the academic definition with which I am aligned "It is a field of research in computer science that develops and studies methods and software that enable machines to perceive their environment and use learning and intelligence to take actions that maximize their chances of achieving defined goals."
My whole point is that the current AI-craze boils down to one single highly-specific method that got added the AI toolbox. It has clear shortcomings and limitations that major tech corporations hide or disguise knowingly, for their profit and our own deception. My take is that we collectively benefit from calling the current breed of AI products what they are: LLMs. For transparency's, correctness' and honesty toward consumers' sakes. The marketing around those products needs to be regulated accordingly.
Now I want to elaborate on why the commonly used taxonomy of AI, as formalised by scientists, or, what you call "my definition" is the only one that matters (with the practical consequence that nobody in the field uses the term "AI" without more qualifiers). Say, you develop a program to play tic-tac-toe. It is something whose complexity is low-enough that it has an analytical solution, with all the states of the game that can be stored and look-up. It could also be trained via convolutional neural networks, a family within machine learning, within AI, on lots of games. Would you call the resulting capability of playing tic-tac-toe "AI"? Theoretically yes, you must (and myself as well). Where we differ is in the importance of specifying how this ability is accomplished. The analytical implementation will be fully explainable, while the neural-network won't. In other words, we won't be able to tell whether the answer from the latter is faithful and desired, or subject to what we nowadays call "hallucination" in a turn of the tech giants to further anthropomorphise LLMs.
Now, maybe more about where I'm coming from. I researched computer vision algorithms for self-driving vehicles for several years. The trolley dilemma and other considerations about what it means for machines to behave "morally" has been ingrained through my formative and academic years. As Engineers and machine learning scientists, we have been evolving our field from predictable analytical methods to new ones with better results but less introspectability. I'm of the standpoint that this is only fine as long as the society is educated on those matters and willingly commits to using those methods with there trade-offs, for about the same reasons we put labels on food products and list known side-effects and their occurring changes in our medication. Because now those computational methods affect us in the real world in very material and tangible manners.
The only brand new definition of AI is the one that came in full marketing speed shortly after ChatGPT, to have us believe that "AI has been solved and is a commodity, now" while all we got were more chatbots.
In the academic fields where this taxonomy matters, nothing much has changed with LLMs, or not more than with DNNs, SVMs, etc. Nobody that's been involved in ML research for more than 5 years seriously thinks "job's done pals, we got to pack it up, after 70 or so years of effort, we've finally figured it out, and that AI we were looking for, we got it".
In 1968 Marvin Minsky defined “artificial intelligence” as “the science of making machines do things that would require intelligence if done by men”
I suspect Alan Turing would also have disagreed with you, but to my knowledge he didn't actually use the precise words "artificial intelligence" so maybe we can disregard him.
But hey, your definition is much better established, right?
What you're proposing is in fact a brand new definition of AI. There are terms in use to describe what you want, AGI and ASI for example are more in that direction.
> But hey, your definition is much better established, right?
I don't like this manner of baselessly singling me out. Once again, there is no "my definition" of AI. Here is the one from wikipedia, that matches the academic definition with which I am aligned "It is a field of research in computer science that develops and studies methods and software that enable machines to perceive their environment and use learning and intelligence to take actions that maximize their chances of achieving defined goals."
My whole point is that the current AI-craze boils down to one single highly-specific method that got added the AI toolbox. It has clear shortcomings and limitations that major tech corporations hide or disguise knowingly, for their profit and our own deception. My take is that we collectively benefit from calling the current breed of AI products what they are: LLMs. For transparency's, correctness' and honesty toward consumers' sakes. The marketing around those products needs to be regulated accordingly.
Now I want to elaborate on why the commonly used taxonomy of AI, as formalised by scientists, or, what you call "my definition" is the only one that matters (with the practical consequence that nobody in the field uses the term "AI" without more qualifiers). Say, you develop a program to play tic-tac-toe. It is something whose complexity is low-enough that it has an analytical solution, with all the states of the game that can be stored and look-up. It could also be trained via convolutional neural networks, a family within machine learning, within AI, on lots of games. Would you call the resulting capability of playing tic-tac-toe "AI"? Theoretically yes, you must (and myself as well). Where we differ is in the importance of specifying how this ability is accomplished. The analytical implementation will be fully explainable, while the neural-network won't. In other words, we won't be able to tell whether the answer from the latter is faithful and desired, or subject to what we nowadays call "hallucination" in a turn of the tech giants to further anthropomorphise LLMs.
Now, maybe more about where I'm coming from. I researched computer vision algorithms for self-driving vehicles for several years. The trolley dilemma and other considerations about what it means for machines to behave "morally" has been ingrained through my formative and academic years. As Engineers and machine learning scientists, we have been evolving our field from predictable analytical methods to new ones with better results but less introspectability. I'm of the standpoint that this is only fine as long as the society is educated on those matters and willingly commits to using those methods with there trade-offs, for about the same reasons we put labels on food products and list known side-effects and their occurring changes in our medication. Because now those computational methods affect us in the real world in very material and tangible manners.