How to ruin the entire Internet, software engineering and personal servers just because some billionaire man children watched Star Trek 30 years ago.
They are coming for everyone, including those who promote this AI nonsense here and who will be disposed of first, because they are the least competent.
I'm a software engineer, and I don't write code anymore. I'm still coming to terms with that, grieving the loss of my old career and getting used to the new career which is more like a technical lead and product person than a computer programmer.
And no, that doesn't make me some kind of "AI hater", or someone unable to see value in LLMs.
There’s a reason the term AGI is used a lot now. What are LLMs if not intelligence that is artificial? Just today I used it to debug code, write a shader (which, to be fair, it’s only slightly better than me at doing), and tell my daughter and I what hedgehogs and foxes eat. Seems pretty intelligent to me.
Of course, not long ago we were using the term largely for things that were basically big chains of conditional statements.
> Seems pretty intelligent to me.
Convenient? Yes. Intelligent? I mean, you can redefine AI to be whatever you want by lowering this bar however low you want. LLMs gave us slightly more convincing chatbots than prev-gen and nobody then would call them intelligent. The only reason we do now is marketing. It's laughable to me that we are still calling that something that's so obviously unable to push back on trivially impossible requests.
> Of course, not long ago we were using the term largely for things that were basically big chains of conditional statements.
No, we weren't? What are you even talking about? We had already built large-enough artificial neural networks whose outcomes couldn't be trivially explained in the 70's. Nobody with a sane mind and an understanding of what they were would call them oracles or intelligent devices. That's where the true genius of Altman lies.
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".
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.
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.
That ship has sailed, you're the one who has to adapt to the usage of the word in the world, not the world to you.