3 ms·

The word AI has come to mean machine learning of any kind, which definitely exists. Maybe you’re thinking of AGI (artificial general intelligence)?

olyjohn 4y ago
AGI, you mean what we used to call... AI?
- brookst 4y ago
  The AGI term came about to distinguish a certain level of capability, just alike ASI is appearing now. I don't see a problem with terminology getting more refined as a field matures. We used to have "cars" and "trucks." These days we have SUVs and sports cars and crossovers and who knows what else. Yes, they used to be "cars". People still find it useful to have more granular categories.
  - thomastjeffery 4y ago
    Yes, but in this case, the word is getting less defined. "AI" used to mean one thing, but now it doesn't, because "AGI" took its place. At no point, however, was this distinction made literal. It can only be found in the surrounding context.
    - brookst 4y ago
      I'd argue that "AI" used to mean a whole universe of things, from self-optimizing rice cookers to computers that figure out the meaning of life, the universe, and everything. Today AI remains a catch-all, but having more narrowly scoped terms like ML, AGI, and ASI lets people talk without tripping over whether or not the rice cooker is going to turn us all into paperclips.
thomastjeffery 4y ago
Yes, but it's still misleading. As soon as someone says the words, "an AI", they have entered a narrative about the thing personified. The original subject is lost to an anthropomorphized facade. The adjective, "General" was originally meant as a classifier for quality, not category. That distinction is still present on the face of the word itself. An AGI is an AI that can compete with human intelligence. Adding "General" to the phrase, "Artificial General Intelligence" does nothing to define "Artificial" or "Intelligence". The distinction I just made is entirely based on symbolic meaning. Words are defined explicitly, and then put together. In this approach, the resulting meaning is independent of context: it's literal. Your distinction takes the opposite approach: an entire phrase - regardless of any symbolic definitions each word is known to have - is defined by the narrative it is surrounded with. In this approach, the resulting meaning is dependent on the context: it's literary. Large Language Models are famous for demonstrating the second approach: they implicitly model whatever patterns exist in a prompt's text, so that they can generate a "continuation" that follows those patterns, without ever needing a literal symbolic definition. This is exciting, because natural language - as the result of humans taking both literal and literary approaches - is ambiguous. The literal approach that traditional programming language parsers use is fundamentally limited to "context-free grammars", because ambiguity can only be resolved with literary context. The problem is that LLMs cannot be literal. They are only capable of inference, so they lose all of the functionally that symbolic definition would provide. The distinction I made earlier has the same limitation that parsers have: it ignores the surrounding narrative context that has evolved away from the original meaning: it's short-sighted. The distinction you made has the same limitation that LLMs have: it ignores the original symbolic meaning that is still present in each word: it's confidently imprecise. What matters is the result: By ignoring the literary context, the definition of "AI" is written in stone, and we lose the ability to talk around it. By ignoring the literal meaning, we imply "AI" tech is (by definition) capable of both literal and literary transformations. We can resolve both limitations by simply using new words. There is no need to say "AI" when "NLP" is more precise.
- circuit10 4y ago
  What does personification have to do with it? I never mentioned that. Are you falling into the “only people are intelligent” thing that I keep seeing? ChatGPT is clearly intelligent, just not as intelligent as a human I don’t see a problem with the term AI, it’s both artificial and intelligent
  - thomastjeffery 4y ago
    Did you even read my comment?
    - circuit10 4y ago
      I skimmed it, did I miss something?
      - thomastjeffery 4y ago
        Evidently.
ted_dunning 4y ago
The word AI has always meant "the intelligent system that we would like to build". Historically, as soon as the system actually works for some task, it gets a new name. Thus expert systems, neural networks, logistic regression were all supposedly AI at one time and now are distinct. Already, we see a bit of a divergence in nomenclature where people distinguish large language models from other classes of techniques. Of course, up to now, it has mostly been techies who have used, abused and redefined the term. More recently, marketing types have adopted the work AI so it may lose all meaning soon.

olyjohn 4y ago

AGI, you mean what we used to call... AI?

brookst 4y ago
The AGI term came about to distinguish a certain level of capability, just alike ASI is appearing now. I don't see a problem with terminology getting more refined as a field matures. We used to have "cars" and "trucks." These days we have SUVs and sports cars and crossovers and who knows what else. Yes, they used to be "cars". People still find it useful to have more granular categories.
- thomastjeffery 4y ago
  Yes, but in this case, the word is getting less defined. "AI" used to mean one thing, but now it doesn't, because "AGI" took its place. At no point, however, was this distinction made literal. It can only be found in the surrounding context.
  - brookst 4y ago
    I'd argue that "AI" used to mean a whole universe of things, from self-optimizing rice cookers to computers that figure out the meaning of life, the universe, and everything. Today AI remains a catch-all, but having more narrowly scoped terms like ML, AGI, and ASI lets people talk without tripping over whether or not the rice cooker is going to turn us all into paperclips.

thomastjeffery 4y ago

Yes, but it's still misleading. As soon as someone says the words, "an AI", they have entered a narrative about the thing personified. The original subject is lost to an anthropomorphized facade. The adjective, "General" was originally meant as a classifier for quality, not category. That distinction is still present on the face of the word itself. An AGI is an AI that can compete with human intelligence. Adding "General" to the phrase, "Artificial General Intelligence" does nothing to define "Artificial" or "Intelligence". The distinction I just made is entirely based on symbolic meaning. Words are defined explicitly, and then put together. In this approach, the resulting meaning is independent of context: it's literal. Your distinction takes the opposite approach: an entire phrase - regardless of any symbolic definitions each word is known to have - is defined by the narrative it is surrounded with. In this approach, the resulting meaning is dependent on the context: it's literary. Large Language Models are famous for demonstrating the second approach: they implicitly model whatever patterns exist in a prompt's text, so that they can generate a "continuation" that follows those patterns, without ever needing a literal symbolic definition. This is exciting, because natural language - as the result of humans taking both literal and literary approaches - is ambiguous. The literal approach that traditional programming language parsers use is fundamentally limited to "context-free grammars", because ambiguity can only be resolved with literary context. The problem is that LLMs cannot be literal. They are only capable of inference, so they lose all of the functionally that symbolic definition would provide. The distinction I made earlier has the same limitation that parsers have: it ignores the surrounding narrative context that has evolved away from the original meaning: it's short-sighted. The distinction you made has the same limitation that LLMs have: it ignores the original symbolic meaning that is still present in each word: it's confidently imprecise. What matters is the result: By ignoring the literary context, the definition of "AI" is written in stone, and we lose the ability to talk around it. By ignoring the literal meaning, we imply "AI" tech is (by definition) capable of both literal and literary transformations. We can resolve both limitations by simply using new words. There is no need to say "AI" when "NLP" is more precise.

circuit10 4y ago
What does personification have to do with it? I never mentioned that. Are you falling into the “only people are intelligent” thing that I keep seeing? ChatGPT is clearly intelligent, just not as intelligent as a human I don’t see a problem with the term AI, it’s both artificial and intelligent
- thomastjeffery 4y ago
  Did you even read my comment?
  - circuit10 4y ago
    I skimmed it, did I miss something?
    - thomastjeffery 4y ago
      Evidently.

ted_dunning 4y ago

The word AI has always meant "the intelligent system that we would like to build". Historically, as soon as the system actually works for some task, it gets a new name. Thus expert systems, neural networks, logistic regression were all supposedly AI at one time and now are distinct. Already, we see a bit of a divergence in nomenclature where people distinguish large language models from other classes of techniques. Of course, up to now, it has mostly been techies who have used, abused and redefined the term. More recently, marketing types have adopted the work AI so it may lose all meaning soon.