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I've been annoyed by the redefinition of artificial intelligence since the LLM boom started. The term AI has no place being used to describe LLMs as far as I ca

I've been annoyed by the redefinition of artificial intelligence since the LLM boom started. The term AI has no place being used to describe LLMs as far as I can tell, unless what goes on inside the black box of an LLM is drastically different than how they are described to function. Predicting the next token based on a compressed dataset of human generated content isn't intelligence in any meaningful definition of the word. That doesn't mean LLMs aren't impressive or useful for certain tasks, but they aren't intelligent. When Altman describes them as reasoning machines he's either lying (likely for marketing purposes) or using a different definition of "reasoning" than most people would. The latest release of GPT is attempting to mimic reasoning, but what they're actually doing is having one system act as an automated prompt engineer in between the GPT model and the end user.

IanCal 2y ago
What redefinition? SVMs were part of AI and they're far simpler. The field of AI has covered basic algorithms for decades before LLMs.
- _heimdall 2y ago
  Do you consider a basic algorithm to be artificial intelligence? You are right though, you van go back further than LLMs and find misuses of the term "artificial intelligence." That doesn't contradict my main point though, that the word has been so redefined as to be pretty meaningless to the understanding of what intelligence is. If we want to consider even basic algorithms to be intelligence, are we boiling down the entire concept of intelligence to mathematical equations?
  - IanCal 2y ago
    > misuses of the term "artificial intelligence." If it's been the way the field has used it for decades, it's not really a misuse. > that the word has been so redefined It's not been redefined though, other than people now wanting to moan about PR and things not being "real" AI when we've had AGI as a term to use right there. > If we want to consider even basic algorithms to be intelligence, are we boiling down the entire concept of intelligence to mathematical equations? Massive side argument, but I think we obey physical laws and are not magical and so fundamentally I can't see another answer.
- HarHarVeryFunny 2y ago
  Not really - machine learning, whether SVMs or ANNs, was called just that until relatively recently when the popular press started to first call ANNs AI, then LLMs. At first there was pushback from ML researchers, but particularly with LLMs they are now embracing it since investors want to invest in "AI". LLMs are really just fancy (deep) pattern recognizers/predictors, conceptually not so different than rule-based expert systems like CYC, which was never called AI. Of course LLMs learn their own rules, which is extremely useful. Other than the pop press wanting to talk about futuristic AI, and investors wanting to invest in it, what also provides cover for LLMs as "AI", is that they are trained to predict/copy human training data, and so appear as smart/dumb as that is, even if they are really no smarter than Searle's Chinese room.
  - IanCal 2y ago
    > machine learning, whether SVMs or ANNs, was called just that until relatively recently when the popular press started to first call ANNs AI, That is absolutely not the case. These things have been in the field of AI for decades. Frighteningly it's nearing two decades since I started my degree in AI and it wasn't a new reference then.
    - HarHarVeryFunny 2y ago
      I remember taking Andrew Ng's Coursera ML course (incl. neural nets and SVMs) when it came out in 2011, and nobody, including him, was calling it AI at that time. I think it was sometime after neural nets really took off after ImageNet 2012 that the press started to call everything AI.
      - IanCal 2y ago
        The field of ai is far older than that, my degree was in artificial intelligence starting in 2005 so before the dnn boom with rbms (I was replicating them only in my masters, I think it was more 2008ish that became a bigger topic?)
        2y ago
Ukv 2y ago
> I've been annoyed by the redefinition of artificial intelligence since the LLM boom started If there's any redefinition, it's being pushed further out. AI was previously used to describe far simpler systems, like expert systems and Deep Blue's alpha–beta search. > Predicting the next token based on a compressed dataset of human generated content isn't intelligence in any meaningful definition of the word I'd claim generating the next token is a sufficiently general task such that success can depend on essentially arbitrary intellectual capabilities. For instance, reliably completing unseen equations like `2335 + 4612 = ` requires ability to perform basic arithmetic. > using a different definition of "reasoning" than most people would. The latest release of GPT is attempting to mimic reasoning I think most people initially have some relatively solid definitions of "learning", "reasoning", "language use", etc. similar to how it's being used there - just that when non-humans meet those definitions there's an inclination to create some distinction between "learning" and an elusive "actual learning". For instance, if something changes to refine its future behavior in response to its experiences (touch hot stove, get hurt, avoid in future) beyond the immediate/direct effect (withdrawing hand) then it can "learn". I think even small microorganisms can learn, with the main requirement being that it has some mutable state (can't learn if you can't change). Yet, others will object that "machine learning" is a misnomer because it's "not actual learning" and instead "just mimicking/simulating".
- AnimalMuppet 2y ago
  For to define "reasoning", you have to deal with (at least) the following sub-questions: 1. What is knowledge? 2. How can knowledge be encoded in a machine? LLMs say that knowledge is encoded in the relationships between words (and, in fact, has been by the corpus of human writing), and that's enough. Expert systems said that knowledge could be encoded in carefully-written rules, and that's enough. I'm pretty sure that any actually intelligent[1] computer is going to have to have more than one flavor of knowledge representation, and be able to shift between them as the situation warrants. [1] Whatever "actually intelligent" may mean. I don't have to know what it is, though, to recognize that what we have so far is inadequate.
  - Ukv 2y ago
MrMcCall 2y ago
You understand. Remember that fools can either be full of horsesh_t, where they don't know that what they believe and repeat is untrue, or bullsh_t, where they know they are lying and doing so for a particular reason. The first step to being a Dune-style truthsayer is to never lie. The deeper truth to that path (which is possible, but rarely travelled) is that it is possible, but we must purposefully seek ever deeper truths about truth and humanity. Our world's lack of this deep honesty, first about oneself and then about others, is a major source of our systemic problems. Another major source is selfishness, but I've discussed that elsewhere. Regardless, most people just love hearing the words flow out of their own mouths, and that tendency seems to be worse for successful tech guys or anyone with a bit of money or with self-righteous fake-religion guys.
FrustratedMonky 2y ago
But I also only write one word at a time. How am I predicting the next word? The latest neuroscience isn't as clear cut that humans aren't similar.
danaris 2y ago
The term "artificial intelligence" is still used, quite correctly, to refer to fully-deterministic algorithms controlling NPCs in video games of all types. The field of "artificial intelligence" still has "machine learning" (of which LLMs are a product) as part of it. The problem is not, and has never been, that the term "AI" was used incorrectly to describe LLMs. It's that people (like Altman) who almost certainly do know better started making marketing claims conflating them with "AGI" (aka "strong AI"), and pushing them as being genuinely "alive" and reasoning. Most of us in the tech field, and a lot of people outside of it (eg, most gamers) fully recognize that "AI" does not automatically mean Skynet. It takes active, deceptive work on the part of the people selling these systems to prime them to make that leap.

IanCal 2y ago

What redefinition? SVMs were part of AI and they're far simpler. The field of AI has covered basic algorithms for decades before LLMs.

_heimdall 2y ago
Do you consider a basic algorithm to be artificial intelligence? You are right though, you van go back further than LLMs and find misuses of the term "artificial intelligence." That doesn't contradict my main point though, that the word has been so redefined as to be pretty meaningless to the understanding of what intelligence is. If we want to consider even basic algorithms to be intelligence, are we boiling down the entire concept of intelligence to mathematical equations?
- IanCal 2y ago
  > misuses of the term "artificial intelligence." If it's been the way the field has used it for decades, it's not really a misuse. > that the word has been so redefined It's not been redefined though, other than people now wanting to moan about PR and things not being "real" AI when we've had AGI as a term to use right there. > If we want to consider even basic algorithms to be intelligence, are we boiling down the entire concept of intelligence to mathematical equations? Massive side argument, but I think we obey physical laws and are not magical and so fundamentally I can't see another answer.
HarHarVeryFunny 2y ago
Not really - machine learning, whether SVMs or ANNs, was called just that until relatively recently when the popular press started to first call ANNs AI, then LLMs. At first there was pushback from ML researchers, but particularly with LLMs they are now embracing it since investors want to invest in "AI". LLMs are really just fancy (deep) pattern recognizers/predictors, conceptually not so different than rule-based expert systems like CYC, which was never called AI. Of course LLMs learn their own rules, which is extremely useful. Other than the pop press wanting to talk about futuristic AI, and investors wanting to invest in it, what also provides cover for LLMs as "AI", is that they are trained to predict/copy human training data, and so appear as smart/dumb as that is, even if they are really no smarter than Searle's Chinese room.
- IanCal 2y ago
  > machine learning, whether SVMs or ANNs, was called just that until relatively recently when the popular press started to first call ANNs AI, That is absolutely not the case. These things have been in the field of AI for decades. Frighteningly it's nearing two decades since I started my degree in AI and it wasn't a new reference then.
  - HarHarVeryFunny 2y ago
    I remember taking Andrew Ng's Coursera ML course (incl. neural nets and SVMs) when it came out in 2011, and nobody, including him, was calling it AI at that time. I think it was sometime after neural nets really took off after ImageNet 2012 that the press started to call everything AI.
    - IanCal 2y ago
      The field of ai is far older than that, my degree was in artificial intelligence starting in 2005 so before the dnn boom with rbms (I was replicating them only in my masters, I think it was more 2008ish that became a bigger topic?)
      - 2y ago

Ukv 2y ago

> I've been annoyed by the redefinition of artificial intelligence since the LLM boom started If there's any redefinition, it's being pushed further out. AI was previously used to describe far simpler systems, like expert systems and Deep Blue's alpha–beta search. > Predicting the next token based on a compressed dataset of human generated content isn't intelligence in any meaningful definition of the word I'd claim generating the next token is a sufficiently general task such that success can depend on essentially arbitrary intellectual capabilities. For instance, reliably completing unseen equations like `2335 + 4612 = ` requires ability to perform basic arithmetic. > using a different definition of "reasoning" than most people would. The latest release of GPT is attempting to mimic reasoning I think most people initially have some relatively solid definitions of "learning", "reasoning", "language use", etc. similar to how it's being used there - just that when non-humans meet those definitions there's an inclination to create some distinction between "learning" and an elusive "actual learning". For instance, if something changes to refine its future behavior in response to its experiences (touch hot stove, get hurt, avoid in future) beyond the immediate/direct effect (withdrawing hand) then it can "learn". I think even small microorganisms can learn, with the main requirement being that it has some mutable state (can't learn if you can't change). Yet, others will object that "machine learning" is a misnomer because it's "not actual learning" and instead "just mimicking/simulating".

AnimalMuppet 2y ago
For to define "reasoning", you have to deal with (at least) the following sub-questions: 1. What is knowledge? 2. How can knowledge be encoded in a machine? LLMs say that knowledge is encoded in the relationships between words (and, in fact, has been by the corpus of human writing), and that's enough. Expert systems said that knowledge could be encoded in carefully-written rules, and that's enough. I'm pretty sure that any actually intelligent[1] computer is going to have to have more than one flavor of knowledge representation, and be able to shift between them as the situation warrants. [1] Whatever "actually intelligent" may mean. I don't have to know what it is, though, to recognize that what we have so far is inadequate.
- Ukv 2y ago

MrMcCall 2y ago

You understand. Remember that fools can either be full of horsesh_t, where they don't know that what they believe and repeat is untrue, or bullsh_t, where they know they are lying and doing so for a particular reason. The first step to being a Dune-style truthsayer is to never lie. The deeper truth to that path (which is possible, but rarely travelled) is that it is possible, but we must purposefully seek ever deeper truths about truth and humanity. Our world's lack of this deep honesty, first about oneself and then about others, is a major source of our systemic problems. Another major source is selfishness, but I've discussed that elsewhere. Regardless, most people just love hearing the words flow out of their own mouths, and that tendency seems to be worse for successful tech guys or anyone with a bit of money or with self-righteous fake-religion guys.

FrustratedMonky 2y ago

But I also only write one word at a time. How am I predicting the next word? The latest neuroscience isn't as clear cut that humans aren't similar.

danaris 2y ago

The term "artificial intelligence" is still used, quite correctly, to refer to fully-deterministic algorithms controlling NPCs in video games of all types. The field of "artificial intelligence" still has "machine learning" (of which LLMs are a product) as part of it. The problem is not, and has never been, that the term "AI" was used incorrectly to describe LLMs. It's that people (like Altman) who almost certainly do know better started making marketing claims conflating them with "AGI" (aka "strong AI"), and pushing them as being genuinely "alive" and reasoning. Most of us in the tech field, and a lot of people outside of it (eg, most gamers) fully recognize that "AI" does not automatically mean Skynet. It takes active, deceptive work on the part of the people selling these systems to prime them to make that leap.