Earlier quoted context omitted.
i think it can figure it out.
Yes but why should it?
Because that's the requirement to be allowed to drive on a public road.
Four Fallacies of Modern AI
61–70 of 96 posts
Re: Four Fallacies of Modern AI
#62This article seems to fall straight into the trap it aims to warn us about. All this talk about "true" understanding, embodiment, etc. is needless antropomorphizing. A much better framework for thinking about intelligence is simply as the ability to make predictions about the world (including conditional ones like "what will happen if we take this action"). Whether it's achieved through "true understanding" (however…
From a practical standpoint, all the talk of "true understanding", "sentience" and the likes is pointless.
The only real and measurable thing is performance. And the performance of AI systems only goes up.
Re: Four Fallacies of Modern AI
#63The author suggests common sense and reasoning is unavoidable traits that are fundamental to humans.
That is also a fallacy from being too immersed in a professional environment filled with deep reasoning and a deep rooted tradition of logic.
In the greater human civilization you will find an abundance of individuals lacking both reasoning and common sense.
Re: Four Fallacies of Modern AI
#64I think the Stochastic Parrots idea is pretty outdated and incorrect. LLMs are not parrots, we don't even need them to parrot, we already have perfect copying machines. LLMs are working on new things, that is their purpose, reproducing the same thing we already have is not worth it. The core misconception here is that LLMs are autonomous agents parroting away. No, they are connected to humans, tools, reference data,…
I think you are on to something. Chasing AGI is - I believe - ultimately useless endeavour, but we can already use the existing tools we have in ingenious and creative ways. And no I don’t mean endless barrage of AI lofi hip hop or the same ”cool” album cover with random kanji that all of them have. For instance, it is pretty amazing to have a private tutor which with you can discuss why Charles XII of Sweden ultimately failed in his war against Russia or why roughly 30% of people seems to have a personality that leans toward authoritanianism - this is how people have learned since the very beginning of language. But conversation is an art and you get out from it what you bring into it. It also does not give you a readymade result which you can immediatedly capitalise on, which is what investors want, but what could and can ultimately be useful to humanity.
However, almost all models (worst is ChatGPT) are made virtually useless in this respect, since they are basically sycophantic yesmen - why on earth does an ”autocorrect on steroids” pretend to laugh at my jokes?
Next step is not to built faster models or throw more computing power at them, bit to learn to play the piano.
Re: Four Fallacies of Modern AI
#65This article seems to fall straight into the trap it aims to warn us about. All this talk about "true" understanding, embodiment, etc. is needless antropomorphizing. A much better framework for thinking about intelligence is simply as the ability to make predictions about the world (including conditional ones like "what will happen if we take this action"). Whether it's achieved through "true understanding" (however…
From a practical standpoint, all the talk of "true understanding", "sentience" and the likes is pointless. The only real and measurable thing is performance. And the performance of AI systems only goes up.
But only goes up in the sense that it's getting closer to a horizontal asymptote. Which is not really that good.
Re: Four Fallacies of Modern AI
#66I think the Stochastic Parrots idea is pretty outdated and incorrect. LLMs are not parrots, we don't even need them to parrot, we already have perfect copying machines. LLMs are working on new things, that is their purpose, reproducing the same thing we already have is not worth it. The core misconception here is that LLMs are autonomous agents parroting away. No, they are connected to humans, tools, reference data,…
You can shuffle a deck of 52 cards, and be reasonably confident that nobody has ever gotten that exact shuffle (or probably ever will, until the universe dies). But at least in this case, we are sure that a deck of 52 cards can be arranged in any permutation of 52 cards. We know we can reach any state from any other state.
This is not the case for LLMs. We don't know what the full state space looks like. Just because the state space that LLMs (lossily) compress, is unimaginably huge, doesn't mean that you can assume that the state you want is one of them. So yeah, you might get a string of symbols that nobody has seen before, but you still have no way of knowing whether A) it's the string of symbols you wanted, and B) if it isn't, whether the string of symbols you wanted can ever be generated by the network at all.
Re: Four Fallacies of Modern AI
#67> But that still leaves a crucial question: can we develop a more precise, less anthropomorphic vocabulary to describe AI capabilities? Or is our human-centric language the only tool we have to reason about these new forms of intelligence, with all the baggage that entails? I don't get the problem with this really. I think LLM's "reasoning" is a very fair and proper way to call it. It takes time and spits out tokens…
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Re: Four Fallacies of Modern AI
#68This article seems to fall straight into the trap it aims to warn us about. All this talk about "true" understanding, embodiment, etc. is needless antropomorphizing. A much better framework for thinking about intelligence is simply as the ability to make predictions about the world (including conditional ones like "what will happen if we take this action"). Whether it's achieved through "true understanding" (however…
"Making predictions about the world" is a reductive and childish way to describe intelligence in humans. Did David Lynch make Mulholland Drive because he predicted it would be a good movie? The most depressing thing about AI summers is watching tech people cynically try to define intelligence downwards to excuse failures in current AI.
Well yes, any creation tries to anticipate some reaction, be it audience, environment, or only the creators one.
A prediction is just a reaction to a present state, which is the simplest definition of intelligence: The ability to (sense and) react to something. I like to use this definition, instead of "being able to predict", because its more generic.
The more sophisticated (and directed) the reaction is, the more intelligent the system must be. Following this logic, even a traffic light is intelligent, at least more intelligent than a simple rock.
From that perspective, the question of why a creator produced a piece of art becomes unimportant to determine intelligence, since the simple fact that he did is sign of intelligence already.
Re: Four Fallacies of Modern AI
#69Earlier quoted context omitted.
From a practical standpoint, all the talk of "true understanding", "sentience" and the likes is pointless. The only real and measurable thing is performance. And the performance of AI systems only goes up.
But only goes up in the sense that it's getting closer to a horizontal asymptote. Which is not really that good.
It does, but the limit isn't "human performance". AI isn't bounded by human performance. The limit is the saturation of the benchmark in question.
Which is solvable with better benchmarks.
Re: Four Fallacies of Modern AI
#70Earlier quoted context omitted.
"Making predictions about the world" is a reductive and childish way to describe intelligence in humans. Did David Lynch make Mulholland Drive because he predicted it would be a good movie? The most depressing thing about AI summers is watching tech people cynically try to define intelligence downwards to excuse failures in current AI.
> Did David Lynch make Mulholland Drive because he predicted it would be a good movie? He made it because he predicted that it will have some effects enjoyable to him. Without knowing David Lynch personally I can assume that he made it because he predicted other people will like it. Although of course, it might have been some other goal. But unless he was completely unlike anyone I've ever met, it's safe to assume th…
> how soon an AI will be able to make a movie that you, AIPedant, will enjoy as much as you've enjoyed Mulholland Drive
As it stands, AI is a tool and requires artists/individuals to initiate a process. How many AI made artifacts do you know that enjoy the same cultural relevance as their human made counterparts? Novels, music, movies, shows, games... anything?
You're arguing that the types of film cameras play some part in the significant identity that makes Mulholland Drive a work of art, and I'd disagree. While artists/individuals might gain cultural recognition, the tool on its own rarely will. A tool of choice can be an inspiration for a work and gain a certain significance (e.g. the Honda CB77 Super Hawk[0]), but it seems that people always strive to look for the human individual behind any process, as it is generally accepted that the complete body of works tells a different story that any one artifact ever can.
Marcel Duchamp's Readymade[1] (and the mere choice of the artist) gave impact to this cultural shift more than a century ago, and I see similarities in economic and scientific efforts as well. Apple isn't Apple without the influence of a "Steve Jobs" or a "Jony Ive" - people are interested in the individuals behind companies and institutions, while at the same time also tend to underestimate the amount of individuals that makes any work an artifact - but that's a different topic.
If some future form of AI will transcend into a sentient object that isn't a plain tool anymore, I'd guess (in stark contrast to popular perception) we'll all lose interest rather quickly.
[0]: https://en.wikipedia.org/wiki/Honda_CB77#Zen_and_the_Art_of_...