5 ms·
This article seems to fall straight into the trap it aims to warn us about. All this talk about "true" understanding, embodiment, etc. is needless antropomorphi
by entropyneur 1y ago
This 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 you define it; I personally doubt you can) or "mimicking" bears no relevance for most of the questions about the impact of AI we are trying to answer.
- wagwang 1y agoPredict and create, that's all that matters.
- cantor_S_drug 1y agoImagine LLM is conscious (as Anthropic wants us to believe). Imagine LLM is made to train on so much data which is far beyond what its parameter count allows for. Am I hurting the LLM by causing it intensive cognitive strain?
- adastra22 1y agoWhy would that hurt?
- cantor_S_drug 1y agoYou are made to memorize entire encyclopedia but you have biological limit of only 1000 facts.
- entropyneur 1y agoI agree that whether AI is conscious is an important question. In fact, I think it's the most important question above our own existential crisis. Unfortunately, it's also completely hopeless at our current level of knowledge.
- AIPedant 1y ago"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.
- simianwords 1y ago"David Lynch made Mullholland Drive because he was intelligent" is also absurd.
- peterashford 1y agoBut "An intelligent creature made Mullholland Drive" is not
- koonsolo 1y agoI look at it the complete opposite way: humans are defining intelligence upwards to make sure they can perceive themselves better than a computer. It's clear that humans consider humans as intelligent. Is a monkey intelligent? A dolphin? A crow? An ant? So I ask you, what is the lowest form of intelligence to you? (I'm also a huge David Lynch fan by the way :D)
- peterashford 1y agoIm not sure what that gets you. I think most people would suggest that it appears to be a sliding scale. Humans, dolphins / crows, ants, etc. What does that get us?
- keiferski 1y agoIt matters if your civilizational system is built on assigning rights or responsibilities to things because they have consciousness or "interiority." Intelligence fits here just as well. Currently many of our legal systems are set up this way, if in a fairly arbitrary fashion. Consider for example how sentience is used as a metric for whether an animal ought to receive additional rights. Or how murder (which requires deliberate, conscious thought) is punished more harshly than manslaughter (which can be accidental or careless.) If we just treat intelligence as a descriptive quality and apply it to LLMs, we quickly realize the absurdity of saying a chatbot is somehow equivalent, consciously, to a human being. At least, to me it seems absurd. And it indicates the flaws of grafting human consciousness onto machines without analyzing why.
- ACCount37 1y agoFrom 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.
- vrighter 1y agoBut only goes up in the sense that it's getting closer to a horizontal asymptote. Which is not really that good.
- ACCount37 1y agoIt 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.
- DonaldFisk 1y agoI think that intelligence requires, or rather, is the development and use of a model of the problem while the problem is being solved, i.e. it involves understanding the problem. Accurate predictions, based on extrapolations made by systems trained using huge quantities of data, are not enough.