Yann LeCun argues AGI is nonsense, pointing to failure of robotics and autonomous systems to match animal intelligence

It's not merely physical agents, it's anything that deals with something else than sequences of discrete symbols.

Any data modality that is high-dimensional, continuous, and possibly noisy is completely out of reach of current generative models.

That includes pretty much all real-world signals (aside from human language, computer languages and mathematics).

Furthermore, you can't have reliable agents unless they have the ability to predict the consequences of their actions and plan accordingly. LLMs simply don't.

@ylecun @andrewgwils What is weird is that you have to spell this out when every AI bro can see it.

Their reality denial is handled by redefining what AGI means. It is now about solving artificial and contaminated LLM benchmarks.

If @ylecun can really make a 10yr old a first-time listener, this will dwarf his contributions to machine learning. Sign me up for the alpha!

@ylecun @andrewgwils I partially agree with Yann here. But I do think we are on the precipice or near car AGI

@caleb_shepherd @andrewgwils 1. Today is a holiday 2. I suggest you look up how long it takes to go from a research concept to a technology breakthrough and a deployed product, and learn what the process involves.

@ylecun @andrewgwils Intellegence is likely nonsense too, then. What is intellegence exactly? Until we agree to a common definition, how can we suggest something is solved?

Based on Tesla FSD v14, we are very close to Level 5 self-driving cars. 1-5 years.

Whether or not it learned it in a few hours is irrelevant to the user.

But your broader point about the G is correct.

@ylecun @andrewgwils The letter G looks like an arrow being diverted too

@ylecun Yes, physical agents are not generally competent yet, and certainly new paradigms are needed for significant further advances in AI. But autonomous vehicles are now generally safer than human drivers under typical driving conditions.

@ChrissGPT @ylecun @andrewgwils Stop saying: "something AGI". Car AGI, coding AGI, Math AGI. AGI by definition has to be general otherwise it's just a "narrow" AI that is good at one specific task. That means it's not able to transfer skills to multiple domains and lack holistic understanding of its environment

@ylecun @andrewgwils If the G is nonsense, what’s the right term for what a teenager has that our best systems don’t?

@BrianCHolt @ylecun @andrewgwils That's my point really. Much of what I see people arguing online is just due to their differences in what something actually means. It gets a little ironic when discussing LLMs.

@ylecun @andrewgwils Precalculated optimal path that doesn't have temporal awareness. Am I wrong?

@fanofaliens @ylecun @andrewgwils The space within the G looks like a hook

@joshfeingold @ylecun @andrewgwils Isn't this assuming the conclusion about what Intelligence is? As in, based on a given definition from computer science, since it doesn't work from that perspective, then the whole construct is suspect?

@ylecun @andrewgwils language models are just the language interface to AI

visual, general machine learning, sensor/vector models ... all of those are forms of AI and an "AGI", if anyone chose to build one, would have access to all of them

@tpgoebel @BrianCHolt @ylecun @andrewgwils Not everything. ;)

@logisti00 @ylecun @andrewgwils Predictive text generation does not equate to comprehension; addressing novel challenges necessitates an architecture capable of genuine problem understanding.

@ylecun @andrewgwils world economy will collapse because of this

@ylecun @andrewgwils Fair point. But you’ve argued this for years. Where are the robots and learning systems that prove your approach? Criticizing “AGI” is easier than demonstrating a better alternative.