Earlier quoted context omitted.
Says the bag of lipids and proteins :)
Mostly water, actually.
Typical. You know they pump the chickens at the grocery store too.
Yann LeCun raises $1B to build AI that understands the physical world
451–460 of 529 posts
Re: Yann LeCun raises $1B to build AI that understands the physical world
#452Earlier quoted context omitted.
> The Wason selection task is the classic example: most people fail a simple conditional reasoning problem unless it’s dressed up in familiar social context, like catching cheaters. I've never heard about the Wason selection task, looked it up, and could tell the right answer right away. But I can also tell you why: because I have some familiarity with formal logic and can, in your words, pattern-match the gotcha tha…
Agree with much of your comment. Though note that as GP said, on the Wason selection task, people famously do much better when it's framed in a social context. That at least partially undermines your theory that its lack of familiarity with the terminology of formal logic.
Maybe the social version just creates a context where "if x then y" obviously does not include "if not x then not y". Everyone knows people over the drinking age can drink both alcoholic and non-alcoholic drinks, so you obviously don't have to check the person drinking the soft drink to make sure they aren't an adult.
Re: Yann LeCun raises $1B to build AI that understands the physical world
#453Earlier quoted context omitted.
> I don't understand this view. How I see it the fundamental bottleneck to AGI is continual learning and backpropagation. Models today are static, and human brains don't learn or adapt themselves with anything close to backpropagation. Even with continuous backpropagation and "learning", enriching the training data, so called online-learning, the limitations will not disappear. The LLMs will not be able to conclude t…
Models don't care. They aren't alive. This is the source of the chasm between here and AGI. You have to fear death to reason about the world and how to behave in it. I guess I just always thought it was obvious that you can't do better than nature. You can do different things, sure, but if a society of unique individuals wasn't the most effective way of making progress, nature itself would not have chosen it. So in a…
"You have to fear death to reason about the world and how to behave in it."
You're onto something there.
If everyone knew they were to die tomorrow, all of a sudden they'd choose to act differently. There is no logical thought process that determines that - it's something else. Something we can't concretely point toward as an object.
Re: Yann LeCun raises $1B to build AI that understands the physical world
#454Earlier quoted context omitted.
> I don't understand this view. How I see it the fundamental bottleneck to AGI is continual learning and backpropagation. Models today are static, and human brains don't learn or adapt themselves with anything close to backpropagation. Even with continuous backpropagation and "learning", enriching the training data, so called online-learning, the limitations will not disappear. The LLMs will not be able to conclude t…
Humans are notoriously bad at formal logic. The Wason selection task is the classic example: most people fail a simple conditional reasoning problem unless it’s dressed up in familiar social context, like catching cheaters. That looks a lot more like pattern matching than rule application. Kahneman’s whole framework points the same direction. Most of what people call “reasoning” is fast, associative, pattern-based. T…
I remember reading about this in a book, 'The enigma of reason', basically it was saying that reasoning was exactly that, we decided and then we came up with a reason for what we had decided and usually not the other way around.
This is because, the 'reasoning' part of our brain came from evolution when we started to communicate with others, we needed to explain our behaviour.
Which is fascinating if you think of the implications of that. In the most part we think we are being logical, but in reality we are pattern matching/impulsive and using our reasoning/logic to come up for excuses for why we have chosen what we had already decided.
It explains a lot about the world and why it's so hard to reason with someone, we are assuming the decision came from reason in the first place, which when you look at such peoples choices, makes sense as it's clear it didn't.
Re: Yann LeCun raises $1B to build AI that understands the physical world
#455Earlier quoted context omitted.
Quoting the Wikipedia article's formulation of the task for clarity: > You are shown a set of four cards placed on a table, each of which has a number on one side and a color on the other. The visible faces of the cards show 3, 8, blue and red. Which card(s) must you turn over in order to test that if a card shows an even number on one face, then its opposite face is blue? Confusion over the meaning of 'if' can only…
People in everyday life are not evaluating rules. They evaluate cases, for whether a case fits a rule. So, when being told: "Which card(s) must you turn over in order to test that if a card shows an even number on one face, then its opposite face is blue?" they translate it to: "Check the cards that show an even number on one face to see whether their opposite face is blue and vice versa" Based on this, many would na…
Exactly. We invented rule-based machines so that we could have a thing that follows rules, and adheres strictly to them, all day long.
Im not sure why people keep comparing machine-behaviour to human's. Its like Economic models that assume perfect rationality... yeah that's not reality mate.
Re: Yann LeCun raises $1B to build AI that understands the physical world
#456Earlier quoted context omitted.
> The Wason selection task is the classic example: most people fail a simple conditional reasoning problem unless it’s dressed up in familiar social context, like catching cheaters. I've never heard about the Wason selection task, looked it up, and could tell the right answer right away. But I can also tell you why: because I have some familiarity with formal logic and can, in your words, pattern-match the gotcha tha…
Quoting the Wikipedia article's formulation of the task for clarity: > You are shown a set of four cards placed on a table, each of which has a number on one side and a color on the other. The visible faces of the cards show 3, 8, blue and red. Which card(s) must you turn over in order to test that if a card shows an even number on one face, then its opposite face is blue? Confusion over the meaning of 'if' can only…
It also doesn't explain why people don't think it necessary to check the 3 to make sure it's not blue (which it would be if "if" meant "if and only if").
Re: Yann LeCun raises $1B to build AI that understands the physical world
#457Justifiable. There are a lot more degrees of freedom in world models. LLMs are fundamentally capped because they only learn from static text -- human communications about the world -- rather than from the world itself, which is why they can remix existing ideas but find it all but impossible to produce genuinely novel discoveries or inventions. A well-funded and well-run startup building physical world models (ground…
> LLMs are fundamentally capped because they only learn from static text -- human communications about the world -- rather than from the world itself, which is why they can remix existing ideas but find it all but impossible to produce genuinely novel discoveries or inventions.
This seems wrong to me on a few levels.
First, there is no way to "experience the world directly," all experience is indirect, and language is a very good way of describing the world. If language was a bad choice or limited in some fundamental way, LLMs wouldn't work as well as they do.
Second, novel ideas are often existing ideas remixed. It's hard/impossible to point to any single idea that sprung from nowhere.
Third, you can provide an LLM with real-world information and suddenly it's "interacting with the world". If I tell an LLM about the US war on Iran, I am in a very real sense plugging it into the real world, something that isn't part of its training data.
Finally, modern LLMs are multi-modal, meaning they have the ability to handle images/video. My understanding is that they use some kind of adapter to turn non-text data into data that the LLM can make sense of.
Re: Yann LeCun raises $1B to build AI that understands the physical world
#458Earlier quoted context omitted.
Quoting the Wikipedia article's formulation of the task for clarity: > You are shown a set of four cards placed on a table, each of which has a number on one side and a color on the other. The visible faces of the cards show 3, 8, blue and red. Which card(s) must you turn over in order to test that if a card shows an even number on one face, then its opposite face is blue? Confusion over the meaning of 'if' can only…
People in everyday life are not evaluating rules. They evaluate cases, for whether a case fits a rule. So, when being told: "Which card(s) must you turn over in order to test that if a card shows an even number on one face, then its opposite face is blue?" they translate it to: "Check the cards that show an even number on one face to see whether their opposite face is blue and vice versa" Based on this, many would na…
Yeah maybe if you phrased it as "Which card(s) must you turn over in order to ensure that all odd-numbered cards are blue?" you'd get a better response?
Re: Yann LeCun raises $1B to build AI that understands the physical world
#459Earlier quoted context omitted.
I think we - in last few months - are very close to, if not already at, the point where "coding" is solved. That doesn't mean that software design or software engineering is solved, but it does mean that a SOTA model like GPT 5.4 or Opus 4.6 has a good chance of being able to code up a working version of whatever you specify, with reason. What's still missing is the general reasoning ability to plan what to build or…
Sure, Claude and other SOTA LLMs do generate about 90% of my code but I feel like we are not closer to solving the last 10% than we were a year ago in the days of Claude 3.7. It can pretty reliably get 90% there and then I can either keep prompting it to get the rest done or just do it manually which is quite often faster.
It's interesting that people don't seem to think the likely outcome might be... capital and labour. Not capital alone.
You see this in construction - the capital is used for certain things and is operated by labour.
Re: Yann LeCun raises $1B to build AI that understands the physical world
#460Earlier quoted context omitted.
Putting stuff you have learned into a markdown file is a very "shallow" version of continual learning. It can remember facts, yes, but I doubt a model can master new out-of-distribution tasks this way. If anything, I think that Google's Titans[1] and Hope[2] architectures are more aligned with true continual learning (without being actual continual learning still, which is why they call it "test-time memorization").…
I have had it master tasks by doing this. The first time it tries to solve an issue it may take a long time, but it documents its findings and how it was able to do it and then it applies that knowledge the next time the task comes up.
There is some things that just don't transfer really well without specific training. I tried to create diagrams in Typst with Cetz (a Processing and Tikz inspired graphing library), and even with documentation, GPT 5.2-thinking can't really do complex nice diagrams like it can in Tikz. It can do simple things that are similar to the shown examples, but nothing really interesting. Typst and specially Cetz is too new for any current model to really "get it", so they can't use it. I need to wait to the next batch of frontier models so that they learn Typst and Cetz examples during pre-training.