[ninetyninenine]

It’s not massively underplaying it imo. AI hype is real. This is revolutionary technology that humanity has never seen before.

But it happened at a time where hype can be delivered at a magnitude never before seen by humanity as well to a degree of volume that is completely unnatural by any standard set previously by hype machines created by humanity. Not even landing on the moon has inundated people with as much hype. But inevitably like landing on the moon, humanity is suffering from hype fatigue.

Too much hype makes us numb to the reality of how insane the technology is.

Like when someone says the only thing stopping LLMs is hallucinations… that is literally the last gap. LLMs cover creativity, comprehension, analysis, knowledge and much more. Hallucinations is it. The final problem is targeted and boxed into something much more narrower then just build a human level AI from scratch.

Don’t get me wrong. Hallucinations are hard. But this being the last thing left is not an underplay. Yes it’s a massive issue but yes it is also a massive achievement to reduce all of agi to simply solving just an hallucination problem.

[NoGravitas]

> Like when someone says the only thing stopping LLMs is hallucinations… that is literally the last gap.

What you are missing here is that the "hallucinations" you don't like and the "results" you do like are, in terms of the underlying process, exactly the same thing. They are not an aberration you can remove. Producing these kinds of results without "hallucinations" is going to require fundamentally different techniques. It's not a "last gap".

[ninetyninenine]

That's not true. There is we just need to find it.

Humans have a condition called schizophrenia where we literally are incapable of differentiating hallucination and reality. What that capability is, is something we need to find out and discover for both ourselves and LLMs.

For example: Mathematically speaking it's possible to know how far away an inferenced point is away from a cluster of real world data. That delta when fed back into the neural network can allow the LLM to know how speculative a response is. From there we can feed the response back into itself for refinement.

[dns_snek]

You're confounding hallucination in humans, which is a purely sensory experience, and hallucinations in LLMs, which seems to be used to describe every kind of mistake and deficiency in LLMs.

And even if we were to cure schizophrenia in humans, just what makes you think that it would apply to LLMs? Having an extremely weak conceptual model of the world and not being able to reason out of rather simple problems (like LLMs struggle with) isn't schizophrenia.

This oversimplified explanation which posits that neural networks are just like human brains has truly gone too far now.

> Mathematically speaking it's possible to know how far away an inferenced point is away from a cluster of real world data.

And mathematically speaking, how would you accomplish this? As you probably know LLMs don't operate on conceptual ideas, they operate on tokens. That's why LLMs tend to fail when asked to do things that aren't well represented in their training data, they don't have a working model of the world even if they can fake it to a certain degree.

[ninetyninenine]

No there is no confounding. When you hallucinate with schizophrenia you know things that are not true and you sense things that are not true. The hallucinations involve both sensory and knowledge.

A weak conceptual model of the world is the problem. But realize humans also have a weak conceptual model of the world as well and make a bunch of hallucinations based on that weak model. For example many people are still making the claim about LLMs that it’s all stochastic parroting when it’s been proven that it’s not. That is an hallucination. Or the people betting (and not) on the financial success of crypto or AI. We don’t know how either of these things will pan out but people on either team act as if they know definitively. A huge part of human behavior is driven by hallucinations that fill in gaps.

> And mathematically speaking, how would you accomplish this? As you probably know LLMs don't operate on conceptual ideas, they operate on tokens. That's why LLMs tend to fail when asked to do things that aren't well represented in their training data, they don't have a working model of the world even if they can fake it to a certain degree.

It’s not an incorrect model of the world as technically both you and an LLM ultimately have an incorrect model of the world and both you and the LLM fake it. The best you can say is that the LLM has a less accurate approximation of the world than you but ultimately both you and the LLM hold an incorrect model and both you and the LLM regularly hallucinate off of it. You also make up bullshit on things not well represented in your own model.

But like I said we are often (and often not) aware of our own bullshit so providing that to the LLM quantitatively will help it too.

The LLM is not just trained on random tokens it’s trained on highly specific groups of tokens and those groups of represent conceptual ideas. So an LLM is 100 percent trained on concepts and tokens are only an encoding of that concept.

If a group of tokens represents a vector then we can for sure calculate distance between vectors. We know that there are also different types of vectors represented at each layer of the feed forward network that encode reasoning and not just the syntactic order of the tokens.

Like literally there is not very much training data of a human giving instructions to someone to write code and the associated code diff. The fact that an LLM can do this to a useable degree without volumes of similar training data speaks to the fact it knows concepts. This is the same tired argument that has been proven wrong. We already know LLMs aren’t just parroting training data as the majority of the agentic coding operations we currently use LLMs for actually don’t have associated training data to copy.

Given that we know all of these embeddings from the training data (the model had to calculate the embeddings at one point) we can encode proximity and distance into the model via addition and subtraction of the magnitude of vectors and from this we extract a number that ascertains distance between vectors embeddings.

Imagine a best fit 2D curve through a scatter plot of data points. But at the same time that curve has a gradient color along it. Red indicates its very close to existing data points blue indicates its far. We can definitely derive and algorithm that calculates the additional “self awareness” dimension here encoded in color and this can extend to the higher dimensional encoding that is the LLM.

If an LLm is aware that the output is red or blue then it can sort of tell that if the line is blue it’s likely to be an hallucination.

[dns_snek]

> It’s not an incorrect model of the world as technically both you and an LLM ultimately have an incorrect model of the world and both you and the LLM fake it.

I should've said that the model is "missing", not "weak" when talking about LLMs, that was my mistake. Yes I'm a human with an imperfect and in many aspects incorrect conceptual model of the world, that is true. The following aren't real examples, they're hyperbolic to better illustrate the category of errors I'm talking about.

If someone asks me "can I stare into the sun without eye protection", my answer isn't going to change based on how the question is phrased because I conceptually understand that the radiation coming from the sun (and more broadly, intense visible radiation emitted from any source) causes irreversible damage to your eyes, which is a fact stored in my conceptual understanding of the world.

However LLMs will flip flop based on tone and phrasing of your question. Asked normally, they will warn you about the dangers of staring into the sun, but if your question hints at disbelief, they might reply "No you're right, staring into the sun isn't that bad".

I also know that mirrors reflect light, which allows me to intuitively understand that staring at the sun through a mirror is dangerous without being explicitly taught that fact.

If you ask an LLM whether staring into a mirror which is pointed at the sun (oriented such that you see the sun through the mirror) is safe, they might agree that it's safe to do so, even though they "know" that staring into the sun is dangerous, and they "know" that mirrors reflect light. Presumably this is because their training data doesn't explicitly state that staring at a mirror is dangerous.

The way the question is framed can completely change their answer which betrays their lack of conceptual understanding. Those are distinctly different problems. You might say that humans do this too, but we don't call that intelligent behavior, and we tend to have a low opinion of those who exhibit this behavior often.

[ninetyninenine]

No it doesn’t. Conceptual understanding is there. But the LLM is not obligated towards correctness. The fact that at one point it gave you the correct answer is indicative that an aspect of it understands the concept.

Like if I told it solve a complex puzzle equation not in its training data and it correctly solved that problem. We know from the low probability of arriving at that solution from random chance that the LLM must know and understand and reason to arrive at that solution.

Now you’re saying you perturb the input with some grammar changes but leave everything else the same and the LLM will now produce a wrong answer. But this doesn’t change the fact that it was able to get the right answer.

Humans can be dumb and inconsistent. LLMs can be dumb and inconsistent too. This happens to be a quirk of the LLM. But you cannot deny that it is intelligent on the sole fact that LLMs can produce output that we know for sure can only be arrived at through reasoning.

[indigoabstract]

I think you would have really enjoyed living in the '50s, when the future was bright and colonizing Mars was basically a solved problem.

What we got instead is a bunch of wisecracking programmers who like to remind everyone of the 90–90 rule, or the last 10 percent.

[ninetyninenine]

Mars wasn't part of the 90-90 rule genius. If you know your history, it's mainly because political interest was lost. Technologically sending someone to mars is 100000000x more feasible than agi simply because the technology and theory exists such that we can do it.

AGI is part of the last 10 percent rule. But like that's the entire issue. 90% is still 90% progress. That is massive. And the hype surrounding LLMs has made people forget how far 90% is. People are going, "LLMs are retarded because it has the IQ of a 5 year old". They don't realize how even getting it to the level of a 5 year old was impossible for decades and decades.

[ninetyninenine]

Wisecracking programmers lol. You talk as if programming is like something to be proud of. It’s one of the most lucrative jobs with ease of entry as a boot camp can turn someone from zero to hero in a year.

And then you mouth off a buzz phrase not even coined by a programmer but repeated to the point of annoyance about how the final 10 percent is always the hardest as if programmers who copy the phrase are so smart.

Bro the last 10 percent being the hardest doesn’t mean the previous 90 percent didn’t happen. The first 90 percent is a feat in itself and LLMs can now even do PRs. That was a feat no one just 5 years ago could have predicted was possible in our lifetimes.

Idiot programmers and their generic wise cracks were the ones saying that AI would never be able to pass the Turing test and this was just 4 years ago.

[taco_emoji]

Oh, buddy, LLM hallucinations are not the only gap left for AGI

[ninetyninenine]

It is. After that it's virtually indistinguishable from chatting with a human