Technology

Jesus fuck... did you actually read your definition?

1. the act or action of grasping with the intellect

Okay, we've replaced the concept of "intelligence" and just swapped it out with "grasping with the intellect".

2. knowledge gained by comprehending

Nice, using the verb word of "comprehending" to define "comprehension".

3. the capacity for understanding fully

Okay, maybe that's a start, but it's incredibly vague.

Also, clicking on the links just resolves back to the most generic circular definition possible. Remind me to never use Merriam-Webster.

At least this gets further: https://en.wiktionary.org/wiki/comprehension

If we settle on "A thorough understanding" and define understanding as... fuck, we're back to this circular bullshit again.

Okay, fine, let's just look at intelligence:

The capacity of mind, especially to understand principles, truths, facts or meanings, acquire knowledge, and apply it to practice; the ability to comprehend and learn; the ability to process sentient experience to generate true beliefs with a justified degree of confidence.

Breaking this down:

• Understand principles, truths - Sure, sounds like an LLM to me. If you ask it to define a scientific principle or fact or Newton's laws, it can tell you what it is.
• facts or meaning - And, depending on the kind of thing we're talking about, it can you tell you why it exists.
• acquire knowledge - While it's not permanent (because context are just thrown away like short-term memories), LLMs have MCPs and other tools to acquire new knowledge.
• apply it in practice - It takes the knowledge it has naturally (training data), acquires new knowledge from its tools, and then applies it to the task it's looking at.
• the ability to comprehend and learn - Here we go with "comprehension" again. But, let's assume we all know what "learn" is. A LLM can learn about things, but the main problem is the context size and how we throw that away each session. But, that can be solved by storing facts elsewhere, Markdown rules, other techniques to condense the immediate context into long-term storage. In humans, we call that "long-term memory". We have our own process to stash short-term memory into long-term memory. We just haven't experimented enough with the long-term storage part in LLMs.
• the ability to process sentient experience - sentient... well, that's a pretty weighted word. I also feel like there's a clear difference between intelligence and sentience. We're now entering the realm of sensation, thought, and feelings, which are entire different parts of the brain.

I also don't think "sensations" are impossible to emulate, either.

There's some semantics here.

Large neural networks have been shown to build parallel models of the world that they learn through their training data. A chess model trained only on chess move notation does have an interior board and state that it tracks, despite never being told about a board, the number of pieces, how they move, etc.

LLMs do something similar with language, mapping tokens to ideas in ways that can produce meaningful and grammatically correct output without needing to be formally taught any of this.

Our brains also do things like this - we observe X and then try to intuit Y, the truth of the world and reason for X.

Where this breaks down is humans can then take our guess for Y and propose Z, and try to find it out in the world. If we can't, then Y is probably wrong, and our new understanding of the world is Y* that is consistent both with X and the absence of Z.

I cannot stress enough how far we are from machines that can do this. They can learn to approximate Y through training, but they have no means to extend that to propose an unobserved Z, no means to update their internal state given the results of that observation (without fully retraining the model on new data - which makes it a new model), and no way to question X given Z - they can talk at great length about data biases but can literally never excise bad data from their training weights. There have been experiments with training models on time-boxed datasets (e.g. stuff up to AD 1880) and asking it about modern concepts - predictably hilarious results.

All the current 'AI' stuff is smoke and mirrors around this very hard problem. The machines can talk, but they can't think (Z) and can't learn (Y*).

If you feed it every chess game, it can tell you which spaces the knight is likely to move to, and which it very unlikely to move to, but it does not understand how the knight moves.

Well, it knows how the knight moves, but it might forget that's it's an important rule after it starts to lose context. That's also how LLMs start to lose track of what's actually on the board.

There are definitely differences between LLMs and Neural Networks, but at the end of the day, they are both just gigabytes of weights. And really, both types are similar to what the human brain possesses. People tend to forget that the human brain isn't just some monolithic thing. There's a lot of difference specialized pieces. If you ask Magnus Carlsen how he calculated the best move on the chess board in a blitz game, he literally cannot tell you! It just appears, because that part of the brain is trained and hardwired to calculate good moves, based on the current board state.

A LLM uses logic and reasoning to arrive at a solution. That's not really compatible with intense pre-trained calculation. You can't talk yourself into a chess move at the speed the game requires. You can quickly think about possibilities, but you have to already have pre-trained calculations to get at least 90-95% of the way there, to pick from a few different lines.

On the other hand, Neural Networks beat the shit out of the greatest grandmasters in chess. Hands down. On average, they are at least +500 ELO to the best GMs. And everybody in the chess world knows this, which is why everybody uses these tools outside of 1v1 human to human games. (Obviously, cheating is taken very seriously, and has made plenty of headlines over the years.) Every GM analyzes their own games using those engines, to pick apart mistakes.

Oh, so, like my coworkers then.

/s

To be honest human spew bullshit all the time when they do not understand something. Or people are confidently incorrect all the time as they are remembering incorrectly. The amount of bullshitting is more a meter for intelligence, not something that discriminate between intelligence or the lack thereof.