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Comment by neilellis | Hacker News Reader

[This is in reply to the comments not the article!]

It's just a statistical model is the logical equivalent of human beings are just a bunch of atoms.

The amount of reductionist thinking that goes on in tech is hilarious. First define AGI then challenge an AI to meet those requirements. If it meets them it is AGI. Put aside your preconceptions of what technology you think is required to achieve the goals and stay empirical.

Note previous definitions of AI have been thrown away as AI passes through them one by one :-)

What goes on inside its 'head' is irrelevant. We still don't know what actually goes on inside our heads and we were damn sure we were intelligent long before we had a clue how our heads worked at all.

Also sentience != AGI. We can't even agree what sentience is in humans and other living beings so I'd stay clear of that one for now :-)

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>It's just a statistical model is the logical equivalent of human beings are just a bunch of atoms.

Not exactly.

One says "human beings are just a bunch of atoms" referring to the low level constituans (in a reductionistic way), but not making an accessment about the abilities emerging from those atoms in their interactions when in the form of a human.

But when one says that GPT is "just a statistical model" they're implying a capacity cap of statistical models, that makes modelling certain thinking behavior impossible (regarless of how impressive the current results are, they might very well be capped to go beyond some limit because of the method -statistically model- involved).

So, you can consider "GPT is just a statistical model" analogous to:

"This engine can't parse a context senstive language because it's just a regular expression engine".

>First define AGI then challenge an AI to meet those requirements. If it meets them it is AGI. Put aside your preconceptions of what technology you think is required to achieve the goals and stay empirical.

The problem is definitions can be slippery, and even famous tests (like the Turing Test) might be found lacking in practice, as we discover that, yes, it can pass this test, but there's still ways off what we consider human-like performance in many areas. So, we should also stay empirical about the definitions, tests, and goals too.

> But when one says that GPT is "just a statistical model" they're implying a capacity cap of statistical models

Except there is no “capacity cap” on statistical models, we have no idea what they are or are not capable of yet.

>Except there is no “capacity cap” on statistical models, we have no idea what they are or are not capable of yet

We do however have the knowledge that the human brain uses different model and topology, not just a bigger scale.

And we do have a good intuitition that scalling LLMs as they are (e.g. not changing the architecture) will give us more of the same kind of capabilities it currently has with the same limitations, not the kind we expect to match human thinking.

Also, empirically we do have an idea of "what they are or are not capable of yet". We had developed them, run them, and scaled them several times.

This.

The fact that it can now use tools (e.g. a calculator) for whatever task that needs precision, closes the AGI loop. It doesn't need to be "sentient" to automate the process of _thinking_.

How do you think? Imagine you want to extract 5 seconds of a video file without any prior experience. You'd probably use some software to execute the task. Well, that's Plugins.

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