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AI is not rule
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this post was submitted on 23 Oct 2023
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My point is not that it can't be intelligent because it's wrong sometimes, my point is the program called "consciousness" is what would end up doing all the work we would recognize as sapient.
A LLM is little more than a really weird dictionary that doesn't let you open it.
The LLM could spit things out and then be retrained but only if "consciousness" can tell it yes or no, feed it info etc.
Currently that consciousness program is entirely computer scientists and there are no promising avenues for replacing them yet.
You missed the point of my "can be wrong" bit. The focus was on the final clause of "and recognize that it was wrong".
But I'm kinda confused by your last post. You say that only computer scientists are giving it feedback on its "correctness" and therefore it can't truly be conscious, but that's trivially untrue and clearly irrelevant.
First, feedback on correctness can be driven by end users. Anyone can tell ChatGPT "I don't like the way you did that," and it would be trivially easy to add that to a feedback loop that influences the model over time.
Second, find me a person who's only feedback loop was internal. People are told "no that's wrong" or "you've messed that up" all the time. That's what makes us grow as people. That is arguably the core underpinning of what makes something intelligent. The ability to take ideas from other people (computer scientists or no), and have them influence the way you think about things.
Like, it seems like you think that the "consciousness program" you describe would count as an intelligence, but then say it doesn't because it's only getting its external information from computer scientists, which seems like a distinction without a difference.
Not my argument: find me a person you'd consider intelligent who is only influenced externally, with no autonomy of their own. I name that person vegetable.
You've never worked with end users, have you? Jesus Christ the last thing you want to give an end users is write access to your model. It doesn't matter what channels hat write access comes through, it will be used to destroy your model.
(Not to mention the extortionate cost of this constant training, but this isn't a discussion about economic feasibility)
Besides that doesn't solve autonomy, which is still an integral aspect of intelligence.
The "consciousness program" is a fiction for illustrative purposes. It doesn't exist, in case you misunderstood me.
I did not miss the point on the wrong bit: but an LLM saying it is uncertain is not the same as saying it is wrong, and LLMs do not evaluate true or false: they transform inputs into outputs. Optionally with a certainty level.
Feeding another LLM with the outputs of the first has shown in some cases to improve accuracy, but that's just hooking 2 models together: not solving the fundamental gaps in reasoning.
If a human encounters an unknown situation it can seek out context to try and figure more out. They can generalize what they know and seek for things that might help them understand more.
An LLM just has an output. It cannot "broaden it's search" or generalize. Anything that did so would be layers on top of the LLM running aforementioned fictious "consciousness", and that consciousness would have a significant amount of complexity in order to perform the functions described here and previously.
An LLM is not an actor, it is math.
You're anthropomorphizing bits.
What about this? These weird little dictionaries have lots of emergent properties we're still exploring.
The paper states that the graphs representing those relations are the result of training LLMs on a very small subset of unambiguous true and false statements.
While these emergent properties may provide interesting avenues to model refinement and inspecting outputs it doesn't change the fact that these weird little dictionaries aren't doing anything truly unexpected. We just are learning the extra data associated with the training data.
It's not far removed from the primary complaint of Gebru's On Stochastic Parrots where she points out the ways that our biases are implicitly trained into LLMs because of the uncontrolled and unexamined inputs: except in this case those biases are the linguistics of truth and lies in unambiguous boolean inputs.
This may provide interesting avenues to model refinement that aren't spitting things out and being retrained by “consciousness” telling it yes or no, or feeding it additional info.
Only if the "direction of truth" exists in the wild with unchecked training data.
That clustering is a representation of the nature of the data fed to the model: all their training data was unambitious true or false... It's not surprising that it clusters.
Cool.