[ben_w]

I refer you to Wachowski & Wachowski (1999)*, building on previous work including Descartes and A. J. Ayer.

To whit: humans can't either, so that's an unreasonable question.

More formally, the tripartite definition of knowledge is flawed, and everything you think you know has a Munchausen trilemma.

* Genuinely part of my A-level in philosophy

[croes]

So we get the same flaws as before with a higher power consumption.

And because it’s fast and easy we now get more fakes, scams and disinformation.

That makes AI a lose-lose not to mention further negative consequences.

[ben_w]

Not if you source your training data from reality.

Are you treating "the internet" as "reality" with this line of questions?

The internet is the map, don't mistake the map for the territory — it's fine as a bootstrap but not the final result, just like it's OK for a human to research a topic by reading on Wikipedia but not to use it as the only source.

[Workaccount2]

Sooner or later someone is going to figure out how to do active training on AI models. It's the holy grail of AI before AGI. This would allow you to do base training on a small set of very high quality data, and then let the model actively decide what it wants to train on going forward or let it "forget" what it wants to unlearn.

[yunohn]

I wasn’t expecting your response to be “the truth is unknowable”, but was hoping for something of more substance to discuss.

[ben_w]

Then you need a more precisely framed question.

1. AI can do what we can do, in much the same way we can do it, because it's biologically inspired. Not a perfect copy, but close enough for the general case of this argument.

2. AI can't ever be perfect because of the same reasons we can't ever be perfect: it's impossible to become certain of anything in finite time and with finite examples.

3. AI can still reach higher performance in specific things than us — not everything, not yet — because the information processing speedup going from synapses to transistors is of the same order of magnitude as walking is to continental drift, so when there exists sufficient training data to overcome the inefficiency of the model, we can make models absorb approximately all of that information.