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Comment by burlesona | Hacker News Reader

I genuinely wonder if we will find there are some inherent tradeoffs to knowledge and understanding such that if we ever have machines that can “think like humans” they would in practice run into human-like cognition limits: ie such machines would be “bad at math” in the same way humans are “bat at math” compared to conventional computers.

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Indeed. I posit that as we get closer and closer to simulating how the human brain works in the pursuit of artificial intelligence, we're going to start seeing more and more of the same "bugs" that humans have (logical fallacies, susceptibility to illusions, mental illness, etc.)

You think your job sucks now, just wait until you're dealing with the general AI over on the UX team that's trying to get your ass fired because it's fostering a 3 year old grudge over that time you said Chappie was stupid.

At first, I thought it was surprising that a language model with a restricted vocabulary (e.g. banning the letter "E") acts significantly more "mentally ill", and then I thought about how I would come across if forced to use that constraint all the time, and I realized that maybe I'd appear mentally ill too!

You can play with LMs with constrained vocabularies here: https://huggingface.co/spaces/Hellisotherpeople/Gadsby

That's an interesting thought. However it's not cognitive limits that make humans bad at math, it's just a "hardware" issue: a human with a piece of paper is much better at math.

Even if neural networks were fundamentally incompatible with conventional computation, I don't see why you couldn't augment a neural network with a conventional ALU to do the numerical computations. This is exactly what humans do with pencil and paper - it's just a bit too slow.

Either the language model would need to know what it's doing or the host program would have to know what the AI is doing. Both seem out of reach. The latter seems more doable since you could hack something up for simple scenarios, but you'd effectively have to match the capabilities of the neural network in a classical way to handle every case (which would render using a neural net moot).

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