I fully believe that this is 90% of what the human brain is doing to solve the same problems, though. Brains just also have a constraint/filter layer, determining which of these bits of "made up bullshit" should be allowed to rise from the layer generating them, to the motor output layer.

Or, to put that another way, flipping the perspective around: our minds could consist of an intelligent, analytical, but utterly unimaginative agent, that sits there listening to a stream of suggestions spewed out by a distinct second agent, one that is "creative" but has no idea about the constraints of things like physics. The brain's analytical agent filters this stream of suggestions, taking notice of the suggestions that seem like they'll make the world change in the ways it "wants"†; and then it does those.

† Or, according to modern perceptual-control research, the agent attempts to predict the world that will occur a few seconds in the future, with a bias toward predicting world-states the reward-system has annotated as being rewarding; and then it looks at what motor commands it "would have" issued in that hypothetical world, and actually issues those. The stream of suggestions, in this model, serve as input to feed the generative model of potential world-states; the executive agent then must notice whether the potential world-state is a "possible" world or an "impossible" world (and whether the motor commands required of it are "possible" or "impossible" inputs), and filter out the "impossible" worlds.

Under this hypothesis, dreaming is the state when your executive responsible for filtering out "impossible" worlds isn't online. So you just get a continuous "impossible" world generated from the streamed suggestions of the creative-but-stupid bullshit-generating agent, with nothing to tear it down—just as seen in these generative AIs. As consciousness returns, the mental predicted world-state is noticed to be impossible by the now-online analytical agent, and is torn down.

This is a good point, and it's a little concerning to me that we will likely soon be inundated not just with spam from lowly-paid shills, but literal bots generating a near endless supply of confident-sounding nonsense.

I am not an expert with machine learning, but it seems (at a very high level) we've done amazingly well at creating models that can recognize and sometimes recreate patterns. But they never seem to have any ability to understand the patterns. I'm sure someone much more knowledgeable could compare it to a child of whatever age (or maybe I'm just completely wrong).

Let's not undersell it. Yes, you can identify inconsistencies in the unicorn example (if you are reading it more carefully than many people, HNers included, read most things), and some of the other examples, like the LoTR one, are more blatantly inconsistent than that. But on the other hand, look at the anti-recycling example: is there a single inconsistency in it? (On Twitter, someone insisted to me that the anti-recycling example proved GPT-2 was merely memorizing human input and spitting it back out - specifically, copying from Reddit, because they found a post of it on Reddit, until I pointed out to them that the timestamp was after OA's announcement.)

When it comes to attacks, it's not the average-case which matters.

Can't wait for those bugs caused by autocorrect.

This has been tried. The code looks kind of plausible, but doesn't quite compile. Some of the other examples (Shakespeare, Latex, wikipedia source code) are pretty good at a structural level. http://karpathy.github.io/2015/05/21/rnn-effectiveness/

This had about 10 million parameters though, compared to OpenAi's 1.5B. I don't think their text is nonsense though. There's some very interesting examples. They have since released publicly a 345M parameter version, there are some nice examples from that model pre-trained with modern poetry on this twitter account: https://twitter.com/rossgoodwin