Hmm, it’s still on the funny side of graph for me. I think it could go on for at least another week.

I almost wanna use some reverse psychology to try and make him stop.

'hey im from sneerclub and we are loving this please dont stop this strike'

(I mean he clearly mentally prepped against arguments and even force (and billionaires), but not someone just making fun of him. Of course he prob doesn't know about any of these places and hasn't build us up to Boogeyman status, but imagine it worked)

The 17 rules also seem to have abuse build in. Documents need to be stored redundantly (without any mention of how many copies that means), and it has a system where people are billed for the data they store. Combine these and storing your data anywhere runs the risk of a malicious actor emptying your accounts. In a 'it costs ten bucks to store a file here' 'sorry we had to securely store ten copies of your file, 100 bucks please'. Weird sort of rules. Feels a lot like it never figured out what it wants to be a centralized or distributed system, a system where writers can make money, or they need to pay to use. And a lot of technical solutions for social problems.

It's because of research in the mid-80s leading to Moravec's paradox — sensorimotor stuff takes more neurons than basic maths — and Sharp's 1983 international release of the PC-1401, the first modern pocket computer, along with everybody suddenly learning about Piaget's research with children. By the end of the 80s, AI research had accepted that the difficulty with basic arithmetic tasks must be in learning simple circuitry which expresses those tasks; actually performing the arithmetic is easy, but discovering a working circuit can't be done without some sort of process that reduces intermediate circuits, so the effort must also be recursive in the sense that there are meta-circuits which also express those tasks. This seemed to line up with how children learn arithmetic: a child first learns to add by counting piles, then by abstracting to symbols, then by internalizing addition tables, and finally by specializing some brain structures to intuitively make leaps of addition. But sometimes these steps result in wrong intuition, and so a human-like brain-like computer will also sometimes be wrong about arithmetic too.

As usual, this is unproblematic when applied to understanding humans or computation, but not a reasonable basis for designing a product. Who would pay for wrong arithmetic when they could pay for a Sharp or Casio instead?

Bonus: Everybody in the industry knew how many transistors were in Casio and Sharp's products. Moravec's paradox can be numerically estimated. Moore's law gives an estimate for how many transistors can be fit onto a chip. This is why so much sci-fi of the 80s and 90s suggests that we will have a robotics breakthrough around 2020. We didn't actually get the breakthrough IMO; Moravec's paradox is mostly about kinematics and moving a robot around in the world, and we are still using the same kinematic paradigms from the 80s. But this is why bros think that scaling is so important.

Not sure if analog turing machines provide any new capabilities that digital TMs do, but I leave that question for the smarter people in the subject of theorethical computer science

The general idea among computer scientists is that analog TMs are not more powerful than digital TMs. The supposed advantage of an analog machine is that it can store real numbers that vary continuously while digital machines can only store discrete values, and a real number would require an infinite number of discrete values to simulate. However, each real number "stored" by an analog machine can only be measured up to a certain precision, due to noise, quantum effects, or just the fact that nothing is infinitely precise in real life. So, in any reasonable model of analog machines, a digital machine can simulate an analog value just fine by using enough precision.

There aren't many formal proofs that digital and analog are equivalent, since any such proof would depend on exactly how you model an analog machine. Here is one example.

Quantum computers are in fact (believed to be) more powerful than classical digital TMs in terms of efficiency, but the reasons for why they are more powerful are not easy to explain without a fair bit of math. This causes techbros to get some interesting ideas on what they think quantum computers are capable of. I've seen enough nonsense about quantum machine learning for a lifetime. Also, there is the issue of when practical quantum computers will be built.

Nice result, not too shocking after IMO performance. A friend of mind told me that this particular competition is highly time constrained for human competitors, i.e., questions aren’t impossibly difficult per se, but some are time sinks that you simply avoid to get points elsewhere. (5 hours on 12 Qs is tight…)

So when you are competing against a data center using a nuclear reactor vs 3 humans running on broccoli, the claims of superhuman performance definitely require an * attached to them.