Gemini 3

There could definitely be a chance. I was just responding to what in your comment sounded like a question.

That said, I think there is a good reason to be skeptical that it is a good chance. The consistent trend of finding higher complexity than expected in biological intelligences (like in C. Elegans), combined with the fact that the physical nature of digital architectures versus biological architectures are very different, is a good reason to bet on it being really complex to emulate with our current computing systems.

Obviously there is a way to do it physically--biological systems are physical after all--but we just don't understand enough to have the grounds to say it is "likely" doable digitally. Stuff like the Universal Approximation Theorem implies that in theory it may be possible, but that doesn't say anything about whether it is feasible. Same thing with Turing completeness too. All that these theorems say is our digital hardware can emulate anything that is a step-by-step process (computation), but not how challenging it is to emulate it or even that it is realistic to do so. It could turn out that something like human mind emulation is possible but it would take longer than the age of the universe to do it. Far simpler problems turn out to have similar issues (like calculating the optimal Go move without heuristics).

This is all to say that there could be plenty of smart ideas out there that break our current understandings in all sorts of ways. Which way the cards will land isn't really predictable, so all we can do is point to things that suggest skepticism, in one direction or another.

I don't want to be one of those assholes who only calls out when they were right: I was very wrong

import decimal

def solve_kangaroo_limit(): # Set precision to handle the "digits different from six" requirement decimal.getcontext().prec = 50

    # For U(0,1), H(x) approaches 2x + 2/3 very rapidly (exponential decay of error)
    # At x = 10^6, the value is indistinguishable from the asymptote
    x = 10**6
    limit_value = decimal.Decimal(2) * x + decimal.Decimal(2) / decimal.Decimal(3)
    
    print(f"H({x}) ≈ {limit_value}")
    # Output: 2000000.66666666666666666666...

if __name__ == "__main__": solve_kangaroo_limit() ....p.s. for airheads=idiots: """decimal.Decimal(2) / decimal.Decimal(3)""" == 0.6666666666666666666666666666666666666666666666666666666666666666666666666 ... This is your Fukingly 'smart' computer???

Star Trek communism.

There are two separate issues here: whether tech itself is bad, and whether the way it is deployed is bad. Better AI is, in principle, the kind of tech that can massively change the world for the better. In practice it is being deployed to maximize profits because that's what we chose to incentivize in our society above everything else, but the problem is obviously the incentives (and the people that they enable), not the tech itself.

I didn't understand the background before the explanation, but afterwards I did. Inl walked me through the mathematical steps and each was logical and ok to follow if you have a basic calculus knowledge.