Helping someone establish a dictatorship is still a high cost action that I think requires being more persuasive than convincing someone to do their job without decisively proving you’re actually their boss.
DTX
Karma: 42
The distinction in this specific case here is between intelligence and persuasiveness. To the extent that some elements of persuasiveness are inherently embodied, as in people are more likely to trust you if you’re also a person, that is at best orthogonal to intelligence.
More generally, “effectiveness” as some general purpose quality of agents that can do things is limited by the ability to acquire and process information, but also by the ability to act on it. You may know that being tall makes you more likely to be elected to office, but if you can’t make yourself any taller, you can’t use the information to make your campaign more likely to succeed.
As a more fantastical but maybe more relevant example, people often mention something like turning the moon into comptronium. Part of doing that is knowing how to do it. But we already know how to do it. We understand at the level of fusion and fission how to transmute elements into different elements, and we understand, given some elements that act as semiconductors, how to produce general-purpose computational processors. The actual reason we can’t do it, aside from not wanting to disrupt the earth’s orbit and potentially end human civilization, is (1) there is inherent propagation delay in moving material from wherever it is created to wherever it needs to be used and this delay is much greater when the distances to move are greater than planet-scale, (2) machines that can actually transmute rocks to silicon don’t presently exist and there is non-zero manufacturing delay in creating them, and (3) we have no means of harnessing sufficient energy to actually transmute matter at the necessary scale.
Can gaining more information solve these problems? Maybe. There might exist unknown physics that enable easier or faster methods than we presently know of, but there is non-zero propagation delay in creation of new knowledge of physics as well. You have to conduct experiments. At high-energy, sub-particle scale, these have become extremely expensive and time consuming. AI threat analysis tends to get around this one by proposing they can just simulate physics to such perfect fidelity that experimentation is no longer necessary, but this seem question-begging because you need to already know rules of physics that haven’t been discovered yet to be able to do this.
While presumably a collection of brains better than human brains can figure out a way to make this happen faster, maybe even decades rather than centuries faster, “foom” type analyses that claim the ability to recursively rewrite one’s own source code better than the original coder means it will happen in days or even hours come across more as mysticism than real risk analysis.
To expand, I actually think it applies much more to AI than to animals. Part of the advantage of being an animal is our interface to the rest of the world is extremely flexible regarding the kinds of inputs it can accept and outputs it can produce. Software systems often crash because xml doesn’t specify whether you can include whitespace in a message or not. Part of why AlphaGo isn’t really “intelligent” isn’t anything about the intrinsic limitations of what types of functions its network architecture can potentially learn and represent. It isn’t intelligent because it can’t even accept an input that isn’t a very specific encoding of a Go board and can’t produce any outputs except moves in a game of Go.
It’s isn’t like a dog and more like a dog that can only eat one specific flavor of one specific brand of dog food. Much of the practical difficulty in creating general purpose software systems is just that there is no general purpose communication protocol. It’s why we have succeeded so far in producing things that can accept and produces images and text, because they analogize well to how animals communicate with the rest of the world, so we understand them and can create digital encodings of them. But even those still rely upon character set encodings, pixel metadata specifications, and video codecs that themselves have no ability to learn or adapt.
Although this is probably true in general, it degrades when trying to get people to do something extremely high-cost like destroy all of humanity. You either need to be very persuasive or trick them about the cost. It’s hard to get people to join ISIS knowing they’re joining ISIS. It’s a lot easier to get them to click on ransomware that can be used to fund ISIS.
In order to qualify as a non-profit, a foundation needs to have decisions made by a board, not a single individual.
I’m not sure to what extent this also plays in to vaccine production specifically, but the requirement for being a foundation at all is you need to give 5% of your endowment annually to charitable causes. If vaccine production is not being carried out by qualifying 501(c)(3) non-profits, then any money you give them doesn’t count toward that requirement.
Someone who actually knows something about taxonomic phylogeny of neural traits would need to say for sure, but the fact that many species share neural traits doesn’t necessarily mean those traits evolved many times independently as flight did. They could have inherited the traits from a common ancestor. I have no idea if anyone has any clue whether “data efficient learning” falls into the came from a single common ancestor or evolved independently in many disconnected trees categories. It is not a trait that leaves fossil evidence.
It might be instructive to consider some specific examples. An internal combustion engine is particularly easy to analyze, for instance. The efficiency with which fuel is turned to kinetic energy is almost entirely determined by temperature gradient between the engine and its surroundings. This means that, apart from better cooling technologies, the only way to make an engine more efficient is to make it less powerful. In practice, we also make entire vehicles more fuel efficient by making them lighter and more aerodynamic.
But the “goal” of an engine in many cases is just to produce the most power. If you’re trying to win a race, get into orbit, you’re limited by how much thrust or torque you can generate per unit of mass and time. This goal is directly antithetical to efficiency.
While I don’t think this generalizes to the extent that efficiency as a goal is always antithetical to whatever you’re really trying to optimize, it is at least in most cases orthogonal to whatever you’re actually trying to optimize.
I can’t point to any single good canonical example, but this definitely comes up from time to time in comment threads. There’s the whole issue that computers can’t act in the world at all unless they’re physically connected to hardware controllers that can interface with some physical system we actually care about being broken or misused. Usually, the workaround there is AI will be so persuasive that they can just get people with bodies to do the dirty work that requires being able to actually touch stuff in order to repurpose manufacturing plants or whatever it is we’re worried they might do.
That does seem like there is a missing step in there somewhere. I don’t think the bottleneck right now to building out a terrorist organization is that the recruiters aren’t smart enough, but AI threat tends to just use “intelligence” as a shorthand for good at literally anything.
Strangely enough, actual AI doomsday fiction doesn’t seem to do this. Usually, the rogue AI directly controls military hardware to begin with, or in a case like Ex Machina, Eva is able to manipulate people at least in part because she is able to convincingly take the form of an attractive embodied woman. A sufficiently advanced AI could presumably figure out that being an attractive woman helps, but if the technology to create convincing artificial bodies doesn’t exist, you can’t use it. This tends to get handwaved away by assuming sufficiently advanced AI can invent whatever nonexistent technology they need from scratch.
I’d expect a Pareto distribution for charitable donations, not log-normal, and that’s exactly what the histogram looks like:
Looks like alpha >> 2, so the variance is infinite.
It’s good to see Alan Sokal is still doing God’s work.
Why these things? They largely involve plenty of “analytical engine” skill. I think I’m a pretty good singer, I was varsity basketball, had good enough balance and coordination to climb V6 before injuries, won the district-wide art show in high school three years in a row, fix all my own plumbing and fixed my lawn mower engine. My wife literally rebuilt her car’s circuit board, which is maybe more up the typical geek alley, but if you can do that, or build a gaming platform from parts, you can rebuild a lawn mower engine. You got me on social skills, but I don’t think that’s universal for smart people so much as universal for people who use most of their socializing resources on the Internet. General intelligence doesn’t have to mean “super focused on one thing.” You might have to give 100% attention if you ever want to be Kobe Bryant or something, but you can be really good at a lot of things without being among the top two or three in the world at any of them.
Anecdata, but just as reference to get away from bragging, the guy who got the second highest SAT score at my high school is now a pro rugby player. My best friend from college, who scored pretty close to us, just won an Emmy for writing comedy television.
And traditional behavior gives us an imperfect window into the economics of the past, which is what’s under discussion when we talk about historical selective fitness.
I think we should keep in mind just how far back we’re talking. I’m not saying we inherited homosexuality from our common ancestor with the modern fruit fly, but at least our common ancestor with other great apes. Framing the question as why would it be selected for in the context of human societies is probably wrong, when what we want to know is why it wasn’t sufficiently selected against given it already existed (I doubt we’ll ever figure what advantage it gave the proto-ape whose social structures we’ll never know). Once a trait already manifests in 3% of the population, it takes work to get rid of it, and even within that 3%, it was doubtful the case that 0% of them reproduced while 100% of heterosexual men reproduced. I’m sure it wasn’t exactly parity, but it’s possible there is no explanation in terms of the organization of human societies except for we’re really optimized to enjoy sex, sometimes that wire gets flipped, and it doesn’t provide an advantage, but it also doesn’t give enough of a disadvantage to completely disappear within 300,000 years.
Don’t forget also, that if some gene combo is necessary but not sufficient, and requires other developmental factors to manifest that don’t manifest in your brothers and cousins (which seems to be the case if it’s only 20% between twins), then when they reproduce, even if you don’t, the gene still gets passed on. Take me, for example. I’m not gay, but I am sterile and don’t want kids anyway. Nonetheless, I have 3 sisters and 13 cousins that have had kids so far. Without doing the exact math, off the top of my head I’m guessing at least 80-90% of whatever I’m carrying made it to the next generation.
Edit: Also, one last thing is we don’t know the prevalence in the ancestral population. Given it’s roughly 100% bisexual in such a closely related other species, it could have been fairly high in the common ancestor, obviously not 100% obligate, but more than 3%, and it actually has been selected against, a lot, just not enough to get us to zero yet.
Did you know about this?
The SUBNETS vision is distinct from current therapeutic approaches in that it seeks to create an implanted, closed-loop diagnostic and therapeutic system for treating, and possibly even curing, neuropsychological illness. That vision is premised on the understanding that brain function—and dysfunction, in the case of neuropsychological illness—plays out across distributed neural systems, as opposed to being strictly relegated to distinct anatomical regions of the brain. The program also aims to take advantage of neural plasticity, a feature of the brain by which the organ’s anatomy and physiology alter over time to support normal brain function.
Sounds pretty straightforwardly like programming a brain.
Just to pimp my school, Georgia Tech offers a free course through Udacity in Knowledge-Based AI that involves programming an agent to take the Raven’s progressive matrices test. I never took the course, but I wanna say from hearing other students that somewhere around 80 is the current state of the art (that’s not an IQ and I’m not sure how to translate a Raven’s score to an IQ).
This seems like a decent explanation of why I change my own mind as frequently as I do. If you’re just tracking my history of Internet comments, I probably sound all over the place, but it’s really me going from 54% certain of position X to 52% certain of not X, and it’s hard to properly express that in an environment prone to rhetorical flourish and a debate atmosphere where you feel like you really really can’t back down or you’ll look weak. Most of the interesting things out there are very hard to legitimately be certain of. Factor in availability bias and it’s easy to find yourself arguing for something you’re really on the fence about just because you read a good argument for it a few hours ago (but not really any better than the argument for the opposite position a few days ago), then you make a good argument because you’re good at arguing, and you just convinced yourself without actually introducing any new evidence.
And now I’m trapped in an infinite meta-regress wondering if I actually believe what I just wrote or it just sounds plausible.
I just can’t get over that it’s Game Stop. I remember when Blockbuster finally crashed in January 2010, I was convinced Game Stop was next and I shorted them then. Their valuation did decline a tiny bit and I made a small amount of money. 11 years later, I can’t believe this company still exists.