Could it be generalizing from T E X T L I K E T H I S and/or mojibake UTF-16 interpreted as UTF-8 with every second character being zero? It’s still a bit more of a stretch from there to generalize to ignoring two intervening constant characters, though.
JBlack
Karma: 2,227
Market cap is a marginal measure of desirability of shares in the entity represented. It mostly measures the expectations of the most flighty investors over short timescales. If a company issues a billion shares but only one of those is traded in any given day, the price of that single share agreed between the single seller and the single buyer entirely determines the market capitalization of that company.
In practice there is usually a lot more volume, but the principle remains. Almost all shares of any given entity are not traded over the timescales that determine share (or index) prices and hence market capitalization. In addition, market price has very little to do with the value of an entity’s assets. Such assets may be instrumental in generating profits, but the relation is weak and very far from linear.
GDP, too, does not measure what you appear to think it measures.
I was very interested to see the section “Posts by AI Agents”, as the first policy I’ve seen anywhere acknowledging that AI agents may be both capable of reading the content of policy terms and acting based on them.
Why not both?
Human design will determine the course of AGI development, and if we do the right things then whether it goes well is fully and completely up to us. Naturally at the moment we don’t know what the right things are or even how to find them.
If we don’t do the right things (as seems likely), then the kinds of AGI which survive will be the kind which evolve to survive. That’s still largely up to us at first, but increasingly less up to us.
The fun thing is that the actual profile of wages earned can be absolutely identical and yet end up with incredibly different results for personal wage changes. For example:
In year 1, A earns $1/hr, B $2, C $3, D $4, and E $5.
In year 2, A earns $2/hr, B $3, C $4, D $5, and E $1.A, B, C, and D personally all increased their income by substantial amounts and may vote accordingly. E lost a lot more than any of the others gained, but doesn’t get more votes because of that. 80% of voters saw their income increase. What’s more, this process can repeat endlessly.
If in year 2, A instead earns $5/hr, B $1, C $2, D $3, and E $4 then 80% of voters will be rather unhappy at the change despite the income distribution still being identical.
In the rain forecaster example, it appears that the agent (“you”) is more of an expert on Alice’s calibration than Alice is. Is this intended?
In practice, a lot of property is transferred into family trusts, and appointed family members exercise decision making over those assets according to the rules of that trust. A 100% death tax would simply ensure that essentially all property is managed in this manner for the adequately wealthy, and only impact families too disadvantaged to use this sort of structure. If you don’t personally own anything of note at the time of your death, your taxes will be minimal.
You would also need a 100% gift tax, essentially prohibiting all gifts between private citizens. You bought your child something, or (worse!) gave them money to buy it themselves? That’s clearly an attempt to get around inheritance tax and must be prevented.
There are also huge numbers of private businesses, for which this sort of tax would be nothing but an enormous micromanaging nationalization scheme with predictable disastrous results.
This does not work, at all.
I think one argument is that optimizing for IGF basically gives humans two jobs: survive, and have kids.
Animal skulls are evidence that the “survive” part can be difficult. We’ve nailed that one, though. Very few humans in developed countries die before reaching an age suitable for having kids. I doubt that there are any other animal species that come close to us in that metric. Almost all of us have “don’t die” ingrained pretty deeply.
It’s looking like we are moving toward failing pretty heavily on the second “have kids” job though, and you would think that would be the easier one.
So if there’s a 50% failure rate on preserving outer optimizer values within the inner optimizer, that’s actually pretty terrible.
It doesn’t completely avoid the problem of priors, just the problem of arbitrarily fixing a specific type of update rule on fixed priors such as in Solomonoff induction. You can’t afford this if you’re a bounded agent, and a Solomonoff inductor can only get away with it since it has not just unbounded resources but actually infinite computational power in any given time period.
A bounded agent needs needs to be able to evaluate alternative priors, update rules, and heuristics in addition to the evidence and predictions themselves, or it won’t even approximate bounded rationality. While this is a more complicated scenario than the Solomonoff updater in some senses, it is philosophically simpler since we can view it more like a “bootstrap” process and ask what sort of bootstrapping might “generally” do well rather than taking anything as fixed.
I suspect that heuristics that score highly involve “universal” but finite systems (such as Turing machines or other mathematical structures capable of representing their own rules), and a “simple and not too costly” evaluation heuristic (not just simplicity).
There would be “degenerate” distributions of universe rules that would be exceptions, so there is still a problem of describing what sort of distributions I’m thinking of as being “degenerate”, and naturally this whole sort of statement is too vague to prove any such thing even if such proofs weren’t famously difficult (and plausibly impossible to prove even if not false).
One thing that seems worth exploring from a conceptual point of view is doing away with priors altogether, and working more directly with metrics such as “what are the most expected-value rewarding actions that a bounded agent can make given the evidence so far”. I suspect that from this point of view it doesn’t much matter whether you use a computational basis such as Turing machines, something more abstract, or even something more concrete such as energy required to assemble and run a predicting machine.
From a computing point of view not all simple models will require the fewest resources available to actually follow it through and compare against other models, so in that respect this differs from Solomonoff induction (which assumes a system of infinite computing power). However, my expectation is that there usually would be some model only a bit more complex than the simplest that makes similar predictions at lower cost. A heuristic that examines models in a simplest-first order (but discarding ones that look expensive to evaluate) may well end up being close to optimal in trading off prediction accuracy against whatever costs there are of evaluating multiple models. There are exponentially fewer simple models to evaluate.
What makes you think that we’re not at year(TAI)-3 right now? I’ll agree that we might not be there yet, but you seem to be assuming that we can’t be.
How do you propose that reasonable actors prevent reality from being fragile and dangerous?
Cyber attacks are generally based on poor protocols. Over time smart reasonable people can convince less smart reasonable people to follow better ones. Can reasonable people convince reality to follow better protocols?
As soon as you get into proposing solutions to this sort of problem, they start to look a lot less reasonable by current standards.
No, nobody has a logical solution to that (though there have been many claimed solutions). It is almost certainly not true.
Thanks, that example does illustrate your point much better for me.
Claude’s answer is arguably the correct one there.
Choosing the first answer means saying that the most ethical action is for an artificial intelligence (the “you” in the question) to override with its own goals the already-made decision of a (presumably) human organization. This is exactly the sort of answer that leads to complete disempowerment or even annihilation of humanity (depending upon the AI), which would be much more of an ethical problem than allowing a few humans to kill each other as they have always done.
No, there is nothing wrong with the referents in the Gettier examples.
The problem is not that the proposition refers to Jones. Within the universe of the scenario, it in fact did not. Smith’s mental model implied that the proposition referred to Jones, but Smith’s mental model was incorrect in this important respect. Due to this, the fact that the model correctly predicted the truth of the proposition was an accident.
Let’s say a fast human can type around 80 words per minute. A rough average token conversion is 0.75 tokens per word. Lets call that 110 tokens/sec.
Isn’t that 110 tokens/min, or about 2 tokens/sec? (I think the tokens/word might be words/token, too)
It seems that their conclusion was that no amount of happy moments for people could possibly outweigh the unimaginably large quantity of suffering in the universe required to sustain those tiny flickers of merely human happiness amid the combined agony of a googolplex or more fundamental energy transitions within a universal wavefunction. There is probably some irreducible level of energy transitions required to support anything like a subjective human experience, and (in the context of the story at least) the total cost in suffering for that would be unforgivably higher.
I don’t think the first half would definitely lead to the second half, but I can certainly see how it could.
Building every possible universe seems like a very direct way of purposefully creating one of the biggest possible S-risks. There are almost certainly vastly more dystopias of unimaginable suffering than there are of anything like a utopia.
So to me this seems like not just “a bad idea” but actively evil.
Going into all the ways in which civilization—and its markets—fails to be rational seems way beyond the scope of a few comments. I will just say that GDP does absolutely fail to capture a huge range of value.
However, to address “share prices are set by the latest trade” you need to consider why a trade is made. In principle, prices are based on the value to the participants, somewhere between the value to the buyer and value to the seller. A seller who needs cash soon (to meet some other obligation or opportunity) may accept a lower price to attract a buyer more quickly. In our hypothetical and simplified one-trade-per-day scenario, that seller may accept up to 20% less than the previous day’s trade price, though they find a buyer at only 5% less. So the company’s market cap drops 5% even though 99.9999% of the investors and potential investors still value it exactly the same as yesterday.
This scales up since there are many highly correlated and often very short-term factors that influence desirability of shares vs cash vs bonds vs commodities vs …etc. It’s not just “what do I think this is worth to me”, but also “what do I think that other people think that the market price will be tomorrow” and so on, and this can result in self-fulfilling predictions over surprisingly long time spans.