I think perhaps a more useful, less subjective definition of AGI is something along the line of:
Can perform most jobs that are:
fully remote
any average human can be trained to do
can communicate by email or text, doesn’t require voice/video calls
Because if it really was generally intelligent why can’t it do that job?
Overall I appreciate what you’re trying to do here, but it’s not convincing.
The biggest problems are in this section:
The first is the machine itself. It is a computational device with audiovisual output. That implies its creators have sensory apparatus that processes both. They see. They hear.
Atmosphere dense enough for pressure wave propagation. Bilateral body plans with clustered sensory organs.
The machine has no idea that light or sounds exist. All it knows is that there’s two forms of output—both of which are interpretable to it. The evidence points equally strongly to creators running in Conway’s game of life as it does to ones in our universe.
You give as an example a situation which is inherently not repeatable, where were forced to make do with reasoning under significant uncertainty and with very limited information, to decide what’s going on out of an incredibly wide hypothesis space. You correctly point out this is hard.
You then say that in a situation where we can perform repeated experiments to exclude one hypothesis P-values work ok.
But in that exact situation Bayesian reasoning works fine. Sure you might not agree on which alternative hypothesis is true but so long as both of you agree there are any alternative hypothesese that make it more likely to see the given results, after a few rounds you’ll have extremely low credence in the original hypothesis.
What does it mean to raise the temperature by a fixed amount before we’ve defined temperature?
Without having read the book, or knowing the history of temperature, an obvious property I’d want my scale of temperature to have is that the temperature of a mixture of identical substances at different temperatures is the weighted average of the constituent parts.
Then you can easily recreate the celcius scale. Let the temperature of freezing water be 0, and the temperature of boiling water be 100.
Mix 99 parts freezing water with one part boiling water, and mix well (and quickly, in an insulated environment). That’s 1 degree celcius.
Repeat for the other 98 combinations.
If we’re lucky the mercury in our thermometer moves up a constant amount each time, and we can avoid mixing things to calibrate our thermometer in the future. If we’re unlucky it doesn’t and we need to painstakingly calibrate all thermometers. If we’re really unlucky a scale that works on one substance doesn’t work on another and then we need to rethink this whole temperature thing again.
Is this standard for the MBTI?
The test we used did, I have very little further knowledge of the MBTI other than what was discussed on this course.
That assumes you’re in a better position to make such investments than the charity?
It’s a good metaphor, but I think one important aspect this misses is overfitting—when you have a lot of parameters the NN can literally memorise small training sets till it gets 100% on the training set and 0% on the test set. Whereas a smaller model is forced to learn the underlying structure, so generalises better.
Hence larger models need a much larger training set even to match smaller models, which is another disadvantage of larger models (besides for higher per token training and inference costs).
Ok, in that case you’re just basically referring to the SSA Vs SIA. That’s an old chestnut, and either way leads to seemingly paradoxical results.
Yes
In universalism the question is then why out of all the parts of the consciousness I could happen to be experiencing, I’m experiencing this particular part.
There is no other deck of cards here. There’s no copy of me to compare myself to, and say how curious that looks exactly like me.
That’s like saying that every game of cards must be rigged, because otherwise the chance of having this particular card order is miniscule...
Do we have the numbers?
what percentage of anthropic shares are owned by employees?
Of those what percentage are owned by EA pulled employees?
Of those how many which actually translate their words into actions once they have the money?
What percentage do we expect them to give, and over what time frame?
How does that compare to current EA related funding?
Making up random numbers (I’ve done zero research)
Not more than 50 billion owned by employeess
Not more than 20 billion owned by EA employees
75% will put their money where their mouth is
And donate 50%. If they spread that over 20 years, at current interest rates that’s about 500 million a year
Givewell raised 400 million last year. Not all of that is from EAs, but not all EAs donated there. Let’s say total of 500 million.
Then if this all happens it about doubles fundings for EA related causes. Is a reasonable chance of that happening worth upfronting donations for?
Fair enough. I found it unreadable in a way I associate with AI (lots of dense words, but tricky to extract the content out of them), and the em dashes are somewhat of a giveaway.
Given how much slop there is I do appreciate if people clarify what they used AI for because I don’t want to wade through a ton of slop which wasn’t even human written.
Thanks for replying.
Hi, was this post written by, or with assistance from, AI?
Thanks
What about persuading politicians that AI safety is a cause that will win them votes? That requires very broad spectrum outreach to get as many ordinary people on board as possible.
Lots of individual mistakes here, that together serve to severely overstate the case made:
Failure to defeat Houthi Rebels: The US Navy’s struggles against the Houthis’ low-cost drone arsenal demonstrate how even non-state actors can challenge NATO air defense. NATO forces found themselves hard-pressed to counter relatively primitive drone attacks.
The Houthis were unable to touch US naval power. What the US couldn’t do is defend ships in a very narrow stretch of water from drone, missile, and speedboat attacks. This is a very specific situation, and it’s like saying there’s an end to US ground power because they couldn’t decisively defeat the Taliban. Asymmetric warfare works, more news at 10.
Also note the drones they were using were far from cheap, often costing hundreds of thousands of dollars.
Economic logic of drone warfare: In Ukraine, drones account for over 70% of combat kills—a proportion likely to increase. The economics are devastating: $1,000 FPV drones routinely destroy $7 million Abrams tanks. Modern tanks are dangerously outdated and the entire armored warfare needs to be redesigned from the ground up to face the realities of modern drone warfare.
You do see the videos where the tank gets blown up by the (closer to $5000) drone, you don’t see the vast majority of videos where it does nothing. Meanwhile how many people were killed by the Abrams tank, and how useful was the Abrams tank when used correctly to break through enemy lines and regain movement—something drones are not really capable of.
Obsolescence of NATO doctrine: NATO’s military doctrine remains rooted in pre-drone warfare assumptions, creating an embarrassing disconnect where peacetime generals lecture battle-hardened Ukrainian officers who possess actual combat experience against a lethal, drone-equipped adversary.
Given that Nato’s doctrine assumes air superiority, which drones barely impact at all, I don’t see how you could possibly draw such a conclusion from the war in Ukraine, where both sides decisively lack air superiority.
Navy should transition from few expensive carriers to distributed drone-launching platforms—hundreds of cheap drone carriers, underwater drone deployments, and autonomous loyal wingmen for the naval air-force.
The seas are huge, and cheap drones are short range and slow. Enemy ships are difficult to find in a vast empty sea. Ships are extremely difficult to destroy or cripple. Communications are almost certainly jammed. For drones to be useful they need to be:
fast
long range
carry a large payload
autonomous
We call such drones “cruise missiles” and they are extensively deployed in all NATO Navies. If you know a way to make them cheaper, then the DoD will almost certainly be very interested.
autonomous loyal wingmen for the naval air-force.
Current drone warfare is all about low cost, slow, short range, non-autonomous technology. Autonomous wingmen would by high cost, fast, long range, autonomous. I don’t see how you could possibly draw conclusions about them from current events.
The Transparent Battlefield: Assume constant observation. Every movement is tracked, every concentration targeted within minutes. Forces must operate dispersed, communicating through secure mesh networks, moving constantly. Resources, command, logistics—everything dispersed, redundant, modular.
This is basically current NATO doctrine, and has been for years.
Technology parity: China has successfully replicated fifth-generation fighter capabilities (notably copying JSF technology through espionage) and is now mass-producing these aircraft at scale.
This is true, but note that China is investing heavily into this stuff (and aircraft carriers), not autonomous drone swarms.
are increasingly vulnerable to drone swarms and hypersonic missiles
I have seen zero evidence of this. Indeed hypersonic missiles are similarly vulnerable to air defences as non hypersonic ones, and come with a whole host of problems of their own.
Small correction: Jordan was not involved in the 1973 war.