Why expect AGIs to be better at thinking than human beings? Is there some argument that human thinking problems are primarily due to hardware constraints? Has anyone here put much thought into parenting/educating AGIs?
Why expect AGIs to be better at thinking than human beings? Is there some argument that human thinking problems are primarily due to hardware constraints? Has anyone here put much thought into parenting/educating AGIs?
I suspect this has been answered on here before in a lot more detail, but:
Also, specifically in AI, there is some precedent for there to be only a few years between "researchers get ...
I'm getting an error trying to load Lumifer's comment in the highly nested discussion, but I can see it in my inbox, so I'll try replying here without the nesting. For this comment, I will quote everything I reply to so it stands alone better.
Isn't it convenient that I don't have to care about these infinitely many theories?
why not?
Why not what?
Why don't you have to care about the infinity of theories?
you can criticize categories, e.g. all ideas with feature X
...How can you know that every single theory in that infinity has feature X? o
Has anyone here put much thought into parenting/educating AGIs?
I'm interested in General Intelligence Augmentation, what it would be like try and build/train an artificial brain lobe and try and make it part of a normal human intelligence.
I wrote a bit on my current thoughts on how I expect to align it using training/education here but watching this presentation is necessary for context.
Because
"[the brain] is sending signals at a millionth the speed of light, firing at 100 Hz, and even in heat dissipation [...] 50000 times the thermodynamic minimum energy expenditure per binary swtich operation"
https://www.youtube.com/watch?v=EUjc1WuyPT8&t=3320s
AI will be quantitatively smarter because it'll be able to think over 10000 times faster (arbitrary conservative lower bound) and it will be qualitatively smarter because its software will be built by an algoirthm far better than evolution
I would probably classify it as suboptimal. It's not a "clear, decisive mistake" to see only black and white -- but it limits you.
In the usual way: additional data points increase the probability of the hypothesis being correct, however their influence tends to rapidly decline to zero and they can't lift the probability over the asymptote (which is usually less than 1). Induction doesn't prove anything, but then in my system nothing proves anything.
What you said in the previous message is messy and doesn't seem to be terribly impactful. Talking about how you can define a loss function or how you can convert scores to a yes/no metric is secondary and tertiary to the core disagreements we have.
the probability of which hypotheses being correct, how much? how do you differentiate between hypotheses which do not contradict any of the data?