Earlier quoted context omitted.
Plot twist: figuring out that objective function may prove as intractable as the original problem! We'll need an objective function writing objective function, and then it's turtles all the way down.
The main objective function in nature is very simple though: maximize the number of copies of your genes. Such an objective enforced in a resource-constrained multi-agent world (as suggested in the slides) could really lead to quite complex sub-objectives which may as well lead to general intelligence. For example, if each agent can process information and perform work, it follows that individuals that have better co…
Where will artificial general intelligence come from?
221–230 of 252 posts
Re: Where will artificial general intelligence come from?
#222Earlier quoted context omitted.
What, oh wise one, is the goal?
Nothing. Evoultion came about randomly, not by intent, so it has no goal.
Re: Where will artificial general intelligence come from?
#223Earlier quoted context omitted.
What, oh wise one, is the goal?
Nothing. Evoultion came about randomly, not by intent, so it has no goal.
Re: Where will artificial general intelligence come from?
#224Earlier quoted context omitted.
this is not a case that happened
As a matter of fact, the mainstream AI community hasn't adopted probabilistic programming, but it does exist .
Is your opinion independent of evidence?
Re: Where will artificial general intelligence come from?
#225Earlier quoted context omitted.
this is not a case that happened
As a matter of fact, the mainstream AI community hasn't adopted probabilistic programming, but it does exist .
Re: Where will artificial general intelligence come from?
#226Earlier quoted context omitted.
> Actually convnets were inspired by Fukushima's Neocognitron, which was itself inspired by visual cortex. That doesn't contradict what I wrote. ConvNets require the synchronization of weights between neurons, which is not considered to be biologically plausible. Some aspects of the architecture (the receptive fields, in this case) may well be plausible, with the complete architecture still implausible.
You're focusing too much on the implementation details and assuming that because they're different that it's not equivalent. The secret sauce here is that network topology (and threshold rules), not the implementation details, are largely what determine the functional properties of that network. Show an electrical engineer the circuit diagram of a 4 bit adder and they'll know it's function immediately. Artificial Neu…
The major advances are the implementation details, and I think many would consider the network topology research constrained not by our imagination but by our implementations.
Re: Where will artificial general intelligence come from?
#227Good luck.
Re: Where will artificial general intelligence come from?
#228Earlier quoted context omitted.
Uh oh, not this again. There is so much woo about using subthreshold FETs to simulate neurons when we don't even know how neurons work. I've seen the work of J. Hasler in school and she seemed to be fond about simulating a type of neuron (winner-take-all) that is hard to train with backpropagation (vanishing gradients just by inspection) and has limited grounds on physical simulation of neurons. Do you have any other…
I think what people (including Hassler) care about is not so much simulating neurons, but the energy efficiency. Once we figure out AGI algorithms, we will want to build them in hardware. This justifies the continuing research in subthreshold FETs, because they could allow very efficient computation (not necessarily biologically realistic).
Re: Where will artificial general intelligence come from?
#229Earlier quoted context omitted.
Well, for biological organisms, it's all about reproductive success. I mean, what exits today reflects what managed to reproduce, and how well. Overall, that has created lots of complexity. But that's just because there are so many niches and ways to be successful in them. What you say about people reflects cooperative behavior that drives reproductive success for shared gene complexes.
It took nature four billion years to invent humans, who are actually - if we're honest - pretty terrible as an example of workable AGI. In fact what nature invented was a persistent colony organism with external memory. Wild solo humans are only a little smarter than wolves individually, but being able to share and externalise invention and learning created a massive advantage. Humans are successful because although…
It can be compressed to "until AIs can talk".