4 ms·

"The audience was split 50-50 on that. If it can accelerate—if it can more than double every 18th months going forward—it would seem like you’d get something li

"The audience was split 50-50 on that. If it can accelerate—if it can more than double every 18th months going forward—it would seem like you’d get something like AI in just a few years. Yet most people thought AI was much further away than biotech 2.0. " Interesting leap - is a large number of transistors on a die enough to enable an acceptable AI? I would have thought that more is necessary, and figuring out when it will happen will not be easily determined by a model like Moore's Law.

seiji 14y ago
Singularitards tend to forget "size of brain" isn't related to "usefulness of brain." We don't have superintelligent elephants and whales running around. Brains are slow, dumb things. They are just have freaky connectivity. On a related note, it's hilarious modern GPUs aren't classified as weapons.
- Estragon 14y ago
  How do you weaponize a GPU?
  - inportb 14y ago
    You could use it for cryptography or cryptanalysis, for example.
_delirium 14y ago
That's a longstanding debate, but yes, a common view is that there's more of an algorithm/formalization bottleneck than a hardware-speed bottleneck. There are some things we in principle know how to do in AI, but just can't practically do fast enough--- those will be fixed by speed increases. But there are a lot of things where we don't even know how, in principle, to write down an algorithm to approach the problem satisfactorily. Hence the notion of "AI-completeness" rather than "NP-completeness" as a colloquial expression for what makes an AI problem hard.
cdavid 14y ago
At least Mc Carthy thought that machines from the 70ies should have enough power to do it, i.e. it is an algorithmic/representation/etc… problem, not a lack of computational power one. If you look at what happens in nature, relatively low-level organisms are capable of some traits that we wish the most powerful computer had (vision of insects, etc…). Also, if you look at a well defined problem like speech recognition, the relationship between computational power and say accuracy is closer to a logarithmic curve than an exponential one. The law of diminishing returns has been the rule most of the time.

seiji 14y ago

Singularitards tend to forget "size of brain" isn't related to "usefulness of brain." We don't have superintelligent elephants and whales running around. Brains are slow, dumb things. They are just have freaky connectivity. On a related note, it's hilarious modern GPUs aren't classified as weapons.

Estragon 14y ago
How do you weaponize a GPU?
- inportb 14y ago
  You could use it for cryptography or cryptanalysis, for example.

_delirium 14y ago

That's a longstanding debate, but yes, a common view is that there's more of an algorithm/formalization bottleneck than a hardware-speed bottleneck. There are some things we in principle know how to do in AI, but just can't practically do fast enough--- those will be fixed by speed increases. But there are a lot of things where we don't even know how, in principle, to write down an algorithm to approach the problem satisfactorily. Hence the notion of "AI-completeness" rather than "NP-completeness" as a colloquial expression for what makes an AI problem hard.

cdavid 14y ago

At least Mc Carthy thought that machines from the 70ies should have enough power to do it, i.e. it is an algorithmic/representation/etc… problem, not a lack of computational power one. If you look at what happens in nature, relatively low-level organisms are capable of some traits that we wish the most powerful computer had (vision of insects, etc…). Also, if you look at a well defined problem like speech recognition, the relationship between computational power and say accuracy is closer to a logarithmic curve than an exponential one. The law of diminishing returns has been the rule most of the time.