11–20 of 142 posts
It seems the author may not have been familiar with AlphaGo Zero, which used substantially less processing power. https://deepmind.com/blog/alphago-zero-learning-scratch/
"But human intelligence is limited by the size of the skull that houses the brain." When you think about it this way, it seems impossible that we haven't duplicated the capability of the human brain in an airplane hangar somewhere. What's going on inside our heads that we can't mimic? That magical algorithm...
Modern supercomputers are still a factor of 10 off of the brains compute power(~1 Exaflop) but I don't think anyone in the field believes that you suddenly get consciousness once you reach a critical mass of compute. It's clearly a software problem.
"But human intelligence is limited by the size of the skull that houses the brain." When you think about it this way, it seems impossible that we haven't duplicated the capability of the human brain in an airplane hangar somewhere. What's going on inside our heads that we can't mimic? That magical algorithm...
this is wrong. look at the size of an elephant brain. they are smart, but not smarter than humans. similarly, bird brains are tiny, but birds have shown significant intelligence, crows in particular have been well studied.
> "DeepMind has found a way around this by employing vast amounts of computer power. AlphaGo takes thousands of years of human game-playing time to learn anything." It seems the author may not have been familiar with AlphaGo Zero, which used substantially less processing power. https://deepmind.com/blog/alphago-zero-learning-scratch/
> Over the course of training, 4.9 million games of self-play were generated, using 1,600 simulations for each MCTS, which corresponds to approximately 0.4 s thinking time per move.
Apologies in advance for the meta-comment (feel free to disregard) about this: > [Opening Paragraph of Article:] One afternoon in August 2010, in a conference hall perched on the edge of San Francisco Bay, a 34-year-old Londoner called Demis Hassabis took to the stage. Walking to the podium with the deliberate gait of a man trying to control his nerves, he pursed his lips into a brief smile and began to speak: [...]…
Earlier quoted context omitted.
Modern supercomputers are still a factor of 10 off of the brains compute power(~1 Exaflop) but I don't think anyone in the field believes that you suddenly get consciousness once you reach a critical mass of compute. It's clearly a software problem.
Estimates vary over many orders of magnitude: https://aiimpacts.org/brain-performance-in-flops/
Apologies in advance for the meta-comment (feel free to disregard) about this: > [Opening Paragraph of Article:] One afternoon in August 2010, in a conference hall perched on the edge of San Francisco Bay, a 34-year-old Londoner called Demis Hassabis took to the stage. Walking to the podium with the deliberate gait of a man trying to control his nerves, he pursed his lips into a brief smile and began to speak: [...]…
Earlier quoted context omitted.
Estimates vary over many orders of magnitude: https://aiimpacts.org/brain-performance-in-flops/
~1 Exaflop leaves room for error.
Earlier quoted context omitted.
We know nothing about how it works, it seems to derive its information and results from somewhere else as if it's hooked some bigger brain ( which can't see ), so analyzing brain alone we don't find anything.
This is inaccurate. We understand much of how it works, how vision, speech, etc work but we don't understand consciousness which is quite different.
> "DeepMind has found a way around this by employing vast amounts of computer power. AlphaGo takes thousands of years of human game-playing time to learn anything." It seems the author may not have been familiar with AlphaGo Zero, which used substantially less processing power. https://deepmind.com/blog/alphago-zero-learning-scratch/
Less power doesn't necessarily mean fewer games. According to the paper on AlphaGo Zero, they trained it on ~4.9 million games. > Over the course of training, 4.9 million games of self-play were generated, using 1,600 simulations for each MCTS, which corresponds to approximately 0.4 s thinking time per move.
That being said, the AlphaGo zero paper ends with the words:
> Humankind has accumulated Go knowledge from millions of games played over thousands of years, collectively distilled into patterns, proverbs and books. In the space of a few days, starting tabula rasa, AlphaGo Zero was able to rediscover much of this Go knowledge, as well as novel strategies that provide new insights into the oldest of games.