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sigh this is why I don't usually get involved in arguments about whether something is considered AI or not; it just devolves into people trying to define and re

sigh this is why I don't usually get involved in arguments about whether something is considered AI or not; it just devolves into people trying to define and redefine what the word 'AI' even means. Final word: I follow this stuff closely and read the research articles, and what we currently have is really great, but it's still just pulling the wool over your eyes. These techniques and algorithms trick you into thinking they are intelligent when in fact they are anything but. What we have now is a parlour trick in comparison to what the brain is doing. When true AI actually comes along, no one will doubt it.

ecopoesis 10y ago
I was unaware that we knew how how human intelligence works. </sarcasm> If we can build systems that can fool us into thinking their intelligent (which I will agree that outside of limited scopes we have not), then we built intelligence.
deleted 10y ago
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enobrev 10y ago
To be fair, I fool people into thinking I'm intelligent all the time.
- sdenton4 10y ago
  Superintelligence will be achieved by: a) Training a system to trick the smartest people on the planet that it's smart, and then b) Asking it to prove itself by solving the Riemann hypothesis.
joesb 10y ago
Or may be you give your brain too much credits.
causalmachine 10y ago
@hacker_9 ('A') Albeit, the human brain calculates roughly 10^16 to 10^18 synaptic operations per second. (Exascale Computation) THIS DAY, artificial neural models calculate 10^14+ synaptic operations per second. BY EXTENSION, artificial cognitive models have reduced larger number of cognitive tasks, as time diverged, in tandem with enhanced parallelism. THUSLY, exascale artificial neural models are but likely by 2020. (Moore's Law) It requires not Einsteinian intellect, such that one observes the prior sequence. SOLELY IDIOTS/THEISTS FAIL TO COMPUTE THE AFORESAID. . . . . . . ('B') By extension, artificial intelligence is enhancing exponentially, particularly on the order of Moore's Law. Here are a sequence COGNITIVE fields/tasks, where sophisticated ARTIFICIAL NEURAL models EXCEED mankind: 1) Language translation (eg: Skype 50+ languages) 2) Legal-conflict-resolution (eg: 'Watson') 3) Self-driving (eg: 'OTTO-Self Driving' ) 5) Disease diagnosis (eg: 'Watson') 6) Medicinal drug prescription (eg: 'Watson') 7) Visual Product Sorting (eg: 'Amazon Corrigon' ) 8) Help Desk Assistance ('eg: Digital Genius) 9) Mechanical Cucumber Sorting (eg: 'Makoto's Cucumber Sorter') 10) Financial Analysis (eg: 'SigFig') 11) E-Discovery Law (eg: ' Social Science Research Network.') 12) Anesthesiology (eg: 'SedaSys') 13) Music composition (eg: 'Emily') 14) Go (eg: 'Alpha Go') ... https://www.youtube.com/watch?v=P_-wn8ghcoY https://www.youtube.com/watch?v=P_-wn8ghcoY https://m.youtube.com/watch?v=7Pq-S557XQU https://m.youtube.com/watch?v=7Pq-S557XQU
TeMPOraL 10y ago
I agree. What I see we have now is lots of bricks - often overly problem-specific (and it's important to stress this; a lot of things that get called "AI" in the news is really lots of compute thrown at super specific heuristics). To call it AI, we still need to connect them together into a system that can use those components to learn and solve problems it didn't see before, without bricking itself in the process.
ethbro 10y ago
> parlour trick in comparison to what the brain is doing That's a good turn of phrase, because it speaks to the real rub between algorithmic insufficiency and hardware insufficiency. From my reading, it seems fair to say that "strong general AI" will require some product of both to be realized and that our current hardware capabilities likely fall short of the minimum requirement (even with optimal algorithms). Were they imagining exascale computing when Quicksort was dreamed up in 1959? No. But does that make it any less important?

ecopoesis 10y ago

I was unaware that we knew how how human intelligence works. </sarcasm> If we can build systems that can fool us into thinking their intelligent (which I will agree that outside of limited scopes we have not), then we built intelligence.

deleted 10y ago

[deleted]

enobrev 10y ago

To be fair, I fool people into thinking I'm intelligent all the time.

sdenton4 10y ago
Superintelligence will be achieved by: a) Training a system to trick the smartest people on the planet that it's smart, and then b) Asking it to prove itself by solving the Riemann hypothesis.

joesb 10y ago

Or may be you give your brain too much credits.

causalmachine 10y ago

@hacker_9 ('A') Albeit, the human brain calculates roughly 10^16 to 10^18 synaptic operations per second. (Exascale Computation) THIS DAY, artificial neural models calculate 10^14+ synaptic operations per second. BY EXTENSION, artificial cognitive models have reduced larger number of cognitive tasks, as time diverged, in tandem with enhanced parallelism. THUSLY, exascale artificial neural models are but likely by 2020. (Moore's Law) It requires not Einsteinian intellect, such that one observes the prior sequence. SOLELY IDIOTS/THEISTS FAIL TO COMPUTE THE AFORESAID. . . . . . . ('B') By extension, artificial intelligence is enhancing exponentially, particularly on the order of Moore's Law. Here are a sequence COGNITIVE fields/tasks, where sophisticated ARTIFICIAL NEURAL models EXCEED mankind: 1) Language translation (eg: Skype 50+ languages) 2) Legal-conflict-resolution (eg: 'Watson') 3) Self-driving (eg: 'OTTO-Self Driving' ) 5) Disease diagnosis (eg: 'Watson') 6) Medicinal drug prescription (eg: 'Watson') 7) Visual Product Sorting (eg: 'Amazon Corrigon' ) 8) Help Desk Assistance ('eg: Digital Genius) 9) Mechanical Cucumber Sorting (eg: 'Makoto's Cucumber Sorter') 10) Financial Analysis (eg: 'SigFig') 11) E-Discovery Law (eg: ' Social Science Research Network.') 12) Anesthesiology (eg: 'SedaSys') 13) Music composition (eg: 'Emily') 14) Go (eg: 'Alpha Go') ... https://www.youtube.com/watch?v=P_-wn8ghcoY https://www.youtube.com/watch?v=P_-wn8ghcoY https://m.youtube.com/watch?v=7Pq-S557XQU https://m.youtube.com/watch?v=7Pq-S557XQU

TeMPOraL 10y ago

I agree. What I see we have now is lots of bricks - often overly problem-specific (and it's important to stress this; a lot of things that get called "AI" in the news is really lots of compute thrown at super specific heuristics). To call it AI, we still need to connect them together into a system that can use those components to learn and solve problems it didn't see before, without bricking itself in the process.

ethbro 10y ago

> parlour trick in comparison to what the brain is doing That's a good turn of phrase, because it speaks to the real rub between algorithmic insufficiency and hardware insufficiency. From my reading, it seems fair to say that "strong general AI" will require some product of both to be realized and that our current hardware capabilities likely fall short of the minimum requirement (even with optimal algorithms). Were they imagining exascale computing when Quicksort was dreamed up in 1959? No. But does that make it any less important?