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You should the CEO’s of big companies bragging with big buzz words like “AI”, “cognitive”. The truth is very few people understand the current state of AI and

by nojvek 9y ago

You should the CEO’s of big companies bragging with big buzz words like “AI”, “cognitive”. The truth is very few people understand the current state of AI and it’s limits. Most of the fancy papers in Arxiv are based on NN’s which are just essentially vector algebraic math. Yes, we made huge leaps in image labeling and speech to text but current AI is a far cry from the full abilities of human brain reasoning and comprehension. The reality is people will say whatever to make a sale / get VC funding. The current economy is running on greed. When the bubble bursts, people will get hurt. Then the economy runs on fear. Fear that AI is hype. IBM has always been about hype. They are riding on the jeopardy wave and exploiting the greed.

rdiddly 9y ago
You can also argue that even the leaps we've managed to make in machine learning (a phrase I prefer over 'AI' not least because it includes the reminder-word 'machine') are mostly quantitative rather than qualitative leaps. Almost anything you can think of that's 'better' lately seems to be mostly thanks to 'more' -- more data collected in one place available for training/processing (thanks to large centralized surveillance companies), and the ability to process more data faster (thanks to faster hardware, and more of it, owned by those selfsame large centralized surveillance companies). We've seen a lot of optimizations and interesting research within the existing paradigm but we haven't seen a serious qualitative breakthrough the likes of which would justify this kind of hype.
tlb 9y ago
"just essentially vector algebraic math" -- True, but only in the sense that anything done by a computer is just iterated Boolean logic. Yet, Wikipedia and Grand Theft Auto and Mathematica have emergent properties beyond the obvious things that Boolean logic can do. So the fact that neural networks are based on vector algebra doesn't prove that they aren't capable of impressive things.
- nojvek 9y ago
  I mean to say the there are tons of problems NNs aren’t good for. If we are on the journey to discover GAI super algorithm that is smart enough to learn very quickly without a shit ton of data, I’d say we still have a long way to go. Anyone claiming they solved cognitive reasoning with a bunch of NNs stacked together in a black box is probably lying to themselves.

by nojvek 9y ago

rdiddly 9y ago

You can also argue that even the leaps we've managed to make in machine learning (a phrase I prefer over 'AI' not least because it includes the reminder-word 'machine') are mostly quantitative rather than qualitative leaps. Almost anything you can think of that's 'better' lately seems to be mostly thanks to 'more' -- more data collected in one place available for training/processing (thanks to large centralized surveillance companies), and the ability to process more data faster (thanks to faster hardware, and more of it, owned by those selfsame large centralized surveillance companies). We've seen a lot of optimizations and interesting research within the existing paradigm but we haven't seen a serious qualitative breakthrough the likes of which would justify this kind of hype.

tlb 9y ago

"just essentially vector algebraic math" -- True, but only in the sense that anything done by a computer is just iterated Boolean logic. Yet, Wikipedia and Grand Theft Auto and Mathematica have emergent properties beyond the obvious things that Boolean logic can do. So the fact that neural networks are based on vector algebra doesn't prove that they aren't capable of impressive things.

nojvek 9y ago
I mean to say the there are tons of problems NNs aren’t good for. If we are on the journey to discover GAI super algorithm that is smart enough to learn very quickly without a shit ton of data, I’d say we still have a long way to go. Anyone claiming they solved cognitive reasoning with a bunch of NNs stacked together in a black box is probably lying to themselves.