Will the End of Moore's Law Halt AI Progress? (mindmatters.ai)

johnnyb (Slashdot reader #4,816) writes: Kurzweil's conception of "The Singularity" has been at the forefront of the media conception of artificial intelligence for many years now. But how close is that to reality? Will AI's be able to design ever-more-powerful AIs? Eric Holloway suggests that the power of AI has been fueled by Moore's law more than AI technology itself, and therefore hitting Moore's Wall will bring AI expansion to a fast halt.
Holloway calls that halt "peak AI...the point where a return on the investment in AI improvement is not worthwhile." He argues that humanity will reach that point, "perhaps soon...."

"So, returning to our original question, whether there is a path to Kurzweil's Singularity, we must conclude from our analysis that no such path exists and that unlimited self-improving AI is impossible."

Comments

Transistors and AI

[reanjr]

If you think AI is just more transistors, you probably aren't doing anything interesting in AI research. How many transistors in the human brain? How many regular transistors are necessary to do the work of one quantum transistor? We don't even know how the brain works, and this asshat is asserting that we'll never be able to build a machine that works the same way.

Kurzweil is a Shill

[Dayze!Confused]

Kurzweil pretends to know what he's talking about because he can fit a graph with lots of tampering with the data. He fails to see that what he calls exponential growth is nothing more than the beginning of a sigmoid function. A good analysis of Moore's law and computational power shows a sigmoid function, as with many technologies they start off slow, build up quickly, then tapper off.

Right and wrong

[Dan East]

He's right and wrong. He is correct that much of the "advancements" in AI has been because of processing power (and dataset size). Most of what I learned in AI in college a quarter century ago forms the foundation of today's AI (and most of what I learned had been developed decades earlier). The reason we have things like Siri isn't because AI is smarter. It's because processing power is so fast and cheap, and because data storage and ram is so large and cheap, that an absolutely massive data set can be crunched to do speaker agnostic recognition to determine what I said. In fact, Apple can run my voice audio through dozens of speech models (male, female, accents, etc) in parallel to find the best result. So he is right - processing power has enabled AI to become far more useful of late.

However, where he is wrong is in the parallelism and scalability. In my above example, many different nodes (maybe located in entirely different datacenters) are doing that processing to find the best match.

AI doesn't need to exist on one processor, and it doesn't need to execute at any particular speed. If we're talking "turing" type AI, and I were to ask it "How are you feeling today?" and the AI takes 5 hours to reply "I feel the same as I always do.", well it is still just as intelligent as if it were responding in real-time. When we have reached that point in AI intelligence then we can throw more processing power at it in many different ways to allow it to process faster. The point is that the intelligence is not bound by the processing speed. Sure, for Siri to be viable commercially and useful to Joe Blow it needs to be fast, but as far as research and advancing the field of AI, that is independent of the processing speed.

And having said all that, AI has not advanced significantly beyond the full realization and expansion of things like neural nets with massive processing power and data sets to be useful in identifying, say, a tree in a photograph. We could have been doing that in 1980 given the processing power and storage capacity we have now.