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Comment by argonaut | Hacker News Reader

I hear this repeated a lot. But I've never actually seen a source from, say, the 90's or the 2000s, actually saying something like "if we're able to classify images better than humans, that's true AI."

I suspect it's just hearsay that's been repeated over and over again.

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There used to be a concept of "AI completeness" which meant that some problems could only be solved by True AI, which would also be able to solve all other human-level problems. Doug Hofstadter writes in Gödel-Escher-Bach that he believes grandmaster-level chess is AI complete. That was obviously false in retrospect, and it's becoming increasingly obvious that there is no AI completeness at all.

For now, I take this blogpost [1] of Andrej Karpathy as a nice measure for AI hardness.

[1] http://karpathy.github.io/2012/10/22/state-of-computer-visio...

This is a good blog post and articulates well many of the issues I have with calling current work on AI subsets "AI". From my point of view, the results of current work with AI subsets are impressive, but they're hitting self-proclaimed benchmarks or someone poorly defined benchmarks and declaring AI status. Creating an advanced Go machine was very cool, but as Lee Sedol played more, he said he began to understand how AlphaGo "thought". The appended reddit thread here [1] has some interesting insight on why pulling back the curtain on current AI claims is appropriate. A lot of the times, "it's just calculations" is appropriate since we don't really have a machine considering what it's given, it's driving towards a singular goal in the best way possible. In the case of AlphaGo, it disregarded Lee Sedol's move because it had no care about the unusualness of it, it just wrote the move off as sub-optimal. When you see behavior like this where the systems are algorithmically defeated, it's very difficult to not be dismissive of the claims of AI.

Again, there is a lot of coolness happening with subsets of AI research, but I don't feel that there is even a clear definition of what AI would entail - spaghetti code to get a desired result doesn't really help either, since it has to be persistent independent successes - to refer to Karpathy's article, it would have to make repeated assertions and understandings of similar photos with a high success rate to really be something spectacular.

[1] https://www.reddit.com/r/baduk/comments/4a7wl2/fascinating_i...

>it's becoming increasingly obvious that there is no AI completeness at all.

Bingo! The "general" part is the ability to learn new structures and task structures from environmental cues, and then construct informed prior beliefs about those new tasks and structures using causal relations to previously-observed tasks and structures. Nothing more!

In the 90's, John McCarthy was calling Go "the new Drosophila of AI". Now, we have AlphaGo that is "just" a bunch of "generic" deep learning with MC. Maybe John McCarthy was right and the ideas behind AlphaGo are the first steps toward true AI. I am very impressed.

The guys at NVIDIA did something very cool recently. They took a simple CNN (convolutional neural net) and in 2-3 days they taught it to drive a car.

Article: How Our Deep Learning Tech Taught a Car to Drive - https://blogs.nvidia.com/blog/2016/05/06/self-driving-cars-3...

These neural nets are really smart, and we don't know exactly how they work, we know only in principle. But if we asked the guy who made the self driving car what is the role of the 17th neuron in the 14th layer, he would probably have no idea. Just like human brains evolve through learning, so do neural nets. Yet many people think they are just clever tricks and not truly intelligent.

That's because they're not intelligent. They're layered function approximators with some specialisations for vision - conv layers. Their structure is designed by humans as is the data they are fed with. Maybe the human brain is the same, IMO it isn't. Humans seek out new experiences and can introspect and at their best assess their level of knowledge of a sibject. DNNs cannot.

Did you read the paper? They did not teach it to drive a car. The car just does lane keeping - it doesn't even do turns or lane changes. This is stuff solved 10+ years ago. And even then it only achieves 98% autonomy on lane keeping - this is a task that needs 100% accuracy. You should not be running into the median every couple miles.

Sounds like McCarthy was right: the point of Drosophila is that it's a very small, simple model organism. You're not supposed to "solve biology" by modelling something in Drosophila, you're just supposed to be able to narrow the field to hypotheses that are not so flagrantly stupid they fail in a fruit-fly.

Is this supposed to disprove my point? The fruit flies of AI hardly sounds impressive.

hey argonaut, we meet again. I think you'd be interested in the parent comments of this[0] thread.

[0]https://news.ycombinator.com/item?id=12322536

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