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Comment by rbanffy | Hacker News Reader

> recognizing arbitrary objects within a larger image has been the Holy Grail of artificial intelligence

The Holy Grail is general AI. Recognizing objects is a side quest, perhaps a required step, but, by no means, the end goal.

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Like so many other things in the field of AI, general object recognition was the "holy grail" because it was assumed that it required AGI. Now we've figured out a way to do general object recognition without AGI.

Whenever we figure out how to do something, we stop calling it AI or AGI. If the trend continues, will we eventually have a general AI, but won't consider it anything special? Will it have been just a small incremental step by then?

I think it will need some kind of breakthrough. Current advancements are probably incremental as you stated, but having an AGI might need some new theory we don't have currently.

Deep learning is the opposite of incremental. For a long time it was not clear whether/how we can learn multi layer networks efficiently. ImageNet changed everything.

Machine learning people basically agree that there weren't any big breakthroughs in deep learning. The success and the hype is mostly a combination of more computing power and more data. The algorithms (convolutional neural network etc.) were invented back in the 1980s and even earlier.

There have been some improvements but they are incremental indeed. More use of ReLU, dropout etc. But it's not a new paradigm at all.

They weren't any recent breakthroughts. But LSTM and ConvNet are breakthroughts. It just took a lot of times to prove it.

Convnets follow pretty naturally from multilayer perceptrons. Perhaps backpropagation was a breakthrough, enabling the training of ANNs on data, instead of hand-tuning.

But the idea of neural nets is very old, going back to Rosenblatt and connectionism.

I think deep, natural language processing will be unambiguous: if you create a machine that says "Yes, I am intelligent, thanks for asking" in a way indistinguishable from a human, it would be hard to disagree. On the other hand, it's entirely possible that that goal will take so much longer than others we'll have incredibly strong AIs affecting our lives before we notice.

Yes, this is called AI effect: https://en.wikipedia.org/wiki/AI_effect

Is there a story somewhere of AI researchers concluding general object recognition was the holy grail of AI?

I get that a lot of people downplay achievements in machine learning by saying it's nothing like AGI, but it's almost a meme now that "once upon a time everyone thought that was the holy grail and they're moving the signposts" even when 1) nobody thought that, or 2) some people thought that and some people didn't think that.

What problems wouldn't be solved in this space by an AGI?

In fact, the first thing you learn in an introductory computer vision class is that Marvin Minsky assigned "computer vision" to an undergrad as a summer project in 1966 (wire up a camera to a computer and write a program to understand images). It was the opposite of a holy grail.

Even today, it's hard to make laymen appreciate the advancements in computer vision because for them "seeing" doesn't seem like a difficult thing. Even stupid chickens can see. A chess program is much more impressive to laypeople.

Well I can't find any specific references, but I definitely recall getting that impression from old machine vision work. Decades of work to get models that were incredibly complex and hand crafted and barely worked. I don't know if I thought it would require AGI, but it would definitely require significant progress towards general AI.

I - for what it is worth - would still say general object recognition, with the emphasis on general, is indeed the holy grail.

The ability to recognize objects like people do is not properly represented by current benchmarks. I can imagine that you can built a perfect robotic "bird spotter" but if you put that in a self-driving car I would not be surprised if it stops for something that's just a shadow, or if you put it on a humanoid it's unable to distinguish its own hand from that of its clone. Imagine two of them cleaning out the dishwasher. :-)

A lot of AI is still working only in lab conditions or restricted application domains. That's why I consider robots and cars so important in driving AI towards the "general" dimension.

Now a nice article that addresses some limitations of vision: https://medium.com/@andrewt3000/tesla-mobileeye-and-deep-lea...

Are you sure? I'm convinced this time is different. Sure, it's been applied to vision first, but I believe the techniques can be applied to almost any sensory input.

Just the recognition part won't cut it. Look at AlphaGo. It too several different techniques combined to beat the best human Go player in the world. That's a step beyond object classification, but still not enough for AGI.

I think a "holy grail" could be understanding complicated intentions and social reasoning. Like "he's only doing that so that it seems that he thinks that the other girl doesn't know that he could otherwise not do the etc. etc."

And general common sense reasoning.

Let me assure you: Only CV researchers thought so :)

I was at one time guilty of this. But hey, I was young and stupid :D

> Now we've figured out a way to do general object recognition without AGI.

I'm pretty sure we'll eventually learn to do anything without AGI, as a narrow task.

The trick with AGI is putting all those little things together. Perhaps that's the actual recipe for it, somehow. Turtles all the way down, who knows how many levels.

I think we're going to get better vision before we get hard AI. Just my two cents.

(Then again, I'm biased.)

Author here - I guess "the holy grail" is overstated, but recognizing arbitrary objects is a tough problem that people have put a ton of time and energy into. I think to really get it right in all cases you may need general AI.

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