Comment by Barrin92 | Hacker News Reader

Comment by Barrin92 | Hacker News Reader

In my opinion the term intelligence itself is misplaced for machine learning tasks. Every problem that is solved with ML and "big data" appears to me to be a perception problem (which wouldn't be surprising because the mechanism is inspired by human vision, not cognition, which it lends itself to naturally).

As a specific example, a few months ago or so openai released their text generation tool and branded it as "too dangerous too release", claiming it could , with the help of AI, generate believable texts.

But what it generated was simply natural sounding gibberish. There were plenty of sentences in the text along the lines of "before the first human walked the earth, humans did..""

What, for me at least, lies at the core of intelligence is understanding semantics. An intelligent system can recognise the sentence above as flawed because it could extract meaning.

Everything coming out of the field of ML seems to me just like sophisticated statistics. In many ways symbolic AI to me still seems more valuable, profit aside.

I agree. Extracting meaning is NOT a math problem, meaning comes from the human context and context is infinite. Hence different humans extract different meanings.

I think AI and ML are great for processing large amounts of data and looking for patterns. Patterns on their own don't mean anything though, it's always up to us to interpret them.

Right, that tool makes gibberish and didn't understand much of anything.

AI research actually started by focusing on symbolic AI but eventually it was found to be too difficult to define all of the symbols. See the Cyc project.

AGI as a field aside from narrow AI/narrow ML has been making useful but not mind-blowing progress for decades. The sidebar and recent posts/post history on Reddit r/agi has useful links for learning about the field. Also more and more posts on r/machinelearning are providing more general purpose tools that address some problems like better semantic understanding.

There is a promising strain of research that is focusing on core AGI requirements. One of the big challenges is bridging the gap between low level sensory information and high level concepts. This is known as the symbol grounding problem. In my mind the approaches tackling that type of challenge have a lot of promise. And the amount of research in that area is growing.

In the text generation tool outlined above (and indeed many of the convnet-based visual networks), the hidden layers are there precisely to extract 'meaning'. The lower layers (closer to the source input) deal with syntax and feed upwards to hidden layers that extract semantic features, which in turn feed upwards to more layers, each with a bigger overview of the semantic features and thus ultimately the context. That's the idea anyway.

>the hidden layers are there precisely to extract 'meaning

That is just wishful thinking, no? I mean, there is no particular reason to think that the hidden layers will actually do this with any high degree of success.

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