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Comment by FirmwareBurner | Hacker News Reader

What's the difference between AI and just pattern matching here?

I mean, you take a data set of how people with X health condition look like, train your model on that set, then the model can, with a certain probability threshold, tell you if other people have X condition as well. But to me, a non-AI average joe programmer, that's more pattern matching, and less AI.

That kind of application was something I remember some colleagues in university were working on over 10 years ago in OpenCV and Tensorflow, using labeled sets of X-rays and CT scans to pattern match conditions for aiding in diagnostics for radiology lab techs, well before the AI explosion.

What am I missing here?

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The history of AI research has been one of moving goalposts. Technologies only stay "AI" until they're shown to be useful. At various points in the past, computing in general, self-modifying code, search algorithms, computer vision, chat bots, neural networks, stable diffusion, large language models, and more, have all been considered "AI" at various points in their development. Occasionally, people will demonstrate that a topic in "AI" has a broader usefulness, and the specific name will be used, and people will argue online whether it's "really AI".

About 15 years ago I learned about several of those topics in an AI course in college, and now they're all frequent occurrences in debates about whether they're really AI.

What is AI, or what counts as AI, has always had a large element of "things seem hard must be AI"

For years most people agreed that a computer beating the best human at chess would be "real" AI. Then they managed it, but people said "not that does not count, you just brute forced it".

What you describe, the automatic construction of classification models was textbook AI for over 20 years (using a whole range of methods), now it's often just seen as "data science".

>What am I missing here?

Perhaps how the pattern matching is performed?

Whether this is "AI" or not, I don't know. But dismissing it as mere "pattern-matching" grossly simplifies the technology and it's potential usefulness.

>I remember colleagues in university were working on over 10 years ago

Yes, this is how we get better technology, by continuing to work on problems.

AI (LLMs etc) exhibits "emergent behavior" properties. things like reasoning and other higher order effects. It could be mimicry (parrotting or cargo culting) but it seems like there is something that that isn't in a bunch of if/else statements or pattern matching.

If this is remotely like my experience with ChatGPT, doesn't that run the risk of hallucinating? Versus pattern matching just telling you "This X-ray shows a 75% chance this person has condition X".

Can you at least interact with it and have it explain to you its reasoning like "so my diagnostic is based on seeing a lump in the lower right hand corner"? That would be an innovation indeed.

> AI (LLMs etc) exhibits "emergent behavior" properties. things like reasoning and other higher order effects.

I would disagree with this statement. The model is _reasoning_ via the method of processing the data since the weights are informed from the relationships of prior similar information, but at its heart the model is still just doing pattern matching. The primary distinction and what I disagree with in your statement is that these AI models are closer to AGI because there aren't a bunch of if/else statements or basic pattern matching.

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