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I'd argue we hit the tipping point more than ten years ago. Finance and a lot of tech (e.g. google) had already been transformed by machine learning at that poi
by rm999 14y ago
I'd argue we hit the tipping point more than ten years ago. Finance and a lot of tech (e.g. google) had already been transformed by machine learning at that point. At my last job we were working with some pretty complex neural network models that were first developed in the early to mid 90s.
AI research died off in the 1960s and 1970s because the results didn't even get close to matching the expectations. Machine learning's current commercial success ensures that won't happen in the foreseeable future. That said, it's worth tempering our enthusiasm. To put it perspective, deep networks are extremely far from recreating general, human-level intelligence. There's a long way to go, and I'm not convinced the path we're on is the 'correct' one to reaching the kind of general AI people see in the movies.
- eli_gottlieb 14y agoI was under the impression that "Turing Test-passing general sapience" was actually a pretty niche field in AI/ML these days. Most everyone seems to have found it cheaper to build a special-purpose machine learning engine/algorithm for their particular problem than to wait for a speaking, reasoning human-equivalent computer.
- Houshalter 14y agoTrue general artificial intelligence would be capable of actually solving problems. Which means it could write code, engineer things, maybe even improve itself, etc. Most machine learning is limited to mindlessly exploring a search space which makes it good mostly at pattern recognition. But they are pretty terrible at solving difficult problems or writing code and things like that. Including special purpose algorithms. We have the learning part down (somewhat), but the intelligence part not so much.