Katja Grace, John Salvatier, Allan Dafoe, Baobao Zhang, Owain Evans
2017 · arXiv
Advances in artificial intelligence (AI) will transform modern life by reshaping transportation, health, science, finance, and the military. To adapt public policy, we need to better anticipate these advances. Here we report the results from a large survey of machine learning researchers on their beliefs about progress in AI. Researchers predict AI will outperform humans in many activities in the next ten years, such as translating languages (by 2024), writing high-school essays (by 2026), driving a truck (by 2027), working in retail (by 2031), writing a bestselling book (by 2049), and working as a surgeon (by 2053). Researchers believe there is a 50% chance of AI outperforming humans in all tasks in 45 years and of automating all human jobs in 120 years, with Asian respondents expecting these dates much sooner than North Americans. These results will inform discussion amongst researchers and policymakers about anticipating and managing trends in AI.
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Note that a very significant fraction do believe human level machine intelligence is likely within about 50 years, and a good few on closer timescales, though the precise numbers are endlessly debatable. Two major AI corporations, DeepMind and OpenAI, are explicitly aiming to create AGI. https://arxiv.org/abs/1705.08807, https://www.youtube.com/watch?v=HOJ1NVtlnyQ
If the article's main claim was "AGI is imminent", that would be a valid criticism. But it isn't (as the article says explicitly). The main claim is that technological progress is hard to forecast in general, especially for those not personally at the cutting edge of the field, and that almost no one right now is even really trying. Therefore, we should be very uncertain about AGI timelines. There's plenty of historical evidence, both in this article and elsewhere, to back up those claims. (edit: I think your point about Winograd as a binary task not being explained clearly is valid, but that's not the article's main focus) (edit 2: As far as I can tell, "trusting the…
experts" here means believing that we are very uncertain about AI timelines, which is essentially this article's main claim. All expert surveys I'm aware of confirm that the average AI expert is uncertain, and that there's also lots of disagreement between experts in the field. See eg. the recent paper by Grace et al.: https://arxiv.org/pdf/1705.08807.pdf) (edit 3: "No expert was surprised with Alphago." just isn't true. See eg. this discussion: https://www.reddit.com/r/baduk/comments/2wgukb/why_do_people.... Hindsight is always 20/20.)
Nope, not at all. Like I said, we have had autonomous vehicles operating in normal traffic for decades now. Autonomous vehicles do not require AGI, far from it. Waymo, Uber, Tesla and more are all competing to bring autonomous vehicles to the mass consumer market and indeed most estimates claim that we'll have autonomous trucks by 2027: https://arxiv.org/abs/1705.08807 I hate how everyone thinks they know enough to talk about AI because it's so buzzy/trendy right now. Modern AI is not pretending to be AGI. No one is claiming to be going for AGI, and whatever successes we have been seeing lately have to do with applied AI in solving specific problems, not AGI. https://en.m.wikipedia.org/wiki/History_of_autonomous_cars BTW did you even look at the survey? Because that's the opinion of actual AI researchers across the world. This is easily Google able info, BTW, clearly your background is not AI.
Modern AI, things like AlphaGo are examples of applied AI. "Common sense" falls within the realm of artificial general intelligence, which is a line of research that's largely abandoned now in favor of applied AI. Modern AI solutions are engineered to solve very very specific problems. You are never going to see attempts to teach "common sense". https://arxiv.org/abs/1705.08807 With that said, the above is what the world's AI researchers think is possible hopefully within my lifetime using just applied AI without the notion of "common sense". Common sense is AGI. That's not the goal anymore. The goal is to do things like self driving cars. Both Google and Tesla have…
placed vehicles on the road that have driven for literally millions of miles. The idea is to build a bunch of classifiers and regression models and use them together in an ensemble to solve your problem. The same approach is being applied successfully to a lot of unrelated fields where deep learning is concerned. Also, modern AI doesn't even pretend to be biological in nature, in fact we'll known researchers like Andrew Ng make a point in saying that they are only biologically inspires and that's where the commonalities end. There are other models like HTM that are way more ambitious and want to come up with a single generalized scheme to solve a broad range of problems, AGI style. These guys think biology is important and are trying to emulate the neocortex. They ARE going for AGI, common sense, etc.
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