That's kind of broad. Also, the robots shown have very little "AI".

Maybe machine learning has reached a peak. It's routine now to make classifiers that are about 90% accurate, and really hard to get much beyond that. What we really have are systems which extract lots of signals from an input set and construct a statistical model that maps signals to results. This works moderately well with enough data, but hits a limit at some point. It's great for the class of problems where that's good enough. Like ad targeting and search. Not so great where a wrong result is a serious problem. Like self-driving cars and medical diagnosis.

I wonder what the next idea will be. I'd like to see progress on "common sense", defined as being able to predict the consequences of real-world actions as a guide to what to do next.

This worth reading - a serious and interesting write up.

I'm concerned by the language around how the economy works here "that suggests that the market expects..." The market doesn't expect a thing - the market is made of a herd that flocks from shiny thing to shiny thing. It's not a rational system and modelling it as such is surely completely debunked at this point?

Also the section on regulation is narrow; the responses cited - putting managers in, licensing a regulator to check outcomes - are weak and old fashioned. Two fundamentals are not mentioned; first an investigative and proselytizing enforcement agency as per areospace that can identify specific routes to failure and then communicate them to the professional and business actors. Secondly professions in the sense of proper engineers who are personally liable and insured and required for the use of the technology (brew what you like in your room, but if you use it on people then prison beckons). Third process and infrasturture that is required for use and that no professional would contemplate life without (like design drawings and stress analysis in engineering).

The section on the political economy of AI is interesting, it put me in mind of the latest William Gibson book "Agency" in there there is a quote - something along the lines of "at the boundary of unauthorized military research and the most reckless kind of commercial use" describing the origination of an AI. The framing and narrative of the book is much more plagent and potent than the literature of economic analysis; the data and logic of the economists so far are not as convincing as fiction and polemic. If I was an economist I would be concerned by that.

Well-sourced conference report. Actually saves me a lot of time. To summarize: it's still the Wild West in AI. But there is broad recognition that governance is essential

I'll just add one more report, as if the dozens already mentioned were not enough. It's from Berkeley's Center For Long-Term Cybersecurity. And it addresses the enormous challenge of securing AI systems from adversarial attack. A glimpse into the vortex of how the "industrialization of AI" creates a self-perpetuating, fractal-like cycle of eternal dependencies. Requiring us to create ever stronger AI to protect and serve the AI on which our new engineering platforms will be founded upon

https://cltc.berkeley.edu/wp-content/uploads/2019/02/CLTC_Cu...

It stands to reason then, the ultimate AI-mediated prediction problem is predicting the impacts of AI itself ;)

At least in my bubble, AI discussions have been going in predictable ways.. and getting grounded in the same places.

First, we try to build the (proverbial) foundation: What is AI? What is intelligence? Is it general? Lots of places to get stuck here. Can machines understand meaning? Is general intelligence statistical. Can it be?

No real way of settling these, so we have poor foundations.

Then we ask: What can it do? When? What will it do? Why? Can it automate driving? Other stuff? How big a deal is this economically? Will all cars be taxis?

At this point, foundations crack. We're trying to predict the economic side-effects of a technology, its viability, timelines, regulation... and we're building these predictions on very abstract foundations. Obviously, it's all too squishy so we end up nowhere.

Anyway, the effects of technology are very hard to predict... especially recent ones. Computerisation of offices has not measurably increased productivity since the 80s/90s[1], for example. A PC landed on every desk. Many more people work at a desk than before. What predictions would we have made in the 90s, when this was starting to look inevitable.

[1]David Graeber, Tyler Cowen & others highlight this point. It's hard to define or measure, so most economists don't. But within wide margins of error, it does not seem to

Had hoped an article like this would shed some light on the environmental impacts of the additional compute required to build and maintain a feature backed by AI. My understanding is it takes significantly more compute + data, and therefore electricity, to build and maintain a feature baked by AI. As AI becomes increasingly accessible, particularly through offerings from cloud providers, the power consumption would increase faster than when folks were exclusively writing scripts/uploading binaries to process transactions. My hope is it's negligible. Haven't had the time to crunch numbers to figure this out as it's out of my daily lane.

Maybe I skimmed the article too fast and missed it while enjoying my coffee and bagel sandwich. Let me know if that happened.

related to ide.mit.edu ?