So, to look at the words:

  The notion that software engineers are not 
  responsible for things that go wrong will be 
  put to rest for good.

  The notion that *some* software engineers *cannot*
  be found as responsible (in part or in whole)
  for *some* things that go wrong will be 
  put to rest in *some* situations.

Meanwhile, there is very obviously a line to be drawn between the programmer that programs their VCR clock to time a recording, the programmer that programmed the VCR as a consumer-grade product intended for purchase by unlicensed individuals, the TV network that broadcast the television show at the time the individual programmed their VCR to record 60 minutes of broadcast on a given channel, and the programmer who locked me out of the firmware on my smart phone.

That sounds like a really hard problem to solve. Not only would you need to have an audit trail for the history of changes to the code, you'd also have to figure out a way for third parties to audit the code and make sure they're looking at the same data that is being released by the project.

Additionally, reacting to arbitrary changes in the environment probably requires more resources than even a multinational corporation can provide. So you'd need a way for third parties to be able to make their own changes to the software, somehow add them to the codebase without creating too much administrative overhead for the core team, and audit those changes in an automated fashion so that they don't create thousands of new exploits.

And how would you even manage the sum total of all these Frankenstein versions of the original software with the changed versions? How would all these geographically disparate groups of programmers even communicate?

We obviously need funding for a lot more CS professors to come up with solutions to these issues.

This tech is almost inevitable. We are creator beings. We want to make something that matches our intelligence-- and we always have. We've made pantheons of Gods to match and overcome us, we've made these autonomous beings in the stories we tell and in the dolls we played with when we were kids. The problem is that we don't know what machine intelligence may do when we let it out in the world. If they can learn the tasks we want them to learn, can they then learn how to learn a task we didn't assign them to? What kind of task would that be? How would we control it?

There may be no controlling it. But if we started focusing on this problem right now we may be able to figure it out. Instead, most everyone in AI is working to pump out as many "smart" things as possible, trying to develop better learning algorithms, get AI to become as human as possible, trying to make machines that behave like us but better, without paying attention to the future costs.

Maybe for the first time in human history we can learn without making the mistake.

- that documentary was pretty unhelpful

- Terminator images are usually inappropriate

- AlphaGo Zero (if not other Alpha•s) was pretty cool"""

(https://twitter.com/Miles_Brundage/status/983063456424308736)

I don't have any survey results to point to, but my impression from following AI researchers from industry/academia is:

* Modern methods are many leaps of understanding behind anything resembling AGI, so any concern about research groups developing a sentient computer program behind close doors with no warning is probably misplaced.

* AI/ML causing large-scale unemployment will be a serious issue eventually, but it's difficult to make a strong case that's it's happening right now.

* The ability to monitor and manipulate individuals using ML/AI is dangerous, doesn't depend on particularly advanced technology, and is already being used by corporations and governments right now. It's a lot easier to get the public worried about terminator-style robots than about (what appear to be) simple advances in advertising or law enforcement.

* There's a strong incentive for those selling "AI technology" to oversell its ability to generalize/improve automatically. To quote Elon (of all people), "It's a mistake to think that technology automatically improves. It does not automatically improve. It only improves if a lot of people work hard to make it better."–this applies to "deep neural networks" as much as anything else.

There remains the possibility that there is a difference between "the set of top experts in the field included in this documentary" and "a survey or statement from the larger share of practitioners within the field". This ... runs into a few additional problems.

Documentaries can (and with some frequency are) selective and nonrepresentational. That is, a documentary's goal is not to Reveal The Statistical Truth, but To Tell a Specific Story. Documentaries are driven by narrative, not random sampling and statistical analysis.

That said, stats can lie and crafted stories can be quite useful.

A large-scale sampling of opinion is also ... largely just that. A large-scale sampling of opinion. Even if that's expert opinion. It is not the same as arriving at a truth (unless the truth you're seeking to arrive at is "what is the typical or general opinion held on some matter by some population?").

The views and concerns of top practitioners within a field is often highly significant. It may still be inaccurate (or be inaccurately portrayed). But these are the people who've worked with a thing for a long time, who've seen what does and doesn't work, what is or isn't hyped. And you'll often find exceptionally strong critics of various fields or technologies.

Leading atomic scientists in the U.S. and USSR both came to oppose nuclear weapons: Robert J. Oppenheimer and Andrei Sakharov. The Father of the Nuclear Navy, USN Admiral Hyman Rickover, came to oppose nuclear power. There are numerous technologists who are now questioning the goal of universal connectivity (myself included, though my qualifications are expressly not available for or any basis for credence). And in the field of AI, there are numerous significant, long-term, and leading researchers who are raising profound questions of advisability and risk.

Or, you know, you could go with the Good Humour guy, Pinker.