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There is a blind spot in AI research

nature.com

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Re: There is a blind spot in AI research

#41

Post by goodplay »

hyperpallium wrote: post #33

I wish HN discouraged clickbait titles, even when the article itself uses that title. One solution is to append the answer to the original title, separated by a "|", as used by: https://www.reddit.com/r/savedyouaclick/ For example: There is a blind spot in AI research | Auto­nomous systems are already ubiquitous, but there are no agreed methods to assess their effects If it's interesting, I'll still click .

I second your proposal.

While I never post a comment on an article before reading it, I almost always read the comments before reading the article to avoid click baits. An brief one-line summary that serves the same role as an abstract would eliminate the need to read the comments first.

It might even help eliminate clickbaity titles in technical articles altogether.

Re: There is a blind spot in AI research

#42

Post by pmyjavec »

ggchappell wrote: post #2

Key quote: “People worry that computers will get too smart and take over the world, but the real problem is that they’re too stupid and they’ve already taken over the world.” -- Pedro Domingos, in The Master Algorithm (2015)

Calling people stupid isn't cool, it's unproductive overall and there is not place for it no matter how educated someone is this.

Society is ill right now and maybe people are hoping artificial intelligence will take them to a better place?

Re: There is a blind spot in AI research

#43

Post by tintor »

pmyjavec wrote: post #42
ggchappell wrote: post #2

Key quote: “People worry that computers will get too smart and take over the world, but the real problem is that they’re too stupid and they’ve already taken over the world.” -- Pedro Domingos, in The Master Algorithm (2015)

Calling people stupid isn't cool, it's unproductive overall and there is not place for it no matter how educated someone is this. Society is ill right now and maybe people are hoping artificial intelligence will take them to a better place?

He was talking about computers, not people.

Re: There is a blind spot in AI research

#44

Post by goodplay »

Animats wrote: post #34

Scary robot video of the month.[1] This is not just a blind, repetitive operation; previous X-rays and laser scans tell it what to do. [1] https://youtu.be/MZIv6WtSF9I?t=245

As morbid as it may be, I wonder if that system can tell the difference between a lamb and a human of comparable size. The system might still able to identify (or misidentify) all targeted joints.

Re: There is a blind spot in AI research

#45

Post by frozenport »

ChoHag wrote: post #10
frozenport wrote: post #4

The problem is the AI community is treating itself like non-experts. Explaining that AI needs to be controlled by telling horror stories of robot domination is good to motivate research work to lay people, but is a distraction for professionals.

So the "robots are going to kill us" meme is good because it makes "the rest of us" interested in the idea of AI?

Kinda, but if you treat your own people (the AI community), like "the rest of us" - well then you disrupt real scientific work.

Re: There is a blind spot in AI research

#46

Post by basch »

hyperpallium wrote: post #33

I wish HN discouraged clickbait titles, even when the article itself uses that title. One solution is to append the answer to the original title, separated by a "|", as used by: https://www.reddit.com/r/savedyouaclick/ For example: There is a blind spot in AI research | Auto­nomous systems are already ubiquitous, but there are no agreed methods to assess their effects If it's interesting, I'll still click .

i like this too

buzzfeed/upworthy had such an impact on the world of journalism, that after an eon of only ever using the original headline, techmeme was forced to start rewriting headlines

here was their post outlining their decision http://news.techmeme.com/130906/headlines

Re: There is a blind spot in AI research

#47

Post by Houshalter »

pmyjavec wrote: post #40
Houshalter wrote: post #37

This is vastly overblown. People already vastlh distrust algorithms. Psychologists have studied it and called it "algorithmic bias". That when given a choice between computer and human, even when the computer makes much better predictions, people distrust it. In almost every domain where there is data and a simple prediction task, even really crude statistical methods outperform "experts". This has been known for dec…

Release the algorithms! In fairness though, it sounds like the point you and others are often making is this. Humans are now considered dumb, bias and unreliable. So we need to invest in some kind of external policing system (AI) to run our world for us and make sure we're doing it right. Basically establish reliance on something external to ourselves? This is sad because it sounds like we're losing faith in ourselve…

Humans are just not good at doing certain kinds of tasks. We can add numbers, but nowhere near as fast as a computer can. Similarly we can see patterns in data, but not to the exact precision of a statistical model that has it's parameters optimally tuned with gradient descent and bayesian inference. Humans will never be as good as statistical algorithms at certain tasks and that's ok.

I see fear about algorithms everywhere. Previous articles insist that algorithms could be unfair or racist. This article suggests things along those lines as well. The EU recently banned perhaps the majority of applications of machine learning, in any place where they might be used to rank individuals. This fear is hugely setting back society and technological progress. And almost every one of these places will have to revert back to human judgement. Which by every measure is far worse and far less fair.

Re: There is a blind spot in AI research

#48

Post by eslaught »

Houshalter wrote: post #47
pmyjavec wrote: post #40

Earlier quoted context omitted.

Release the algorithms! In fairness though, it sounds like the point you and others are often making is this. Humans are now considered dumb, bias and unreliable. So we need to invest in some kind of external policing system (AI) to run our world for us and make sure we're doing it right. Basically establish reliance on something external to ourselves? This is sad because it sounds like we're losing faith in ourselve…

Humans are just not good at doing certain kinds of tasks. We can add numbers, but nowhere near as fast as a computer can. Similarly we can see patterns in data, but not to the exact precision of a statistical model that has it's parameters optimally tuned with gradient descent and bayesian inference. Humans will never be as good as statistical algorithms at certain tasks and that's ok . I see fear about algorithms ev…

The algorithms themselves may not 'choose' to discriminate, but they certainly can be used in a way that causes discrimination due to an oversight on the part of the algorithm's designer, even if not intended.

See e.g.:

Fairness as a Program Property, Aws Albarghouthi, et al, FATML 16 http://pages.cs.wisc.edu/~aws/ (Note: I can't find the paper link, maybe the conference hasn't occurred yet, but Aws gave a pre-talk on this topic already.)

Part of the problem with algorithms is that they allows us to be sloppy in our assignment of responsibility. We think "the computer can't be biased", which is of course true, but ignore the fact that the human designer of an algorithm could have made a mistake. And because of the nature of computer programs, these mistakes can be arbitrarily subtle. The above paper (I'm recalling from the talk now) applies certain probabilistic reasoning to prove that certain kinds of programs are "fair" for a certain population distribution and for a very limited set of language features (e.g. no loops). But static analysis is a very hard problem and it is unlikely we'll ever see a solution that generalizes well to anything we'd recognize as a useful programming language.

Edit (finishing my line of thought): So certainly bias exists in either case. I'm not trying to claim that using algorithms increases bias. However, algorithms can cause the decision process to be opaque, and in that sense 'hide' the bias. Unfortunately, it seems that if we want to use algorithms in these settings, we'll need either rigorous models like the above that are amenable to static analysis, or else give up and return to where we were before.

Re: There is a blind spot in AI research

#49

Post by kevindeasis »

hyperpallium wrote: post #33

I wish HN discouraged clickbait titles, even when the article itself uses that title. One solution is to append the answer to the original title, separated by a "|", as used by: https://www.reddit.com/r/savedyouaclick/ For example: There is a blind spot in AI research | Auto­nomous systems are already ubiquitous, but there are no agreed methods to assess their effects If it's interesting, I'll still click .

This would save lots people time

Re: There is a blind spot in AI research

#50

Post by lqdc13 »

Houshalter wrote: post #37

This is vastly overblown. People already vastlh distrust algorithms. Psychologists have studied it and called it "algorithmic bias". That when given a choice between computer and human, even when the computer makes much better predictions, people distrust it. In almost every domain where there is data and a simple prediction task, even really crude statistical methods outperform "experts". This has been known for dec…

Why do humans distrust "algorithms"? Maybe they had past experiences where algorithms behaved worse than humans?

A recent example from recently: facebook replaced human-curated news with machine-curated, they started trending fake news [2].

Another example is algorithms that try to help you during automated phone calls, hence people always try to get to a human. This is because the speech-to-concept parsing/mapping is flawed or because they're not programmed to perform some specific tasks.

Another example is self-driving cars. Google cars have been involved in more accidents per mile than average humans[1].

In general, people's intuition is built through repeated encounters, that's why it is so great.

[1] https://www.bloomberg.com/news/articles/2015-12-18/humans-ar... [2] http://www.theverge.com/2016/8/30/12702478/facebook-trending...

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