Hacker News Reader

Comment by laurent123456 | Hacker News Reader

> For example, how many human jobs did AI replace in 2016? If you gave professional pundits a multiple choice question listing these three answers: 3 million, 300,000 and none, I suspect very few would choose the correct answer, which is of course “none”.

It's probably very hard to evaluate the impact of AI, but their answer is not any better (nor sourced) than that of the professional pundits they criticise (by the way, I can't find any article of anybody claiming that jobs have been lost due to AI). Rubbish theregister article as usual.

View on HN

The way in which technology replaces jobs is not usually explicit. Cell phones and email and automated voice mail systems replaced a lot of receptionists. Desktop computers replaced pretty much all the typists in the typing pool and a many secretaries. Smart phones replaced a lot of the secretaries that were left.

The thing about AI is that it doesn't seem like AI in the rear view mirror. Grammar check and voice recognition don't seem like a big deal twenty years later. But it was science fiction in real life twenty years ago.

> The thing about AI is that it doesn't seem like AI in the rear view mirror.

This. Related link: https://en.wikipedia.org/wiki/AI_effect

I'm guessing that a Data or HAL would be different than examples of narrow AI.

It's really hard to define AI too. We are working on linear regression for predicting sales. Would that qualify of AI?

To us it's just a bunch of algorithms on Apache Spark but for our end users it's magic, it's AI.

I read a good explanation of different terminologies of AI here on HN not long time back.

The problem with AI is that people are expecting the AI, and what we have is an AI. Predicting sales takes some amount of intelligence. A fish couldn't do it, for example. Nor could an untrained human. So if a computer predicts sales from data given to it, yes, it's an artificial intelligence.

Now people will judge it and say that it's not AI because it can't do this or that or because it's too specific to a domain. But why would you buy an AI that's as smart as a human in every way and then use it just to predict sales? An AI replicates one part of human intelligence. The AI is a fully functional artificial human. Two completely independent ideas. We haven't had a single AI capable of fully replacing a human in every way, but is that something we really want or need? Or are domain specific AIs good enough?

Anything that takes the repetitive cognitive load off a human and puts it into a specially trained computer is an AI.

> Anything that takes the repetitive cognitive load off a human and puts it into a specially trained computer is an AI.

This is just watering out the term AI to the point of uselessness and/or ridicule.

I mean, by your definition, the steam-engine and punched-hole-card powered Jaquard looms of the early 17th century are an AI. Of course, those were the precursor to Babbage's general mechanical computer, so they were actually "specially trained computers".

Edit: 19th century, not 17th. "There are two really hard problems in programming: cache invalidation, naming things, and off-by-one errors."

As far as I know, the steam engine didn't replace anything that used to require human thought. A mechanical loom isn't replacing human intelligence either. Yes, it replaced a human performing an action, but humans brains weren't exactly racked with figuring out how to sew or weave.

Think about the word "artificial" and the word "intelligence". If it used to take a human thinking and reasoning about something to perform a task but now a computer is doing it, that is by its very definition, "artificial intelligence". A steam engine or mechanical loom don't replace human brains, they replace human hands. Which is why I said "cognitive load".

There has certainly been advances. It does not happen overnight - we stand on the shoulders of giants. The advances in speech recognition is in the mainstream media today but decades of work and research have contributed to the advances. Similarly the work today will contribute to future advances and what 2016 did was give the readers of mainstream media a glimpse of what we will see in the years to come.

I think there is a problem in this article in conflating the news about the coming wave of technological unemployment with "AI only"

I've had the opportunity this year to pay very close attention to the AI debate since my girlfriend is doing her masters in social policy and her focus is on technological unemployment. (and the policy implications of the same)

This is an umbrella term under which AI lives under. Essentially any job which goes away to automation falls under technological unemployment.

Consider the 60,000 foxconn employees http://www.bbc.com/news/technology-36376966 rendered jobless because of TEU. Their further plans to neat fully-automate http://www.theverge.com/2016/12/30/14128870/foxconn-robots-a... their factories.

Those aren't necessarily AI but they are unemployed. The distinction isn't really that important.

Now if we consider the public policy estimates of the number of jobs "at risk" of TEU in the next 5 to 10 years, we are looking at as much as 50% to 60%.

One particularly interesting line in one of her papers was that the estimates were as much as 40% "unless natural language processing reaches pairity with humans in the next 5 years" ... Microsoft achieved that pairity that a few months ago. http://blogs.microsoft.com/next/2016/10/18/historic-achievem...

Kristin Lee at the white house recently published some really interesting papaer on AI, Authomation and the social impact of it https://www.whitehouse.gov/blog/2016/12/20/artificial-intell... my girlfriend sent me the link with the subject "the whitehouse just stole my work!" - though it's really just parallel evolution, they're saying basically what she's coming out with too. (incidentally, if anyone reads this and has Kristin's contact info, I'd love to introduce her to my girlfriend, I think they'd be able to trade ideas)

Anyways - suffice it to say - Artifical Intelligence is only "fake news" if you've got your head in the sand or you decide to creatively misinterpret the literature.

Well said. One thing to point though, NLP leans towards the understanding of the semantic meaning of a sentence, while the article you referred here talks about speech recognition, which focuses on primarily audio to text.

Comparing to speech recognition, NLP currently doesn't enjoy the same level of success by adapting DL technique, AFAIK. By success, I mean parity with human level performance. Current techniques are far from understanding reasonably complex commands/requirement expressed in everyday language and react to it correspondingly.

IMHO, this is as hard as solving the AGI problem itself. Having a chatbot that people can talk to without realizing it is AI or not, is practically indistinguishable from mastering true intelligence, as is defined by Turing Test.

Reply on news.ycombinator.com