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  • So once I added your RSS feed to our site, every single article that is published on

  • Vox.com is getting sent through a feed and were just like automatically creating a

  • video for it.”

  • Oh my god that is so crazy.

  • Every single article through our feed.”

  • Every single article.”

  • This is Wibbitz.

  • It’s one of the companies automating news video production.

  • You might call this the robot coming for my job.

  • So this article, our algorithm will just intelligently summarize it into just a quick

  • 30 second to 1 minute video.

  • And then based on the keywords in the article, it’s gonna match relevant media to it.”

  • It’s pretty impressive when you think about all the ways it could get confused.

  • In the beginning it was very rough.

  • People with the same names would confuse it.

  • Turkey the country and turkey the animal would be another example.”

  • Their product was built with machine learning algorithms, and it became more accurate over time.

  • The result is a video made in a few seconds that’s not drastically different from what

  • a human would make in several hours, given the same constraints.

  • Wibbitz is part of a rapidly growing industry of so-calledAI-poweredproducts.

  • The number of companies mentioning artificial intelligence in their earnings calls has skyrocketed

  • in the past 3 years.

  • But the truth is that the termartificial intelligenceisn’t very well defined.

  • What happens with AI is that initially lots of things are called artificial intelligence.

  • It used to be the expert systems; the kind of systems that fly airplanes were called

  • artificial intelligence.

  • Then once they were working and routine and everyone takes them for granted, then they

  • are not called AI anymore.”

  • Right now when people talk about AI, theyre mostly talking aboutmachine learning

  • - a subfield of computer science that dates back at least to the 1950s.

  • And the methods that are popular today aren’t fundamentally different from algorithms invented

  • decades ago, So why all the interest and investment right now?

  • I asked Manuela Veloso, the head of the machine learning department at Carnegie Mellon.

  • You have to understand that there is something very important about these past years.

  • It's data.

  • We humans became collectors of data.

  • Fitbits, GPSes, pictures, I mean look how much credit card purchases, how much data

  • is around.”

  • Certain machine learning algorithms really thrive on big data, as long as computers have

  • the processing power to handle it, which they do now.

  • If computers are the cannon and the internet is gunpowder, these are the fireworks and

  • they have only just begun.

  • In his book, Pedro Domingos offers a nice simple way of understanding

  • supervised machine learning.

  • He says: “Every algorithm has an input and an output:

  • the data goes into the computer, the algorithm does what it will with it, and out comes the

  • result.

  • Machine learning turns this around: in goes the data and the desired result and out comes

  • the algorithm that turns one into the other.”

  • The algorithms are trained to find statistical relationships

  • in the data that allow it to make good guesses when presented with new examples.

  • That means we no longer have an easy rule for what kinds of tasks computers can and

  • cannot do.

  • Ten years ago, I could have said with confidence, we know how this works to computerize something

  • you need to understand all the steps, then you script the steps and get a dumb machine

  • to do it and just follow mechanistically the process that you would have followed.

  • But now we have machines, I shouldn't say we, I don't make them.

  • People have developed machines that learn from data.

  • That makes it harder to say what set of jobs are going to become substituted, readily substituted

  • by automation, and which will be complemented.”

  • A study by the McKinsey Global Institute gets at this question by looking at the many tasks

  • that make up 800 different occupations.

  • And they grouped those tasks into 7 categories: 3 that are highly susceptible to automation

  • with currently-demonstrated technologies, and 4 that are not.

  • Things like managing people, they include things like creativity, they include things

  • like decision-making or judgment.

  • And caring work that requires empathy or human interaction, with an emotional content to

  • associate with it.

  • Those are much harder things to automate.”

  • The report concluded that while most jobs include some tasks that can be automated,

  • less than 5% of occupations can be fully automated.

  • So this idea of occupations and jobs changing may actually be a bigger effect than the question

  • of jobs disappearing, although of course, there are some jobs that will disappear or

  • at least decline.”

  • That’s because most jobs are made up of a bunch of different tasks and most of today’s

  • AI can only do one task.

  • Don’t get me wrong.

  • They can be really good at that task.

  • A deep neural network watched 5000 hours of BBC news with captions and now it can read

  • lips  better than human professionals.

  • And machine learning algorithms trained on images of tumors can predict lung cancer survival

  • better than human pathologists.

  • The mistake is to assume that these focused applications can add up to

  • a more general intelligence.

  • Or that they learn like we do, which is simply not the case.

  • When they get the right answer it’s tempting to assume they understand what they see.

  • Only when they make a mistake do we get a glimpse at how different their process is

  • from our own.

  • It’s pattern recognition masquerading as understanding.

  • That’s why researchers can easily trick a learning algorithm into mislabeling a picture.

  • “A lot of machine learning, at this point, is very superficial and very brittle.

  • It's based on immediately observable features, which may or may not be essential to what's

  • going on.”

  • Last year the director Oscar Sharp produced a short film that was written by a neural

  • network trained on sci-fi movie scripts.

  • The principle is completely constructed of the same time.”

  • It was all about you to be true.”

  • You didn’t even see the movie with the rest of the base.”

  • “I don’t know.”

  • “I don’t care.”

  • It’s great.

  • It makes no sense.

  • Because it doesn’t have what a 5-year-old child has, which is an abstract model of how

  • the world works, why things happen, or what a story is.

  • And why should it?

  • We evolved these things over millions of years.

  • So there's a lot it can do, much more than before but I mean, we humans are amazing,

  • I think. We are very broad, see.”

  • AI applications will keep getting better.

  • Robot voices used to sounds like this.

  • Now they can sound like this.

  • Which means Wibbitz will so be able to offer natural-sounding narration.

  • Algorithms are also starting to analyze video frames.

  • IBM trained a system to select the scenes for a movie trailer.

  • So instead of just pulling generic clips, Wibbitz might pull specific ones.

  • But there’s no clear path toward a more human-like intelligence which includes common

  • sense, curiosity, and abstract reasoning.

  • “I think AI is as good as the content that

  • goes through it.

  • So you can’t really expect AI to do magic which some people expect it to do.”

  • Machine learning algorithms can translate 37 languages but they don’t know what a

  • chair is for.

  • Theyre nothing like us, and that’s what makes them such a powerful tool.

  • Wibbitz will never make this video, but AI could help me make a better one.

So once I added your RSS feed to our site, every single article that is published on

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