If You Can Turn It Into A Video Game, AI Can Master It

Not long ago the idea that a computer could kick butt playing old school video games seemed like sci fi. In the last few years, it was possible. New techniques knocked the learning time from months to 10 hours. Now researchers have shown that if you throw enough computer chips — 768 CPU cores — at the problem, it can crack it in 21 minutes.

AI researchers are still working on building AI that can handle modern, complex multiplayer games, but at this point that looks like it’ll take years, not decades. And what that means is that if you can create a “good enough” simulation of a task as a game, AI can master it — and at the rate we’re going, AI will be able to master it faster than a human could learn the task let alone get good at it.

Today, we can’t simulate many tasks accurately enough in a game for this technique to pose a threat. But then again, we havent had tons of researchers and incredibly wealthy companies trying to. Similalry, there’s also a big difference between an AI in a virtual world and a robot doing the task for real in the physical world, but how much this difference will make as we get better and better at building “good enough” simulations isn’t clear.

There are two important lessons from this research. First, anybody who says they’re sure robots/AI aren’t a threat to jobs is kidding themselves.

For example, if AI today can — after we’ve spent the time to create a “good enough” simlation — master one type of new skills in 21 minutes, the argument that lots of new jobs will be created to replace the eliminated ones is a lot less reasuring. The reason why that was true in the past was that it took at least a decade if not much longer for new jobs to get automated. Who knows if that will still be true?

All of which is yet another reason to stop obsessing over trying to predict the future and start obsessing over building a more just economy regardless of how many jobs get eliminated.

Second, in robots/AI, size matters — and that’s a real concern for anyone who cares about equality of opportunity. Right now, we’ve got two types of remarkably effective ML/AI technqiues:

  • Those that require massive datasets
  • Those that require massive computing firepower

What an individual or a small team can accomplish today is pretty impressive — check out Fast.AI‘s classes for examples. But for every new trick small fry have at their disposal, the big players have a lot more tricks at their fingertips. And there’s no sign this disparity is likely to change in the future.

That’s why thinking about and organizing for an economy that works for everyone is so critical — even if robots/AI don’t create mass unemployment, this tech could end up creating even greater disparities in wealth that could destroy our society.

Patreon Stats Show We’re a Long, Long Way From “YouTube Done Right”

The Makers All framework argues we need to build grassroots power at the heart of the economy

to ensure that everyone, not just a handful of corporations and the wealthy, gets a seat at the table where decisions get made about the legal and de facto rules governing creative works so that both the creative bounty and the profits are widely shared (e.g., “YouTube Done Right”)

Just how far are we from that goal? Here are some sobering stats about the popular site Patreon.

Founded by musician (and former YouTuber) Jack Conte in 2013, the service reached 50,000 monthly active content creators last year. With the support of one million monthly active patrons, the company estimated that creators would earn $150 million last year.

But how many of these creators are really benefiting?

Graphtreon.com has graphed all active Patreon campaigns since March 2015. According to the website, the platform now has 79,420 creators. Yet, only 1,393 creators (under 2%) earned the federal monthly minimum wage of $1,160 a month in October 2017. Even worse, if changed to $15 per hour, only 0.8%, or 635 creators, made that amount.

In addition, Graphtreon found that the vast majority on the platform earned between $1 and $100 a month.

It wouldn’t be surprising if a good chunk of the 50,000 monthly active content creators didn’t earn much money; many might just be dipping their toe in the water. Even so, these numbers are dismal.

The problem isn’t that no one’s making money.

Successful Patreon creators on the channel earn up to five-digit incomes per month.

But the distribution is skewed heavily towards the top.

And that’s the point the Makers All framework is arguing: if we want to have an economy where many people can make much or all of their living from digital creative works, increasing the number of people with the skills to make these creative works is only part of the story. We’ll need to change the rules of the digital road, and that’s going to require large scale community organizing to build grassroots power.

Lousy Jobs and the Paradox of Automation’s Last Mile

The New York Times rang in the new year with an article about Stanford researcher Timnit Gebru, whose team “analyzed 50 million images and location data from Google Street View” and crunched out a number of interesting observations that previous image analysis couldn’t manage. As you’d expect, project used some heavy duty AI. But human labor was also critical to their success:

But first, a database curated by humans had to train the A.I. software to understand the images.

The researchers recruited hundreds of people to pick out and classify cars in a sample of millions of pictures. Some of the online contractors did simple tasks like identifying the cars in images. Others were car experts who knew nuances like the subtle difference in the taillights on the 2007 and 2008 Honda Accords.

“Collecting and labeling a large data set is the most painful thing you can do in our field,” said Ms. Gebru, who received her Ph.D. from Stanford in September and now works for Microsoft Research.

But without experiencing that data-wrangling work, she added, “you don’t understand what is impeding progress in A.I. in the real world.”

Once the car-image engine was built, its speed and predictive accuracy was impressive. It successfully classified the cars in the 50 million images in two weeks. That task would take a human expert, spending 10 seconds per image, more than 15 years.

According to researchers Mary Gray and Siddharth Suri, this low-paid human labor is the dirty little secret fueling the AI revolution: the “paradox of automation’s last mile.”

Whether it is Facebook’s trending topics; Amazon’s delivery of Prime orders via Alexa; or the many instant responses of bots we now receive in response to consumer activity or complaint, tasks advertised as AI-driven involve humans, working at computer screens, paid to respond to queries and requests sent to them through application programming interfaces (APIs) of crowdwork systems. The truth is, AI is as “fully-automated” as the Great and Powerful Oz was in that famous scene from the classic film, where Dorothy and friends realize that the great wizard is simply a man manically pulling levers from behind a curtain. This blend of AI and humans, who follow through when the AI falls short, isn’t going away anytime soon. Indeed, the creation of human tasks in the wake of technological advancement has been a part of automation’s history since the invention of the machine lathe.

We call this ever-moving frontier of AI’s development, the paradox of automation’s last mile: as AI makes progress, it also results in the rapid creation and destruction of temporary labor markets for new types of humans-in-the-loop tasks. By 2033, economists predict that tech innovation could convert 30% of today’s full-time occupations into augmented services completed “on demand” through a mix of automation and human labor. In short, AI will eliminate some work as it opens up opportunities for redefining what work humans do best. These AI-assisted augmented services, delivered by people quietly working in concert with bots, are poised to enhance our daily productivity but they also introduce new social challenges.

So it may well be that robots/AI don’t end up creating mass unemployment. But that doesn’t mean we won’t end up in a dystopian future, where many people are trapped in very low wage jobs that help propel AI/robotics to new heights, creating vast amounts of wealth for the 1%.

If we don’t want to end up in a dystopian future, let’s make 2018 the year we stop fighting over what robots/AI will do to jobs and start fighting for a better future for all.