Learning to Learn

“I didn’t know robots had advanced so far,” a reader remarked after last week’s post about how computers are displacing knowledge workers. What changed to make that happen? The machines learned how to learn. This is from Artificial Intelligence Goes Bilingual—Without A Dictionary, Science Magazine, Nov. 28, 2017.

“Imagine that you give one person lots of Chinese books and lots of Arabic books—none of them overlapping—and the person has to learn to translate Chinese to Arabic. That seems impossible, right?” says… Mikel Artetxe, a computer scientist at the University of the Basque Country (UPV) in San Sebastiàn, Spain. “But we show that a computer can do that.”

Most machine learning—in which neural networks and other computer algorithms learn from experience—is “supervised.” A computer makes a guess, receives the right answer, and adjusts its process accordingly. That works well when teaching a computer to translate between, say, English and French, because many documents exist in both languages. It doesn’t work so well for rare languages, or for popular ones without many parallel texts.

[This learning technique is called] unsupervised machine learning. [A computer using this technique] constructs bilingual dictionaries without the aid of a human teacher telling them when their guesses are right.

Hmmm… I could have used that last year, when my wife and I spent three months visiting our daughter in South Korea. The Korean language is ridiculously complex; I never got much past “good morning.”

Alpha Go match

Go matches were a standard offering on the gym TV’s where I worked out in Seoul. (Imagine two guys in black suits staring intently at a game board — not exactly a riveting workout visual.) Like the Korean language, Go is also ridiculously complex, and mysterious, too:  the masters seem to make moves more intuitively than analytically. But the days of human Go supremacy are over. Google wizard and overall overachiever Sebastian Thrun[1] explains why in this conversation with TED Curator Chris Anderson:

sebastian thrun TED

“Artificial intelligence and machine learning is about 60 years old and has not had a great day in its past until recently. And the reason is that today, we have reached a scale of computing and datasets that was necessary to make machines smart. The new thing now is that computers can find their own rules. So instead of an expert deciphering, step by step, a rule for every contingency, what you do now is you give the computer examples and have it infer its own rules.

“A really good example is AlphaGo. Normally, in game playing, you would really write down all the rules, but in AlphaGo’s case, the system looked over a million games and was able to infer its own rules and then beat the world’s residing Go champion. That is exciting, because it relieves the software engineer of the need of being super smart, and pushes the burden towards the data.

“20 years ago the computers were as big as a cockroach brain. Now they are powerful enough to really emulate specialized human thinking. And then the computers take advantage of the fact that they can look at much more data than people can. AlphaGo looked at more than a million games.  No human expert can ever study a million games. So as a result, the computer can find rules that even people can’t find.”

Thrun made those comments in April 2017. AlphaGo’s championship reign was short-lived:  six months later it lost big to a new cyber challenger that taught itself without reviewing all that data. This is from AlphaGo Zero Shows Machines Can Become Superhuman Without Any Help, MIT Technology Review, October 18, 2017.

AlphaGo wasn’t the best Go player on the planet for very long. A new version of the masterful AI program has emerged, and it’s a monster. In a head-to-head matchup, AlphaGo Zero defeated the original program by 100 games to none.

Whereas the original AlphaGo learned by ingesting data from hundreds of thousands of games played by human experts, AlphaGo Zero started with nothing but a blank board and the rules of the game. It learned simply by playing millions of games against itself, using what it learned in each game to improve.

The new program represents a step forward in the quest to build machines that are truly intelligent. That’s because machines will need to figure out solutions to difficult problems even when there isn’t a large amount of training data to learn from.

“The most striking thing is we don’t need any human data anymore,” says Demis Hassabis, CEO and cofounder of DeepMind [the creators of AlphaGo Zero].

“By not using human data or human expertise, we’ve actually removed the constraints of human knowledge,” says David Silver, the lead researcher at DeepMind and a professor at University College London. “It’s able to create knowledge for itself from first principles.”

Did you catch that? “We’ve removed the constraints of human knowledge.” Wow. No wonder computers are elbowing all those knowledge workers out of the way.

What’s left for human to do? We’ll hear from Sebastian Thrun and others on that topic next time.

[1] Sebastian Thrun’s TED bio describes him as “an educator, entrepreneur and troublemaker. After a long life as a professor at Stanford University, Thrun resigned from tenure to join Google. At Google, he founded Google X, home to self-driving cars and many other moonshot technologies. Thrun also founded Udacity, an online university with worldwide reach, and Kitty Hawk, a ‘flying car’ company. He has authored 11 books, 400 papers, holds 3 doctorates and has won numerous awards.”

The Super Bowl of Economics: Capitalism vs. Technology

Flippy

Technology is the odds-on favorite.

In the multi-author collection Does Capitalism Have a Future?, Randall Collins, Emeritus Professor of Sociology at the University of Pennsylvania, observes that capitalism is subject to a “long-term structural weakness,” namely “ the technological displacement of labor by machines.”

Technology eliminating jobs is nothing new. From the end of the 18th Century through the end of the 20th, the Industrial Revolution swept a huge number of manual labor jobs into the dustbin of history. It didn’t happen instantly:  at the turn of the 20th Century, 40% of the USA workforce still worked on the farm. A half century later, that figure was 16%.

I grew up in rural Minnesota, where farm kids did chores before school, town kids baled hay for summer jobs, and everybody watched the weather and asked how the crops were doing. We didn’t know we were a vanishing species. In fact, “learning a trade” so you could “work with your hands” was still a moral and societal virtue. I chose carpentry. It was my first fulltime job after I graduated with a liberal arts degree.

Another half century later, at the start of the 21st Century, less than 2% of the U.S. workforce was still on the farm. In my hometown, our GI fathers beat their swords into plowshares, then my generation moved to the city and melted the plows down into silicon. And now the technological revolution is doing the same thing to mental labor that the Industrial revolution did to manual labor — only it’s doing it way faster, even though most of us aren’t aware that “knowledge workers” are a vanishing species. The following is from The Stupidity Paradox:  The Power and Pitfalls of Functional Stupidity at Work:

“1962… was the year the management thinker Peter Drucker was asked by The New York Times to write about what the economy would look like in 1980. One big change he foresaw was the rise of the new type of employee he called ‘knowledge workers.’

“A few years ago, Steven Sweets and Peter Meiksins decided they wanted to track the changing nature of work in the new knowledge intensive economy. These two US labour sociologists assembled large-scale statistical databases as well as research reports from hundreds of workplaces. What they found surprised them. A new economy full of knowledge workers was nowhere to be found.

“The researchers summarized their unexpected finding this way:  for every well-paid programmer working at a firm like Microsoft, there are three people flipping burgers at a restaurant like McDonald’s. It seems that in the ‘knowledge’ economy, low-level service jobs still dominate.

“A report by the US Bureau of Labor Statistics painted an even bleaker picture. One third of the US workforce was made up of three occupational groups:  office and administrative support, sales and related occupations, and food preparation and related work.”

And now — guess what? — those non-knowledge workers flipping your burgers might not be human. This is from “Robots Will Transform Fast Food” in this month’s The Atlantic:

“According to Michael Chui, a partner at the McKinsey Global Institute, many tasks in the food-service and accommodation industry are exactly the kind that are easily automated. Chui’s latest research estimates that 54 percent of the tasks workers perform in American restaurants and hotels could be automated using currently available technologies—making it the fourth-most-automatable sector in the U.S.

“Robots have arrived in American restaurants and hotels for the same reasons they first arrived on factory floors. The cost of machines, even sophisticated ones, has fallen significantly in recent years, dropping 40 percent since 2005, according to the Boston Consulting Group.

“‘We think we’ve hit the point where labor-wage rates are now making automation of those tasks make a lot more sense,’ Bob Wright, the chief operations officer of Wendy’s, said in a conference call with investors last February, referring to jobs that feature ‘repetitive production tasks.’

“The international chain CaliBurger, for example, will soon install Flippy, a robot that can flip 150 burgers an hour.”

That’s Flippy’s picture at the top of this post. Burger flippers are going the way of farmers — the Flippies of the world are busy eliminating one of the three main occupational groups in the U.S. And again, a lot of us aren’t aware this is going on.

Burger flipping maybe to particularly amenable to automation, but what about other knowledge-based jobs that surely a robot couldn’t do — like, let’s say, writing this column, or managing a corporation, or even… practicing law?

More to come.

Check out Kevin’s latest LinkedIn Pulse article:  Leadership and Life Lessons From an Elite Athlete and a Dying Man.