How long ago is long ago? How fast is fast?

So apparently, the first iPhone came out 10 years ago today. That kind of hit me.

The first iPod came out in October 2001, 16 years ago.

The World Wide Web was made open to the public in 1991, 26 years ago and it didn't become widespread for a while after that let alone a commonly understood concept.

Does all of that seem like a time long, long ago and far away?

Or does it perhaps not seem so long ago at all?

Ten years isn't a very long time to completely change the way people communicate and spend their free moments. Twenty six years isn't very long to change the entire way we interact with knowlege, information and each other. If you're over 40 these are things you remember happening and which were preceded by a whole prior world where those technologies could not even be conceived of. The world is indeed changing quickly, but some people have internalized that change as normal while other feels constantly pushed back into their seat, as if on a plane, waiting for the acceleration to end and the cruising phase to begin.

So here's the truth, the acceleration phase isn't going to end any time soon.

For example, it might seem the peak of cell phone technology must surely be approaching as each iteration of those supercomputers in our pockets seems more and more similar to the last. But the power underneath them and the abilities they can connect across the internet are only going to continue accelerating in power and complexity. The continuing acceleration is being driven by applications of the latest research from Artificial Intelligence and Machine Learning.

Many people are justifiably worried about employment in a world running at this pace. They feel like the world is moving along and leaving them behind. In any country there are many reasons for unemployment but technological change is a large part of it and going forward it will be an increasingly important part of that. So maybe people need to ponder about what 'long ago' and 'fast' mean to them these days. Even more importantly, we all need to talk about this more deeply and start finding solutions that allow society to continue to grow and flourish even if the nature of technology in our lives and the nature employment itself changes in the coming years.

Here are some recent articles on the topic for further thoughts:





On the Importance of Being AlphaGo

Victory for #teammachine! Plus a very informative consolation prize for #teamhuman.

This is a response to a couple articles (Beautiful AI and Go, Go and Weakness of Decision Trees) from my friend over at "SE,AG" about the recent match between DeepMind's AlphaGo and World Champion Go player Lee Sedol.:
"Is the Horizon effect something you can just throw more machine learning at? Isn't this what humans do?"
That's what I was going to say in response, but you beat me to it.

Obviously, you could build deeper trees. But the reason these games, even chess, are hard to handle is you can't build larger trees indefinitely. And would it be really satisfying if AlphaGo built ridiculously deep trees, peering into the future more than any human could, in order to defeat a human's uncanny ability to built compact trees using heuristics?

That was why the DeepBlue victory felt a little hollow, as far as I remember, it was mostly tree search with lots of tricks. But AlphaGo uses treesearch, a database of amateur games it has watched and Reinforcement Learning. This means it can really learn from each game it plays, even from playing itself (enter player 0). This is the most exciting thing for me about this victory, that it's a powerful use of RL with deep learning to solve something no one thought would be possible for another decade or two using compute power alone.

Also, on a more pedantic point, AlphaGo doesn't use Decision Trees, which are good for learning how to classify or predict data. Rather it uses Monte Carlo Tree Search, which is an area of research in decision making that simulates forward different scenarios based on the current policy to evaluate how good it is. In many algorithms these trees can be throw away and the policy is updated to take the actual action forward. I think this is what goes on people's head when they play a game like chess or Go. But humans are very good and intuitively picking just the branches with the most promise.

I haven't looked into it in detail yet but I image that part of the Deep Learning element that Alpha Go uses is for building this kind of intuition, so that it doesn't treat all possible choices equally.

How Bad is it Going to Get?

Here's another great article about impending job losses over the next decade due to advanced automation, and it's not just factories anymore. As the tasks that robots and all kinds of  intelligent software systems become more subtle we may approach a point where there there will be a net decrease in total jobs.

"They also said that robots and so-called digital agents will displace more jobs than they create by 2025."

Yet articles like this always include caveats :

"White House economists said they don't have enough information to judge whether increased automation will help or hurt the U.S. economy. For example, new jobs could emerge to develop and maintain robots or other new forms of technology."

This has always been the case in previous ages such as the industrial revolution and the mass production factories of the 1950s. In those eras, the next generation could always learn a new trade and the economy expanded as a whole. I'm an optimist, I believe that the when the children of today grow up they will find ways to add value and 'work' that may be impossible for us to forsee.

But the current revolution requires something those previous ones did not.

"according to the White House, the key is to maintain a "robust training and education agenda to ensure that displaced workers are able to quickly and smoothly move into new jobs."

They're right, current workers in many careers will no longer be able to do the jobs they have. They can retrain, but how fast? These changes are now happening faster than a generation. The user drivers of today putting taxi drivers and dispatchers out of work through technology are funding research into self driving taxis that will lead to the destruction of that entire type of job.

The amount of training required to get the safe jobs that the article talks about takes years and very particular aptitudes. We need to begin to address as a society about all the ways citizens can contribute. Is worn for a wage the only system available? Some Nations will have a better chance of having this discussion than others. But for the nation most at risk to this trend, the United States, I fear that discussion will be incredible hard to even start.