What the machine age means for the way you learn

By the time you finish reading the next three lines, the Visa network will have processed 10 million transactions. In this second, Google is analyzing 56,000 searches that will churn out billions of hits. At this very moment about three million emails have arrived, and most of them weren’t sent by humans, but by machines.

The US government has set up a research center called BRAIN (the Brain Research through Advancing Innovative Neurotechnology initiative), and they’re investing astronomical sums. Elon Musk, the founder of Tesla, invented Neuralink to create a connection between human brains and computers: in other words, the goal is to actually download our brains. McKinsey estimates that in 2015, investments in Artificial Intelligence (AI) ranged from 20 to 30 billion dollars. AI experts (who are still very few, and earning ridiculous amounts of money) agree that by 2030 we’ll see what’s called a technological singularity, which marks the moment in time when machine intelligence will surpass human intelligence.

How is that possible? The human brain contains around 100 billion neuron with 100 trillion chemical and electronic connections. Put those connections to work and do the math: the human brain is still the most complex machine on the planet. So the realization that in a decade’s time AI will exceed human intelligence – well, that makes us a little nervous, to say the least.

Have we already lost? What can we do? In this article I’m not laying down the battle lines for a war between humans and machines; that’s a decades-old debate. Instead I want to point out a way that we can move from an obsolete paradigm of learning to a different one, both at an individual level and in our society as a whole. In other words, with AI, Machine Learning, the ability of computers to learn without being programmed, Deep Learning, and science that’s progressing faster than ever, we humans have to rethink what it means to learn.

In the old paradigm, there are three stages in the life of a human being: studying and learning up until about 20, followed by 30 to 40 years of work, if we’re lucky enough to have a job. Then if we’re still alive we’ll enjoy our retirement, if we’re lucky enough to have a pension, as long as possible in the hopes our health holds out. All societies are structured this way. What’s changed?

- Life Expectancy: From around 40 years in the early 1800s, we’re at nearly 75 today, and we’ll hit 90-96 by 2050, when there will be a billion people over 90, and 50% of all the people in the world under 27.

- Pension Systems are unsustainable. Right now in Europe there are four workers per pensioner; in 2040 there will only be two workers per pensioner.

- Job Market: Unemployment rates will keep on rising for people with a low or medium level of education, while the fortunate few will be the winners. AI specialists, scientists, experts in computer science, robotics, and nanotechnology are likely to thrive in the 4th Industrial Revolution, a major shift in the way our economy works as man and machine get ever closer together.

And as if all that were not enough:

- The Fourth Industrial Revolution: What’s the difference between this one and the other three? The speed of this revolution is exponential, not linear. And it’s not only changing products, services, and costs, but it’s changing us, the way we humans relate to one another, and to AI.

What does this mean for us?

We can no longer think in terms of the old paradigm, an obsolete system from the first industrial revolutions. In the new reality that’s rapidly emerging, we’ll live to be 90 or 100 and we’ll have to keep on learning all our lives.

In other words, to balance out machine learning, we have to become learning machines. We can’t afford to disconnect, we have to stay intellectually active in some way. But be careful here, I’m not talking about being plugged into social media 24/7. That drains our attention and our critical thinking. Instead what I mean is to train our contextual intelligence and keep it in good shape, spurred on by insatiable curiosity and the drive for self-improvement.

Rewarding skills now center on people and knowledge. The capacity to collaborate, to understand complexities and adapt to them, to have an overall vision, not one compartmentalized into super specializations, to grow empathy and build trust - things that AI will never be able to do. In other words, as I see it, we’ll lose the race in speed, but we can win the race for depth and compassion.

This means rethinking the curriculum at school and university. For instance, Finland has completely upended the way subjects are taught in school, starting in the first grade. Instead of only studying, say, geography, history or math in isolation, they will also piece their knowledge together. Students will consider World War II, for example, by looking at geopolitics, economics, history and geography. So they learn how to handle complexity, to ‘put together the pieces’ that aren’t separated. It’s how they develop system thinking. To learn like human beings we need to be perennially curious and avoid the trap of over-specializing. We need to know that failure is a learning process. As the old saying goes, we learn from our mistakes, and we’ll never stop learning.

In the Sistine Chapel, God gives the gift of life to Man in what may be the most famous painting in the world. But what many people might not notice is that Michelangelo depicts God with a strange shape: the shape of the human brain. So what Michelangelo is perhaps telling us is that God gives us both life and intelligence. Today more than ever before we have to use this intelligence, guided by our moral compass, with our seatbelt securely fastened for the bumpy road ahead.

First published in Italian here.