Not only has she cracked the code for successful online dating in her book the “Data, A Love Story” but the founder of the Future Today Institute, Amy Webb convincingly explains “the future” isn’t a concept that just happens to us submissively. In her Washington Post Best Seller that has also been named #1 Best Seller on Amazon and recipient of the Gold Axiom Medal Winner for Best Book About Business & Technology- “The Signals Are Talking”- the NYU Stern School of Business professor presents us with the opportunity to see ahead, giving us the chance to forecast what’s to come, as Amy regularly has to answer to the question “What does the future hold for us?” when advising her Fortune 500 and Global 1000 clients.

How do we predict the future when we live in what seems to be an increasingly unpredictable world?

MIT mathematician Edward Lorenz once observed that “only one thing can happen next,” and that the impact of that act — whatever it is — changes everything else that follows. You know this as the “butterfly effect,” but this is the heart of chaos theory. Because we acknowledge that chaos is real and that chaotic events will at some point occur, as futurists our goal is not to predict, with total accuracy, what will happen x-number of years from now. Instead, the goal is to reduce the ambiguity — to develop probable, plausible and possible scenarios using data and evidence.

What is essential to predicting what’s next? Are there skills that can be taught and learned?

There’s a neurological trick your brain plays on you the moment you start trying to figure out the future of something: you wind up looking at the world through a pinhole and missing all of the other adjacent and related signals that make up the more complete picture of what’s over the horizon. For example, one of our clients wanted us to forecast the future of cars, with a timeframe of the year 2037. Well, that’s twenty years from now. Most companies — not to mention government agencies, universities and everyday people — can’t move at the speed of technology. Asking me to forecast the future of “cars” assumes that our existing technology won’t change much. Instead, I reframed the question to: What’s the future of people, pets and objects moving from point A to point B? This illustrates the most essential skill in seeing the future: understanding that weak signals come from lots of different places, include those from outside your usual frame of reference. The frameworks I describe in my book “The Signals Are Talking” are new strategic ways of thinking. Anyone can learn how to think like a futurist.

How do you differentiate between a meaningful trend and a temporary fad? What trends do you see as significant and lasting?

Typically, trends meet four general characteristics. Real trends are rarely “trendy” — that’s to say that they don’t pop up quickly and disappear as soon as the next technology attracts our attention. First, a trend is driven by a basic human need, one that is catalyzed by new technology. Most people don’t realize this, but we’ve been trying to automate transportation for hundreds of years. Self-driving cars are just the latest manifestation of that trend. Second, trends are timely, but they persist. Did you know that GE actually developed a self-driving car and track back in the 1950s? Engineers have been working on self-driving car prototypes for decades. Third, trends evolve as they emerge. Our current crop is road legal and can now drive alongside other cars on the highway. Finally, trends usually materialize as a series of un-connectable dots, which begin out on the fringe and move to the mainstream.

Technology has upended many of our established ways of learning. What’s on the horizon that has the potential to disrupt it even further?

Artificial Intelligence is the next era of computing. It’s more than a trend — it’s a fundamentally different way of approaching machines. At the moment, we are teaching machines to think just like we do. The next step is to allow machines to learn on their own, in unsupervised environments. We ought to be asking ourselves who is doing the teaching — and what machines are learning from us.

Throughout the disruption and amid the noise, what is lasting and durable in education and learning?

We can automate lots of tasks, but very high-level critical thinking and relating to other people are cognitive tasks that only humans can perform. Knowing that, the most lasting and durable facet of education are those courses, training sessions and degrees that focus on critical reasoning. It seems counter-intuitive, but the best possible preparation for a future among thinking machines is a rigorous liberal arts education.

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