What Watson Taught Me About The Future and How To Prepare Future Generations For It


Publication date:

May 04, 2021


Sometimes, we run into something and cannot quite believe our eyes when we do. We just know that we might be looking at the future.

Two such experiences happened to me. One was my first visit to the TUMO Center For Creative Technologies in Yerevan, Armenia more than a decade ago. That visit let me to imagine what education might evolve into in years to come. The other experience happened more recently when I had the opportunity to see Watson in action. That experience led me to imagine how  collaborative engagements involving humans and intelligent systems might shape our future. My focus here will be on the latter experience and its impact on education.

At a private workshop on artificial intelligence (AI) in Madrid, Spain, the director of IBM’s innovation lab showed us a video of an application of their Watson system. Seeing that video made me reflect on what the future might hold in store for all of us and what that would require from the education we provide to future generations today. After all, these generations will have to be functional in that future and, hence, our responsibility to equip them for it.

The future projected in the video was what Satya Nadella, the CEO of Microsoft, calls the future of ambiant intelligence; i.e., all of us surrounded by intelligent devices that will empower us in more ways than one. Watson is one of those intelligent devices. Think of it as a direct report to you but one that is not human.

Watson is a computer system that combines cognitive computing with AI. Here is how IBM describes Watson: “Watson is a cognitive technology that processes information more like a human than a computer – by understanding natural language, generating hypotheses based on evidence, and learning as it goes. And learn it does. Watson “gets smarter” in three ways: by being taught by its users, by learning from prior interactions, and by being presented with new information. This means organizations can more fully understand and use the data that surrounds them, and use that data to make better decisions.”

Before I describe what the Watson video was about, let me put that experience in perspective. First, this was 5 years ago and there is good reason to believe that Watson (and other intelligent systems like it) have evolved and are even more powerful today than they were then. Second, I saw the video in the company of some of the leading AI experts in the world. All of them told me that Watson was indicative of what was to come but that it was not leading edge anymore. Hence, where I saw a future, the experts in the field were already working on a horizon well beyond it. If we care to look, the future is already with us.

What was the Watson video about? It showed two senior partners at a management consulting company discussing a client project with Watson. The project was about a possible acquisition and the partners could be seen asking questions which Watson answered. All their interactions  were in spoken English. Whatever the senior partners asked, Watson had an answer ready and gave it without hesitation and often backed it up with relevant data. It was just fascinating to see the interaction between two intelligent humans and an intelligent machine. That picture inspired me to reflect on what a future with ambient intelligence really means and might imply for education.

Whatever shape or form these intelligent machines will take on is debatable but not really that important. What is more important is to reflect on what these intelligent machines are already capable of and  might eventually be capable of. That knowledge would give us insight into what tasks we might be able to delegate to them and which ones we might not be able to delegate. It would also help us identify what human intelligence will be needed to effectively and efficiently collaborate with such systems. That knowledge is crucial for education today

Watching Watson interact intelligently with the consulting partners, two questions popped into my mind. First, how are humans going to interact with such powerful and empowering systems?  Being able to rely on intelligent systems such as Watson will revolutionize complex decision making. Different styles of interaction are likely to emerge and we should probably start exploring what those might be. We are really at the dawn of senior managers having intelligent systems on their teams. How does one lead/manage/motivate a team where some members are intelligent systems such as Watson?

Second, what will happen to all the junior consultants, a prized job for many graduating MBAs? When I looked at what Watson was doing, I realized that this was pretty much the work junior consultants do for the first few years when they join consulting companies. Watson was doing their job fast, effortlessly, flawlessly, and  – let us not forget – with zero emotions. Watson needs other kinds of human support but the system is clearly able to replace most, if not all, junior consultants. That ought to get business schools and MBA candidates thinking.

With an image of the future taking shape in my mind, I started reflecting on what that future implies for education. Two questions pose themselves immediately. First, what skills and competencies will humans need to optimize human-machine collaborative decision making; and are we teaching any of those in our schools today? Second, what skills and competencies are we teaching that might become obsolete because intelligent systems like Watson will perform them with human (or better) accuracy?

It is clear to me that Watson-like systems will be able to handle and manipulate data as well as any expert data analyst. If there is anything in the data, these systems will find it. But they will remain machines that cannot really think the way we humans do. In other words, how they deploy their data analytic skills will depend on human intervention and questioning. In the Watson video, I did not see the intelligent system asking any questions. The two partners were the ones asking the questions. What is evident is that inquisitive intelligence will be needed to drive those intelligent systems.

Machine-learning systems such as Watson also canot think beyond the data they were trained on and learned from. Just as us humans, they have comfort zones (i.e., “data” comfort zones). They can learn from data but they cannot imagine beyond data. That is a unique capability we have as humans; i.e., we can and do abstract beyond what we observe. We might need to hone that capability for the human-machine collaborative future.

It is also apparent that intelligent systems such as Watson will be able to do rational analyses and decision making effortlessly and flawlessly. What they lack, however, is intuition. As Einstein pointed out, human intuition is truly a unique and powerful gift. Intuitive judgement is what drives us. In education, however, we emphasize the rational over the intuitive. Worse, we have made the latter suspect.

As I discuss in my book, Rough Diamonds (https://geni.us/RoughDiamonds), we might have to put intuition back into the driver’s seat. As an inspiring analogy, think of the cockpit crew in a modern airliner: we see a pilot, co-pilot, and flight engineer supported by intelligent systems. When the autopilot is engaged, their role is reduced to monitoring these systems. They rely on their intuitive intelligence to judge when to support and when to supplant those systems. That intuition is honed in flight simulators all through their flying careers.  We might well need something similar for decision makers who will be supported and empowered by intelligent systems.

Clearly, education should not clone students to be intelligent systems. It should focus on developing and honing human intelligence that can uniquely strengthen the collaborative engagement with such systems. We should not train students to be Watsons but we should train them to be able to work with Watson and secure collaborative decision making that is better than what either machine or human could do on their own. As the future is already here, we better get to work.

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