We must pay more attention to artificial intelligence in the classroom
Artificial Intelligence has the potential to make us smarter, healthier and more entrepreneurial.
This is one of my conclusions from evaluating healthcare start-ups for government investment. I have been doing this for over five years and it’s an excellent way to keep connected to the latest business and technological trends.
One trend that I have noticed recently is that AI-based applications have gradually started to dominate the business plan proposals that I receive. We see AI solutions in the diagnosis of patients, image analysis, drug development, clinical trials, fall detection systems, etc.
So, when a student made the following observation in a class that I was teaching on business organization, I thought it was the most obvious thing in the world:
“Artificial intelligence is poised to transform the way we live, work and learn. It will have a huge impact on the way we do and organize business.”
Of course, the comment wasn’t very precise, and the term “AI” has many subsets. Nevertheless, it chimed with my experience of evaluating the healthcare proposals. AI — however we want to define it — is everywhere. And it is already a game-changing cluster of technologies.
But what surprised me, was the reaction of other people in the room. What seemed to be a simple statement of fact was rejected — quite aggressively — by others:
“The media headlines are just feeding the hype around AI . . . There are a lot of misconceptions about AI . . . AI doesn’t have anything to do with real “human” intelligence . . . The AI applications out there aren’t particularly intelligent/smart . . . We are still in the very early stage of discovery . . . We should not waste too much time on AI yet.”
This experience made me think about a more general and serious problem:
We need to “level up” everyone’s knowledge of AI.
Much more attention needs to be paid to AI literacy. The issue is too important to limit the discussions to computer science or engineering. “Artificial intelligence” must become a mandatory subject at every level of education.
Building AI Literacy
Such mandatory training should focus on three areas:
#Understanding the Technology
The AI skeptics I met were under the impression that “artificial intelligence” is still a field of study that belongs to an elite group of “AI specialists.” Surely, the world’s best “AI researchers” are continually making breakthrough discoveries. But what the skeptics ignore is the fact that we see more and more implementations and applications of AI in our daily lives.
The fast development of better algorithms and computing power in combination with the increasing availability and digitization of information leaves no area of our lives unaffected. Think HR, marketing, law, energy, agriculture, shopping. The fact that we (as consumers) are unconsciously sharing more and more data will only improve the currently available applications.
But the pervasiveness of these technologies is matched by a lack of visibility. By and large, we are unaware of how much AI-related technologies already impede upon our lives. Everybody needs to have a basic understanding of the working of AI to understand the world where they are living.
It makes no sense to leave the people who will be most affected by these applications and solutions uneducated.
#Participating in the Design of the Technology
More knowledge about algorithms will help non-AI researchers contribute to debates on how to improve the performance of algorithms and to limit abuse. And this is in everyone’s best interests.
Big Data and predictive analytics are going to play a crucial role in the activities of knowledge workers. It will spark a revolution in how research is conducted. It will also transform how customers are identified, products are advertised, conflicts are solved, organizations are managed and coordinated.
Data-driven technologies will contribute to solutions to the global challenges as defined in the United Nations Sustainable Development Goals (SDGs). These goals are related to poverty, inequality, climate, environmental degradation, prosperity, and peace and justice.
But for “AI” to reach its full potential, it is my experience that it is necessary for non-AI experts to be involved in the development of AI. Currently, mainly AI specialists are engaged in the discussions regarding this field of research. It is important that a more diverse group of people will be actively involved in the AI discussions and experiments. Such involvement will not only help identify different technical and ethical problems but, more importantly, allow us to find innovative solutions and reveal new possibilities.
#Navigating a World of Intelligent Machines
Finally, being aware of how algorithms and other forms of AI work will make all of us smarter in how we engage with technology. More knowledge about how algorithms operate will make us more sophisticated consumers of “artificial intelligence” solutions. This is a necessary first step in preparing ourselves for a world where we will co-exist (live and work) with intelligent machines.
It will also encourage us to take AI more seriously and focus on the social skills that will become crucial in a word of artificial intelligence: creativity, social skills, working in teams, and the ability to engage in a life-long self-learning exercise.
I am convinced. We must better prepare the next generation for an AI-centered future. This means that we need to encourage all students — and not only students of computer science — to engage with AI technology and to think about what it means for the different careers they wish to pursue.
And this means rethinking and “de-siloing” education. Too much time is spent transferring pieces of knowledge that are easily found online or will become irrelevant in the future.
What is even more worrisome is that this knowledge is defined according to strict disciplinary boundaries or fields of research that emerged in the Middle Ages (economics, law, biology, physics, etc.). Somehow this makes no sense in a world of artificial intelligence.
These boundaries must become more fluid and data-analytics, algorithms and machine learning must be integrated into all fields of education. Also, not only are students less and less interested in these “traditional topics,” but the younger generation knows that our tech-driven world requires different knowledge, skills, and training.