Building the superhuman, one A.I. routine at a time

The term “artificial intelligence” was first coined in 1955 by John McCarthy, widely known as the father of A.I. Think about what the world was like then, and where we are now. A.I. has evolved, but its premise is still the same: Researchers and technologists are creating software that operates systems and machines built to understand and simulate human tasks and mimic our thought process.

While science fiction (both old and new) has depicted the future of A.I. as insubordinate robots who usurp their creators and attempt to take over the world, the reality is that we’re much closer to developing machines and systems that enhance our own abilities and complement our own minds.

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But humanizing the A.I. that works with us only becomes possible in systems based on knowledge rather than pure automation of the tasks we want it to perform. Knowledge-based A.I. operates within IT, but its power extends beyond IT systems, enabling organizations to uncover issues in business processes and have a much greater impact on the business.

Likewise, knowledge-based A.I. learns over time, becoming more intelligent and capable of creating outcomes that help us. It’s a system more closely related to how we learn and retain information in our brains, holding information and memory that we can return to over and over for insights about future problems.

Recently, Northwestern University professor Ken Forbus and his team developed a structure-mapping engine (SME) model, fostering analogical problem-solving capabilities such as “capturing the way humans spontaneously use analogies between situations to solve moral dilemmas.” Essentially, the model gives computers the ability to rationalize as well as imitate the human cognitive process. This new development gives us a preview of what the future of work looks like. While we apply A.I. in analytics to predict outcomes or to automate mundane tasks, we’re also making major strides toward developing A.I. that is interactive and helps us make critical decisions.

In another example, researchers at the National Research Nuclear University in Moscow are working on an “emotional” computer with the ability to understand context, develop rapport with humans through emotional actions and eventually build trust. We can expect the human side of A.I. to become the norm as this and other work establish reliance on and credence in AI systems. These systems are advancing, but A.I. has a long way to go before it can truly understand our brains.

Humanizing A.I. will also have a profound impact on the future of education. Using computer vision, animatronics, motors, and A.I., the Cozmo robot recognizes faces and adapts to users over time through familiarization. Imagine using Cozmo in our classrooms, helping students observe and identify emotions, develop critical thinking through games, and identify behavioral differences.

This represents an example of how future generations will experience technology in the classroom in profoundly different ways as A.I. capabilities grow. It will affect both how students learn and what they’re taught. Considering the strong possibility that A.I. will render most repeatable tasks moot, students will only need to possess basic coding knowledge to create an application or program. The weight and burden of most coding — from writing to refinement — can and will be carried out by A.I. systems.

Paradoxically, this means students will need an understanding of A.I. and coding. In the classroom, generations of students will experience A.I. that will learn from engaging with us, while it amplifies the ability of the teacher as well. It’s one of the many scenarios in which the technology will present fundamental challenges even as it helps us.

For organizations and individuals to benefit from A.I., however, we must incorporate knowledge-based systems that curate information from across technologies and business units, automatically learn from that information, and use that knowledge to intelligently drive automated systems and amplify existing capabilities. In this way, everyone in an organization — along with its customers and partners — can enhance their performance thanks to the collective, evolving knowledge of A.I. that learns, remembers, and informs.

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A.I.’s power is not in machine learning or automation but knowledge — e.g., the discovery, creation, and management of knowledge.

In every instance, the future of work and education depends upon the human component of A.I. It’s important to think of A.I. as a service — it exists to improve how we work, learn, and live. It amplifies our ability to solve problems large and small. A.I. will find not just new solutions but new problems, and that is its most exciting potential: It frees us to think and collaborate to tackle new, interesting, and life-changing challenges.

And that brings us to the ultimate potential: As A.I. becomes more human, it will transform us into superhumans.