These days, conversations on almost any topic — be it finance, health care, art, the economy, music, or even religion — do not seem complete without a lively, and more or less informed, exchange on AI and on machine learning. The crux of the discussion typically rests on the role of humans in the increasingly large number of enterprises that depend on machines for decision making and manufacturing. In this context, a distinction that may prove useful in thinking about a future society of humans and “intelligent” machines is that proposed back in the 60s in the field of psychology between fluid and crystallized intelligence. As recently pointed out by Sarah Harper, taken to its logical end point, this idea may yield some possibly counter-intuitive conclusions regarding the parts to be played by AI and by different generations in the workplace.
Fluid intelligence relates to the ability to solve new problems by applying well-defined logical rules, such as by means of inductive or deductive reasoning. Fluid intelligence does not depend on any external prior knowledge about the world and the problem domain. In contrast, crystallized intelligence is the capacity to build on one’s experience and knowledge to acquire new skills and to solve problems.
In humans, fluid intelligence tends to decrease with age, while crystallized intelligence follows an inverse trend, peaking much later in life. Machines appear to have surpassed humans in terms of fluid intelligence, given their unprecedented capability to recognize patterns in large volumes of data and to optimize actions over long time horizons. But building general-purpose skills based on expertise in a computer, that is, generating artificial crystallized intelligence, is broadly considered to be unattainable with current AI techniques (listen to Obama’s eloquent explanation of this point!). Current state-of-the-art machine learning methods in fact cannot even explain why they output given decisions.
So there you have it — in a system that can leverage the fluid intelligence of sophisticated AI tools, the crystallized intelligence borne out of the experience of older women or men may become more valuable than the speed and flexibility of fresh graduates. Considering the predictions of an increased lifespan, this sounds like good news — can it be that expertise is not dead after all?