Speed of thought is something we like to imagine operates at a single high-velocity setting. But that’s just not the case. Some modes of cognition are painfully slow, such as pondering the bewildering panoply of investment options available under your company’s retirement plan. But some other modes are instantaneous, such as speaking your native language, recognizing an old friend, or sensing when your life may be in danger.
None of this is news to anybody who studies cognitive psychology or has followed advances in artificial intelligence, aka AI, over the past several decades. Different modes of cognition have different styles, speeds and spheres of application.
When we speak of “cognitive computing,” we’re generally referring to the ability of automated systems to handle the conscious, critical, logical, attentive, reasoning mode of thought that humans engage in when they, say, play “Jeopardy!” or try to master some academic discipline. This is the “slow” cognition that Nobel-winning psychologist/economist Daniel Kahneman discussed in his recent IBM Colloquium speech.
As anybody who ever watched an expert at work will attest, this “slow” thinking can move at lightning speed when the master is in his or her element. When a subject-domain specialist is expounding on their field of study, they often move rapidly from one brilliant thought to the next. It’s almost as if these thought-gems automatically flash into their mind without conscious effort.
This is the cognitive agility that Kahneman examined in his speech. He described the ability of humans to build skills, which involves mastering “System 2” cognition (slow, conscious, reasoning-driven) so that it becomes “System 1” (fast, unconscious, action-driven). Not just that, but an expert is able to switch between both modes of thought within the moment when it becomes necessary to rationally ponder some new circumstance that doesn’t match the automated mental template they’ve developed. Kahneman describes System 2 “slow thinking” as well-suited for probability-savvy correlation thinking, whereas System 1 “fast thinking” is geared to deterministic causal thinking.
In reading the referenced article, several insights came to me.
First, Kahneman’s “System 2” cognition—slow, rule-centric and attention-dependent—is well-suited for acceleration and automation on big data platforms such as IBM Watson. After all, a machine can process a huge knowledge corpus, myriad fixed rules and complex statistical models far faster than any mortal. And a big-data platform doesn’t have the limited attention span of a human; consequently, it can handle many tasks concurrently without losing its train of thought.
Also, Kahneman’s “System 1” cognition—fast, unconscious, action-driven—is not necessarily something we need to hand to computers alone. We can accelerate it by facilitating data-driven interactive visualization by human beings, at any level of expertise. When a big-data platform drives a self-service business intelligence application such as IBM Cognos, it can help users to accelerate their own “System 1” thinking by enabling them to visualize meaningful patterns in a flash without having to build statistical models, do fancy programming, or indulge in any other “System 2” thought.
So it’s clear to me that cognitive computing is not simply limited to the Watsons and other big-data platforms of the world. Any well-architected big data, advanced analytics, or BI platform is, for all intents and purposes, a cognitive-computing platform. To the extent it uses machines to accelerate slow “System 2” cognition and/or provides self-service visualization tools to help people speed up their wetware’s “System 1” thinking, it’s a cognitive-computing platform.
The core criterion is whether the system, however architected, has the net effect of speeding up any form of cognition, executing on hardware and/or wetware.