I recently read an article “Building Your Cognitive Technology” byTom Davenport. What I find of interest in articles like this a lack of an understandable discussion on what makes cognitive computing cognitive. Yes, they spend countless works describing attributes (data types, capable of learning, transparent, etc.); but very little, if any, on the computational approach for cognitive computing.
Take, for example, the use of Emergent Phenomenon, which is used to achieve highly complex, often cognitive behavior. Emergence is a process through which large complex patterns of behavior (cognitive by nature) can be achieved through the interaction of simpler processes (activities), each of which do not exhibit complex behavior. Think of 10,000 ants swarming to regulate a hive to within 1 degree celsius. Ants have no formal communication, no sight, no central command and control, just pheromones as a mean of marking their trail. Their ability to regulate birthing hives is a type of emergent phenomenon that was not programmed, is just emerges.
By the way, think about how one would design, implement, and test an emergent system. These systems “behave” a lot like offspring in the developmental stages of life (infants, children, teenagers, young adults, etc.). We do not “design” them, but yet through repeated complex interactions with their environment (people, places, and things), they grown to achieve amazing capabilities.
As such, true cognitive computing is more Emergent that Designed. However, today’s solutions tends to simulate cognitive behavior (fake the behavior without understanding their structure) rather than emulating its capabilities (takes on structural similarities that result in comparable characteristics). A very simplistic example is traditional genetic algorithms, originally developed by Holland. His approach uses a series of zeros and ones to encode information, from which evolutionary principles (selection, crossover, mutation, etc.) are applied. Over a series of evolutions, populations of these strings can exhibit complex behaviors.
This, however, is not the way nature works. Instead genetic evolution is encoded in nucleotides (C, T, A G) through which more complex expressions of value can be made. While a subtle difference, it is that difference between simulation and emulation which holds back true evolutionary-based cognitive progress.
Until such time that our computational approach to solve changes, that is more emulation (emergent) than simulation (design), cognitive computing will mostly be the things through which marketing manipulates the buying masses and not the means through which our silicon-based helper nurtures its human masters.
Categories: Cognitive Computing