Cognitive Computing: More Human Than Artificial Intelligence
Mistaking cognitive computing for just another AI misses the important contributions this computing platform offers.
In 2011, two episodes of Jeopardy stunned the world when the best Jeopardy players in the history squared off against IBM’s Watson Cognitive Computing System and were soundly beaten. For many, this was the moment when artificial intelligence probably became a very real thing in their minds; one contestant even scrawled “I, for one, welcome our future computer overlords” on his answer in his final losing round. He likely spoke for many in the audience.
Watson dominated a game where nuanced wordplay was intrinsic to the challenge of the contest, where contestants needed to provide the question that fit an answer shrouded in double meaning. For humans, Jeopardy is a unique cognitive exercise—as anyone playing along at home can attest to—but for a machine that can be thwarted by a reCAPTCHA challenge on a web page, Watson’s success was a monumental achievement in computing that has implications for the future of practical, everyday technology.
Cognitive Computing vs. Artificial Intelligence
Calling cognitive computing a form artificial intelligence isn’t wrong, but it misses a fundamental distinction that makes it so remarkable.
When we talk about artificial intelligence, often we are talking about something that is necessarily an incredible sophisticated functional algorithm. That is, an AI is a very, very complex decision tree—one we may not even be able to follow ourselves—that when given a specific input, will produce a predictable output.
This is how autonomous vehicles work, by taking in a starting point and a destination as input and navigating between the two according to a mind-bogglingly long sequence of if-else statements.
If the light is red, stop; otherwise, proceed. No human input needed.
This is a radical simplification, but this is essentially what most people are talking about when they talk about AI. An AI is something that finds the best possible way to do something within a given set of parameters and makes a decision or takes action as a result. This applies to autonomous vehicles as much as it does to high-speed trading platforms on Wall Street.
This article originally appeared on InterestingEngineering.com. To read the full article, click here.
Nastel Technologies uses machine learning to detect anomalies, behavior and sentiment, accelerate decisions, satisfy customers, innovate continuously. To answer business-centric questions and provide actionable guidance for decision-makers, Nastel’s AutoPilot® for Analytics fuses:
- Advanced predictive anomaly detection, Bayesian Classification and other machine learning algorithms
- Raw information handling and analytics speed
- End-to-end business transaction tracking that spans technologies, tiers, and organizations
- Intuitive, easy-to-use data visualizations and dashboards
If you would like to learn more, click here