Both AI and Cognitive Computing may look extremely alike, but like we mentioned above there is a small difference between both methods.
Firstly, artificial intelligence does not work at mimicking human thought processes. The concept behind AI is to not mimic human thought and processes, but to solve a problem through the use of the best possible algorithm. This can be illustrated through an example of a car, which stays on course and avoids a collision. The processes in AI are not looking to process data in the same way as it would be processed by humans, but they’re looking to process it through the best known algorithm present. Processing data the way humans do it is a far more fault-prone and complex algorithm. And, we all know that a self-driven car isn’t giving suggestions to the driver, it’s responsible for all the decisions in driving.
Secondly, cognitive computing is not responsible for making decisions for humans, instead it is responsible for complementing or supplementing our own cognitive abilities of decision making. AI in medicine would be all about making the right decisions pertaining to a patient or the preferred mode of treatment, and minimizing the role of the doctor. Cognitive computing, on the contrary, would be more focused on achieving evidence that could supplement the human expert into making more flawless medical diagnoses.
This article originally appeared on datasciencecentral.com. To read the full article, click here.
Although many IT organizations field basic analytic tools sufficient to keep MTTR to an acceptable level, they need more sophisticated capabilities to answer questions like: “How does the performance of IT activities and operations impact our business?” And, “Is there a way to understand these dynamic interplays in real-time to optimize intelligent day-to-day management of the business?”