You'll read a great deal on these pages about Cognitive Computing. It's a term we're sure you'll hear more of in the trade press in the coming months. Among the players carving out this category along with Scianta Analytics are IBM Watson, Microsoft Cognitive Computers, Google DeepMind, Cisco Cognitive Threat Analytics, and Cognitive Scale. Let's talk for a moment about what's meant by the term Cognitive Computing.
Machine Learning vs Cognitive Computing
This is the Year of the Machine Learning Revolution!
Machine learning became mainstream in 2016. For the first time in history, machine learning is not only available to big companies like Google, Amazon or Apple. Startups have started building products and services using machine learning.
Entrepreneur Magazine – January 12, 2017
This is the Year of Machine Learning!
So, that’s pretty exciting, right? We’re always intrigued when we hear that pronouncement. Frankly we’ve heard the claim every year for more than a decade. What that headline really means is that this is the year the particular pundit who wrote the headline discovered machine learning. The truth is, machine learning has been around and thriving for more than half a century. As exciting as the headline is, machine learning is very old news.
At Scianta Analytics, our roots are in machine learning, but we’re so much more than that. We’re a Cognitive Computing company. To understand what that means, let’s take a look at some definitions.
“The field of study that gives computers the ability to learn without being explicitly programmed.”
Arthur Samuel – MIT, IBM, Stanford
Author of Computer Checkers at IBM – 1959
“Systems that learn at scale, reason with purpose and interact with humans naturally.”
Computing, Cognition and the Future of Knowing
Dr. John E. Kelly III
Senior Vice President, IBM Research and Solutions Portfolio – October 2015
“The natural evolution of machine learning, Cognitive Computing attempts to imbue, in computer systems, the same insight and understanding we see in humans.”
Chief Scientist, Scianta Analytics
Speaking at Splunk's .conf User Conference – September 2013
It turns out that the “Year of Machine Learning” was around 1959. We prefer to think of 2017 as “The Year of Cognitive Computing.”
Scianta Analytics is a cognitive computing company. We have evolved the fundamentals of machine learning that were developed in the mid-twentieth century, into the human-centered, semantic-reasoning-enabled, analytics technology for the new millennium: Cognitive Computing.
So, what’s the deal with cognitive computing? How is it different than traditional machine learning? As Dr.Cox has said, cognitive computing is the natural evolution of machine learning. It strives to deliver the same insight and understanding from computer systems that we see in humans.
That takes more than simple machine learning. It takes a cognitive approach to the problem of analytics and decision making. That requires qualitative expression and semantic reasoning. And those require fuzzy logic.
Scianta Analytics Chief Scientist Earl Cox began studying fuzzy logic with Dr. Latfi Zadeh at Berkeley in the 1970s. Dr. Zadeh, often referred to as “the father of fuzzy logic”, wrote the forward to Earl’s first book, The Fuzzy Systems Handbook: A Practitioner's Guide to Building, Using, and Maintaining Fuzzy Systems in 1993. Dr. Cox’s continued work on fuzzy logic theory and its real-world application in analytics and cognitive modeling and decision making became the foundation of Scianta Analytics’ semantic reasoning and cognitive computing IP.
Is Cognitive Computing the Same as Artificial Intelligence?
No. While there is overlap, there are important distinctions between AI and cognitive computing. For some detail, we’ll refer to our friends at IBM. Speaking about IBM Watson’s work in health care, IBM puts it this way:
In an artificial intelligence system, the system would have told the doctor which course of action to take based on its analysis. In cognitive computing, the system provides information to help the doctor decide. In many cases, a doctor will indeed make the same choice that Watson suggests, but the exceptions are where human experience and judgment become most important.
“The objective of <cognitive computing based analytics> is to do the ‘heavy lifting’ for people.”
Michael Karasick, VP of Cognitive Computing for IBM Research
We admit, there’s quite a bit of overlapping grey area between cognitive computing and AI. The difference then becomes more one of design intent rather than cognitve capabilities. Here's an easy way to think about it:
The Machine Intelligence built into Scianta Analytics Extreme Vigilance is implemented, for the most part, in the form of Cognitive Computing. Scianta's machine intelligence is infused with semantic reasoning – that is, the ability to "think" and communicate qualitatively. It is optimized to deliver to your analysts the Deep Insight they need to make the smartest decisions in important use cases.
The Machine Intelligence built into the underlying Scianta Cognitive Modeler platform is also capable of delivering true Artificial Intelligence. The SCM platform's AI capabilities have begun to be leveraged in Scianta Analytics new Command and Control to drive enterprise Orchestration and Automation with systems like Phantom.