How is cognitive computing different from traditional computing
Cognitive computing has been an exciting concept on the horizon of the tech industry for some time now, but many people are still confused about how it differs from traditional computing.
This article will show you what cognitive computing is and how it’s different from the current approaches to computer processing, including machine learning and neural networks.
We’ll also examine some of the most significant areas where cognitive computing has already begun to make an impact in both business and consumers’ everyday lives.
What Is Cognitive Computing?
IBM has termed its business model Cognitive Computing because it turns massive amounts of data into insights and then leverages those insights to solve problems.
The basis for Cognitive Computing is analytics, where machines are given access to massive sets of data and use algorithms to make sense of it. Some have criticized IBM’s Cognitive Computing moniker as being a bit misleading—but they have a point.
Even if you discount cognitive tools like Watson as mere analytics tools, there are some real distinctions between what we think of when we think traditional computing and what we mean by cognitive computing.
In fact, many people have written about how cognitive tools add something important that wasn’t possible with traditional computing.
The most common argument centers on inference: that is, allowing systems to determine logical truths in an automated way rather than forcing humans do all of that work themselves.
The Foundation of Cognitive Computing – Machine Learning
Cognitive computing has a lot of buzz behind it, but what does it really mean for IT? Traditional computer systems allow users to search for and access data quickly, giving people fast answers.
Cognitive systems analyze data and draw conclusions in near real time—and perhaps even faster. The idea of putting artificial intelligence into a computer system sounds like something out of science fiction, but there’s an explanation behind what makes these systems tick that’s rooted in science fact.
There are two fundamental principles that underlie every form of cognitive system: pattern recognition and probabilistic modeling.
Pattern recognition focuses on how computers identify relevant information based on past experience or knowledge; basically, when computers observe a situation (or stream of data), they can identify patterns.
Probabilistic modeling is more about determining cause-and-effect relationships within sets of information; again, using past experience or knowledge, computers can determine which factors influenced specific events or results and use those findings to predict future outcomes.
So how do these things relate to each other? Let’s say you have some raw data—say, spreadsheets with detailed sales figures by region.
The Potential of Cognitive Computing
Cognitive computing represents a huge opportunity for organizations, helping them use data more effectively and efficiently.
Cognitive computers can identify patterns and trends in data, provide insights about what’s happening now or predict future behaviors.
That information can be used to make smarter business decisions. Most importantly, cognitive computers learn from their interactions with you, which means they are constantly getting better at delivering relevant content and services.
This learning process makes cognitive computing especially beneficial for industries like financial services and healthcare where accuracy is paramount.
The Difference Between Traditional vs. Cognitive Computing
Despite what some would have you believe; cognitive computing isn’t just a flash in the pan. IBM has been developing its artificial intelligence technology for decades and currently has more than 1,500 AI applications out in the world.
That doesn’t count all of IBM’s AI patents or partnerships with other research institutions, startups and businesses—or its most recent acquisition of Red Hat for $34 billion.
However, traditional computer programs operate differently from their cognitive counterparts because they don’t learn like humans do. Traditional software can only follow a series of predefined commands that must be encoded by engineers at certain points along their execution path.
Cognitive systems can instead focus on interacting with humans and processing raw data sets to develop predictive algorithms to make real-time adjustments. While that may sound complicated, it makes sense when you take into account how Watson works in hospitals.
Instead of offering treatment advice based off a list of known best practices, Watson relies on treating each patient as an individual who needs personalized care.
While Watson will always show doctors information about similar patients who received treatments—similarities which it identified through unstructured data collection and analysis—it will also present them with new approaches based off patient data gathered in real time.
What Is Natural Language Processing (NLP)?
Natural language processing (NLP) uses computational methods to allow computers to understand human language and, in particular, to enable systems to gain insight from human-language data.
The ultimate goal of NLP is for computers to be able to communicate in natural language with human users. Such systems would allow people to give queries in their own words and retrieve information that is meaningful for them instead of having to follow strict syntax rules when interacting with a computer.
In addition, users will have access anytime, anywhere through devices such as smartphones or tablets.
Cognitive systems have been around for a while in certain applications, like voice recognition and image recognition.
However, they’ve traditionally been specialized applications with limited generalization capabilities.
Cognitive systems are gaining momentum as organizations recognize just how valuable these technologies can be; you’ll find cognitive technology in many of your favorite tech products, including Apple’s Siri and Amazon’s Alexa (home assistant), Google’s self-driving cars, SnapChat filters (face detection) and augmented reality games.
Since Amazon has not disclosed its sales numbers, it isn’t clear which technology has garnered more attention between artificial intelligence (AI) and machine learning (ML).
But both AI and ML will have huge impacts on industries ranging from finance to healthcare to manufacturing over time.