In today’s competitive business world, sales professionals know the value of CRM software. But there’s a new kid on the block that could make CRM software obsolete. Cognitive computing (or “computer intelligence”) is a new form of computing that relies on data mining, pattern recognition, and other data science techniques.
Cognitive computing is the cutting edge of machine learning. A relatively new technology, cognitive computing relies on learning how humans think and working to mimic that with computers. To do this, cognitive computing systems use things like massive data and artificial intelligence to see what humans do and replicate the same behavior without human input. The cognitive computing revolution is happening in supervised learning (aka machine learning), and it is having a significant impact on research and the future of AI.
Cognitive computing isn’t some futuristic magic, either. Cognitive computing technology has been used in a variety of business applications, including customer service, analytics, marketing, and finance. And IBM Watson, the cognitive computing platform developed by IBM, is now being used by businesses of all sizes.
Neural networks are revolutionizing machine learning, and researchers are pushing the boundaries of human intelligence and supercomputing capabilities by learning systems that mimic the brain’s natural ability to process information. Now, researchers are using cognitive computing to develop machine learning systems that can “learn” on their own from raw data—and are predicting a massive increase in machine learning applications in the future.
Machine learning is one of the hottest fields in tech right now, and it’s largely thanks to advances in artificial intelligence. Cognitive computing, a subset of machine learning, is the study of how machines learn, and it’s revolutionizing supervised learning.
Supervised learning is a branch of machine learning that has seen major advancements in recent years. In supervised learning, the computer is given historical data to predict what may happen in the future. It learns through pattern recognition, lending itself well to a wide range of industries from healthcare to robotics to self-driving cars.
Until recently, machine learning (ML) was a black box: data scientists would feed data into an algorithm, and the ML system would spit out a recommendation on how to best respond to, say, a customer looking for a travel package. But over the past few years, advances in ML have seen breakthroughs in the form of deep learning, which has shown potential as the next computing platform. But it’s not the only ML method out there.
Ever since the first AI algorithms were developed, computer scientists could teach computers how to think. And machine learning has allowed AI systems to learn from data using algorithms like linear regression, logistic regression, and decision trees. While those algorithms have enabled computers to learn, they are iterative, meaning they ask a computer to make predictions based on what it has already learned.
Machine learning is an exciting and fast-developing area of technology. There are many different ways to apply machine learning, but one of the most intriguing is cognitive computing, which uses artificial intelligence to create more intelligent and autonomous systems. Machine learning relies on programmed algorithms, and these algorithms change and evolve to become more accurate. Cognitive computing software, however, learns as it develops. Cognitive computing allows machines to learn directly from the environment, building up a retained knowledge of normal and what isn’t.
Machine learning has created huge advancements in the artificial intelligence (AI) space through the years. AI algorithms have the potential to perform tasks in the same way that humans do, such as recognizing images, speech, and text. Now, machine learning is becoming even more sophisticated with the rise of cognitive computing. Cognitive computing combines AI with a deep understanding of human intelligence. Cognitive computing enables machines to understand their environments, humans, and each other. This understanding allows machines to integrate the computational power of machine learning and AI with the high-level, abstract representations of human intelligence.
As machine learning algorithms evolve, it’s becoming increasingly clear that having human-like intelligence is probably a long way off. The “cognitive computing” field is pursuing solutions to this “intelligence gap,” starting with supervised learning.