Cognitive computing and artificial intelligence represent the next big wave in supercomputing. But do you know the difference between them? And where does machine learning and deep learning fit in? Stay right here and we’ll explain.
What is artificial intelligence?
The Turing Archive for the History of Computing defines artificial intelligence (AI) as “the science of making computers do things that require intelligence when done by humans.”
AI development is primarily concerned with enabling computers to solve complex problems. But it’s about results, not specifically building machines or algorithms that can think like you and I do. Even the famous ‘Turing Test’, developed by British mathematician Alan Turing in 1950 is an ‘imitation game’ at heart, one that challenges a computer to ‘mimic’ human responses.
You’ll find task-orientated AI at work today in a variety of places, from the Netflix recommendation engine to the natural language processing (NLP) used in Amazon’s Alexa-powered Echo.
Elsewhere AI provides the brainpower behind the face recognition technology in Google Photos, the computer vision systems in driverless cars and even the spam filters in your email.
Machine learning and deep learning
Some of these AIs use machine learning. This subset of artificial intelligence enables researchers, data scientists, engineers and analysts to construct algorithms that can learn from and make predictions based on data. Rather than following a specific set of rules or instructions, an algorithm is trained to spot patterns in large amounts of data.
Deep learning takes this idea further, processing information in layers where the result/output from one layer becomes the input for the next one.
AI is a broad term. Perhaps too broad. The Atlantic argues that ‘artificial intelligence’ has been hijacked by companies wanting to make their software algorithms sound smarter than they really are. Chat bots are often classed as AIs, for example, when they are “mostly glorified phone trees, or else clever, automated Mad Libs.”
What is cognitive computing?
Cognitive computing might be closer to the idea of artificial intelligence. What’s the difference? As VDC Research IoT analyst Steve Hoffenberg explains, an AI and a cognitive computing system would approach a data intensive task quite differently.
Imagine that both an AI and a cognitive system had to analyse a huge database of medical records and journal articles to determine treatment for a patient. “In an artificial intelligence system,” says Hoffenberg, “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.”
Cognitive systems are designed to solve problems the way humans solve problems, by thinking, reasoning and remembering. As Saffron Technology explains, this approach gives cognitive computing systems an advantage allowing them to “learn and adapt as new data arrives” and to “explore and uncover things you would never know to ask about.”
Ultimately, AI and cognitive computing systems are “based on the ability of machines to sense, reason, act and adapt based on learned experience,” says Intel CEO Brian Krzanich. “[They will] relieve us of a wide range of tasks, such as driving, firefighting and mining, and many more. We will [also[ see unprecedented developments in medicine, scientific discovery, education and how work gets done.”
For a greater insight into the future of computing, read: What is neuromorphic computing (and why might we need it)?