Computers are getting to be more intelligent, although machine intelligence involves more than a single concept. It's common to hear "AI," "cognitive computing," "machine learning" and "deep learning" used in everyday conversation. Whether you're a practitioner, IT leader, or business leader, you should understand the differences. Following are some basic explanations that explain the value of each.
Cognitive computing is the sensory branch of machine intelligence. Using sensors and algorithms computers can see, hear and feel, although there are also efforts underway that address smell and taste. Cognitive computing brings human capabilities to computers so they can think, behave and act like humans, cognitive computing is the way laymen experience artificial intelligence.
Image sensors give computers sight, and microphones enable them to hear. Text to speech and speech to text technologies enable them to communicate with humans using human language. Alexa, Siri, Cortana and Google Assistant are examples of the latter. Cognitive computing is about adding artificial sensory capabilities to computers and adding a brain to computers. This ability makes computers think, behave, and act like humans.
Artificial Intelligence brings decision making capabilities to computers, which we experience every day in the form of recommendation engines. Although AI is not a new technology, cloud compute and storage economics have enabled its explosive growth and mainstream use.Think of AI as the brain behind cognitive computing. While cognitive computing is the sensory capabilities, AI can exist without those capabilities. A popular emerging AI use case is the self-driving car. Essentially, AI is the "brain" behind intelligent software applications.
Machine learning requires massive amounts of data from which patterns can be recognized and predictions can be made.
The classic machine learning training example is teaching a computer to differentiate between cats and dogs, or different breeds of cats and dogs. Such capabilities are now being used to identify objects and individuals in crime investigations, for example. Meanwhile, businesses are using the predictive aspects to improve customer service, security and business efficiencies. Machine learning is learning from past data, historical trends, identifying patterns, and then predicting what's next. That's an oversimplified definition, but it is the best way to think about it.
Deep learning uses neural networks that mimic the physiology and function of the human brain. Neural networks include several layers of neurons, leading to the use of the term "deep learning".
Deep learning takes the concept of machine learning, where you let computers learn from historical data, and then applies the way a human brain thinks. Deep learning uses neural science and neurological techniques. Deep learning is the most advanced form of machine learning, and it is becoming the preferred way to train computers.