It is difficult to understand what is happening in the field of artificial intelligence (AI). New developments seem to happen weekly, and companies use different words to describe their products.
The terms artificial intelligence, cognitive intelligence, autonomous machines, and machine learning are all thrown around. New research from ReportLinker (www.reportlinker.com), a market research solution, says AI) is a vision, a goal, and a set of technologies. Its breadth and complexity make it a difficult subject to understand and explain.
Adding to the confusion is the number and variety of terms used. Machine learning (ML), deep learning, neural networks, and predictive analytics describe different AI approaches. Other marketing terms such as cognitive computing or autonomous machines further muddy the water.
The term artificial intelligence is often used to refer to artificial general intelligence (AGI). This is a type of AI that can transfer learning from one domain to another. AGI will be able to apply learning techniques to gain new skills without pre-programming. This AI is also called ‘strong AI’ and would be indistinguishable from a human mind.
Artificial super-intelligence (ASI) is the second type of AI. This is an extension of AGI, and would be superior to humans in every domain—from logic to creativity and from social intelligence to persuasion. It is this type of AI that forms the basis of media and cultural stories about the future of AI and robotics.
The most ground-breaking developments are occurring in the third category: artificial narrow intelligence (ANI). ANI systems have the ability to complete pre-defined and limited tasks. ANI is already part of software such as Google Search, Netflix, and Apple Siri. Once ANI algorithms are embedded in software, the threshold for what constitutes ANI shifts higher.
ML is the AI approach that gives machines the ability to learn from data. Other AI approaches, symbolic and statistical, use a different rule-based approach.