Artificial Intelligence & Machine Learning : You Need to Know Most Used Fundamental Terminology of It
Suddenly, artificial intelligence (AI) is everywhere. For decades, the dream of creating machines that can think and learn like humans seemed like it would be perpetually out of reach, but now artificial intelligence is embedded in the phones we carry everywhere, the websites we use every day and, in some cases, even in the appliances we use around our homes.
The market researchers at IDC have predicted that companies will spend $12.5 billion on cognitive and AI systems in 2017, 59.3% more than they spent last year. And by 2020, total AI revenues could top $46 billion.
In many cases, AI has crept into our lives and our work without us realizing it. A recent survey of 235 business executives conducted by the National Business Research Institute and sponsored by Narrative Science found that while only 38% of respondents thought they were using AI in their workplace, 88% of them were actually using AI-based technologies like predictive analytics, automated reporting and voice recognition and response.
This highlights one of the big issues with artificial intelligence: A lot of people don’t really understand what AI is.
Adding more confusion to the mix, researchers and product developers who work in AI throw around a lot of technical terms that can be baffling to the uninitiated. If they don’t work directly on AI systems, even veteran IT professionals sometimes have difficulty explaining the differences between machine learning and deep learning or defining what exactly a neural network is.
With those tech pros in mind, we’ve put together a slideshow that defines 12 of the most important terms related to artificial intelligence and machine learning. These are the AI jargon IT and business leaders are most likely to encounter, and understanding these words can go a long way towards providing a foundational understanding of this burgeoning area of technology.
Cynthia Harvey is a freelance writer and editor based in the Detroit area. She has been covering the technology industry for more than fifteen years
What is artificial intelligence? In the simplest terms, an artificial intelligence is a machine that can think the way people think.
From the earliest days of computing, machines have been good at performing logical tasks like solving simple math problems. However, other tasks, like carrying on a conversation, identifying whether the animal in picture is a dog or cat, or recognizing whether a person is happy or sad, are much more difficult for computers.
The phrase “artificial intelligence” was first used in reference to these tasks that are easy for humans and difficult for machines at a computer science workshop in 1956. At the conclusion of the workshop, the attendees devoted themselves to figuring out “how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves.”
To this day, AI researchers continue to work on the areas outlined by these early AI pioneers. Fields like natural language processing, image recognition and machine learning have become subspecialties within the overall category of AI. Artificial intelligence research has also expanded to encompass other areas, such as social intelligence, creativity, autonomous vehicles, recommendation engines and much more.
Machine learning is a subset of the larger artificial intelligence category. Going back to the proposal from that first artificial intelligence workshop, machine learning is the part of artificial intelligence that focuses on giving computers the ability to “improve themselves” over time as a result of experience. An early computer scientist named Arthur Samuel explained that machine learning enables computers “to learn without being explicitly programmed,” and his machine learning definition is frequently quoted.
Computer scientists have come up with a lot of different ways to help computers to learn.
