12 Artificial Intelligence Terms You Need to Know

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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.

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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.

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