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In a world where buzzwords come and go, artificial intelligence has been remarkably durable. Since it first emerged as a concept in the 1950s, there has been a relatively constant flow of technologies, products, services, and companies that purport to be AI. It is quite likely that a solution you are investing in today is being referred to as AI-enabled or machine-learning-driven.
But, is it really?
The reality today for most organizations is that AI and machine learning form a rather small piece of the overall analytics pie. Indeed, research conducted by London-based investment firm MMC Ventures revealed that 40% of Europe’s artificial intelligence startups did not use any AI at all. Furthermore, the offerings of many startups and analytics providers, even if quite advanced, fall short of even basic AI.
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We define AI as any computer-based system that observes, analyzes, and learns. The key here is that these systems are iterative — they get better and more accurate as they collect and analyze more data, without explicit intervention from humans. As the term implies, these are machines that learn, however simple the learning may be.
What Isn’t AI
Just as it is important to define what characterizes a system as AI, it’s equally important to identify what isn’t AI. Mistaking advanced analytics and computing techniques for AI and machine learning can often lead to confusion, and the following section details some of the most common AI fallacies for leaders to understand.
1. Just because a system uses an algorithm and advanced statistics, that doesn’t make it AI.
An algorithm is simply a set of predefined steps or rules to solve a problem. These can be simple (think of an if-then statement) or very complex (think of a chess-playing machine). However, most algorithms are static: They will always return the same output given the same input. That is, they do not adapt or learn.
These algorithms are often coded using standard statistical models, like correlation or regression, that are very good at identifying trend lines in well-defined data. These trend lines allow them to offer predictions of future states based on a set of past states. However, true AI is able to work with data that is not well structured, well defined, or even numeric.