5 artificial intelligence (AI) types, defined
Artificial intelligence (AI) is redefining the enterprise’s notions about extracting insight from data. Indeed, the vast majority of technology executives (91 percent) and 84 percent of the general public believe that AI is the “next technology revolution,” according to Edelman’s 2019 Artificial Intelligence (AI) Survey. PwC has predicted that AI could contribute $15.7 trillion to the global economy by 2030.
AI, in short, is a pretty big deal. However, it’s not a monolithic entity: There are multiple flavors of cognitive capabilities. Understanding the various types of AI, how they work, and where they might add value to the business is critical for both IT and line-of-business leaders.
[ What’s next? Read also: 10 AI trends to watch in 2020 and How big data and AI work together. ]
Let’s break down five types of AI and sample uses for them:
ML is perhaps the most relevant subset of AI to the average enterprise today. As explained in the Executive’s guide to real-world AI, our recent research report conducted by Harvard Business Review Analytic Services, ML is a mature technology that has been around for years.
ML is a branch of AI that empowers computers to self-learn from data and apply that learning without human intervention. When facing a situation in which a solution is hidden in a large data set, machine learning is a go-to. “ML excels at processing that data, extracting patterns from it in a fraction of the time a human would take, and producing otherwise inaccessible insight,” says Ingo Mierswa, founder and president of the data science platform RapidMiner.
ML powers risk analysis, fraud detection, and portfolio management in financial services; GPS-based predictions in travel; and targeted marketing campaigns, to list a few examples.
ML learning can get better at completing tasks over time based on the labeled data it ingests, explains ISG director of cognitive automation and innovation Wayne Butterfield, or it can power the creation of predictive models to improve a plethora of business-critical tasks.
An explainer article by AI software company Pathmind offers a useful analogy: Think of a set of Russian dolls nested within each other. “Deep learning is a subset of machine learning, and machine learning is a subset of AI, which is an umbrella term for any computer program that does something smart.”
In our plain English primer on deep learning, we offer this basic definition: the branch of AI that tries to closely mimic the human mind. With deep learning, CompTIA explains, “computers analyze problems at multiple layers in an attempt to simulate how the human brain analyzes problems. Visual images, natural language, or other inputs can be parsed into various components in order to extract meaning and build context, improving the probability of the computer arriving at the correct conclusion.
