3 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.

Three important kinds of AI
Let’s break down five types of AI and sample uses for them:

Machine learning (ML)

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 use cases

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 improve by completing tasks over time-based on the tagged data you ingest, explains ISG director of cognitive automation and innovation, Wayne Butterfield, or can drive the creation of predictive models to improve a great deal. number of critical business tasks.

Deep learning

An explanatory article from the IA software company Pathmind offers a useful analogy: Think of a set of Russian dolls nested together. "Deep learning is a subset of machine learning, and machine learning is a subset of AI, which is a general 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.”

Deep learning uses so-called neural networks, which "learn from the processing of the tagged data supplied during training, and uses this answer key to learn what characteristics of the input are needed to build the correct output," according to an explanation provided by deep AI. "Once a sufficient number of examples have been processed, the neural network can begin processing new and invisible inputs and successfully return accurate results."

Deep learning use cases

Deep learning powers product and content recommendations for Amazon and Netflix. It works behind the scenes of Google’s voice- and image-recognition algorithms. Its capacity to analyze very large amounts of high-dimensional data makes deep learning ideally suited for supercharging preventive maintenance systems, as McKinsey pointed out in its Notes from The AI Frontier: Applications and Value of Deep Learning: “By creating additional layers of data, such as audio and image data, from other sensors, including relatively inexpensive ones such as microphones and cameras, neural networks can improve and possibly replace more traditional methods. AI's ability to predict failures and enable planned interventions can be used to reduce downtime and operating costs while improving production performance. "

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