Mapping AI techniques to problem types

Mapping AI techniques to problem types

A view of new analytics techniques, focused on specific potential applications in business and the public sector.

As artificial intelligence technologies advance, so does the definition of which techniques constitute AI (see sidebar, “Deep learning’s origins and pioneers”).1 For the purposes of this paper, we use AI as shorthand specifically to refer to deep learning techniques that use artificial neural networks. In this section, we define a range of AI and advanced analytics techniques as well as key problem types to which these techniques can be applied.

Neural networks and other machine learning techniques

We looked at the value potential of a range of analytics techniques. The focus of our research was on methods using artificial neural networks for deep learning, which we collectively refer to as AI in this paper, understanding that in different times and contexts, other techniques can and have been included in AI. We also examined other machine learning techniques and traditional analytics techniques (Exhibit 1). We focused on specific potential applications of AI in business and the public sector (sometimes described as “artificial narrow AI”) rather than the longer-term possibility of an “artificial general intelligence” that could potentially perform any intellectual task a human being is capable of.

Artificial intelligence, machine learning, and other analytics techniques examined for this research.

Neural networks are a subset of machine learning techniques. Essentially, they are AI systems based on simulating connected “neural units,” loosely modeling the way that neurons interact in the brain. Computational models inspired by neural connections have been studied since the 1940s and have returned to prominence as computer processing power has increased and large training data sets have been used to successfully analyze input data such as images, video, and speech. AI practitioners refer to these techniques as “deep learning,” since neural networks have many (“deep”) layers of simulated interconnected neurons. Before deep learning, neural networks often had only three to five layers and dozens of neurons; deep learning networks can have seven to ten or more layers, with simulated neurons numbering into the millions.

In this paper, we analyzed the applications and value of three neural network techniques:

  • Feed forward neural networks. One of the most common types of artificial neural network. In this architecture, information moves in only one direction, forward, from the input layer, through the “hidden” layers, to the output layer. There are no loops in the network. The first single-neuron network was proposed in 1958 by AI pioneer Frank Rosenblatt. While the idea is not new, advances in computing power, training algorithms, and available data led to higher levels of performance than previously possible.
  • Recurrent neural networks (RNNs). Artificial neural networks whose connections between neurons include loops, well-suited for processing sequences of inputs, which makes them highly effective in a wide range of applications, from handwriting, to texts, to speech recognition. In November 2016, Oxford University researchers reported that a system based on recurrent neural networks (and convolutional neural networks) had achieved 95 percent accuracy in reading lips, outperforming experienced human lip readers, who tested at 52 percent accuracy.
  • Convolutional neural networks (CNNs). Artificial neural networks in which the connections between neural layers are inspired by the organization of the animal visual cortex, the portion of the brain that processes images, well suited for visual perception tasks.

We estimated the potential of those three deep neural network techniques to create value, as well as other machine learning techniques such as tree-based ensemble learning, classifiers, and clustering, and traditional analytics such as dimensionality reduction and regression.

For our use cases, we also considered two other techniques—generative adversarial networks (GANs) and reinforcement learning—but did not include them in our potential value assessment of AI, since they remain nascent techniques that are not yet widely applied in business contexts. However, as we note in the concluding section of this paper, they may have considerable relevance in the future.

  • Generative adversarial networks (GANs). These usually use two neural networks contesting each other in a zero-sum game framework (thus “adversarial”). GANs can learn to mimic various distributions of data (for example text, speech, and images) and are therefore valuable in generating test datasets when these are not readily available.
  • Reinforcement learning. This is a subfield of machine learning in which systems are trained by receiving virtual “rewards” or “punishments,” essentially learning by trial and error. Google DeepMind has used reinforcement learning to develop systems that can play games, including video games and board games such as Go, better than human champions.

Problem types and the analytic techniques that can be applied to solve them

In a business setting, those analytic techniques can be applied to solve real-life problems. For this research, we created a taxonomy of high-level problem types, characterized by the inputs, outputs, and purpose of each. A corresponding set of analytic techniques can be applied to solve these problems. These problem types include:

  • Classification. Based on a set of training data, categorize new inputs as belonging to one of a set of categories. An example of classification is identifying whether an image contains a specific type of object, such as a truck or a car, or a product of acceptable quality coming from a manufacturing line.
  • Continuous estimation. Based on a set of training data, estimate the next numeric value in a sequence. This type of problem is sometimes described as “prediction,” particularly when it is applied to time series data. One example of continuous estimation is forecasting the sales demand for a product, based on a set of input data such as previous sales figures, consumer sentiment, and weather. Another example is predicting the price of real estate, such as a building, using data describing the property combined with photos of it.
  • Clustering. These problems require a system to create a set of categories, for which individual data instances have a set of common or similar characteristics. An example of clustering is creating a set of consumer segments based on data about individual consumers, including demographics, preferences, and buyer behavior.
  • All other optimization. These problems require a system to generate a set of outputs that optimize outcomes for a specific objective function (some of the other problem types can be considered types of optimization, so we describe these as “all other” optimization). Generating a route for a vehicle that creates the optimum combination of time and fuel use is an example of optimization.
  • Anomaly detection. Given a training set of data, determine whether specific inputs are out of the ordinary. For instance, a system could be trained on a set of historical vibration data associated with the performance of an operating piece of machinery, and then determine whether a new vibration reading suggests that the machine is not operating normally. Note that anomaly detection can be considered a subcategory of classification.
  • Ranking. Ranking algorithms are used most often in information retrieval problems in which the results of a query or request needs to be ordered by some criterion. Recommendation systems suggesting next product to buy use these types of algorithms as a final step, sorting suggestions by relevance, before presenting the results to the user.
  • Recommendations. These systems provide recommendations, based on a set of training data. A common example of recommendations are systems that suggest the “next product to buy” for a customer, based on the buying patterns of similar individuals, and the observed behavior of the specific person.
  • Data generation. These problems require a system to generate appropriately novel data based on training data. For instance, a music composition system might be used to generate new pieces of music in a particular style, after having been trained on pieces of music in that style.

Exhibit 2 illustrates the relative total value of these problem types across our database of use cases, along with some of the sample analytics techniques that can be used to solve each problem type. The most prevalent problem types are classification, continuous estimation, and clustering, suggesting that meeting the requirements and developing the capabilities in associated techniques could have the widest benefit. Some of the problem types that rank lower can be viewed as subcategories of other problem types—for example, anomaly detection is a special case of classification, while recommendations can be considered a type of optimization problem— and thus their associated capabilities could be even more relevant.

Problem types and sample techniques.

This article is a reprint of a chapter from the April 2018 McKinsey Global Institute discussion paper,Notes from the AI frontier: Insights from hundreds of use cases.”

About the author(s)

Michael Chui is a partner in McKinsey’s San Francisco office, where James Manyika is a senior partner. Rita Chung is a consultant in the Silicon Valley office; Nicolaus Henke is a senior partner in the London office; Sankalp Malhotra is a consultant in the New York office, where Pieter Nel is a digital expert; and Mehdi Miremadi is a partner in the Chicago office.

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