Artificial intelligence is rapidly becoming one of the most important technologies of our era. Over the past few years, the necessary ingredients have come together to take AI across the threshold: powerful, inexpensive computer technologies; huge amounts of data; and advanced algorithms, especially machine learning. Machine learning has enabled AI to get around one of its biggest obstacles, the so-called Polanyi’s paradox.

Explicit knowledge is formal, codified, and can be readily explained to people and captured in a computer program. But tacit knowledge, a concept first introduced in the 1950s by scientist and philosopher Michael Polanyi, is the kind of knowledge we’re often not aware we have, and is therefore difficult to transfer to another person, let alone capture in a computer program.

“We can know more than we can tell,” said Mr. Polanyi in what’s become known as Polanyi’s paradox. This common sense phrase succinctly captures the fact that we tacitly know a lot about the way the world works, yet aren’t able to explicitly describe this knowledge. Tacit knowledge is best transmitted through personal interactions and practical experiences. Everyday examples include speaking a language, riding a bike, and easily recognizing many different people, animals and objects.

Machine learning, and related advances like deep learning, have enabled computers to acquire tacit knowledge by being trained with lots and lots of sample inputs, thus learning by analyzing large amounts of data instead of being explicitly programmed. Machine learning methods are now being applied to vision, speech recognition, language translation, and other capabilities that not long ago seemed impossible but are now approaching or surpassing human levels of performance in a number of domains.

As its domain of applications continues to expand, machine learning is raising serious concerns on its impact on automation and the future of work. In a December 2017 Science article, MIT professor Erik Brynjolfsson and CMU professor Tom Mitchell explore this question by identifying eight key criteria that help distinguish tasks that are suitable for machine learning, from those where machine learning is less likely to be successful.

Learning a function that maps well-defined inputs to well-defined outputs. Such functions include classification and predictions, such as the likelihood of defaulting on a loan application. These amount to statistical correlations without necessarily capturing causal effects.

Large (digital) data sets exist or can be created containing input-output pairs. The bigger the training data sets, the more accurate the learning. One of the key features of deep learning algorithms is that, unlike classic analytic methods, there’s no asymptotic data size limit beyond which they stop improving.

The task provides clear feedback with clearly definable goals and metrics. “ML works well when we can clearly describe the goals, even if we cannot necessarily define the best process for achieving those goals,” they write. Machine learning is particularly powerful when there are specific, system-wide performance metrics.

No long chains of logic or reasoning that depend on diverse background knowledge or common sense. “ML systems are very strong at learning empirical associations in data but are less effective when the task requires long chains of reasoning or complex planning that rely on common sense or background knowledge unknown to the computer.”

No need for detailed explanation of how the decision was made. Explaining to a human the reasoning behind a particular decision or recommendation made by a machine learning algorithm is quite difficult, because its methods are so different from those used by humans.

A tolerance for error and no need for provably correct or optimal solutions. Machine learning algorithms derive their solutions based on statistics, assigning probabilities to the different options it evaluates. It’s rarely possible to train them with 100% accuracy.

The phenomenon or function being learned should not change rapidly over time. “In general, ML algorithms work well only when the distribution of future test examples is similar to the distribution of training examples.” If the function changes rapidly over time, retraining is typically required, requiring the acquisition of new training data.

No specialized dexterity, physical skills, or mobility required. Machine learning systems have already surpassed human levels of performance in a number of tasks. However, while the digital AI brains of robots are doing quite well, their physical capabilities are still quite clumsy compared to humans, especially when dealing with unstructured tasks and environments.

The Science article includes fairly elaborate supplementary materials to help evaluate what the current generation of ML systems can and cannot do.

“Although economic effects of ML are relatively limited today, and we are not facing the imminent end of work as is sometimes proclaimed, the implications for the economy and the workforce going forward are profound,…” write Mssrs. Brynjolfsson and Mitchel write. “Thus, a better understanding of the precise applicability of each type of ML and its implications for specific tasks is critical for understanding its likely economic impact.”