Limitations of Machine Learning

A silver bullet for solving all problems, but sometimes it is not the answer.

"If a typical person can perform a mental task in less than one second, we can probably automate it with AI now or in the near future."

Limitation 1 — Ethics

Machine learning, a subset of artificial intelligence, has revolutionized the world as we know it in the past decade.

This amount of data, coupled with the rapid development of processor power and computer parallelization, has now made it possible to obtain and study huge amounts of data with relative ease.

It is easy to understand why machine learning has had such a profound impact on the world, what is less clear is exactly what its capabilities are, and perhaps more importantly, what its limitations are.

Limitation 2 — Deterministic Problems

Machine learning, a subset of artificial intelligence, has revolutionalized the world as we know it in the past decade.

This amount of data, coupled with the rapid development of processor power and computer parallelization, has now made it possible to obtain and study huge amounts of data with relative ease.

It is easy to understand why machine learning has had such a profound impact on the world, what is less clear is exactly what its capabilities are, and perhaps more importantly, what its limitations are.

Limitation 3 — Data

Many machine learning algorithms require large amounts of data before they begin to give useful results. Reusing data is a bad idea, and data augmentation is useful to some extent, but having more data is always the preferred solution. Having a lack of good ground truth data can also limit the capabilities of your model.

Limitation 4 — Misapplication

There is purported to be a "crisis of machine learning in academic research" whereby people blindly use machine learning to analyze systems that are either deterministic or stochastic in nature. For reasons discussed in limitation two, applying machine learning on deterministic systems will succeed, but the algorithm which not be learning the relationship between the two variables, and will not know when it is violating physical laws.

Limitation 5 — Interpretability

Interpretability is one of the primary problems with machine learning. An AI consultancy firm trying to pitch to a firm that only uses traditional statistical methods can be stopped dead if they don't see the model as interpretable. For this reason, interpretability is a paramount quality that machine learning methods should aim to achieve if they are to be applied in practice.

CONCLUSION

"Solutionism" is the belief that, given enough data, machine learning algorithms can solve all of humanity's problems. A neural network can never tell us how to be a good person, and do not understand Newton's laws of motion or Einstein's theory of relativity. There are also fundamental limitations grounded in the underlying theory of machine learning, called computational learning theory.