AI News, Why is machine learning 'hard'?

Why is machine learning 'hard'?

There have been tremendous advances made in making machine learning more accessible over the past few years.

Online courses have emerged, well-written textbooks have gathered cutting edge research into an easier to digest format and countless frameworks have emerged to abstract the low level messiness associated with building machine learning systems.

In some cases these advancements have made it possible to drop an existing model into your application with a basic understanding of how the algorithm works and a few lines of code.

Machine learning remains a hard problem when implementing existing algorithms and models to work well for your new application.

This difficulty is often not due to math - because of the aforementioned frameworks machine learning implementations do not require intense mathematics.

Regular software engineering requires awareness of the trade offs of competing frameworks, tools and techniques and judicious design decisions.

In standard software engineering when you craft a solution to a problem and something doesn’t work as expected in most cases you have two dimensions along which things could have gone wrong: algorithmic or implementation issues.

The debugging process then becomes a matter of combining the signals you have about the bug (compiler error messages, program outputs etc.) with your intuition on where the problem might be.

The reason this is 'exponentially' harder is because if there are n possible ways things could go wrong in one dimension there are n x n ways things could go wrong in 2D and n x n x n x n ways things can go wrong in 4D.

For example, signals that are particularly useful are plots of your loss function on your training and test sets, actual output from your algorithm on your development data set and summary statistics of the intermediate computations in your algorithm.

This is a key skill that you develop as you continue to build out machine learning projects: you begin to associate certain behavior signals with where the problem likely is in your debugging space.

After much trial and error I eventually learned that this is often the case of a training set that has not been correctly randomized (it was an implementation issue that looked like a data issue) and is a problem when you are using stochastic gradient algorithms that process the data in small batches.

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