When a person drives, there are many things that are quickly noticed and then ignored. What gains attention are those things that might be a danger. A pedestrian who might walk out into the road, a light turning yellow, an adjacent car drifting into the same lane, all of those need special attention. The same thing is true in the world of business computing. For instance, a sudden increase in sales is great, but the company needs to track that anomalous increase back to its cause in order to identify and replicate the reason.

One of the interesting changes in terminology is that of the meaning of machine learning (ML). In the olden days, way back in the 1980s, machine learning referred almost exclusively to the to artificial intelligence tools of expert systems and deep learning (DL). Today, with the massive increase in computer performance, many algorithms used in business intelligence can discover many things about data and have been combined with the older techniques under an expanded definition of ML. To understand why the added complexity involved in training and deploying DL systems is useful in certain circumstances, this article describes the basics of optimization and explains what DL adds to business understanding. Optimization is mathematical speak for finding the maximum or minimum value of some function. For instance, one of the most discussed concepts in business is that of maximizing profit.

Criminal recidivism is when a released criminal goes back to crime. From charging crimes through probation, the criminal justice system is constantly looking for ways to better predict which criminals are more likely to remain legal on release and who is a risk of recidivism. Bias can create inaccuracies through weighing variables incorrectly, and machine learning might provide a way of limiting bias and improving recidivism predictions. A recent study by Julia Dressel and Hany Farid, published in Science Advances, points to the limitations of deterministic algorithms with fixed parameters for the task of such predictions. The study analyzes the Correctional Offender Management Profiling for Alternative Sanctions (COMPAS) software, a package used by court systems to predict the likelihood of recidivism in criminal defendants. The lessons learned lead me to a discussion about the promise of machine learning (ML) systems – specifically, deep learning.

The core of most modern Machine Learning (ML) systems is automated neural networks (ANNs). The training of ANN's requires large data sets. One misconception of those data sets is the idea that "if we get enough data, we can make the system 100% accurate." Yes, that can happen, but it's not what we really want. Many methods can be used to group data into relevant categories.

The core of most modern Machine Learning (ML) systems is automated neural networks (ANNs). The training of ANN's require large data sets. One misconception of those data sets is the idea that "if we get enough data, we can make the system 100% accurate." Yes, that can happen, but it's not what we really want. Many methods can be used to group data into relevant categories.

If you've read any tech media recently, then you're probably hearing a lot about artificial intelligence (AI). Some people herald it as the promise of the future, while others are skeptical -- even fearful -- of its impacts on society, culture, and our workplaces. As it turns out, the buzz around AI has mostly resulted in a lot of conflicting emotions. A recent Atlassian user survey found that 87 percent of respondents said artificial intelligence (AI) will change their job in the next three years. Almost the same number said that some part of their job could be done by AI.