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.

One of my biggest complaints about terminology in the industry is the claim that data from conversations is "unstructured data". After all, how do people communicate, either in voice or in a written language, if there was no structure that aids meaning? Syntax is the structure of language, and it clearly aids in defining semantics, or the meaning of the communications. To understand how computers are rapidly improving, it's important to look at how natural language is different from what computers have historically processed. From flat file sequential data storage models to relational databases (RDBMS), there is a decade's long history of rigidly structured data.

Developers know a lot about the machine learning (ML) systems they create and manage, that's a given. However, there is a need for non-developers to have a high level understanding of the types of systems. Artificial neural networks and expert systems are the classical two key classes. With the advanced in computing performance, software capabilities and algorithm complexity, analytical algorithm can arguably be said to have joined the other two. This article is an overview of the three types.

Developers know a lot about the machine learning (ML) systems they create and manage, that's a given. However, there is a need for non-developers to have a high level understanding of the types of systems. Artificial neural networks and expert systems are the classical two key classes. With the advanced in computing performance, software capabilities and algorithm complexity, analytical algorithm can arguably be said to have joined the other two. This article is an overview of the three types.

In business software, the computer chip has been forgotten. Robotics has been more tightly tied to individual hardware devices, so manufacturing applications are still a bit more focused on hardware. The current state of Artificial Intelligence (AI), in general, and Deep Learning (DL) in specific, is more tightly tying hardware to software than at any time in computers since the 1970s. While my last few "management AI" articles were about overfit and bias, two key risks in a machine learning (ML) system. This column digs deeper to address the question many managers, especially business line managers, might have about the hardware acronyms constantly mentioned in the ML ecosystem: Graphics Processing Unit (GPU) and Field Programmable Gate Array (FPGA). It helps to understand that the GPU is valuable because it accelerates the tensor (math) processing necessary for deep learning applications.

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.