Jean-François Puget, a data professional who holds the title of IBM distinguished engineer, delivered a keynote presentation at the Strata Data Conference in London recently. The theme of his talk was the consideration of humans when bringing in automated processes. His main example focused the resistance people can have to new automated processes that work that they are not used to, but he also spoke of bias in artificial intelligence saying it "can be a bit more complex." Puget gave an example that he had worked on with his team, a study on facial recognition. They looked at members of parliament from all over the world and ran experiments with facial recognition services from major vendors.

What programming language should one learn to get a machine learning or data science job? It is debated in many forums. I could provide here my own answer to it and explain why, but I'd rather look at some data first. After all, this is what machine learners and data scientists should do: look at data, not opinions. So, let's look at some data.

Machine learning first took root in the 1950s and has slowly progressed over six decades through 2010. Since then, IBM and companies such as Google, Microsoft and Facebook have been integrating and improving the technology at a rapid pace. And now many other companies are joining the bandwagon. On February 23, Apple Inc. announced that it is expanding its artificial intelligence and machine learning team in Seattle. The expanded team will join forces with the company's headquarters team in Cupertino to develop innovative AI features for future product and services.

As machine learning becomes more accessible through avenues such as Intel Corp.'s BigDL and IBM Corp. opening Watson's core machine learning components up to businesses, some developers and industry insiders are cautioning against getting too dazzled by the potential without considering the human role. However much data those programs can process, in the end, "what you do with the results of algorithms is key," said Jean-Francois Puget, Ph.D. (pictured), distinguished engineer, machine learning and optimization, IBM Analytics, at IBM. Puget spoke with Dave Vellante (@dvellante) and Stu Miniman (@stu), co-hosts of theCUBE, SiliconANGLE Media's mobile live streaming studio, at the IBM Machine Learning Launch Event in New York, NY. He offered his perspective on machine learning and its applications. "For most people, machine learning equals machine learning algorithms," Puget said. "When you look at newspapers or blogs, social media, it's all about algorithms. Our view [is] that sure, you need algorithms for machine learning, but you need steps before you run algorithms, and after."

As machine learning becomes more accessible through avenues such as Intel's BigDL and IBM opening Watson's core machine learning components up to businesses, some developers and industry insiders are cautioning against getting too dazzled by the potential without considering the human role. However much data those programs can process, in the end, "what you do with the results of algorithms is key," said Jean-Francois Puget, Ph.D. (pictured), distinguished engineer, machine learning and optimization, IBM Analytics, at IBM. Puget spoke with Dave Vellante (@dvellante) and Stu Miniman (@stu), co-hosts of theCUBE, SiliconANGLE Media's mobile live streaming studio, at the IBM Machine Learning Launch Event in New York, NY. He offered his perspective on machine learning and its applications. "For most people, machine learning equals machine learning algorithms," Puget said. "When you look at newspapers or blogs, social media, it's all about algorithms. Our view [is] that sure, you need algorithms for machine learning, but you need steps before you run algorithms, and after."

Join The Brussels Data Science Community, Spark Summit Europe attendees, and Spark ML and machine learning experts Nick Pentreath and Jean-Francois Puget for a talk on an Apache Spark–based, end-to-end machine learning system. A round of Spark and machine learning lightning talks will follow. Many resources are available for building basic recommendation models using Spark. But how does a practitioner go from the basics to creating an end-to-end machine learning system, including deployment and management of models for real-time serving? In this session, we'll demonstrate how to build such a system based on Spark ML and Elasticsearch.

We consider a common type of symmetry where we have a matrix of decision variables with interchangeable rows and columns. A simple and efficient method to deal with such row and column symmetry is to post symmetry breaking constraints like DOUBLELEX and SNAKELEX. We provide a number of positive and negative results on posting such symmetry breaking constraints. On the positive side, we prove that we can compute in polynomial time a unique representative of an equivalence class in a matrix model with row and column symmetry if the number of rows (or of columns) is bounded and in a number of other special cases. On the negative side, we show that whilst DOUBLELEX and SNAKELEX are often effective in practice, they can leave a large number of symmetric solutions in the worst case. In addition, we prove that propagating DOUBLELEX completely is NP-hard. Finally we consider how to break row, column and value symmetry, correcting a result in the literature about the safeness of combining different symmetry breaking constraints. We end with the first experimental study on how much symmetry is left by DOUBLELEX and SNAKELEX on some benchmark problems.

One common type of symmetry is when values are symmetric. For example, if we are assigning colours (values) to nodes (variables) in a graph colouring problem then we can uniformly interchange the colours throughout a colouring. For a problem with value symmetries, all symmetric solutions can be eliminated in polynomial time. However, as we show here, both static and dynamic methods to deal with symmetry have computational limitations. With static methods, pruning all symmetric values is NP-hard in general. With dynamic methods, we can take exponential time on problems which static methods solve without search.