CHENNAI: With automation and artificial intelligence emerging in several sectors, more prominently in the IT sector, employees are looking at upskilling' or re-skilling themselves by gaining new skills such as Android Development, Machine Learning, and so on. This is directly reflecting on the kind of courses people are searching for and enrolling in. Udacity, a US-based online educational platform, recently released a survey that reflects on the kind of interest being shown in online courses. In Chennai, some of the popular online courses include those that involve data analysis, deep learning, android development, machine learning, and frontend web developer jobs. The platform which carries out online learning found that the three most popular courses in the country which saw the highest de mand were Android Development, Machine Learning and Deep Learning.

I am giving a talk (in French) at the 85th edition of the ACFAS congress, May 9. I will discuss the engineering aspects of doing machine learning. But more importantly, I will discuss how Semantic Web techniques, technologies and specifications can help solving the engineering problems and how they can be leveraged and integrated in a machine learning workflow. The focus of my talk is based on my work in the field of the semantic web in the last 15 years and my more recent work creating the KBpedia Knowledge Graph at Cognonto and how they influenced our work to develop different machine learning solutions to integrate data, to extend knowledge structure, to tag and disambiguate concepts and entities in corpuses of texts, etc. One thing we experienced is that most of the work involved in such project is not directly related to machine learning problems (or at least related to the usage of machine learning algorithms). And then I recently read a survey conducted by CrowdFlower in 2016 that support what we experienced.

As marketers strive to engage in more meaningful conversations with their audience, understanding which words, phrases, sentences and even content formats resonate with particular audience members is key. Last year we saw progress in lexical analysis with the goal of finding content or text that drove overall marketing success. It did this by analyzing successful campaign content versus unsuccessful content. I believe 2017 will see that work get personalized by combining content analysis at the campaign level with content analysis at the individual level. The interconnected data makes it possible.

In this article, I present a few modern techniques that have been used in various business contexts, comparing performance with traditional methods. The advanced techniques in question are math-free, innovative, efficiently process large amounts of unstructured data, and are robust and scalable. Implementations in Python, R, Julia and Perl are provided, but here we focus on an Excel version that does not even require any Excel macros, coding, plug-ins, or anything other than the most basic version of Excel. It is actually easily implemented in standard, basic SQL too, and we invite readers to work on an SQL version. In short, we offer here an Excel template for machine learning and statistical computing, and it is quite powerful for an Excel spreadsheet.

As you think about what you want to do with your life, at first, a career in artificial intelligence might not be at the top of your list. When you dream of changing the world, it might be hard to connect the dots between building computer models of human intelligence and eliminating hunger or giving individuals the ability to walk again. Maybe you think you'd have to become a coding pro before you can really start solving problems. Or maybe you can't envision a career in artificial intelligence because you have never met anyone like you working in AI. If you have heard anything about AI in the media, it's probably about autonomous vehicles, gaming, or robots taking over the world.

The first step is often the hardest to take, and when given too much choice in terms of direction it can often be debilitating. This post aims to take a newcomer from minimal knowledge of machine learning in Python all the way to knowledgeable practitioner in 7 steps, all while using freely available materials and resources along the way. The prime objective of this outline is to help you wade through the numerous free options that are available; there are many, to be sure, but which are the best? What is the best order in which to use selected resources? It would probably be helpful to have some basic understanding of one or both of the first 2 topics, but even that won't be necessary; some extra time spent on the earlier steps should help compensate.

Dr. Ayanna Howard (1972 –) has some impressive credentials. She is a noted expert in the area of Artificial Intelligence. She is often referred to as an "old school Blerd" (Black Nerd). Her motivation to pursue a career in the sciences was fueled by watching TV shows such as, The Bionic Woman, Star Trek, and Wonder Woman" as a child. Howard has worked as a roboticist and Motorola Foundation Professor at Georgia Tech's Institute for Robotics and Intelligent Machines.

In the debate about the impact of automation and robotics on the future of work, there is often a reductive push toward a Robocalypse, in which machines take all of the jobs. While a total displacement of humans is unlikely, a number of different types of jobs face an existential threat. This is typically low-skill, low-education, and low-income work that often includes significant manual labor and predictively repetitive tasks. According to a recent report by the McKinsey Global Institute, some sectors, such as manufacturing and transportation, have high technical potential for automation. But other sectors, such as education, management, professionals, information and health care, have much lower automation potential.

Feature extraction and dimension reduction for networks is critical in a wide variety of domains. Efficiently and accurately learning features for multiple graphs has important applications in statistical inference on graphs. We propose a method to jointly embed multiple undirected graphs. Given a set of graphs, the joint embedding method identifies a linear subspace spanned by rank one symmetric matrices and projects adjacency matrices of graphs into this subspace. The projection coefficients can be treated as features of the graphs. We also propose a random graph model which generalizes classical random graph model and can be used to model multiple graphs. We show through theory and numerical experiments that under the model, the joint embedding method produces estimates of parameters with small errors. Via simulation experiments, we demonstrate that the joint embedding method produces features which lead to state of the art performance in classifying graphs. Applying the joint embedding method to human brain graphs, we find it extract interpretable features that can be used to predict individual composite creativity index.

Instability and variability of Deep Reinforcement Learning (DRL) algorithms tend to adversely affect their performance. Averaged-DQN is a simple extension to the DQN algorithm, based on averaging previously learned Q-values estimates, which leads to a more stable training procedure and improved performance by reducing approximation error variance in the target values. To understand the effect of the algorithm, we examine the source of value function estimation errors and provide an analytical comparison within a simplified model. We further present experiments on the Arcade Learning Environment benchmark that demonstrate significantly improved stability and performance due to the proposed extension.