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.

Machine Learning has become an integral part of day to day life, particularly in the Digital World. Companies such as Google, Facebook, Netflix, and Amazon now use Machine Learning to continually update their services and algorithms. So far in this series, we have explored the basic principles, current applications and potential use of Machine Learning in SEO and Social. In this blog, we will look at some of the tools and applications that are actively being used in the market. The Machine Learning solutions below range from easy to use off-the-shelf solutions to code-heavy custom built solutions and platforms.

One question that always pops up in any machine learning problem: Which algorithm should I use? What do the algorithms do anyways? After briefly going over a typical machine learning process, we have a closer look at third step, i.e. building the model: What algorithms are out there? Which one should we use? One of Microsoft's Data Scientist, Brandon Rohrer, has written a nice three-part blog series on introducing data science with no jargon: Furthermore, there is one really neat cheat sheet created by Microsoft's Data Science team on when to use which algorithm: Finally, one last resource that I hihgly recommend: Top 10 data mining algorithms in plain English.

Not enough women are going into computer science. "I remember walking into one of the classes at Stanford and just deciding not to take the class because I was one of only three women there, and I just felt so intimidated," recalled Catherina Xu, one of the co-presidents for Women in Computer Science at Stanford University. Incidents like this are happening all across the country, and partly due to the lack of women in the field, there is now a shortage of computer science majors -- and it's going to get even worse. By 2024, the National Center for Women and Information Technology predicts that there will be 1.1 million computing-related job openings, and only 41% of those jobs will be filled. And get this: The percentage of women in the field has been declining since the 1980s.

Deep reinforcement learning (RL) can acquire complex behaviors from low-level inputs, such as images. However, real-world applications of such methods require generalizing to the vast variability of the real world. Deep networks are known to achieve remarkable generalization when provided with massive amounts of labeled data, but can we provide this breadth of experience to an RL agent, such as a robot? The robot might continuously learn as it explores the world around it, even while deployed. However, this learning requires access to a reward function, which is often hard to measure in real-world domains, where the reward could depend on, for example, unknown positions of objects or the emotional state of the user. Conversely, it is often quite practical to provide the agent with reward functions in a limited set of situations, such as when a human supervisor is present or in a controlled setting. Can we make use of this limited supervision, and still benefit from the breadth of experience an agent might collect on its own? In this paper, we formalize this problem as semisupervised reinforcement learning, where the reward function can only be evaluated in a set of "labeled" MDPs, and the agent must generalize its behavior to the wide range of states it might encounter in a set of "unlabeled" MDPs, by using experience from both settings. Our proposed method infers the task objective in the unlabeled MDPs through an algorithm that resembles inverse RL, using the agent's own prior experience in the labeled MDPs as a kind of demonstration of optimal behavior. We evaluate our method on challenging tasks that require control directly from images, and show that our approach can improve the generalization of a learned deep neural network policy by using experience for which no reward function is available. We also show that our method outperforms direct supervised learning of the reward.

Imagine that, in 20 or 30 years, a company creates the first artificially intelligent humanoid robot. She looks like a person, talks like a person, interacts like a person. If you were to meet Ava, you could relate to her even though you know she's a robot. Ava is a fully conscious, fully self-aware being: She communicates; she wants things; she improves herself. She is also, importantly, far more intelligent than her human creators.

Yogi Berra once said, "It's tough to make predictions, especially about the future." It is tough indeed, but enterprises that can make probabilistic predictions about customers, business processes, and operations will have an edge over enterprises that can't. These predictions don't have to be macroscopic to be consequential. Predictions about what a customer is likely to buy next. Predictions about marketing content that will resonate with a prospect.