Artificial intelligence (AI) is clearly a growing force in the technology industry. Chatbots and virtual assistants are becoming a key part of new products, and robots are taking center stage at conferences and showing potential in their roles in various industries like retail and manufacturing. Meanwhile, companies such as Google, Microsoft, and Salesforce are integrated AI as an intelligence layer into the entire tech stack. Yes, AI is now having its moment. This isn't the AI that pop culture has conditioned us to expect; it's not sentient robots or Skynet, or even Tony Stark's Jarvis assistant.

The CIFAR deep learning summer school in Toronto has been training the top AI researchers entering or finishing Ph.D. programs since 2005. Over 1,200 students from 60 different countries applied, of which 200 were selected to attend. Attendees represent some of the leading AI labs in the world, Montreal Institute of Learning Algorithms (MILA), University College London, University of Toronto, University of Alberta, Berkeley, NYU, Columbia, CMU, MIT, ETH Zurich, and Stanford. Every year, the school has trained the next generation of top AI researchers which now hold top posts at AI companies like Google, Facebook, Tesla, and Uber. During an intense 10-day period, students learn the tricks of the trade from top AI researchers like deep learning pioneers Yoshua Bengio (MILA), Geoff Hinton (UofT), and reinforcement learning pioneer, Richard Sutton (University of Alberta, Google Deepmind).

Interest in the use of Artificial Intelligence (AI) in schools is growing. More educators are participating in important conversations about it as understanding develops around how AI will impact the work of teachers and schools. In this post I want to add to the conversation by raising some issues and putting forward some questions that I believe are critical. To begin I want to suggest a definition of the term'Artificial Intelligence' or AI as it is commonly known. What do we mean by'Artificial Intelligence'?

A world renowned pioneer in social robotics, Cynthia Breazeal splits her time as an Associate Professor at MIT, where she received her PhD and founded the Personal Robots Group, and Founder and Chief Scientist of Jibo, a personal robotics company with over $85 million in funding. While Breazeal's work has won numerous academic awards, industry accolades, and media attention, she had to fight early skepticism in the 1990s from other experts in robotics and AI. At the time, robots were seen as physical and industrial tools, not social or emotional companions. Her first social robot, Kismet, was unfairly called out in popular press as "useless". Breazeal bucked the trend with a very different vision: "I wanted to create robots with social and emotional intelligence that could work in collaborative partnership with people. In 2-5 years, I see social robots helping families with things that really matter, like education, health, eldercare, entertainment, and companionship." She hopes her work and influence will inspire others to create robots "not only with smarts, but with heart, too."

To learn Chinese in this room, talk to the floating panda head. The Mandarin-speaking avatar zips around a 360-degree restaurant scene in an artificial intelligence-driven instruction program that looks like a giant video game. Rensselaer Polytechnic Institute students testing the technology move inside the 12-foot-high, wrap-around projection to order virtual bean curd from the panda waiter, chat with Beijing market sellers and practice tai chi by mirroring moves of a watchful mentor. "Definitely less anxiety than messing it up with a real human being," says Rahul Divekar, a computer science graduate student working on the project. "So compared to that anxiety, this is a lot more easy."

We show that many machine learning goals, such as improved fairness metrics, can be expressed as constraints on the model's predictions, which we call rate constraints. We study the problem of training non-convex models subject to these rate constraints (or any non-convex and non-differentiable constraints). In the non-convex setting, the standard approach of Lagrange multipliers may fail. Furthermore, if the constraints are non-differentiable, then one cannot optimize the Lagrangian with gradient-based methods. To solve these issues, we introduce the proxy-Lagrangian formulation. This new formulation leads to an algorithm that produces a stochastic classifier by playing a two-player non-zero-sum game solving for what we call a semi-coarse correlated equilibrium, which in turn corresponds to an approximately optimal and feasible solution to the constrained optimization problem. We then give a procedure which shrinks the randomized solution down to one that is a mixture of at most $m+1$ deterministic solutions, given $m$ constraints. This culminates in algorithms that can solve non-convex constrained optimization problems with possibly non-differentiable and non-convex constraints with theoretical guarantees. We provide extensive experimental results enforcing a wide range of policy goals including different fairness metrics, and other goals on accuracy, coverage, recall, and churn.

Robot manipulation is increasingly poised to interact with humans in co-shared workspaces. Despite increasingly robust manipulation and control algorithms, failure modes continue to exist whenever models do not capture the dynamics of the unstructured environment. To obtain longer-term horizons in robot automation, robots must develop introspection and recovery abilities. We contribute a set of recovery policies to deal with anomalies produced by external disturbances as well as anomaly classification through the use of non-parametric statistics with memoized variational inference with scalable adaptation. A recovery critic stands atop of a tightly-integrated, graph-based online motion-generation and introspection system that resolves a wide range of anomalous situations. Policies, skills, and introspection models are learned incrementally and contextually in a task. Two task-level recovery policies: re-enactment and adaptation resolve accidental and persistent anomalies respectively. The introspection system uses non-parametric priors along with Markov jump linear systems and memoized variational inference with scalable adaptation to learn a model from the data. Extensive real-robot experimentation with various strenuous anomalous conditions is induced and resolved at different phases of a task and in different combinations. The system executes around-the-clock introspection and recovery and even elicited self-recovery when misclassifications occurred.

There has been a gap between artificial intelligence and human intelligence. In this paper, we identify three key elements forming human intelligence, and suggest that abstraction learning combines these elements and is thus a way to bridge the gap. Prior researches in artificial intelligence either specify abstraction by human experts, or take abstraction as a qualitative explanation for the model. This paper aims to learn abstraction directly. We tackle three main challenges: representation, objective function, and learning algorithm. Specifically, we propose a partition structure that contains pre-allocated abstraction neurons; we formulate abstraction learning as a constrained optimization problem, which integrates abstraction properties; we develop a network evolution algorithm to solve this problem. This complete framework is named ONE (Optimization via Network Evolution). In our experiments on MNIST, ONE shows elementary human-like intelligence, including low energy consumption, knowledge sharing, and lifelong learning.

The MRes RCA programme provides early and mid-career art and design researchers with the intellectual, technical and professional tools with which to complete high-quality research projects. The programme is a uniquely interdisciplinary degree, and the first to be taught across all four Schools of the RCA. Over a full-time year it offers training in practice and theory-led research methods for critical studies in art and design. As demonstrated by the graduating students' work, MRes RCA supports students from diverse backgrounds. Students come from previous study both in art and design and in related disciplines such as history, political sciences and psychology, and with experience working in the creative industries, as practising architects, designers and artists.

I'm a graduate student in a lab that seemingly has an instrument for everything. You name it, we've got a robot that can do it. The convenience and efficiency can't be beat. But when I first joined the lab, I feared that these tools would make grad students like me obsolete. I thought the single quality that defined a great scientist was perfect experimental technique, and that scientists are essentially supposed to function as living, breathing instruments.