Almost two years ago, Lean In and Getty images partnered to address bias in the way women are portrayed in stock photography by creating The Lean In Collection, over 6,000 images portraying women as leaders and/or partners. In March of this year, data from Procter & Gamble's Always Confidence and Puberty Survey revealed that more than half of girls surveyed felt that female emoji are stereotypical, while 75 percent wanted to see girls portrayed more progressively. In July, Google announced that the Unicode Consortium had approved 11 new professional emoji--such as farmer, mechanic and welder--with both female and male options, and in a range of skin tones. They also approved a set of male and female versions of existing emoji. But now it's not only stock photos and emoji that are at issue.

Last week at RoboBusiness, I was fortunate to hear James Kuffner, CTO of Toyota's Research Institute (TRI), present his vision of a million connected robots. If this premise sounds scary, I am sorry. Halloween is just around the corner. Seriously, it is worth digging into in greater detail. According to Kuffner, connectivity speeds are as important to cloud robotics as the growth of processing speeds or Moore's Law.

It was December 2012, and Doug Burger was standing in front of Steve Ballmer, trying to predict the future. Ballmer, the big, bald, boisterous CEO of Microsoft, sat in the lecture room on the ground floor of Building 99, home base for the company's blue-sky R&D lab just outside Seattle. The tables curved around the outside of the room in a U-shape, and Ballmer was surrounded by his top lieutenants, his laptop open. Burger, a computer chip researcher who had joined the company four years earlier, was pitching a new idea to the execs. He called it Project Catapult. The tech world, Burger explained, was moving into a new orbit.

Very flexible Need some effort to learn Easy to learn and use (still have some flexibility) You can modify it if you can write TensorFlow or Theano Interface of TensorFlow or Theano or If you want to learn theano: 51.

A London NHS hospital trust has teamed up with tech giant Google to share patient data so it can save more lives. Doctors at the Royal Free say partnering with the artificial intelligence arm of Google - DeepMind - could free up over half a million hours per year, currently spent on paperwork, towards direct patient care. Medical staff will get'breaking news' style alerts about their patients. Privacy campaigners are concerned about data breaches. Information on more than 1.6 million patients a year will be shared with a subsidiary of Google.

Robots have become commonplace in many aspects of life including health care, military and security work. Yet until recently little thought has been given outside of academic circles to the ethics of robots. Silicon Valley Robotics recently launched a Good Robot Design Council -- which has launched "5 Laws of Robotics" guidelines for roboticists and academics -- on the ethical creation, marketing and use of robots in everyday life. Robots should comply with existing law, including privacy. Robots are products; they should be safe, reliable and not misrepresent their capabilities.

University researchers in Swansea have trained computers to detect cancer cells using artificial intelligence algorithms. Using similar technology to face and fingerprint recognition software, the computers have been taught to recognise cells and pinpoint them. It means cancer cells can be identified quicker, speeding up diagnosis times. The project is in collaboration with specialists in the US, Germany, London and Newcastle. Prof Paul Rees, from the university's college of engineering, said in the past, finding cancer cells had been like "looking for a needle in a haystack" and the new method was a "world-leading development".

Standard deep reinforcement learning methods such as Deep Q-Networks (DQN) for multiple tasks (domains) face scalability problems. We propose a method for multi-domain dialogue policy learning---termed NDQN, and apply it to an information-seeking spoken dialogue system in the domains of restaurants and hotels. Experimental results comparing DQN (baseline) versus NDQN (proposed) using simulations report that our proposed method exhibits better scalability and is promising for optimising the behaviour of multi-domain dialogue systems.

Functional MRI (fMRI) and diffusion MRI (dMRI) are non-invasive imaging modalities that allow in-vivo analysis of a patient's brain network (known as a connectome). Use of these technologies has enabled faster and better diagnoses and treatments of neurological disorders and a deeper understanding of the human brain. Recently, researchers have been exploring the application of machine learning models to connectome data in order to predict clinical outcomes and analyze the importance of subnetworks in the brain. Connectome data has unique properties, which present both special challenges and opportunities when used for machine learning. The purpose of this work is to review the literature on the topic of applying machine learning models to MRI-based connectome data. This field is growing rapidly and now encompasses a large body of research. To summarize the research done to date, we provide a comparative, structured summary of 77 relevant works, tabulated according to different criteria, that represent the majority of the literature on this topic. (We also published a living version of this table online at http://connectomelearning.cs.sfu.ca that the community can continue to contribute to.) After giving an overview of how connectomes are constructed from dMRI and fMRI data, we discuss the variety of machine learning tasks that have been explored with connectome data. We then compare the advantages and drawbacks of different machine learning approaches that have been employed, discussing different feature selection and feature extraction schemes, as well as the learning models and regularization penalties themselves. Throughout this discussion, we focus particularly on how the methods are adapted to the unique nature of graphical connectome data. Finally, we conclude by summarizing the current state of the art and by outlining what we believe are strategic directions for future research.

Traditional classification algorithms assume that training and test data come from similar distributions. This assumption is violated in adversarial settings, where malicious actors modify instances to evade detection. A number of custom methods have been developed for both adversarial evasion attacks and robust learning. We propose the first systematic and general-purpose retraining framework which can: a) boost robustness of an \emph{arbitrary} learning algorithm, in the face of b) a broader class of adversarial models than any prior methods. We show that, under natural conditions, the retraining framework minimizes an upper bound on optimal adversarial risk, and show how to extend this result to account for approximations of evasion attacks. Extensive experimental evaluation demonstrates that our retraining methods are nearly indistinguishable from state-of-the-art algorithms for optimizing adversarial risk, but are more general and far more scalable. The experiments also confirm that without retraining, our adversarial framework dramatically reduces the effectiveness of learning. In contrast, retraining significantly boosts robustness to evasion attacks without significantly compromising overall accuracy.