The problem of detecting whether a test sample is from in-distribution (i.e., training distribution by a classifier) or out-of-distribution sufficiently different from it arises in many real-world machine learning applications. However, the state-of-art deep neural networks are known to be highly overconfident in their predictions, i.e., do not distinguish in- and out-of-distributions. Recently, to handle this issue, several threshold-based detectors have been proposed given pre-trained neural classifiers. However, the performance of prior works highly depends on how to train the classifiers since they only focus on improving inference procedures. In this paper, we develop a novel training method for classifiers so that such inference algorithms can work better. In particular, we suggest two additional terms added to the original loss (e.g., cross entropy). The first one forces samples from out-of-distribution less confident by the classifier and the second one is for (implicitly) generating most effective training samples for the first one. In essence, our method jointly trains both classification and generative neural networks for out-of-distribution. We demonstrate its effectiveness using deep convolutional neural networks on various popular image datasets.

It's been a typical week for typical college student BINA48. On Monday, BINA attended her robot ethics class. On Tuesday, the second-semester student had an excused absence to ring the bell at the stock exchange, and soon BINA will be assistant-teaching a kindergarten class and getting a face-lift at Hanson Robotics. But that hasn't stopped the robot, which looks like the bust of a flesh-and-blood woman, from completing a Philosophy of Love course at Notre Dame de Namur University in Belmont, California. Programmed to be social, BINA48 presented her final project along with a human student, demonstrating that the robot could retain and present a philosophical perspective on love.

These are all introductory content and are the more popular ones you'll find.

Data science is an emerging field, and roles, as well as qualifications, aren't clear-cut at the moment. Given the murkiness surrounding the field and the potential lack of analytics expertise at companies seeking to hire a data scientist or team of data scientists, the task of building an analytics team or hiring a company's first data scientist can be daunting. However, with a brief overview of data scientist types and example questions to assess each type, hiring managers can provide recruiters with a more tailored profile and better assess candidates on skills likely needed to fill the role. Data scientists typically have skills in 3 main areas: mathematics/statistics/machine learning, coding/software engineering, and expertise in the industry in which they seek employment (see chart below). Most mature data scientists have a strong skills in 2 of these 3 areas, yielding software/math folks (who are typically found in tech companies or production roles), math/domain folks (more of a traditional statistician or scientific researcher), or software/domain (less common but often involved in data pipelines and business intelligence roles).

We have entered the Age of Artificial Intelligence (AI). And, while many of us have heard how AI will impact market segments like manufacturing or R&D, I find myself wondering: What about other areas of business--like L&D? How will AI affect learning and development? As James Paine points out, "It wasn't so long ago that artificial intelligence was reserved to the realm of science fiction according to the public." AI grew exponentially in 2017 and is projected to be even bigger in 2018.

A teacher's role on any given day is a combination of content expert, engineer, detective, psychologist, diagnostician, and researcher. Teachers are expected to have students master a set of concepts and information in a defined period of time. They must adapt these plans to the unique characteristics of each student. When a student struggles, a teacher must understand the exact nature of the issue–is it a gap in prerequisite knowledge, a learning challenge of the student, and/or a failure of the presentation to engage the student? Then, the teacher needs to modify their instruction based on their hypothesis of the cause of the issue, all while keeping the student positive about their effort, informed about their progress, and passionate about staying engaged.

Two Imperial undergraduate students have launched a new multidisciplinary Machine Learning Society. Undergraduates Harry Berg (Mechanical Engineering) and Haron Shams (Design Engineering) have set up the Imperial College Machine Learning Society to get students involved in and inspired by technology that's going to change the world. Here they tell us more about what inspired them, what happened on launch day and their plans for the future. Image above: Antonia Creswell teaches the audience about the history of machine learning, specifically deep learning. Harry: We really wanted to emphasise the interdisciplinary potential of machine learning – it's not just for computing students, or postgraduates – we're keen to give everyone, particularly undergrad students, the opportunity to get involved.

In recent years the customer experience landscape has seen the emergence of chatbots, virtual digital assistants, and AI. By automating repetitive tasks these tools have saved costs, allowing humans to focus on more complex issues. The long term impact of AI and machine learning applications, however, is potentially tremendous and far reaching. Machine learning refers to the ability of information systems or computer programs to learn and improve from experience, without being programmed. Essentially, the machine interprets existing data using algorithms allowing the computer program or information system to find hidden insights without being explicitly programed where to look.

"Reweighting rewards in embodied evolution to achieve a balanced distribution of labour," in Proceedings of the 14th European Conference on Artificial Life ECAL 2017 (Cambridge, MA: MIT Press), 44–51.

The $100,000 Nittany AI Challenge would not be possible without the support of companies that share our belief in the power of innovation to advance higher education. These companies believe in the potential for artificial intelligence to improve higher education and have given their time and resources to help set each team up for success. The following AI companies have offered or will offer workshops, training resources, and/or mentoring to help teams better understand and leverage their technologies.