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.

Last week I joined Toni Townes-Whitley, Josh Bersin and chief learning officers from around the world at the Microsoft Global Learning Summit to discuss the importance of future-proofing your workforce through learning and skill development. It was a pleasure to present to this group, and it reminded me of my own (very recent) experience in learning and skill development: When our CEO, Satya Nadella, asked me to lead HR at Microsoft. At that point, I'd led Microsoft Services for six years, and before that I held operations, strategy, management and development roles. What made Satya think I could take on a role as Chief People Officer for a global workforce of 115,000 people? We both took a leap of faith, and with his help, the help of the Microsoft Senior Leadership Team, my industry peers, and all 1,500 employees in HR at Microsoft, I have leaned into--and learned into--my role.

Visual analytics systems combine machine learning or other analytic techniques with interactive data visualization to promote sensemaking and analytical reasoning. It is through such techniques that people can make sense of large, complex data. While progress has been made, the tactful combination of machine learning and data visualization is still under-explored. This state-of-the-art report presents a summary of the progress that has been made by highlighting and synthesizing select research advances. Further, it presents opportunities and challenges to enhance the synergy between machine learning and visual analytics for impactful future research directions.

Asynchronous distributed machine learning solutions have proven very effective so far, but always assuming perfectly functioning workers. In practice, some of the workers can however exhibit Byzantine behavior, caused by hardware failures, software bugs, corrupt data, or even malicious attacks. We introduce \emph{Kardam}, the first distributed asynchronous stochastic gradient descent (SGD) algorithm that copes with Byzantine workers. Kardam consists of two complementary components: a filtering and a dampening component. The first is scalar-based and ensures resilience against $\frac{1}{3}$ Byzantine workers. Essentially, this filter leverages the Lipschitzness of cost functions and acts as a self-stabilizer against Byzantine workers that would attempt to corrupt the progress of SGD. The dampening component bounds the convergence rate by adjusting to stale information through a generic gradient weighting scheme. We prove that Kardam guarantees almost sure convergence in the presence of asynchrony and Byzantine behavior, and we derive its convergence rate. We evaluate Kardam on the CIFAR-100 and EMNIST datasets and measure its overhead with respect to non Byzantine-resilient solutions. We empirically show that Kardam does not introduce additional noise to the learning procedure but does induce a slowdown (the cost of Byzantine resilience) that we both theoretically and empirically show to be less than $f/n$, where $f$ is the number of Byzantine failures tolerated and $n$ the total number of workers. Interestingly, we also empirically observe that the dampening component is interesting in its own right for it enables to build an SGD algorithm that outperforms alternative staleness-aware asynchronous competitors in environments with honest workers.