We clarify what fairness guarantees we can and cannot expect to follow from unconstrained machine learning. Specifically, we characterize when unconstrained learning on its own implies group calibration, that is, the outcome variable is conditionally independent of group membership given the score. We show that under reasonable conditions, the deviation from satisfying group calibration is upper bounded by the excess risk of the learned score relative to the Bayes optimal score function. A lower bound confirms the optimality of our upper bound. Moreover, we prove that as the excess risk of the learned score decreases, it strongly violates separation and independence, two other standard fairness criteria. Our results show that group calibration is the fairness criterion that unconstrained learning implicitly favors. On the one hand, this means that calibration is often satisfied on its own without the need for active intervention, albeit at the cost of violating other criteria that are at odds with calibration. On the other hand, it suggests that we should be satisfied with calibration as a fairness criterion only if we are at ease with the use of unconstrained machine learning in a given application.

Deep learning has been widely adapted to many different problems, such as image classification [1], speech recognition [2] and natural language processing [3], and has demonstrated state-of-the-art results for these problems. Despite the promises, deep neural networks (DNNs) remain challenging to deploy in on-device edge scenarios such as mobile and other consumer devices. Due to the limited computational resources available in such on-device edge scenarios, many recent studies [4, 5, 6, 7] have put greater efforts into designing small, low-footprint deep neural network architectures that are more appropriate for embedded devices. A particularly interesting approach for enabling low-footprint deep neural network architectures is the concept of knowledge distillation [8], where the performance of a smaller network is significantly improved by leveraging a teacher-student strategy where the smaller network is trained to mimic the behaviour of a larger teacher network. With much of the research around distillation focused on distilling knowledge from larger networks to smaller networks, there is little research focused on leveraging the concept of distillation for distilling knowledge encapsulated in the training data itself into a reduced form. By producing data with reduced data dimension, one can achieve input-efficient deep neural networks with significantly reduced computational costs. In this study, we explore a concept we will call progressive label distillation, where a series of teacher-student network pairs are leveraged to progressively generate distilled training data.

What happens when a serial entrepreneur with a Master's degree in robotics goes out looking for a problem to solve? For InVia Robotics founder and CEO Lior Elazary, it leads to a new army of self-learning, self-guided warehouse robots. Elazary had already helped found web hosting company HostPro Inc., and Edgecast Networks Inc., a content delivery network. Then he set out to apply the lessons from his graduate study and decided at first to focus on home robotics. "I thought there might be an opportunity in in-home elderly care," he said.

We are looking for outstanding Data Engineers to join our team. This is a great opportunity for a Data Science Consultant to join a consulting firm that offers a variety of projects and a structured learning and development path. You will work alongside a talented team of consultants who all share your passion in building great solutions and learning new skills. Skills in Data Engineering and Machine Learning: as a data science consultant you will have proficiency in one or more of Python, R, Scala, Matlab/Octave, Java, C/C, Go, Javascript, Clojure etc. You will also have either hands on experience or good knowledge of the one of the following concepts such as supervised learning, unsupervised learning, reinforcement learning, deep learning, feature engineering, natural language processing, computer vision, signal processing etc.

Is there any truth to the notion that college instructors who implement active learning receive lower teaching evaluations? Henderson et al. present data from college physics instructors who attended a new-faculty workshop and attempted to incorporate active learning into their introductory course. Contrary to common belief, 48% of these instructors reported an increase in student evaluations, 32% reported no change, and only 20% reported a decrease in their evaluations. The authors acknowledge the limitations of the study, including the nature of self-reported data as well as changes in student evaluations over time, yet provide the overall recommendation that instructors (and institutions) should not let perceived anxiety over negative student evaluations be a reason to avoid implementing evidence-based teaching practices.

Thursday's top Amazon deals are on things you'll love. If you make a purchase by clicking one of our links, we may earn a small share of the revenue. However, our picks and opinions are independent from USA Today's newsroom and any business incentives. When everyone else is checking their email, reading the news, or scrolling through Facebook as they start their day, I'm over here poring over Amazon's deals to try and find those hidden gems and exciting sales on products that are actually awesome. Some days, it can be hard to find more than one or two that really wow me, but today it took no time at all to spot these all-star deals on vacuums, soundbars, smart speakers, protein powder, and tax software.

Despite detailed analysis being conducted around the benefits of artificial intelligence (AI) in various industries, its effect on education has been relatively unexplored. Global innovation foundation Nesta has begun a research project to explore the future of AI in education and found a relatively modest – but fast-growing – bank of academic literature focusing on the topic. As the literature on AI in education grows, however, they also expect to see its scope widening. Early academic literature was, typically, focused on how AI could be used to solve'Bloom's 2-Sigma Problem' and replicate the'gold standard' of education: one-to-one tutoring. However, academics, researchers and technologists are now describing experiments where AI is focused on whole range of different elements – from enabling collaboration between peers to assessing complicated skills, like creativity.

We can build robot teachers, or even robot teaching assistants. And if the answer is yes, what's the right mix of human and machine in the classroom? To get a fresh perspective on that question, this episode we take you to China, where a couple of us from EdSurge recently traveled for a reporting trip. One of the events we attended was a two-day conference about artificial intelligence in education organized by a company called Squirrel AI. It's vision felt unusually utopian.

This post discusses semi-supervised learning algorithms that learn from proxy labels assigned to unlabelled data. Note: Parts of this post are based on my ACL 2018 paper Strong Baselines for Neural Semi-supervised Learning under Domain Shift with Barbara Plank. Unsupervised learning constitutes one of the main challenges for current machine learning models and one of the key elements that is missing for general artificial intelligence. While unsupervised learning on its own is still elusive, researchers have a made a lot of progress in combining unsupervised learning with supervised learning. This branch of machine learning research is called semi-supervised learning. Semi-supervised learning has a long history. For a (slightly outdated) overview, refer to Zhu (2005) [1] and Chapelle et al. (2006) [2].

Care and Feeding is Slate's parenting advice column. In addition to our traditional advice, every Thursday we feature an assortment of teachers from across the country answering your education questions. Have a question for our teachers? Email askateacher@slate.com or post it in the Slate Parenting Facebook group. This week's Ask a Teacher panel: Matthew Dicks, fifth grade, Connecticut Cassy Sarnell, preschool special education, New York Carrie Bauer, middle and high school, New York Amy Scott, eighth grade, North Carolina My fourth-grade daughter is a joy to be around, a good friend, and a well-behaved student.