Yiling Chen (yiling@seas.harvard.edu) is Gordon McKay Professor of Computer Science at Harvard University, Cambridge, MA. Arpita Ghosh (arpitaghosh@cornell.edu) is an associate professor of information science at Cornell University, Ithaca, NY. Michael Kearns (mkearns@cis.upenn.edu) is a professor and National Center Chair of Computer and Information Science at the University of Pennsylvania, Philadelphia, PA. Tim Roughgarden (tim@cs.stanford.edu) is an associate professor of CS at Stanford University, Stanford, CA. Jennifer Wortman Vaughan (jenn@microsoft.com) is a senior researcher at Microsoft Research, New York, NY.

Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)

This year's CES displayed a pretty broad mix in terms of production areas, with plenty of representation for categories ranging from wearables, to health, to VR and beyond – but the clear winners were two companies, and both succeeded primarily through partnerships than by direct demonstration of their own consumer products. Of the two, Nvidia had a much more direct presence; the GPU-maker's CEO Jen-Tsun Huang had the opening keynote, and packed a lot of news into the three-part presentation. Nvidia also had its own showfloor presences, including a self-driving car demonstration featuring BB8, its own test car, and an Audi Q7 equipped with the same software and hardware that could also drive itself, with no human behind the wheel. Nvidia's core business has become the core business of virtually every other tech company of size and significance; AI is one of, if not the primary area of interest and investment at Google, Facebook, Apple and others, and Nvidia's GPUs make it possible to create the neural nets and server systems that back machine learning, image recognition and other technologies under the broad AI umbrella. Meanwhile, Nvidia's consumer business is also making some big leaps.

Chinese internet giant Baidu and state-owned passenger vehicle manufacturer BAIC Motor Corp have formed a strategic partnership that will see the companies produce and promote autonomous vehicle technology in China. Announced on Friday at the Consumer Electronics Show (CES) 2017, Baidu's autonomous driving research and development arm, Baidu Intelligent Vehicle, will work with the automaker on two key projects, the first of which is the launch of a BAIC-built vehicle equipped with Baidu's telematics solutions at the Shanghai auto show in April. Level 3 autonomy still requires human drivers to be present and capable of taking the steering wheel if needed to prevent accidents, however continuous monitoring isn't required. As part of the partnership, the companies will also collaborate on HD maps for use in autonomous driving, and BAIC will provide OEM solutions including CarLife, CoDriver, and MapAuto for Baidu's intelligent vehicle program. CarLife, a smartphone integration application for connected cars, has been deployed in nearly 150 car models from 60 automakers including Audi, Mercedes-Benz, Hyundai, and Shanghai GM, and counts more than 700,000 car owners as active users.

Constructing activity budgets for marine animals when they are at sea and cannot be directly observed is challenging, but recent advances in bio-logging technology offer solutions to this problem. Accelerometers can potentially identify a wide range of behaviours for animals based on unique patterns of acceleration. However, when analysing data derived from accelerometers, there are many statistical techniques available which when applied to different data sets produce different classification accuracies. We investigated a selection of supervised machine learning methods for interpreting behavioural data from captive otariids (fur seals and sea lions). We conducted controlled experiments with 12 seals, where their behaviours were filmed while they were wearing 3-axis accelerometers.

A Convolutional Neural Network (CNN) is comprised of one or more convolutional layers (often with a subsampling step) and then followed by one or more fully connected layers as in a standard multilayer neural network. The architecture of a CNN is designed to take advantage of the 2D structure of an input image (or other 2D input such as a speech signal). This is achieved with local connections and tied weights followed by some form of pooling which results in translation invariant features. Another benefit of CNNs is that they are easier to train and have many fewer parameters than fully connected networks with the same number of hidden units. In this article we will discuss the architecture of a CNN and the back propagation algorithm to compute the gradient with respect to the parameters of the model in order to use gradient based optimization.

Chatbots and A.I. are some of the leading innovations that are taking the tech industry by storm. Companies are pushing to better incorporate these tools to help them streamline, organize, and better communicate with their consumers. But how can these tools help companies who are already at the forefront of their niche? Slack, one of the leading companies for real-time collaboration and digital communication, is pushing towards the chatbot revolution as well. After recently launching their own VC fund and app store, Slack is taking things to the next level with investing an unspecified amount in a wide variety of chatbot companies, from Automat to Butter and Sudo.

Earlier this month, Apple made a splash when it told the artificial intelligence research community that the secretive company would start publishing AI papers of its own. Not even a month later, it's already starting to make good on that promise. Apple has published its very first AI paper on December 22. (The paper was submitted for publication on November 15.) The paper describes a technique for how to improve the training of an algorithm's ability to recognize images using computer-generated images rather than real-world images. In machine learning research, using synthetic images (like those from a video game) to train neural networks can be more efficient than using real-world images.

A 2017 Chrysler Pacifica hybrid minivan equipped with Waymo's self-driving vehicle technology. Waymo, the company born from Alphabet's Google Self-Driving Car research project, faces mounting competition to perfect technology needed for fully autonomous vehicles. After staying low key about its progress, the latest indications from the new company are that it's far along the path to making such vehicles a reality by taking cost out of the components and boosting overall performance and reliability. John Krafcik, Waymo's chief executive officer, said at the Automobili-D conference in Detroit that the latest sensors, software, artificial intelligence and other components -- all developed and built in-house -- are being used for a fleet of 100 Chrysler Pacifica minivans, the first batch of which will begin public road tests in California and Arizona this month, he said. Keeping development and production in-house has led to major cost savings, including a 90% reduction for the laser Lidar sensor riding atop the new Pacificas.

What to watch for in distributed systems, SRE, serverless, containers and more. Forecasting trends is tricky, especially in the fast-moving world of systems operations and engineering. This year, at our Velocity Conference, we have talked about distributed systems, SRE, containerization, serverless architectures, burnout, and many other topics related to the human and technological challenges of delivering software. We think this is important enough that we re-focused the entire Velocity conference on it. Site Reliability Engineering--is it just ops?