Robert Hoyle Brown is a member of the Center for the Future of Work, a global think tank with a charter from Cognizant Technology Solutions to examine how work is changing, and will change, in response to the emergence of the Age of Algorithms, Automation and AI. His research emphasis has been on the topics of robotics, automation, privacy and augmented reality and their impact on business processes. Since joining Cognizant in 2014, he has worked extensively with its Digital Operations practice as head of strategy, as well as Cognizant's Business Accelerator leadership to drive the development of its intelligent automation strategy, messaging and go-to-market outreach. He was the lead author on the Center for the Future of Work whitepapers "The Robot and I: How New Digital Technologies Are Making Smart People and Businesses Smarter by Automating Rote Work" (2015), "Every Move You Make: The Future of Privacy in the Age of the Algorithm" (2018), "Augmenting the Reality of Everything" (2017), and "The 2nd Half of the Chessboard: The Work Ahead in Media & Entertainment" (2018). He was also a co-author of "21 Jobs of the Future and 21 More Jobs of the Future: A Guide to Getting – and Staying – Employed Over the Next 10 Years", as well as Cognizant's Jobs of the Future Index (2018). He is also a frequent blogger at www.futureofwork.com.

Robert Hoyle Brown is a member of the Center for the Future of Work, a global think tank with a charter from Cognizant Technology Solutions to examine how work is changing, and will change, in response to the emergence of the Age of Algorithms, Automation and AI. His research emphasis has been on the topics of robotics, automation, privacy and augmented reality and their impact on business processes. Since joining Cognizant in 2014, he has worked extensively with its Digital Operations practice as head of strategy, as well as Cognizant's Business Accelerator leadership to drive the development of its intelligent automation strategy, messaging and go-to-market outreach. He was the lead author on the Center for the Future of Work whitepapers "The Robot and I: How New Digital Technologies Are Making Smart People and Businesses Smarter by Automating Rote Work" (2015), "Every Move You Make: The Future of Privacy in the Age of the Algorithm" (2018), "Augmenting the Reality of Everything" (2017), and "The 2nd Half of the Chessboard: The Work Ahead in Media & Entertainment" (2018). He was also a co-author of "21 Jobs of the Future and 21 More Jobs of the Future: A Guide to Getting – and Staying – Employed Over the Next 10 Years", as well as Cognizant's Jobs of the Future Index (2018). He is also a frequent blogger at www.futureofwork.com.

How would you feel being watched, tracked and identified by facial recognition cameras everywhere you go? Facial recognition cameras are now creeping onto the streets of Britain and the U.S., yet most people aren't even aware. As we walk around, our faces could be scanned and subjected to a digital police line up we don't even know about. There are over 6 million surveillance cameras in the U.K. – more per citizen than any other country in the world, except China. In the U.K., biometric photos are taken and stored of people whose faces match with criminals – even if the match is incorrect. As director of the U.K. civil liberties group Big Brother Watch, I have been investigating the U.K. police's "trials" of live facial recognition surveillance for several years. Understandably, people are shocked when they see facial recognition cameras appearing on their streets – and many people strongly object to it.

San Francisco's move to ban facial recognition software has worried the industry, which would prefer regulation. But what should regulation look like? We talk to the experts on both sides.

Tree ensembles, such as random forests and AdaBoost, are ubiquitous machine learning models known for achieving strong predictive performance across a wide variety of domains. However, this strong performance comes at the cost of interpretability (i.e. users are unable to understand the relationships a trained random forest has learned and why it is making its predictions). In particular, it is challenging to understand how the contribution of a particular feature, or group of features, varies as their value changes. To address this, we introduce Disentangled Attribution Curves (DAC), a method to provide interpretations of tree ensemble methods in the form of (multivariate) feature importance curves. For a given variable, or group of variables, DAC plots the importance of a variable(s) as their value changes. We validate DAC on real data by showing that the curves can be used to increase the accuracy of logistic regression while maintaining interpretability, by including DAC as an additional feature. In simulation studies, DAC is shown to out-perform competing methods in the recovery of conditional expectations. Finally, through a case-study on the bike-sharing dataset, we demonstrate the use of DAC to uncover novel insights into a dataset.

Many continuous control tasks have easily formulated objectives, yet using them directly as a reward in reinforcement learning (RL) leads to suboptimal policies. Therefore, many classical control tasks guide RL training using complex rewards, which require tedious hand-tuning. We automate the reward search with AutoRL, an evolutionary layer over standard RL that treats reward tuning as hyperparameter optimization and trains a population of RL agents to find a reward that maximizes the task objective. AutoRL, evaluated on four Mujoco continuous control tasks over two RL algorithms, shows improvements over baselines, with the the biggest uplift for more complex tasks. The video can be found at: \url{https://youtu.be/svdaOFfQyC8}.

For bidirectional joint image-text modeling, we develop variational hetero-encoder (VHE) randomized generative adversarial network (GAN) that integrates a probabilistic text decoder, probabilistic image encoder, and GAN into a coherent end-to-end multi-modality learning framework. VHE randomized GAN (VHE-GAN) encodes an image to decode its associated text, and feeds the variational posterior as the source of randomness into the GAN image generator. We plug three off-the-shelf modules, including a deep topic model, a ladder-structured image encoder, and StackGAN++, into VHE-GAN, which already achieves competitive performance. This further motivates the development of VHE-raster-scan-GAN that generates photo-realistic images in not only a multi-scale low-to-high-resolution manner, but also a hierarchical-semantic coarse-to-fine fashion. By capturing and relating hierarchical semantic and visual concepts with end-to-end training, VHE-raster-scan-GAN achieves state-of-the-art performance in a wide variety of image-text multi-modality learning and generation tasks. PyTorch code is provided.

Compressed sensing (CS) provides an elegant framework for recovering sparse signals from compressed measurements. For example, CS can exploit the structure of natural images and recover an image from only a few random measurements. CS is flexible and data efficient, but its application has been restricted by the strong assumption of sparsity and costly reconstruction process. A recent approach that combines CS with neural network generators has removed the constraint of sparsity, but reconstruction remains slow. Here we propose a novel framework that significantly improves both the performance and speed of signal recovery by jointly training a generator and the optimisation process for reconstruction via meta-learning. We explore training the measurements with different objectives, and derive a family of models based on minimising measurement errors. We show that Generative Adversarial Nets (GANs) can be viewed as a special case in this family of models. Borrowing insights from the CS perspective, we develop a novel way of improving GANs using gradient information from the discriminator.

The sequence-to-sequence (seq2seq) model for neural machine translation has significantly improved the accuracy of language translation. There have been new efforts to use this seq2seq model for program language translation or program comparisons. In this work, we present the detailed steps of using a seq2seq model to translate CUDA programs to OpenCL programs, which both have very similar programming styles. Our work shows (i) a training input set generation method, (ii) pre/post processing, and (iii) a case study using Polybench-gpu-1.0, NVIDIA SDK, and Rodinia benchmarks.

While machine learning systems show high success rate in many complex tasks, research shows they can also fail in very unexpected situations. Rise of machine learning products in safety-critical industries cause an increase in attention in evaluating model robustness and estimating failure probability in machine learning systems. In this work, we propose a design to train a student model -- a failure predictor -- to predict the main model's error for input instances based on their saliency map. We implement and review the preliminary results of our failure predictor model on an autonomous vehicle steering control system as an example of safety-critical applications.