The 2019 Snap Research Fellowship program is designed to foster a strong collaboration between Snap Research and the brightest, most driven doctoral students from across the world. We aim to provide students additional resources and freedom to pursue innovative ideas and shape them into tangible cutting-edge research and products. Applicants must be PhD students currently enrolled at an accredited college or university and carrying out exceptional research in areas of computer science relevant to Snap, including Artificial Intelligence, Computational Imaging, Computer Graphics, Computer Vision, Data Mining, Data Science, Human-Computer Interaction, Machine Learning, Natural Language Processing, Security, and Speech and Audio Processing, or another related field. A committee of Snap researchers will review applicants and choose the Fellowship recipients based on: the relevance and quality of their research, their academic performance in technical coursework, their understanding of Snap products, and their technical and personal skills (problem solving, communication, leadership, organizational skills, ability to work in teams, and more).All Fellowship recipients will be selected, and Internship offers will be made, at the sole discretion of the committee. The internships will take place at Snap's offices in either the US or the UK.

We discuss deep reinforcement learning in an overview style. We draw a big picture, filled with details. We discuss six core elements, six important mechanisms, and twelve applications, focusing on contemporary work, and in historical contexts. We start with background of artificial intelligence, machine learning, deep learning, and reinforcement learning (RL), with resources. Next we discuss RL core elements, including value function, policy, reward, model, exploration vs. exploitation, and representation. Then we discuss important mechanisms for RL, including attention and memory, unsupervised learning, hierarchical RL, multi-agent RL, relational RL, and learning to learn. After that, we discuss RL applications, including games, robotics, natural language processing (NLP), computer vision, finance, business management, healthcare, education, energy, transportation, computer systems, and, science, engineering, and art. Finally we summarize briefly, discuss challenges and opportunities, and close with an epilogue.

Deep reinforcement learning enables algorithms to learn complex behavior, deal with continuous action spaces and find good strategies in environments with high dimensional state spaces. With deep reinforcement learning being an active area of research and many concurrent inventions, we decided to focus on a relatively simple robotic task to evaluate a set of ideas that might help to solve recent reinforcement learning problems. We test a newly created combination of two commonly used reinforcement learning methods, whether it is able to learn more effectively than a baseline. We also compare different ideas to preprocess information before it is fed to the reinforcement learning algorithm. The goal of this strategy is to reduce training time and eventually help the algorithm to converge. The concluding evaluation proves the general applicability of the described concepts by testing them using a simulated environment. These concepts might be reused for future experiments.

The increasing popularity of cell phones has made them the most personal and ubiquitous communication devices nowadays. Typically, the ringing notifications of mobile phones are used to inform the users about the incoming calls. However, the notifications of inappropriate incoming calls sometimes cause interruptions not only for the users but also the surrounding people. In this paper, we present a data-driven approach to infer the opportune moments for such phone call interruptions based on user's unavailability, i.e., when a user is unable to answer the incoming phone calls, by analyzing individual's past phone log data, and to discover the corresponding phone silent mode configuring rules for the purpose of minimizing call interruptions in an automated intelligent system. Experiments on the real mobile phone datasets show that our approach is able to identify the opportune moments for call interruptions and generates corresponding silent mode configuring rules by capturing the dominant behavior of individual users' at various times-of-the-day and days-of-theweek. Received on XXXX; accepted on XXXX; published on XXXX Keywords: Mobile phones, phone log data, temporal context, user modeling, phone ringer mode, interruptions, unavailability, personalization, intelligent systems.

We propose a population-based Evolutionary Stochastic Gradient Descent (ESGD) framework for optimizing deep neural networks. ESGD combines SGD and gradient-free evolutionary algorithms as complementary algorithms in one framework in which the optimization alternates between the SGD step and evolution step to improve the average fitness of the population. With a back-off strategy in the SGD step and an elitist strategy in the evolution step, it guarantees that the best fitness in the population will never degrade. In addition, individuals in the population optimized with various SGD-based optimizers using distinct hyper-parameters in the SGD step are considered as competing species in a coevolution setting such that the complementarity of the optimizers is also taken into account. The effectiveness of ESGD is demonstrated across multiple applications including speech recognition, image recognition and language modeling, using networks with a variety of deep architectures.

We present a predictor-corrector framework, called PicCoLO, that can transform a first-order model-free reinforcement or imitation learning algorithm into a new hybrid method that leverages predictive models to accelerate policy learning. The new "PicCoLOed" algorithm optimizes a policy by recursively repeating two steps: In the Prediction Step, the learner uses a model to predict the unseen future gradient and then applies the predicted estimate to update the policy; in the Correction Step, the learner runs the updated policy in the environment, receives the true gradient, and then corrects the policy using the gradient error. Unlike previous algorithms, PicCoLO corrects for the mistakes of using imperfect predicted gradients and hence does not suffer from model bias. The development of PicCoLO is made possible by a novel reduction from predictable online learning to adversarial online learning, which provides a systematic way to modify existing first-order algorithms to achieve the optimal regret with respect to predictable information. We show, in both theory and simulation, that the convergence rate of several first-order model-free algorithms can be improved by PicCoLO.

Data Science is an amalgamation of data interference, algorithm development, and technology that enables professionals to solve analytically complex problems. Data Science is essentially analyzing and churning out findings or insights from data. This scrutiny of data helps organizations in improving and making smarter decisions. Machine Learning is the science of enabling computers or systems to act without being explicitly programmed. It stresses on the development of computer programs that can access and learn from data.

As we've talked about "Going Broad" and "Embracing Fuzziness," we've mentioned cause-and-effect relationships, and understanding upstream and downstream impacts, and correlating "fuzzy" inputs. So by this point, you understand that linking data is more important than just collecting data. So maybe this will be a very short chapter. There it is, 4 sentences and we're all done. Just within the last few weeks you've heard someone say "We'll collect the data, and then we'll figure out what to do with it."

This weekend is WIRED's 25th Anniversary festival. We started it off with three conversations with brilliant CEOs about the future of work: Patrick Collison of Stripe, Stacy Brown-Philpot of TaskRabbit, and Jeff Weiner of LinkedIn. Here is the transcript of my talk with Weiner. Nicholas Thompson: One thing that I love about you is that your career dates to 1994 and an essay that you read in WIRED magazine. So, explain how a review of a Nicholas Negroponte book led you to become who you are. I'm not sure I'd be sitting in this seat today if it weren't for WIRED. I was first introduced to the internet prior to its commercialization while I was still in school as a senior at Wharton undergrad. I was on a consulting project with a buddy of mine and three DuPont engineers who were interested in leveraging this thing called the Internet for desktop teleconferencing.

Technology giant Huawei aims to help train one million artificial intelligence (AI) talents in the next three years to boost the fast-expanding sector. Huawei will provide free online training, organise boot camps and collaborate with industry players. It will also set up a one billion yuan (S$199 million) fund for universities and research institutes to support AI talent development. Mr Zheng Yelai, Huawei's vice-president and president of its cloud business unit, announced this yesterday, the last day of the Huawei Connect Conference in Shanghai. The move is in line with China's push to become a global AI powerhouse in the next decade.