Recognized as a global leader in interactive and digital entertainment, Sony Interactive Entertainment (SIE) is responsible for the PlayStation brand and family of products and services. PlayStation has delivered innovation to the market since the launch of the original PlayStation in Japan in 1994. The PlayStation family of products and services include PlayStation 4, PlayStation VR, PlayStation Vita, PlayStation 3, PlayStation Store, PlayStation Plus, PlayStation Video, PlayStation Music, PlayStation Now, PlayStation Vue, PlayStation Original and acclaimed PlayStation software titles from SIE Worldwide Studios. Headquartered in San Mateo, California, SIE is a wholly-owned subsidiary of Sony Corporation and has global functions and regional headquarters in California, London and Tokyo. The Staff Machine Learning Engineer will deliver optimizable interactions across PlayStation experiences and systems.

Attention & interpretability: modern neural networks are hard to interpret because we haven't built tools to make it easy to analyze their decision-making processes. Part of the reason why we haven't built the tools is that it's not entirely obvious how you get a big stack of perceptual math machinery to tell you about what it is thinking in a way that is remotely useful to the untrained eye. The best thing we've been able to come up with, in the case of certain vision and language tasks, is attention where we visualize what parts of a neural network – sometimes down to an individual cell or'neuron' within it – is activating in response to. This can help us diagnose why an AI tool is responding in the way it is. This component is general, working across different neural network architectures (a first, the researchers claim), and only requires the person to fiddle with it at its input or output points.

Microsoft Research Asia in collaboration with Chinese Science Academy and Peiching Union University has developed a prototype which translates sign language into spoken language and spoken language into sign language in real-time. This revolutionary technique will enable hearing-impaired individuals to communicate effectively by simply using sign language. Similarly, Microsoft Seeing AI is aimed at helping people who are visually-impaired to understand more about who and what is around them. A research project, Seeing AI, combines image recognition and natural language processing to describe a person's surroundings, read text, answer questions and even identify emotions on people's faces. Seeing AI can be used through smartphone app or smart glass app and can help people to achieve more.

I haven't said much about Workey for a few months. It has raised another $8 million and is opening up a New York office, focusing on NY as well as on San Francisco. The Israeli company says it worked with Amazon, Dell EMC, Oracle, and Yahoo during its Beta. Among Workey's features is one called social power: "Workey will promote your anonymous profile based on your connections, and suggest which friends can vouch for you for each opportunity." I monkeyed around with Workey a bit, and one interesting feature is that next to job listings, it pops up little notes indicating you're reasonably qualified, like "your proven record on Twitter puts you ahead of the pack for this opportunity."

By Vivek Wadhwa "Carnage in Indian IT," read a headline about retrenchments in its outsourcing industry as markets stagnate and US visa restrictions erode profits. The Indian IT industry generates $150 billion in revenue but is facing an existential crisis largely of its own making because it became complacent and overconfident even as technologies and markets changed. It can survive only if it exits the business that brought it success and reinvents itself. India's outsourcing boomed during the Y2K crisis of the late '90s because there was an urgency in repairing corporate IT systems. Once chief information officers (CIOs) became comfortable with having their systems maintained across the globe, they started outsourcing large-scale projects to Indian companies, and billion-dollar contracts were announced almost every week. But with the advent of tablets and smartphones and their applications in the 2010s, users gained access to better technology than the companies' IT departments could provide.

Gboard update includes new enhancements to make typing and searching easier. Google has added some fun and helpful new features to Gboard, which highlight its latest machine learning capabilities. In a blog post, Google showed off a new doodle-search function for emojis, predictive phrase suggestions for texting, and enhanced results for searches made from within the keyboard. Look for the enhanced feature to pop up in the latest update for Gboard in the Google Play Store. The feature you're likely to use most often is the new predictive phrase feature, which is integrated into the same space as word predictions.

We consider the problem of approximate Bayesian parameter inference in non-linear state-space models with intractable likelihoods. Sequential Monte Carlo with approximate Bayesian computations (SMC-ABC) is one approach to approximate the likelihood in this type of models. However, such approximations can be noisy and computationally costly which hinders efficient implementations using standard methods based on optimisation and Monte Carlo methods. We propose a computationally efficient novel method based on the combination of Gaussian process optimisation and SMC-ABC to create a Laplace approximation of the intractable posterior. We exemplify the proposed algorithm for inference in stochastic volatility models with both synthetic and real-world data as well as for estimating the Value-at-Risk for two portfolios using a copula model. We document speed-ups of between one and two orders of magnitude compared to state-of-the-art algorithms for posterior inference.

Symmetry-based state space pruning techniques have proved to greatly improve heuristic search based classical planners. Similarly, abstraction heuristics in general and pattern databases in particular are key ingredients of such planners. However, only little work has dealt with how the abstraction heuristics behave under symmetries. In this work, we investigate the symmetry properties of the popular canonical pattern databases heuristic. Exploiting structural symmetries, we strengthen the canonical pattern databases by adding symmetric pattern databases, making the resulting heuristic invariant under structural symmetry, thus making it especially attractive for symmetry-based pruning search methods. Further, we prove that this heuristic is at least as informative as using symmetric lookups over the original heuristic. An experimental evaluation confirms these theoretical results.

In multi-agent path finding (MAPF) the task is to find non-conflicting paths for multiple agents. Recently, a SAT-based approach was developed to solve this problem and proved beneficial in many cases when compared to other search-based solvers. In this paper, we introduce SAT-based unbounded- and bounded-suboptimal algorithms and compare them to relevant search-based algorithms.

In this paper we study the k goal search problem (kGS), which is the problem of solving k shortest path problems that share the same start state. Two fundamental heuristic search approaches are analyzed: searching for the k goals one at a time, or searching for all k goals together in a single pass. Key theoretical properties are established and a preliminary experimental evaluation is performed.