This week in apps: Instagram face filters, Medium audio stories, Google Assistant on iOS and more


Reading all the news from Google I/O may have kept you too busy to keep up with this week's app news. We've kept up for you. Each week we round up the most important app news along with some of the coolest new and updated apps to help you stay in the loop with everything you need on your phone.Here's what caught our eye this week. If you're looking for more, make sure to check out last week's roundup of top apps. Whether at home or on the go, your Assistant is here to help.

CES 2019: The PC gear and smart home tech we can't wait to see


More than 4,400 exhibitors showed off their hardware at CES 2018. That's a lot of gadgets, and the show can become an unmanageable circus if you don't enter with a game plan--and that counts for people following the action at home, as well. To give you a little head start, here's our cheat sheet on what to look for at CES 2019. It's pretty easy to predict what AMD will be revealing at CES--because the company has already told us. AMD chief executive Lisa Su will host a keynote address on Wednesday, Jan. 9 where she'll talk up the company's 2019 plans to "catapult computing, gaming, and visualization technologies forward with the world's first 7nm high-performance CPUs and GPUs."

A Combinatorial-Bandit Algorithm for the Online Joint Bid/Budget Optimization of Pay-per-Click Advertising Campaigns

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Pay-per-click advertising includes various formats (e.g., search, contextual, and social) with a total investment of more than 140 billion USD per year. An advertising campaign is composed of some subcampaigns-each with a different ad-and a cumulative daily budget. The allocation of the ads is ruled exploiting auction mechanisms. In this paper, we propose, for the first time to the best of our knowledge, an algorithm for the online joint bid/budget optimization of pay-per-click multi-channel advertising campaigns. We formulate the optimization problem as a combinatorial bandit problem, in which we use Gaussian Processes to estimate stochastic functions, Bayesian bandit techniques to address the exploration/exploitation problem, and a dynamic programming technique to solve a variation of the Multiple-Choice Knapsack problem. We experimentally evaluate our algorithm both in simulation-using a synthetic setting generated from real data from Yahoo!-and in a real-world application over an advertising period of two months.

Machine learning is the new face of enterprise data


While the complexity of the searching and result-ranking technology behind Apple's Siri would likely elude most of its users, the value of a context-sensitive personal assistant certainly has not. Yet while Siri spawned a new generation of anthropomorphic digital assistants, researchers in machine learning and artificial intelligence (AI) are taking the concept much further to help enterprises catch up to the growth of data. Industrial products distributor Coventry Group is among the latest companies to jump onto the trend. The company, whose fasteners, fluid systems, gasket and hardware divisions collectively employ around 650 people, is working with Adelaide-based data-analytics specialist Complexica to apply that company's AI technology – personified as Larry, the Digital Analyst – to guide decisions around sales and pricing strategies. Introducing Larry – a collection of algorithms delivered on a software-as-a-service (SaaS) basis via Amazon's cloud – to Coventry's business is a two to four month process that will see the technology finetuned to the company's operating parameters.

A Planning Framework for Persistent, Multi-UAV Coverage with Global Deconfliction Artificial Intelligence

Planning for multi-robot coverage seeks to determine collision-free paths for a fleet of robots, enabling them to collectively observe points of interest in an environment. Persistent coverage is a variant of traditional coverage where coverage-levels in the environment decay over time. Thus, robots have to continuously revisit parts of the environment to maintain a desired coverage-level. Facilitating this in the real world demands we tackle numerous subproblems. While there exist standard solutions to these subproblems, there is no complete framework that addresses all of their individual challenges as a whole in a practical setting. We adapt and combine these solutions to present a planning framework for persistent coverage with multiple unmanned aerial vehicles (UAVs). Specifically, we run a continuous loop of goal assignment and globally deconflicting, kinodynamic path planning for multiple UAVs. We evaluate our framework in simulation as well as the real world. In particular, we demonstrate that (i) our framework exhibits graceful coverage given sufficient resources, we maintain persistent coverage; if resources are insufficient (e.g., having too few UAVs for a given size of the enviornment), coverage-levels decay slowly and (ii) planning with global deconfliction in our framework incurs a negligibly higher price compared to other weaker, more local collision-checking schemes. (Video: