Important emails have a habit of arriving at inconvenient times. Boomerang is a plugin for Gmail, Outlook and Android that lets you temporarily dismiss messages from your inbox, to reappear in a few hours or days when you're better able to deal with them. You can also pause your inbox entirely, to suspend the torrent of interruptions while you're busy, and schedule outgoing messages to be sent at specified times. You get 10 free uses a month; after that, monthly subscriptions start at $5. The idea couldn't be simpler: you set up a list of possible dates and times and then your invitees drop by the Doodle website and tick the options that work for them. You'll quickly be able to see at a glance when everyone is available and since recipients don't need to create their own Doodle accounts, it's friction-free.
Using voice commands has become pretty ubiquitous nowadays, as more mobile phone users use voice assistants such as Siri and Cortana, and as devices such as Amazon Echo and Google Home1 have been invading our living rooms. These systems are built with speech recognition software that allows their users to issue voice commands2. Now, our web browsers will become familiar with to Web Speech API, which allows users to integrate voice data in web apps. With the current state of web apps, we can rely on various UI elements to interact with users. With the Web Speech API, we can develop rich web applications with natural user interactions and minimal visual interface, using voice commands.
Indian-origin researchers have developed a new system that uses Artificial Intelligence algorithms and a smartphone app to instantly distinguish between genuine and fake versions of the same product. The system works by deploying a dataset of three million images across various objects and materials such as fabrics, leather, pills, electronics, toys and shoes. "The classification accuracy is more than 98 per cent, and we show how our system works with a cellphone to verify the authenticity of everyday objects," said Lakshminarayanan Subramanian, Professor at New York University. The system is scheduled to be presented on August 14 at the annual KDD Conference on Knowledge Discovery and Data Mining in Halifax, Nova Scotia, Canada. The system described in the presentation is commercialised by Entrupy Inc., an New York University start-up founded by Ashlesh Sharma, Vidyuth Srinivasan, and Subramanian.
Digitizing plant specimens is opening up a whole new world for researchers looking to mine collections from around the world. Computer algorithms trained on the images of thousands of preserved plants have learned to automatically identify species that have been pressed, dried and mounted on herbarium sheets, researchers report. The work, published in BMC Evolutionary Biology on 11 August1, is the first attempt to use deep learning -- an artificial-intelligence technique that teaches neural networks using large, complex data sets -- to tackle the difficult taxonomic task of identifying species in natural-history collections. It's unlikely to be the last attempt, says palaeobotanist Peter Wilf of Pennsylvania State University in University Park. "This kind of work is the future; this is where we're going in natural history."
Machine learning and other forms of artificial intelligence will likely infiltrate all levels of the IT infrastructure stack, but some architectures will take to it more readily than others. And while it is tempting to view AI in terms of the changes it will bring to the data center, the more imminent and profound impact will be on the Internet of Things (IoT). Particularly on the edge, AI offers the only viable means of assessing and coordinating the data flows from massive numbers of devices – many of them imbued with their own levels of intelligence – to produce results that are both meaningful and timely. Much of the storage and processing of IoT workloads will take place on the edge, so it only makes sense that it will incorporate intelligence as a core asset. According to AutomatedBuildings.com founder Ken Sinclair, a new generation of edge controllers is poised to deliver on the promises of autonomy for everything from cars to washing machines and, yes, even buildings.
It's been a little over a year since our Digital Banking Tracker heralded the "Dawn of Banking Voice Technology." Santander U.K. started the trend by launching a voice assistant within SmartBank, its mobile banking app for students, before rolling it out to the general public. In the early days, adopters of the new technology mostly used it to check card spend at the end of the week or month. By winter, customers were able to move beyond simply asking questions and begin conducting actual transactions using their voice -- although they still had to log in to the app using a password. Meanwhile, it's been exactly a year since Barclays announced news that it was doing away with passwords and replacing them with voice recognition technology for identity verification.
In this paper, we present UbuntuWorld 1.0 LTS - a platform for developing automated technical support agents in the Ubuntu operating system. Specifically, we propose to use the Bash terminal as a simulator of the Ubuntu environment for a learning-based agent and demonstrate the usefulness of adopting reinforcement learning (RL) techniques for basic problem solving and troubleshooting in this environment. We provide a plug-and-play interface to the simulator as a python package where different types of agents can be plugged in and evaluated, and provide pathways for integrating data from online support forums like AskUbuntu into an automated agent's learning process. Finally, we show that the use of this data significantly improves the agent's learning efficiency. We believe that this platform can be adopted as a real-world test bed for research on automated technical support.