Facebook has confirmed a report from earlier today saying it's working on an artificial intelligence-based digital voice assistant in the vein of Amazon's Alexa and Google Assistant. The news, first reported by CNBC, indicates Facebook isn't giving up on a vision it first put out years ago, when it began developing an AI assistant for its Messenger platform simply called M. This time around, however, Facebook says it is focusing less on messaging and more on platforms in which hands-free interaction, via voice control and potentially gesture control, is paramount. "We are working to develop voice and AI assistant technologies that may work across our family of AR/VR products including Portal, Oculus and future products," a Facebook spokesperson told The Verge today, following the initial report. That means Facebook may not position the product as a competitor to Alexa or similar platforms, but as more of a feature exclusive to its growing family of hardware devices. CNBC reported that the team building the assistant is working out of Redmond, Washington under the direction of Ira Snyder, a general manager at Facebook Reality Labs and a director of augmented and virtual reality at the company.

Artificial Intelligence (AI) has been omnipresent and the latest in the block is Military. In recent times, AI has become a critical part of modern warfare. Compared with the conventional systems, military establishments churning enormous volumes of data are capable to integrate AI on a more unified process. Ensuring operational efficiency, AI improves self-regulation, self-control and self-actuation of combat systems, credit to its inherent computing coupled with accurate decision-making capabilities. Taking into account the enormous capability Artificial intelligence (AI) holds in the modern-day warfare, many of the world's most powerful countries have increased their investments into military and self-security.

Just a week after it was announced, Google's new AI ethics board is already in trouble. The board, founded to guide "responsible development of AI" at Google, would have had eight members and met four times over the course of 2019 to consider concerns about Google's AI program. Those concerns include how AI can enable authoritarian states, how AI algorithms produce disparate outcomes, whether to work on military applications of AI, and more. Of the eight people listed in Google's initial announcement, one (privacy researcher Alessandro Acquisti) has announced on Twitter that he won't serve, and two others are the subject of petitions calling for their removal -- Kay Coles James, president of the conservative Heritage Foundation think tank, and Dyan Gibbens, CEO of drone company Trumbull Unmanned. Thousands of Google employees have signed onto the petition calling for James's removal.

The inevitable march toward automation continues, analysts from the McKinsey Global Institute and from Tata Communications wrote in separate reports this week. Artificial intelligence's growth comes as no surprise -- a survey from Narrative Science and the National Business Research Institute conducted earlier this year found that 61 percent of businesses implemented AI in 2017, up from 38 percent in 2016 -- but this week's findings lay out in detail the likely socioeconomic impacts in the coming decade. The McKinsey models predict that 70 percent of companies will adopt at least one form of AI -- whether computer vision, natural language, virtual assistants, robotic process automation, or advanced machine learning -- by 2020. And Tata found unbridled enthusiasm among business leaders for an AI-dominated future; in a survey of 120 of them, 90 percent said they expect AI to enhance decision-making. McKinsey and Tata both contend that's a good thing.

The inevitable march toward automation continues, analysts from the McKinsey Global Institute and from Tata Communications wrote in separate reports this week. Artificial intelligence's growth comes as no surprise -- a survey from Narrative Science and the National Business Research Institute conducted earlier this year found that 61 percent of businesses implemented AI in 2017, up from 38 percent in 2016 -- but this week's findings lay out in detail the likely socioeconomic impacts in the coming decade. The McKinsey models predict that 70 percent of companies will adopt at least one form of AI -- whether computer vision, natural language, virtual assistants, robotic process automation, or advanced machine learning -- by 2020. And Tata found unbridled enthusiasm among business leaders for an AI-dominated future; in a survey of 120 of them, 90 percent said they expect AI to enhance decision-making. McKinsey and Tata both contend that's a good thing.

"We spent all those years adopting EHRs, and now we're wanting to get the most out of them. Now we have the digital data, so it should be more liquid and in control of patients and put to use in the care process, even if I go to multiple sites for my care." As ONC and CMS prepare to digest the voluminous public comment on their proposed interoperability rules, especially the emphasis on exchange specs such as FHIR and open APIs, he sees the future only getting brighter for these types of advances as data flows more freely. "We're in the interoperability business, and we like having data being more available and more liquid, and systems being more open to getting data out of them," Woodlock said. "A lot of customers are starting to embark on their journey with with FHIR, and they're really bullish on this as well: having a standards-based API way to interact with medical record medical record data," he added.

Few biometric technologies are sparking the imagination quite like facial recognition. Equally, its arrival has prompted profound concerns and reactions. With artificial intelligence and the blockchain, face recognition certainly represents a significant digital challenge for all companies and organizations - and especially governments. In this dossier, you'll discover the 7 face recognition facts and trends that are set to shape the landscape in 2019. Let's jump right in .

Beacon is unlike any other member of staff at Staffordshire University. It is available 24/7 to answer students' questions, and deals with a number of queries every day – mostly the same ones over and over again – but always stays incredibly patient. That patience is perhaps what gives it away: Beacon is an artificial intelligence (AI) education tool, and the first digital assistant of its kind to be operating at a UK university. Staffordshire developed Beacon with cloud service provider ANS and launched it in January this year. The chatbot, which can be downloaded in a mobile app, enhances the student experience by answering timetable questions and suggesting societies to join.

We describe a chemical robotic assistant equipped with a curiosity algorithm (CA) that can efficiently explore the state a complex chemical system can exhibit. The CA-robot is designed to explore formulations in an open-ended way with no explicit optimization target. By applying the CA-robot to the study of self-propelling multicomponent oil-in-water droplets, we are able to observe an order of magnitude more variety of droplet behaviours than possible with a random parameter search and given the same budget. We demonstrate that the CA-robot enabled the discovery of a sudden and highly specific response of droplets to slight temperature changes. Six modes of self-propelled droplets motion were identified and classified using a time-temperature phase diagram and probed using a variety of techniques including NMR. This work illustrates how target free search can significantly increase the rate of unpredictable observations leading to new discoveries with potential applications in formulation chemistry.

Pitch or fundamental frequency (f0) extraction is a fundamental problem studied extensively for its potential applications in speech and clinical applications. In literature, explicit mode specific (modal speech or singing voice or emotional/ expressive speech or noisy speech) signal processing and deep learning f0 extraction methods that exploit the quasi periodic nature of the signal in time, harmonic property in spectral or combined form to extract the pitch is developed. Hence, there is no single unified method which can reliably extract the pitch from various modes of the acoustic signal. In this work, we propose a hybrid f0 extraction method which seamlessly extracts the pitch across modes of speech production with very high accuracy required for many applications. The proposed hybrid model exploits the advantages of deep learning and signal processing methods to minimize the pitch detection error and adopts to various modes of acoustic signal. Specifically, we propose an ordinal regression convolutional neural networks to map the periodicity rich input representation to obtain the nominal pitch classes which drastically reduces the number of classes required for pitch detection unlike other deep learning approaches. Further, the accurate f0 is estimated from the nominal pitch class labels by filtering and autocorrelation. We show that the proposed method generalizes to the unseen modes of voice production and various noises for large scale datasets. Also, the proposed hybrid model significantly reduces the learning parameters required to train the deep model compared to other methods. Furthermore,the evaluation measures showed that the proposed method is significantly better than the state-of-the-art signal processing and deep learning approaches.