Back in August 2019, the BBC made some waves with the news that it was developing a voice assistant called Beeb, an English language "Alexa" of its own that could interact with and control its array of radio and TV services, and its on-demand catalogue, and able to understand the array of accents you find in across the BBC's national footprint to boot. Ten months on, it's releasing its first live version of the service in the form of a beta to a select group of early adopters: UK-based members of the Windows Insider Program, a beta-testing, bug-seeking, early-adopter group popular in the Windows community, with over 10 million users globally. The idea with the limited release beta -- according to Grace Boswood, COO of BBC Design and Engineering -- will be to get Insiders to try out various features and stress test Beeb in the early beta, while at the same time giving the BBC a trove of usage data that can help it continue to train Beeb further, ahead of a wider release. The BBC is not naming a date yet for the general release. When you are a member in the UK, you have to be using the latest release of Windows 10, and then you download Beeb BETA form the Windows App Store.)

Wind farms have traditionally made less money for the electricity they produce because they have been unable to predict how windy it will be tomorrow. "The way a lot of power markets work is you have to schedule your assets a day ahead," said Michael Terrell, the head of energy market strategy at Google. "And you tend to get compensated higher when you do that than if you sell into the market real-time. "Well, how do variable assets like wind schedule a day ahead when you don't know the wind is going to blow?" Terrell asked, "and how can you actually reserve your place in line?" Here's how: Google and the Google-owned Artificial Intelligence firm DeepMind combined weather data with power data from 700 megawatts of wind energy that Google sources in the Central United States. Using machine learning, they have been able to better predict wind production, better predict electricity supply and demand, and as a result, reduce operating costs. "What we've been doing is working in partnership with the DeepMind team to use machine learning to take the weather data that's available publicly, actually forecast what we think the wind production will be the next day, and bid that wind into the day-ahead markets," Terrell said in a recent seminar hosted by the Stanford Precourt Institute of Energy. Stanford University posted video of the seminar last week. The result has been a 20 percent increase in revenue for wind farms, Terrell said. The Department of Energy listed improved wind forecasting as a first priority in its 2015 Wind Vision report, largely to improve reliability: "Improve Wind Resource Characterization," the report said at the top of its list of goals. "Collect data and develop models to improve wind forecasting at multiple temporal scales--e.g., minutes, hours, days, months, years." Google's goal has been more sweeping: to scrub carbon entirely from its energy portfolio, which consumes as much power as two San Franciscos. Google achieved an initial milestone by matching its annual energy use with its annual renewable-energy procurement, Terrell said. But the company has not been carbon-free in every location at every hour, which is now its new goal--what Terrell calls its "24x7 carbon-free" goal. "We're really starting to turn our efforts in this direction, and we're finding that it's not something that's easy to do.

Natural Language Processing (NLP) is a subfield of artificial intelligence that assists computers with understanding human language. Utilizing NLP, machines can understand unstructured online information so we can gain significant insights. As computer technology advances past their artificial requirements, companies are searching for better approaches to exploit. A sharp increase in computing speed and capacities has led to new and highly intelligent software systems, some of which are prepared to supplant or augment human services. The rise of natural language processing (NLP) is probably the best example, with intelligent chatbots prepared to change the universe of customer service and beyond.

Dr Luciano Zuccarello grew up in the shadow of Mount Etna, an active volcano on the Italian island of Sicily. Farms and orchards ring the lower slopes of the volcano, where the fertile soil is ideal for agriculture. But the volcano looms large in the life of locals because it is also one of the most active volcanoes in the world. More than 29 million people globally live within 10km of a volcano, and understanding volcanoes' behaviour – and being able to predict when they are going to erupt or spew ash into the air – is vital for safeguarding people's wellbeing. However, predicting volcano behaviour is difficult, especially if they have been dormant, and monitoring them can be challenging since taking samples or deploying equipment poses physical dangers.

Making purchases with your voice is convenient, but it's far from secure. Google is attempting to change that when using Assistant by introducing an optional voice verification test. As The Verge reports, the new security feature relies on Google Assistant's Voice Match and it's being rolled out slowly as part of a limited pilot program to test how well it works with smart speakers and smart displays. The Voice Match training feature was updated recently to include phrases so that Assistant could more accurately determine who is issuing commands. With better accuracy, Google clearly feels Voice Match is good enough to now act as an extra layer of security.

This position requires a U.S. person or the ability to obtain an Export Authorization from the appropriate government agency for non-U.S. Raytheon BBN Technologies (BBN) is looking for creative, talented individuals to join our world-class Speech, Language, and Multimedia group and to help us advance the state-of-the-art in our areas of operation. Our work ranges from seminal research and development to advanced fielded solutions. Our research activities drive the development of industry-leading 24 7 solutions and their deployment into demanding user environments. At BBN, Staff Scientists work with a team of experienced staff to design and implement new techniques in a variety of technologies, including speech recognition, speaker ID, language ID, machine translation, information extraction, question-answering, machine learning, NLP, document image processing, and video analysis.

According to the World Health Organization, more than one billion people worldwide have disabilities. The field of disability studies defines disability through a social lens; people are disabled to the extent that society creates accessibility barriers. AI technologies offer the possibility of removing many accessibility barriers; for example, computer vision might help people who are blind better sense the visual world, speech recognition and translation technologies might offer real-time captioning for people who are hard of hearing, and new robotic systems might augment the capabilities of people with limited mobility. Considering the needs of users with disabilities can help technologists identify high-impact challenges whose solutions can advance the state of AI for all users; however, ethical challenges such as inclusivity, bias, privacy, error, expectation setting, simulated data, and social acceptability must be considered. The inclusivity of AI systems refers to whether they are effective for diverse user populations.

At Deepgram, an end-to-end deep learning speech recognition system is used to create a completely different solution, which makes collecting speech data faster, more accurate and reliable, and truly meets the needs of enterprise companies. Deepgram's innovation is to use artificial intelligence to process text and graphics, so that they form mixed custom models, and then fully train these models to enable them to use files from telephone and podcasts to recorded meetings and videos. The innovative method of Deepgram voice storage can help customers search for words according to their pronunciation, even if they are misspelled, Deepgram can find them. Deepgram CEO Stephenson said that Deepgram's model automatically picks up the noise profile of the microphone, as well as background noise, audio coding, transmission protocol, accent, price point (ie energy), emotion, conversation theme, speech rate, product name and language. In addition, he claims that they can improve speech recognition accuracy by 30% compared to industry benchmarks, increase transcription speed by 200 times, and process thousands of simultaneous audio streams.

Artificial intelligence as a discipline consists of hundreds of individual technologies, concepts, and applications. These terms have become increasingly important as STEM education expands and there is a boom in practical household and consumer-facing applications for the technology. Despite that, there is a lack of consistency in how many AI concepts are discussed, not just at the STEM education level, but in popular entertainment, science writing, and even at times in scientific journals. To address this, we need to standardize how we describe AI and its many subsets, and accurately define these terms both in general and specific to individual technologies and applications of those technologies. We discuss some of the most commonly misused and what they really mean.

Back in 2016, Bang & Olufsen announced its first compact Bluetooth speaker. The Beoplay A1 is a small disc-shaped device with big sound and 24-hour battery life. Like all B&O gear, it also came with a premium price to go with those premium materials. Today, the company is introducing the 2nd-generation model, which is now called the Beosound A1. And while there are a number of updates, the biggest new feature is Alexa voice control.