No matter where in the country you live, chances are you get hit with severe weather every once in a while. In New England, it's nasty Nor'Easters and blizzards, while tornadoes are a greater threat in the Midwest and South. Plus, just about everyone is subject to heavy rainfall and thunderstorms from time to time. When severe weather is headed in your direction, you're generally forced to hunker down at home (unless you're supposed to evacuate, in which case, please do so). As long as your electricity and internet are still working, though, your Amazon Echo can help you weather the storm!

The number of companies using artificial intelligence in healthcare has increased from less than 20 in 2012 to 100 last year, according to GlobalData Healthcare estimates. Growth is expected to accelerate with the AI healthcare market set to reach $6.6bn by 2021, a 40 percent growth from its current size, research from Accenture shows. The three most cost-saving uses of AI in healthcare are robot assisted surgery, virtual nursing assistants, and administrative workflow assistance, Accenture has found. Although healthcare AI is widely used in the US, take up has been slower in the UK though healthcare apps are gaining traction. Babylon is an AI app which uses speech recognition to check symptoms and connect patients with doctors while MedyMatch helps A&E departments make better decisions under extreme pressure.

The bot solution gives Predictive Analytics, NLP, and Unified Architecture. It turns the necessary channels into intelligent assistants that make personalized service and real work possible. An employee tries to open an application and notices that it is not working. He checks if there were any exceptions in the previous two days and finds that there were many exceptions. It takes him a few hours to determine the exceptions and categorize them.

Apple has apologised for allowing contractors to listen to voice recordings of Siri users in order to grade them. The company made the announcement after it completed a review of the grading programme, which had been triggered by a Guardian report revealing its existence. According to multiple former graders, accidental activations were regularly sent for review, having recorded confidential information, illegal acts, and even Siri users having sex. "As a result of our review, we realise we have not been fully living up to our high ideals, and for that we apologise," Apple said in an unsigned statement posted to its website. "As we previously announced, we halted the Siri grading program. We plan to resume later this fall when software updates are released to our users."

The concept of self-programming computers was closer to science fiction than reality just ten years ago. Today, we feel comfortable conversing with smart personal assistant like Siri and keep wondering just how Spotify guessed what we like. It's not just the mobile apps that are becoming more "intelligent". Advertising encouraging us to interact and install those apps has made its way onto a way new quality level as well. Thanks to advances in machine learning (ML), the baseline technology for AI, mobile advertising industry is now undergoing significant transformation.

How customers engage with brands and the way they shop is now a new ballgame altogether. Digital engagement with products is quickly replacing the traditional approaches; in fact, it has set new standards to keep up with customer expectations. According to the Global Consumer Insights Survey 2019 by PWC, nearly a third of consumers buy products online weekly or more frequently, and this number has increased steadily by 5% points year on year. Marketing and selling products with trusted and persuasive product data has a significant impact on improving customer experience and growing revenue. Brands are investing more and more in enhancing quality, reliability, and accessibility of product data to stay one step ahead in the digital business arms race. Customers can initiate their product research anywhere and analyze the prices/offers before they ever interact with the brand.

The relationship between humans and robots is a tricky thing. If the latter looks too much like the former, but is still clearly a machine, people think it's creepy, even repulsive--a feeling that's become known as the "uncanny valley." Or, as is sometimes the case, the human, with "Star Wars" or "The Jetsons" as his or her reference points, is disappointed by all the things the robot can't yet do. Then, there is the matter of job insecurity--the fear of one day being replaced by a tireless, unflappable, unfailingly consistent device. Human-robot interactions can be even more complicated for one group in particular--older adults.

If online dating has gotten stale, here's an app that may change that with this one element.

Businesses and consumers are experiencing a gap in perception when it comes to the use of AI. While almost two-thirds (63%) of businesses believe that chatbots and virtual assistants make it easier for customers to get their issues resolved, only one-third (33%) of consumers agree, according to a customer experience benchmark report from NICE inContact. There is an even bigger gap when it comes to businesses believing that customers would like to use their virtual assistants (such as Amazon Alexa/ECHO or Google Home) to interact with them. While 68% of businesses carry this perception – and despite ownership of such devices continuing to increase in the US – only 30% of customers actually want to interact with businesses using them. Even so, businesses are continuing to look to the future when it comes to voice ordering.

We describe a method for selecting relevant new training data for the LSTM-based domain selection component of our personal assistant system. Adding more annotated training data for any ML system typically improves accuracy, but only if it provides examples not already adequately covered in the existing data. However, obtaining, selecting, and labeling relevant data is expensive. This work presents a simple technique that automatically identifies new helpful examples suitable for human annotation. Our experimental results show that the proposed method, compared with random-selection and entropy-based methods, leads to higher accuracy improvements given a fixed annotation budget. Although developed and tested in the setting of a commercial intelligent assistant, the technique is of wider applicability.