Now that AI home assistants are winning the hearts (and living rooms) of people around the world, it's only a matter of time before consumers want their bots to improve their health. Some are doubtlessly already using Amazon Echo's Alexa, Google Home, or Apple's Siri for easy health hacks, like setting medication reminders and looking up possible drug interactions and side effects. Can I have a margarita now?") But we expect bigger ideas soon and bet that efforts like Merck's Amazon Challenge will yield hands-free health help that's far more useful. The ideas that will stick will be those developed from the patients' point of view.

Robo-advisory services are often associated with millennials. This makes sense since these services' client base skews younger than that of traditional wealth management firms. Wealthfront, for instance, has previously reported that 60% of its customers are younger than 35, while Betterment has said that 75% of its customers are under the age of 50. Robo-advisers also offer features that millennials prefer: simple and transparent fee structures, an intuitive digital user experience, and personalization based on the user's appetite for risk. Plus, today's robo-advisers are aggressively positioning themselves as the millennials' answer to traditional, stodgy wealth management firms, as evidenced by their marketing campaigns.

Data scientists are one of the most hirable specialists today, but it's not so easy to enter this profession without a "Projects" field in your resume. You need experience to get the job, and you need the job to get the experience. Seems like a vicious circle, right? Statsbot's data scientist Denis Semenenko wrote this article to help everyone with making the first simple, but yet illustrative data science projects which can take less than a week of work time. This means that you need to formulate the problem, design the solution, find the data, master the technology, build a machine learning model, evaluate the quality, and maybe wrap it into a simple UI.

Amazon announced its first touchscreen smart speaker, the Amazon Echo Show in May. The device will cost $229.99 Just like other products from the Echo range, the Echo Show will have artificial–intelligence based voice command support from the company's Alexa voice assistant. The smart speaker, with a touchscreen, will have additional capabilities on top what existing smart speakers offer -- its touchscreen, for instance, sets it apart from other smart speakers in the market such as Apple's HomePod and Google Home. The idea behind adding a touchscreen to a smart speaker is that it is a touchscreen, which you don't really have to touch since all its functionality is voice-based.

From devices that order our groceries to smart toys that speak to our children, high-tech home gadgets are no longer the stuff of science fiction. But even as they transform our lives, they put families at risk from criminal hackers taking advantage of security flaws to gain virtual access to homes, a report warns. It comes as manufacturers routinely install technology into new household products that allows them to connect to the'internet of things', an umbrella term for devices that can go online. Scary: Which? said ethical hackers broke into the CloudPets toy and made it play its own voice messages. This lets them work with smartphones and'home hubs' such as Amazon's Echo and its virtual assistant Alexa, as well as the Google Home device.

It's a type of artificial intelligence that allows computers to learn new data and apply it to a service, all without being explicitly programmed to do so. There are a few obvious examples, like chatting with Amazon's Alexa in your living room or Siri on your iPhone. Machine learning can also be more subtle, like when Google suggests a new route after sneakily cross-referencing your common destinations with a daily traffic report. Similar conveniences have made their way into customer service via machine learning. The difference is that while the aforementioned machine learning examples learn and adapt to your daily routines, AI tools for customer service focus more on the customer's journey and the workflows of support staffs.

Recent advances in neural networks have inspired people to design hybrid recommendation algorithms that can incorporate both (1) user-item interaction information and (2) content information including image, audio, and text. Despite their promising results, neural network-based recommendation algorithms pose extensive computational costs, making it challenging to scale and improve upon. In this paper, we propose a general neural network-based recommendation framework, which subsumes several existing state-of-the-art recommendation algorithms, and address the efficiency issue by investigating sampling strategies in the stochastic gradient descent training for the framework. We tackle this issue by first establishing a connection between the loss functions and the user-item interaction bipartite graph, where the loss function terms are defined on links while major computation burdens are located at nodes. We call this type of loss functions "graph-based" loss functions, for which varied mini-batch sampling strategies can have different computational costs. Based on the insight, three novel sampling strategies are proposed, which can significantly improve the training efficiency of the proposed framework (up to $\times 30$ times speedup in our experiments), as well as improving the recommendation performance. Theoretical analysis is also provided for both the computational cost and the convergence. We believe the study of sampling strategies have further implications on general graph-based loss functions, and would also enable more research under the neural network-based recommendation framework.

ABI Research predicts the number of businesses adopting artificial intelligence (AI) technologies worldwide will grow considerably, up from 7,000 this year to nearly 900,000 in 2022, a CAGR of 162%. AI is no longer limited to science fiction and movies, with significant strides being made in cloud processing, storage capacity, and machine learning algorithms to enable computer systems to surpass humans in winning strategy games and television shows. Increasingly, businesses are applying these technological advancements to deliver automation and innovation that equal or exceed human capabilities. "Even though nearly one million businesses will adopt AI by 2022, it will not be a great fit for every company," says Jeff Orr, Research Director at ABI Research. "Many businesses will have to adapt their corporate governance policies to deal with the lack of a guaranteed outcome when implementing machine learning. While most enterprises start using machine learning to analyze their existing business for insights, the technologies have far-reaching application in specific industries, ranging from reduction of false positives in fraud detection to powering conversational interfaces for chatbots and virtual assistants."

Federal agencies are a step closer to automating some of their common customer service processes using artificial intelligence. The General Services Administration recently wrapped a pilot that walked federal agencies through the process of building chatbots and other intelligent personal assistants similar to Apple's Siri and Amazon's Alexa. Graduates of that pilot have developed some basic prototypes--a single chatbot that lets users access Small Business Administration licenses, Internal Revenue Service tax credits, Forest Service park permits, and Health and Human Services Department benefits, for one. But prototypes weren't the point of the pilot, GSA's Emerging Citizen Technology Office lead Justin Herman told Nextgov--instead, it was to help agencies understand what they'd need before they can fully deploy intelligent personal assistants. One finding, Herman said, was that agencies need to assess their cloud services, as chatbots and voice-controlled virtual assistants would need to pull information from the internet.

But a startup from San Francisco called BabelOn is working on a particularly unique offshoot of this technology. In a nutshell, BabelOn wants to make it a trivial matter to translate your own voice into another language, even if you don't speak that language yourself. The company says its combo of software and custom-built hardware can analyze what makes up your voice and then use that to recreate speech that sounds just like you, in a language of your choosing. Initially, the company wants to use its technology for things like improving dubbed films or localizing video games, but eventually it wants to be able to translate your speech in real time, say while you're on a Skype call. Microsoft has done this for a while, translating Skype voice calls on the fly, but BabelOn promises that its translations will sound like you, not an anonymous Siri- or Cortana-like digital voice.