Since its launch many of us have become familiar with Alexa, Amazon's cloud-based voice service. Built in the cloud, Alexa is a device that gets smarter the more you use it. By adapting to your speech patterns, vocabulary, and preferences, Alexa has the ability to answer questions, play music, hear the news, get weather and traffic reports and control your smart home. Recently, Amazon upgraded their device with a camera and introduced us to Alexa Echo Look. Only $20 more expensive than the camera-free Echo speaker, Amazon's Alexa Echo Look is priced at an affordable $200.

The back end of last year, and the early part of this one, have been the time for mega-hype on the impacts of Artificial Intelligence – and there is still plenty of time for many of the direst predictions to come true. But it is now also a time when some of the smaller, more focused applications of AI are starting to appear, and it is here that most of its beneficial possibilities will probably be unearthed. Take, for example, the recent introduction of the Dynatrace Artificial Virtual Intelligence System, or DAVIS for short. This is an AI-powered digital virtual assistant aimed squarely at IT operations managers who may be looking for a different way of doing application or digital performance management. The users want snappier response times and there's more data than ever coming out of the monitoring tools.

Locality sensitive hashing (LSH) is a powerful tool for sublinear-time approximate nearest neighbor search, and a variety of hashing schemes have been proposed for different dissimilarity measures. However, hash codes significantly depend on the dissimilarity, which prohibits users from adjusting the dissimilarity at query time. In this paper, we propose {multiple purpose LSH (mp-LSH) which shares the hash codes for different dissimilarities. mp-LSH supports L2, cosine, and inner product dissimilarities, and their corresponding weighted sums, where the weights can be adjusted at query time. It also allows us to modify the importance of pre-defined groups of features. Thus, mp-LSH enables us, for example, to retrieve similar items to a query with the user preference taken into account, to find a similar material to a query with some properties (stability, utility, etc.) optimized, and to turn on or off a part of multi-modal information (brightness, color, audio, text, etc.) in image/video retrieval. We theoretically and empirically analyze the performance of three variants of mp-LSH, and demonstrate their usefulness on real-world data sets.

A smorgasbord of products and solutions, FinTech describes the dynamic landscape of financial services driven by technology. From startups offering existing services at lower costs, to disruptive innovations shaking up the way we manage our finances, the FinTech industry is set to radically alter consumer behaviour and business processes. Global investment in FinTech hit a whopping $5.3 billion (link) for Q4 of 2016. JP Morgan, alone, invested $600 million (during FY 2016) on Fintech initiatives they said included things like developer API's and "interesting developments" in the bill payments area. Things are continuing to rosy for FinTech in 2017, as financial institutions continue to invest in new and emerging technologies (chart) that will either disrupt or protect them from disruption.

Those rumors of Apple unveiling a Siri-enabled speaker just got a little more tangible. Bloomberg sources claim that Apple has started manufacturing the voice-controlled device. It might be announced at WWDC on June 5th, the insiders say. Just don't expect to buy one right away, though. If the rumor is accurate, the speaker "will not be ready" to ship until later in the year.

Apple has started manufacturing the Siri speaker overseas, Bloomberg reported Wednesday, citing sources close to the matter. The report comes after KGI Securities analyst Ming-Chi Kuo said in May there's a more than 50 percent chance Apple will unveil its Siri speaker at the Worldwide Developers Conference. Tuesday's report said the device will debut as soon as WWDC next week, but the gadget will not be ready to ship until later this year. It was reported earlier this month that Apple employees were testing the Siri speaker in their homes for several months. The device reportedly entered an advanced prototype stage last fall.

Based on a first look, it doesn't. We listened to Rubin speak and spent some time with the Essential phone at the Code conference here. We now have a better idea of what issues Rubin's new phone is trying to solve than the ones kind of buried on the new Essential website. Rubin says he wants to "solve consumer problems" with both his new phone, out in June starting at $699, and other products down the line, such as an Amazon Echo-like speaker. For the Essential phone, Rubin and staffers say you won't need to buy the accessory protection because it's not needed.

It might sound impressive that artificial intelligence (AI) powers virtual assistants such as Alexa and Siri – but when it comes to their ability to converse using natural language, they're actually still quite limited. That's why Microsoft is trying a new tactic. Microsoft's Project Mélange, housed in its India office, is using code-mixing (moving between multiple languages within a distinct conversation or even a single sentence) to teach AI how to have more human-like conversations. India is a perfect location for this work, because it's a multi-lingual society in which many people are mixing languages regularly. Using big data analytics and machine learning in realtime, the company is enabling virtual assistants to understand various accents, contexts, languages, and nuances.

Artificial Intelligence (AI) is advancing so rapidly that even its developers are being caught off guard. Google co-founder Sergey Brin said in Davos, Switzerland, in January that it "touches every single one of our main projects, ranging from search to photos to ads … everything we do … it definitely surprised me, even though I was sitting right there." The long-promised AI, the stuff we've seen in science fiction, is coming and we need to be prepared. Today, AI is powering voice assistants such as Google Home, Amazon Alexa and Apple Siri, allowing them to have increasingly natural conversations with us and manage our lights, order food and schedule meetings. Businesses are infusing AI into their products to analyze the vast amounts of data and improve decision-making.