Microsoft wants to make Cortana more human-like. The software giant announced it's acquiring artificial intelligence startup Semantic Machines to help its voice assistants and chatbots carry on more natural conversations with humans. Microsoft didn't disclose how much it paid in the deal, but said Berkeley, California-based Semantic would bring a'revolutionary new approach to building conversational AI'. Microsoft's Cortana is about to get smarter. The firm said it bought Semantic Machines to make its digital assistant more human.

Apple may have released the HomePod just a few months ago, but rumors are already circulating about what's in store next for the smart speaker. The latest is that the iPhone maker could be preparing to release a cheaper version of the $350 HomePod, according to Chinese site Sina. The device would be priced at a significant discount of up to $200, and would be available through Apple's Beats audio brand. Apple might be releasing a cheaper, smaller version of its HomePod speaker. The report from Sina doesn't mention when a cheaper HomePod would go on sale, but noted that Apple is discussing the plans with one of its suppliers, a Taiwanese chip manufacturer called MediaTek.

Last year, Gartner predicted that 30% of all web browsing will be done without a screen by 2019. More importantly, nearly 1/3rd of all searches would be voice led in 2018. Interestingly, most of these voice searches might not come from your smart phones, but from your Amazon Echo or Google Home or Apple Homepod! Home assistants are taking the world by storm, and in the process, they're changing user behavior in a way quite similar to the way smartphones changed the way people interacted with their devices and the internet back when they first launched. Among its competitors, Amazon's Alexa is definitely a clear leader and has the most mature offering.

The Basware Assistant uses natural language processing and artificial intelligence to create a new and simplified way for people to interact with Basware's e-procurement solution. They can communicate with the Basware Assistant like they would with a person, to search for orders and purchase requests using vendor and item names, as well as ID and document numbers. By giving people the ability to specify what they are looking, it eliminates the need for having to navigate a series of screens to reach their intended purchase. Through its natural language processing and AI capabilities, the virtual assistant improves system usability, taking another step in streamlining the overall procurement experience. Not only does it help people find purchase orders and order requests more quickly and save them time, it also reduces the training required for new people to buy with Basware.

It's time to revisit the discussion on recommendation engines. In this installment, I'm going to provide you a conceptual overview of the topic, and then, following that I'll show you how to build a recommendation engine in R. Ready? Before showing you how to build a recommendation engine in R, I need to get you up-to-speed on the concepts behind how recommendation engines work. In case you're totally new to marketing data science, let me illustrate the recommendation engine concept a little before proceeding. You know how, when you go buy something on Amazon, you see related products under the heading of'People who purchased this item also purchased…' (or something like that).

Many women use dating apps like Tinder and Bumble to confirm their attractiveness rather than find a partner. New research into our swiping habits habits has found that men swipe with an eye for casual sex while girls prefer to use dating apps for an ego boost. This is because women get a kick out of being perceived as a potential partner by other users, scientists said. 'Women use dating apps to feel better about themselves more than men do,' said study coauthor Dr Mons Bendixen, from the Norwegian University of Science and Technology in Trondheim. Lead author Ernst Olav Botnen added: 'Men tend to report a desire for casual sex and short-term relationships as a reason for using dating apps.

If Google I/O 2017 was all about Google's restructuring effort and the resonance of artificial intelligence brewing in Google Labs, I/O 2018 was more about humanizing those AI efforts by integrating them into its line of products, particularly Google Assistant, which powers Google Home and is an integral part of a bunch of other Google and non-Google devices. Google Assistant itself was the successor to Google Now. It enabled Android users, for the first time, to engage in a two-way conversation with their Android devices, something iOS users were already enjoying with Siri. During I/O 2016 while introducing Google Assistant, CEO Sundar Pichai called it, "The future of computing as an ambient experience that extends beyond devices." Two of the very big Google launches at the event -- Google Allo, a messaging app with a resident chatbot, and Google Home, a smart speaker to rival Amazon Echo powered by Alexa -- were both powered by Google Assistant, an outcome of Google's continual efforts in natural language processing, deep learning, and text-to-speech over the years.

Back in November of 2015, Google officially confirmed they had implemented a machine learning AI called RankBrain into their query-filtering process. Rankbrain learns from each user query and applies these learnings to each successive query. Machine learning AI helps Google recognize the natural language people use when they type online and then use this information to provide the most relevant search results. The goal is to optimize the user experience by providing nothing but the highest quality information. RankBrain will play a large role in processing voice search queries as the popularity of home assistants like Alexa and Google Home continue to rise.

LinkedIn made it passé to send dozens of résumés to potential employers. Now Debut is finding the perfect match for graduates with personality tests and math quizzes, degrees be damned. It's a British competitor to New York-based WayUp, and investors here seem to like its targeted approach to recruitment: the startup has just raised $6.7 million (£5 million) to make its recommendation algorithms for job-seeking graduates smarter. Debut already has 60 corporate clients, including Google, Apple and Barclays, that pay it an annual subscription to parse the data flood of graduate applicants who have completed psychometric and personality tests on its app. "Most jobs applications have generic information," says founder Charlie Taylor, a former consultant at Ernst & Young who also ranked on Forbes' 30 Under 30 in European tech in 2017.

Microsoft has bought Semantic Machines, an artificial intelligence start-up, as it looks to boost its efforts in developing conversational AI. Berkeley, California-based Semantic's approach to AI is using machine learning to add context to conversations with chatbots. This means taking information received by AI and applying it to future dialogue. The firm's speech recognition team previously led automatic speech recognition development for Apple's personal assistant Siri. In its announcement Monday, Microsoft did not disclose any financial details of the acquisition.