Last week's big news from Facebook's (FB) F8 conference was Facebook's long-anticipated foray into the online dating game. Alphabet's (GOOG) Google I/O is happening this week in Mountain View, California and featured an impressive demo of its state-of-the-art AI capabilities. Here in Seattle, the "counter-programming" as it were, is the Microsoft Build 2018 Developer Conference. Perhaps as something of a direct response to Facebook's recent high-profile issues with data privacy as well as Google's own troubled past on this topic, Microsoft (MSFT) CEO Satya Nadela in his opening keynote pointedly addressed the issue of civic and ethical responsibility for technology companies. This underscores the message he gave in a recent interview on NBC's Today Show, where he said that'Trust' will push the company ahead of Amazon (NASDAQ:AMZN) and Google.

Microsoft today announced that it has acquired Semantic Machines to bolster its conversational AI offerings -- like Cortana, the Azure Bot Service, and Microsoft Cognitive Services. Semantic Machines works in areas like speech synthesis, deep learning, and natural language processing. Semantic Machines describes itself as a company bent on creating conversational AI that enables machines "to communicate, collaborate, understand our goals, and accomplish tasks." It could help Microsoft compete with conversational computing initiatives from Amazon's Alexa, Apple's Siri, Google's Assistant, and Samsung's Bixby. In addition, Semantic Machines has assembled a cadre of experts in the conversational AI arena, like Larry Gillick, former chief scientist for Siri at Apple, and well-known researchers like UC Berkeley professor Dan Klein and Stanford University professor Percy Liang.

Considering its work with Cortana, Microsoft's acquisition of Semantic Machines makes perfect sense. Announced Sunday, the purchase is designed to bolster not just Microsoft digital voice assistant Cortana but also social chatbots like XiaoIce, which has had up to 30 billion conversations across China, Japan, the United States, India and Indonesia. Berkeley, California-based Semantic Machines describes itself as developing the fundamental technology to allow humans to interact naturally with computers. It's led by tech entrepreneur Dan Roth, UC-Berkeley Professor Dan Klein and Stanford University Professor Percy Liang. "With the acquisition of Semantic Machines, we will establish a conversational AI center of excellence in Berkeley to push forward the boundaries of what is possible in language interfaces," said David Ku, chief technology officer of Microsoft AI & Research. "Combining Semantic Machines' technology with Microsoft's own AI advances, we aim to deliver powerful, natural and more productive user experiences that will take conversational computing to a new level."

In recent months, edge computing has become a buzzword. But what exactly is edge computing? Let's time travel back 10 years to the era of personal computing. Back then, we typed our documents in Word, listened to music and played media from CDs we bought and stored our photos on the computer hard drive. Our one personal computer- a desktop or laptop, was the central hub of every computing activity we did back then.

You are likely very familiar with artificial intelligence, even if you don't recognize it as such: all your tech assistants, from Siri to Alexa, run on the technology, but just because it's ubiquitous, doesn't mean it couldn't be made better: more inclusive, more diverse, and more useful. And no one is in a better place to figure out how than Kriti Sharma, Vice President of AI at SAGE, and one of Forbes' 30 Under 30. "When I was 15, I got some exposure to a government research facility where a scientist could use his voice to make a computer do something," Sharma tells Bustle. "That was magical for me. How awesome would it be if I had my own personal machine that could understand my voice?"

Microsoft has acquired a natural language AI firm called Semantic Machines to help Cortana and other bots carry on natural conversations, it announced. The tech "uses the power of machine learning to enable users to discover, access and interact with information and services in a much more natural way, and with significantly less effort," wrote Microsoft AI & Research CTO David Ku. What's more, Microsoft will establish a "conversational AI center" in Berkeley to amp up its language tech and "take conversational computing to a new level." Microsoft pointed out that while Siri, Cortana, Google Assistant and Alexa can execute commands, they can't carry on a conversation in any meaningful way (Google's much-debated Duplex conversation aside). Semantic Machines, however, has developed tech that can understand entire chats, not just orders to do this or that. "For rich and effective communication, intelligent assistants need to be able to have a natural dialogue instead of just responding to commands," said Ku. Semantic Machines should be able to help Microsoft with all that via its primary product, the Conversation Engine.

Vymo, a software-as-a-service start-up, is gearing up for expansion across Asia Pacific, following a round of engagements with key banking and insurance customers. On the back of customer success in Singapore and Hong Kong, the start-up is currently building strong pipeline within the wider region. As part of this expansion, Anurag Srivastava, former head of financial services at Kofax, has been appointed head of APAC for the business, reporting directly to CEO, Yamini Bhat. "We will follow this up with building the regional capability of customer success, delivery and support teams in key strategic markets in Asia Pacific apart from relationships with cloud solution providers and services partners for local deployments," Bhat said. A developer of an AI-personal assistant used by some leading banks and insurers sees the APAC region as a major growth opportunity.

Microsoft has acquired Semantic Machines, a "conversational AI" startup in Berkley, Calif., to further its work in building machines that can converse naturally with humans. Semantic aims to advance the state of voice-based AI, like virtual assistants Alexa, Siri, Cortana and others, from understanding and responding to commands to being able to have compete conversations. The company is led by accomplished startup entrepreneurs, a former chief speech scientist for Apple's Siri and leading AI researchers and professors from Stanford and University of California Berkley. With the acquisition, Microsoft will set up a "center of excellence" focused on pushing "forward the boundaries of what is possible in language interfaces," David Ku, corporate vice president and chief technology officer of Microsoft AI & Research, wrote in a blog post announcing the acquisition. "Combining Semantic Machines' technology with Microsoft's own AI advances, we aim to deliver powerful, natural and more productive user experiences that will take conversational computing to a new level," Ku wrote.

Over the last two years, academic researchers have identified various methods that they can transmit hidden commands that are undetectable by the human ear to Apple's Siri, Amazon's Alexa, and Google's Assistant. According to a new report from The New York Times, scientific researchers have been able "to secretly activate the artificial intelligence systems on smartphones and smart speakers, making them dial phone numbers or open websites." This could, perhaps, allow cybercriminals to unlock smart-home doors, control a Tesla car via the App, access users' online bank accounts, load malicious browser-based cryptocurrency mining websites, and or access all sort of personal information. In 2017, Statista projected around 223 million people in the U.S. would be using a smartphone device, which accounts for roughly 84 percent of all mobile users. Of these 223 million smartphones users, around 108 million Americans are using the Android Operating System, and some 90 million are using Apple's iOS (operating system).

We have seen a variety of Recommender Systems. But we left an important issue aside: How do we evaluate RecSys? Before answering that question per se, I want to make emphasys on something. Using just one error metric can give us a limited view of how these systems work. We should always try to evaluate with different methods our models, almost as picky as your ex, but prorizing quick iteration with the lowest cost possible.