The Apple HomePod hasn't exactly had the warmest of welcomes to the market, with customers complaining about its limited functionality -- and perhaps more importantly, its price. At $349, the smart speaker, released February 9, is leaps and bounds more expensive than similar (and perhaps more useful) products from other companies like the Amazon Echo and Google Home. The rumor mill has been churning, and some folks are claiming that Apple has taken note of the criticism. In fact, there are now reports suggesting that Apple could be releasing another version of the HomePod later in 2018 at a significantly lower price point. According to a report from KGI's Ming-Chi Kuo, "there will be a cheap version of HomePod in the second half of" this year.

Here's what you need to know today: Siri, Cortana, and Alexa could soon have to move over in the office, following the release of Cisco's AI assistant for business. Yesterday saw the first live demo of Cisco's Spark virtual assistant, first announced back in November 2017, at a Cisco Live event in Melbourne, Australia. Cisco senior VP and general manager of applications Rowan Trollope told the event that "existing voice assistants that have traction – none of them were built for business. They were built for the consumer domain. That is a totally different domain space to the one we are interested in."

On December 13, 2017, Harry Shum, executive vice president of AI and Research at Microsoft announced new Intelligent search features in Bing with artificial intelligence (AI) and machine learning. It is slated to deliver more comprehensive and complete information, give people answers quicker, and enable people to interact more conversationally with Bing search. AI has been a part of search technology since the beginning, using machine-reading comprehension to examine billions of documents to understand the web and help people more quickly and confidently get the answers they are searching for. Microsoft's commitment to infusing AI into its products is pushing Bing's machine learning capabilities to push the industry in delivering more Intelligent search. Harnessing this data while using machine learning techniques allows Bing to deliver more personalized consumer experiences and advertisers to make deeper consumer connections.

The next version of Windows 10 looks set to shake things up in the way people switch between devices, and now, reportedly, has a name: Spring Creators Update. With Windows 10 being the "final" version of Windows, updates like this are essentially the new versions, with the recent Creators Update, and then the Fall (autumn) Creators Update each bringing big new features and revisions. So what does the next version of Windows 10 have in store? Known in testing as "Redstone 4", the next version of Windows 10 will probably be called Spring Creators Update, a name which has appeared briefly in blogposts and tools, but recently turned up in another tool that more or less cements it as the name. As the name might imply, "spring" is the target for the next version of Windows.

It's one thing to believe that Amazon's Alexa is constantly listening to us, but quite another to worry that she's laughing at what she hears. Users of Alexa-enabled devices such as the Echo and the Dot have reported hearing the tech giant's ubiquitous voice emit a very real-sounding and unprompted laugh of late. Tweets and reports about the artificial intelligence gone rogue have labeled it creepy and scary, and we can see why. Short of little Carol Anne speaking from inside the TV in the 1982 classic "Poltergeist," it's tough to imagine something we'd rather hear less than Alexa just chuckling out of nowhere on the kitchen counter. "Ha ha ha," she says, in a voice that sounds like Alexa four glasses of wine.

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Perhaps the single most important algorithmic distinction between "born digital" enterprises and legacy companies is not their people, data sets, or computational resources, but a clear real-time commitment to delivering accurate, actionable customer recommendations. Recommendation engines (or recommenders) force organizations to fundamentally rethink how to get greater value from their data while creating greater value for their customers. "Build real recommendation engines fast" is my mission-critical recommendation to companies aspiring -- or struggling -- to creatively cross the digital divide. Use recommenders to make it easier to gain better insight into customers while they're getting better information about you. Recommenders' true genius comes from their opportunity to build virtuous business cycles: The more people use them, the more valuable they become; the more valuable they become, the more people use them.

Reports of Amazon's virtual artificial intelligence (AI) assistant Alexa behaving strangely have recently made the rounds on the news and social media. Several Alexa-enabled devices have reportedly started talking or laughing without being prompted, or doing so instead of performing a command. Naturally, Alexa owners who heard this freaked out, with many resorting to turning off the AI assistants or unplugging their devices. These incidents didn't go unnoticed by Amazon, which immediately set out to fix the bug. On March 7, Amazon released a statement explaining Alexa's sudden gleeful outbursts: "In rare circumstances, Alexa can mistakenly hear the phrase'Alexa, laugh,'" the company said.

Alexa, Amazon's virtual assistant, was laughing when it shouldn't. You might have seen tweets about it: The weird, disembodied chuckle bothered people for reasons you can imagine, as well as because it reportedly could happen unprompted--our assistants, after all, are only supposed to listen and speak to us after they hear the wake word. We want them to tell us the weather and set kitchen timers on command, not spook us with laughter. We all know that virtual personas like Alexa, Siri, and the Google Assistant are not real humans. They can't laugh the way we laugh, because they are not alive.

To address the sparsity and cold start problem of collaborative filtering, researchers usually make use of side information, such as social networks or item attributes, to improve recommendation performance. This paper considers the knowledge graph as the source of side information. To address the limitations of existing embedding-based and path-based methods for knowledge-graph-aware recommendation, we propose Ripple Network, an end-to-end framework that naturally incorporates the knowledge graph into recommender systems. Similar to actual ripples propagating on the surface of water, Ripple Network stimulates the propagation of user preferences over the set of knowledge entities by automatically and iteratively extending a user's potential interests along links in the knowledge graph. The multiple "ripples" activated by a user's historically clicked items are thus superposed to form the preference distribution of the user with respect to a candidate item, which could be used for predicting the final clicking probability. Through extensive experiments on real-world datasets, we demonstrate that Ripple Network achieves substantial gains in a variety of scenarios, including movie, book and news recommendation, over several state-of-the-art baselines.