A majority of consumers are confused about what artificial intelligence really does, and have misplaced fears that inhibit them from embracing AI-based technology, according to a new study. Despite that, "these fears are often eased once they gain firsthand AI experience – which ironically many enjoy today without even realizing it," notes the study, conducted by Pegasystems Inc. In a survey of 6,000 customers in six countries, Pegasystems found that consumers appear hesitant to fully embrace AI devices and services. Only one in three (36 percent) are comfortable with businesses using AI to engage with them – even if this typically results in a better customer experience. "Almost three quarters (72 percent) express some sort of fear about AI, with one quarter (24 percent) of respondents even worried about robots taking over the world," the study finds.

Artificial intelligence (AI) will "significantly transform" the insurance industry in the next three years, with insurers investing in AI to empower agents, brokers and employees to enhance the customer experience with automated personalized services, faster claims handling and individual risk-based underwriting processes, according to a new report from Accenture. The Technology Vision for Insurance 2017 report, called Technology for People, released on Wednesday by the global professional services company, found that while the technology will be empowering, insurers face challenges integrating AI into their existing technology. Insurers cite issues such as data quality, privacy and infrastructure compatibility. The report is based on the insights of a technology advisory board, interviews with industry technologists and a survey of more than 550 insurance executives across 31 countries in North America, Europe, Asia-Pacific, Africa and South America, Accenture noted in a press release. The goal of the survey was to identify the key issues and priorities for technology adoption and investment.

Google Home is finally getting a feature that its users have been clamoring for since it launched last year: it can now distinguish between different voices and respond with personalized responses. Until now, you could only connect a single account to a Google Home, and that meant you could only get info about one person's calendar, for example. If you're single or don't have roommates, that's perfectly fine, but the moment you live with somebody else, that's a pretty obvious problem. With today's update, which should be rolling out in the U.S. as you read this (and which is coming to the U.K. in the coming months), up to six people can share a Google Home. So now, when you ask your morning commute, you'll get the info about your route and not your spouse's.

How many virtual assistants can you fit in one smartphone? European network operator Orange is hoping there's room to squeeze in one more. With the right apps, you can already talk to Alexa, Cortana and Google through your smartphone -- and maybe also to Siri or Bixby if you went with one of the big brands. Djingo will be able to answer questions, send text messages, place calls, play music and video from Orange's set-top box, and control smart home devices. It draws on the company's research into linguistics and artificial intelligence, and will even offer financial advice in conjunction with a new banking service Orange is launching, the company said.

The assistants in our living rooms are supposed to be as personal as they are on our phones, but it's never really felt that way. Anyone who walks into our homes can say, "OK, Google," or "Hey, Alexa" and get access to whatever they want, from upcoming appointments to the kind of music we like, even addressing them by your name. But a new feature rolling out to Google Home looks to add a personal touch to the impersonal home speaker. Instead of being tied to a single account, Google will now let you set up multiple accounts on your Home device (up to six in all), so you'll be able to get specific results from Google Assistant. And what's more, it will recognize your voice so you won't have to ask it to switch from a different account.

Google's Home device is late to the domestic AI assistant game, but it can do one trick that Alexa can't right now: Tell family members apart just by their voices. Up to six people can link their Google accounts to one Home device, then train Google's Assistant to recognize their voices. Once that's done, it'll be able to distinguish you from your spouse or other family members and give you pertinent info, like your schedule or traffic on your usual route. As it stands, the lack of multi-user support is a nuisance. While Home can still give everyone general information and trivia via the Knowledge Graph (and more erratic "featured snippets"), it can only create appointments and do other user-specific stuff for one designated Google account.

Think you've obsessed over your dating profile pics and finally managed to get them just right? Sorry, but you'd better think again. The dating app Hinge sifted through thousands of their users photos and figured out what gets those likes and what gets ignored. The results are pretty surprising. Hinge's profile requires you to add six photos, and people can like them and start conversations based on them individually. So they can really tell what kind of photos illicit the most positive responses.

Cortana is one of our favorite tools for getting things done more quickly and easily in Windows 10. So we're pretty pleased that Windows 10 Creators Update brings a handful of improvements to our favorite digital assistant. After using them for a few days, we've pinpointed three that can help improve your productivity right now. As helpful as it is to have Cortana at arm's reach, it's now useful when you're across the room, too, thanks to a new full-screen mode. Anytime your computer is unlocked and idle, you can summon your virtual assistant by saying, "Hey, Cortana" from anywhere within earshot and a screen-size interface will pop open.

Amazon is the exception to nearly every rule in business. Rising from humble beginnings as a Seattle-based internet bookstore, Amazon has grown into a propulsive force in at least five different giant industries: retail, logistics, consumer technology, cloud computing, and most recently, media and entertainment. The company has had its share of missteps -- the expensive Fire phone flop comes to mind -- but is also rightly known for strokes of strategic genius that have put it ahead of competitors in promising new industries. This was the case with the launch of cloud business AWS in the mid-2000s, and more recently the surprising consumer hit in the Echo device and its Alexa AI assistant. Today's Amazon is far more than just an "everything store," it's a leader in consumer-facing AI and enterprise cloud services. And its insatiable appetite for new markets mean competitors must always be on guard against its next moves.

Machine learning has long powered many products we interact with daily--from "intelligent" assistants like Apple's Siri and Google Now, to recommendation engines like Amazon's that suggest new products to buy, to the ad ranking systems used by Google and Facebook. More recently, machine learning has entered the public consciousness because of advances in "deep learning"--these include AlphaGo's defeat of Go grandmaster Lee Sedol and impressive new products around image recognition and machine translation. While much of the press around machine learning has focused on achievements that were not previously possible, the full range of machine learning methods--from traditional techniques that have been around for decades to more recent approaches with neural networks--can be deployed to solve many important (but perhaps more prosaic) problems that businesses face. Examples of these applications include, but are by no means limited to, fraud prevention, time-series forecasting, and spam detection. InfoQ has curated a series of articles for this introduction to machine learning eMagazine covering everything from the very basics of machine learning (what are typical classifiers and how do you measure their performance?), to production considerations (how do you deal with changing patterns in data after you've deployed your model?), to newer techniques in deep learning.