Since Siri debuted on the iPhone 4s back in 2011, voice assistants have gone from unworkable gimmick to the basis for smart speaker technology found in one in six American homes. "Before Siri, when I talked about [what I do] there were blank stares," Tom Hebner, head of innovation at Nuance Communications, which develops cutting edge A.I. voice technology, told Digital Trends. "People would say, 'Do you build those horrible phone systems? That was one group of people's only interaction with voice technology." According to eMarketer forecasts, almost 100 million smartphone users will be using voice assistants by 2020.

Black & Veatch, an employee-owned, global leader in building critical human infrastructure in energy, water, telecommunications, and government services, is on a journey to leverage AI-assisted bots, called virtual experts, to better capture and interact with engineering knowledge and standards. The goal of this emerging effort is to experiment with ways to better capture knowledge and expertise within the company. Ultimately, this initiative would lead to a reduced amount of time required to locate desired information and create an opportunity for continued innovation to better support the future. Knowledge and standards were generally captured in written form, which led to an abundance of Microsoft Word documents that were difficult to search and a burden to continually refresh. Up to this point, access to content was through best practice document and folder organization and traditional search functions.

Question: Is there a way for me to reboot my Wi-Fi network when I'm on vacation so I can reconnect to all of my smart home devices? Answer: The popularity of smart home devices has put a whole new emphasis on having a solid Internet connection at home. Web cams, motion sensing lights and your perimeter monitoring devices can't alert you if they lose their connection to your network. You've probably seen how rebooting your computer can solve lots of simple issues. The same holds true for your router and Internet modem because they're essentially small computer systems.

In 2011, America's favorite quiz show, Jeopardy!, exposed viewers to what was for many their first brush with artificial intelligence when IBM's Watson faced off against two of the show's greatest champions, Ken Jennings and Brad Rutter. Watson stumbled a bit on the first day but dominated day two and ended the three-round competition with more than three times its opponents' winnings. "I, for one, welcome our new computer overlords," Jennings wrote on his video screen after Watson's win. While artificial intelligence has developed tremendously since then and AI assistants like Siri and Alexa have proliferated in our pockets and homes, there are still plenty of scenarios where these assistants don't seem so smart. A team of researchers at the University of Maryland capitalized on this in an article published in the 2019 issue of the journal Transactions of the Association for Computational Linguistics, creating a set of questions that stumped computers in order to learn more about how they think -- and perhaps teach them to think even better.

While examples of artificial intelligence are numerous across business, AI is still often perceived to be a nascent, still emerging force. In fact, AI is widely deployed. It is critical to the tech platforms of many businesses, across finance and retail and healthcare and media. AI and deep learning examples are so myriad, in fact, that choosing the representative AI example below was a matter of picking among the excess. While the examples of AI below are very different from one another, they all share one common trait: the more data they are fed, the more they learn.

Some are loathing its emergence. Others can't wait to see what it will do for the future of business. Perhaps you've already heard about how it's going to affect manufacturing, healthcare and retail. But did you know that there are massive implications for the restaurant industry as well? The best time to get started with AI is now.

Women have been made into servants once again. Apple's Siri, Amazon's Alexa, Microsoft's Cortana, and Google's Google Home peddle stereotypes of female subservience--which puts their "progressive" parent companies in a moral predicament. People often comment on the sexism inherent in these subservient bots' female voices, but few have considered the real-life implications of the devices' lackluster responses to sexual harassment. By letting users verbally abuse these assistants without ramifications, their parent companies are allowing certain behavioral stereotypes to be perpetuated. Everyone has an ethical imperative to help prevent abuse, but companies producing digital female servants warrant extra scrutiny, especially if they can unintentionally reinforce their abusers' actions as normal or acceptable. In order to substantiate claims about these bots' responses to sexual harassment and the ethical implications of their pre-programmed responses, Quartz gathered comprehensive data on their programming by systematically testing how each reacts to harassment. The message is clear: Instead of fighting back against abuse, each bot helps entrench sexist tropes through their passivity. And Apple, Amazon, Google, and Microsoft have the responsibility to do something about it.

We propose a novel learning framework to answer questions such as "if a user is purchasing a shirt, what other items will (s)he need with the shirt?" Our framework learns distributed representations for items from available textual data, with the learned representations representing items in a latent space expressing functional complementarity as well similarity. In particular, our framework places functionally similar items close together in the latent space, while also placing complementary items closer than non-complementary items, but farther away than similar items. In this study, we introduce a new dataset of similar, complementary, and negative items derived from the Amazon co-purchase dataset. For evaluation purposes, we focus our approach on clothing and fashion verticals. As per our knowledge, this is the first attempt to learn similar and complementary relationships simultaneously through just textual title metadata. Our framework is applicable across a broad set of items in the product catalog and can generate quality complementary item recommendations at scale.

A final beta version of Apple's iOS 13 was found sporting some pretty major flaws just a week before the operating system is set to be released on devices everywhere. As reported by The Verge, researcher Jose Rodriguez discovered a flaw that enables one to access a phone's list of contacts by initiating a FaceTime call. Once a call is placed, Rodriguez shows how, using the voice-over accessibility feature through the iPhones virtual assistant, Siri, all of the contacts in the phone can be accessed, revealing email addresses, phone numbers, names, and any other information stored in the phone's contact list. The flaw, which Rodriguez reported to Apple in July after examining public betas of iOS 13, is similar to one found by the researcher in the operating system's predecessor, iOS 12.1. Though iOS 13 has yet to be released, betas of the new operating system have been available for months, meaning anyone who downloaded the preliminary versions has been unknowingly walking around with the glitch in their device.

Therefore, collaborative filtering is not a suitable model to deal with cold start problem, in which it cannot draw any inference for users or items about which it has not yet gathered sufficient information. But once you have relative large user -- item interaction data, then collaborative filtering is the most widely used recommendation approach. And we are going to learn how to build a collaborative filtering recommender system using TensorFlow. We are again using booking crossing dataset that can be found here. So, our final dataset contains 3,192 users for 5,850 books.