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.

Artificial intelligence is helping us explore the universe, diagnose diseases, and program autonomous cars, among thousands of other use cases across dozens of industries, including, especially, marketing. Even amidst its tangible applications, AI is still in its infancy – through marketers believe that it's a significant game-changer… and many are doing something about it. A new decade is upon us – as the AI hype begins to dissipate and the proverbial clouds start to clear, which trends do retail marketers need to watch for in 2020? Whether you're looking to get started with AI, expand your results from it, or fine-tune existing use cases, we scoured the net, talked to experts, and did our own research to give you the top AI trends to keep in mind over the next year. Our 2017 research showed that 26% of marketers planned to implement RTM more than 12 months ahead – so, around now.

The core technologies that have enabled the rapid progress and swift adoption of smart virtual assistants include NLP text mining, Voice Analytics and Machine Learning. NLP text mining analyzes and derives insights from the written word, enabling virtual assistants to understand whether the customer's feedback is positive, negative or neutral. Voice Analytics utilizes speech recognition software to extract useful data or detect stress in a customer's voice, enabling the virtual assistant to engage proactively and appropriately. Machine Learning, meanwhile, enables the virtual assistant to perform the cognitive functions typically associated with the human mind, such as understanding, reasoning, problem solving, learning, and even interacting with the surrounding environment.

Facebook Research and MIT researchers are using the popular video game'Minecraft' to build a new AI assistant that can juggle multiple tasks at once. Facebook is using the popular video game Minecraft to help train a new artificially intelligent assistant that, in the future, could help humans perform a wide range of tasks with a broad range of spoken commands. Facebook Research and MIT researchers quietly published a paper in July outlining how they intend to use Minecraft to train an AI assistant that can multitask rather than perform one task at superhuman levels. "In this work, we have argued for building a virtual assistant situated in the game of Minecraft, in order to study learning from interaction, and especially learning from language interaction," the researchers explain in the published paper. According to the researcher team, Minecraft is the perfect environment to train artificial intelligence because it's what is known as a "sandbox" game, which allows players to roam freely, fight, craft, explore and build objects in an online world.

The HomePod didn't get much (read: any) airtime at Apple's iPhone 11 event earlier this week, but that's not to say Apple has forgotten about it entirely. In fact, the HomePod is set to get a raft of new features before the year is out – even if some of them should have been present in the first place. Kicking off the feature-add this month is the introduction of radio station playback. From September 30, you'll be able to ask Siri to play one of around 100,000 global radio stations, and the smart helper will track it down using TuneIn, iHeartRadio and Radio.com. A little later in the year, a few more extras will be coming to the HomePod.