In 2011, Apple unveiled its first iPhone with artificial intelligence, a personal assistant named Siri that could answer questions and help keep track of our daily lives. The AI revolution had begun, and it gave way to higher resolution cameras on phones, such as the then-new iPhone 4S, microphones and cameras in the home, everything from connected speakers, security devices, computers and even showers and sinks. By the end of the decade, we were carrying and or living with devices that are capable of tracking our every movement. Counties and states are selling our personal information to data brokers to resell it back to us, in the form of "people search engines." Facebook and Google have refined their tracking skills, in the pursuit of selling targeted advertising to marketers, that many people believe they are listening to us at all times. They are that good at serving up ads based on our interests, whether we want it or not.

Autoencoders are a common building block of Deep Learning architectures, where they are mainly used for representation learning. They have also been successfully used in Collaborative Filtering (CF) recommender systems to predict missing ratings. Unfortunately, like all black box machine learning models, they are unable to explain their outputs. Hence, while predictions from an Autoencoder-based recommender system might be accurate, it might not be clear to the user why a recommendation was generated. In this work, we design an explainable recommendation system using an Autoencoder model whose predictions can be explained using the neighborhood based explanation style. Our preliminary work can be considered to be the first step towards an explainable deep learning architecture based on Autoencoders.

Though used extensively, the concept and process of machine learning (ML) personalization have generally received little attention from academics, practitioners, and the general public. We describe the ML approach as relying on the metaphor of the person as a feature vector and contrast this with humanistic views of the person. In light of the recent calls by the IEEE to consider the effects of ML on human well-being, we ask whether ML personalization can be reconciled with these humanistic views of the person, which highlight the importance of moral and social identity. As human behavior increasingly becomes digitized, analyzed, and predicted, to what extent do our subsequent decisions about what to choose, buy, or do, made both by us and others, reflect who we are as persons? This paper first explicates the term personalization by considering ML personalization and highlights its relation to humanistic conceptions of the person, then proposes several dimensions for evaluating the degree of personalization of ML personalized scores. By doing so, we hope to contribute to current debate on the issues of algorithmic bias, transparency, and fairness in machine learning.

Machine learning, a subset of artificial intelligence (AI), enables analysis of massive quantities of data. While it generally delivers faster, more accurate results in order to identify profitable opportunities or dangerous risks, it may also require additional time and resources to train it properly. Combining machine learning with AI and cognitive technologies can make it even more effective in processing large volumes of information. There are those who still associate artificial intelligence (AI) and machine learning (ML) with science fiction novels and movies like the Matrix. In reality, machine-learning is already with us, seeping into our everyday lives without much fanfare.

Deepak: AI applications in the Indian pharma industry has definitely not caught up as much as it has in some other industries like e-commerce or Financial services. With AI there is always a need for scale (as scale allows learning to happen faster) and most of the attempts have hence been consumer or patient facing. In the developed world we do hear about large scale solutions in imaging and diagnosis, in clinical trials and in pipeline success measurement. Specifically, within Abbott we have identified a few focus areas namely forecasting, decision tree applications like attrition prediction, robotic process automation in internal data management processes and in delivering superlative user experience for the salesforce through Maya, our virtual assistant for the sales reps and in recommendation engines in KnowledgeGenie. Deepak: AI has a huge role to play in managed services and backend support.

San Francisco (CNN Business)In 2010, artificial intelligence was more likely to pop up in dystopian science-fiction movies than in everyday life. And it certainly wasn't something people worried might take over their jobs in the near future. A lot has changed since then. AI is now used for everything from helping you take better smartphone photos and analyzing your personality in job interviews to letting you buy a sandwich without paying a cashier. It's also becoming increasingly common -- and controversial -- when used for surveillance, such as facial-recognition software, and for spreading misinformation, as with deepfake videos that purport to show a person doing or saying something they didn't.

Anything you do online can come back to bite you. It's been a decade full of lessons: who to trust, when to speak out and how to stream big events online after you've broken up with your cable company. In 2010, the first iPhone was only three years old. Uber and Lyft didn't exist, and neither did Google Assistant and Siri, Instagram or streaming video. We've come a long way since then, but the next 10 years won't be easy.

Banking communications and press releases show conversational interfaces accounting for 38.87% of the AI use-cases at banks. In truth, most chatbots are pilot projects with little to no evidence of ROI; they're touted in the press to make banks appear more modern and convenient to customers. Emerj's AI in Banking Vendor Scorecard and Capability Map found conversational interface vendors score lowest in terms of funding and the AI talent they employ (2.4 out of 4.0). The average customer service vendor in banking raises $16 million, far less than the average vendor in financing and loans ($49 million), fraud and cybersecurity ($48 million), and compliance ($44 million). Companies raise more money when their products have traction, and conversational interface vendors make up only 5.5% of the total funding for AI vendors.

If the idea of asking Alexa or Google to turn on and off your lights appeals to you, and you're not doing it already, the holidays could be a great time to finally get to it. "Competition is growing and prices are dropping which makes now the best time to make your home," smart, says YouTuber Steve Siems, who has a channel called "Steve Does," devoted to smart home reviews and installation. He suggests starting small, with a connected speaker, then adding smart switches and bulbs before venturing further with doorbells and other products. "See what you like and what you need more of," he says. "No need to buy 10 smart plugs then realize you only need three for what you want to do. By the time you use the other seven plugs, something newer, better, and cheaper will be out."

Google Assistant and Alexa may pretend to have "personality," but they really don't. Telling a joke when asked does not make any of them a great raconteur. This is fine for two reasons. First, it's not what they're for, and second, giving an artificial creation personality is very, very difficult. Gatebox, the company behind the eponymous product, is succeeding where others have either failed, or aren't even trying.