Fox News Flash top entertainment and celebrity headlines are here. Check out what clicked this week in entertainment. Netflix's viral true-crime documentary "The Tinder Swindler" had millions of viewers crying foul after it began streaming on the platform Feb. 2. The mind-bending story from director Felicity Morris, who also produced the Emmy-winning series, "Don't F**k with Cats: Hunting an Internet Killer," chronicles the depths a Tinder user by the name of Shimon Hayut, now 31, would go to to charm women around the world into loaning him money – to the tune of an estimated $10 million. Hayut posed as Simon Leviev and claimed to be the son of a diamond mogul on the popular dating app. It was only when a group of women banded together to expose Leviev that his scheme was foiled, and he was ultimately convicted of fraud, theft and forgery.

Remember in 2017, Elon Musk said that artificial intelligence would replace humanity in the next five years? While working on artificial intelligence for Tesla cars, he concluded that society had approached the moment when artificial intelligence could become significantly smarter than people. "People should not underestimate the power of the computer,'' Musk said. "This is pride and an obvious mistake." He must know what he's talking about, being one of the early investors of DeepMind, a Google subsidiary that developed an AI that could beat humans at Go and chess. AI is really good at many "human" tasks -- diagnosing diseases, translating languages, and serving customers.

The following blog post is part of Machine Learning in Production (17634) coursework at Carnegie Mellon University. In this discussion, we will consider the scenario of Movie Recommendation with respect to the following two frameworks. Now that we have mentioned the resources and formed a basic blog premise let's talk about some standard definitions. Movie recommendations are challenging because we can approach it in multiple ways, especially since we have numerous data points that can be modeled for the recommendation. Let's try to analyze some of the problems.

How to Do It is Slate's sex advice column. Send it to Stoya and Rich here. My partner and I (man and woman in our mid-30s) want to open profiles on an adult dating site (Feeld, probably?) to connect with couples and singles. We've had ethically non-monogamous encounters at adult resorts, but haven't tried a dating site to meet people closer to home in hopes of landing on more "social swinging" relationships. There are a wealth of swinging/lifestyle podcasts with episodes about dating profiles, and omitting your face from "public" photos on the site (that is, visible to all members) is uniform advice. Of course, most often this is to avoid being identified on the site.

Artificial intelligence (AI) is intelligence demonstrated by machines, as opposed to natural intelligence displayed by animals including humans. Leading AI textbooks define the field as the study of "intelligent agents": any system that perceives its environment and takes actions that maximize its chance of achieving its goals. Some popular accounts use the term "artificial intelligence" to describe machines that mimic "cognitive" functions that humans associate with the human mind, such as "learning" and "problem solving", however, this definition is rejected by major AI researchers. AI applications include advanced web search engines (e.g., Google), recommendation systems (used by YouTube, Amazon and Netflix), understanding human speech (such as Siri and Alexa), self-driving cars (e.g., Tesla), automated decision-making and competing at the highest level in strategic game systems (such as chess and Go). As machines become increasingly capable, tasks considered to require "intelligence" are often removed from the definition of AI, a phenomenon known as the AI effect.

Co-founder & CTO at Orient Software, a software outsourcing company that employs top software engineering talent from Vietnam. Artificial intelligence isn't a new technology, but its impact is only starting to be felt, as businesses and individuals begin to understand the possibilities that AI can offer. AI is set to transform business like never before, creating new opportunities for entrepreneurs, business leaders and workers in every industry. AI is quickly finding its way into our everyday lives. It may even soon be difficult to tell where it stops and humanity begins.

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We're all familiar with the ways in which voice assistants like Siri and Alexa have become a staple of household life: harried cooks everywhere can relate to the question, "Alexa, how many tablespoons are in a cup?" But recent developments point to voice assistants doing more than answering straightforward questions. Technologies like Google Assistant can actually offer life advice. And according to researchers from the Universitat Pompeu Fabra in Barcelona, it doesn't hurt to listen: they've done a study that indicates embracing life coaching from a voice assistant can improve well-being. What does that mean for AI? Human life coaches help people pinpoint their priorities and goals in life, from professional to personal, then work to identify what steps individuals can take to achieve those goals.

LivePerson believes that artificial intelligence (AI) and automation are the foundation for transforming the conversational experience and customer service. This requires altering the way agents operate and the way brands interact with consumers. LivePerson is a leading conversational AI company that creates digital experiences that are curiously human. Conversational AI allows humans and machines to interact using natural language, including speech or text. Already consumer preference has shifted from calling to messaging in personal life.

Traditionally, when recommender systems are formalized as multi-armed bandits, the policy of the recommender system influences the rewards accrued, but not the length of interaction. However, in real-world systems, dissatisfied users may depart (and never come back). In this work, we propose a novel multi-armed bandit setup that captures such policy-dependent horizons. Our setup consists of a finite set of user types, and multiple arms with Bernoulli payoffs. Each (user type, arm) tuple corresponds to an (unknown) reward probability. Each user's type is initially unknown and can only be inferred through their response to recommendations. Moreover, if a user is dissatisfied with their recommendation, they might depart the system. We first address the case where all users share the same type, demonstrating that a recent UCB-based algorithm is optimal. We then move forward to the more challenging case, where users are divided among two types. While naive approaches cannot handle this setting, we provide an efficient learning algorithm that achieves $\tilde{O}(\sqrt{T})$ regret, where $T$ is the number of users.