The web link selection problem is to select a small subset of web links from a large web link pool, and to place the selected links on a web page that can only accommodate a limited number of links, e.g., advertisements, recommendations, or news feeds. Despite the long concerned click-through rate which reflects the attractiveness of the link itself, the revenue can only be obtained from user actions after clicks, e.g., purchasing after being directed to the product pages by recommendation links. Thus, the web links have an intrinsic \emph{multi-level feedback structure}. With this observation, we consider the context-free web link selection problem, where the objective is to maximize revenue while ensuring that the attractiveness is no less than a preset threshold. The key challenge of the problem is that each link's multi-level feedbacks are stochastic, and unobservable unless the link is selected. We model this problem with a constrained stochastic multi-armed bandit formulation, and design an efficient link selection algorithm, called Constrained Upper Confidence Bound algorithm (\textbf{Con-UCB}), and prove $O(\sqrt{T\ln T})$ bounds on both the regret and the violation of the attractiveness constraint. We conduct extensive experiments on three real-world datasets, and show that \textbf{Con-UCB} outperforms state-of-the-art context-free bandit algorithms concerning the multi-level feedback structure.

Users of Netflix, Spotify, or social media companies like FB and LinkedIn are already a customer of Hyper Personalization, where these companies are algorithmically providing extreme personalization to the users. Technology like Artificial Intelligence, Machine Learning, and Deep Learning is employed by these sites to cypher through consumers' likes, dislikes, interactions, and other related items to provide an experience which is unique to an individual. Idea behind Hyper Personalization is very simple... no two persons are alike, and providing them experience based on their unique behavioral persona would make them more satisfied and loyal. We as consumer must understand that we are being observed and analyzed (digitally in this case), for the system to provide this individualized experience. In other words, we are trading privacy for Hyper Personalization.

Cambridge Analytica, the data firm at the centre of this year's Facebook privacy row, is closing and starting insolvency proceedings. The company has been plagued by scandal since the Observer reported that the personal data of about 50 million Americans and at least a million Britons had been harvested from Facebook and improperly shared with Cambridge Analytica. Cambridge Analytica denies any wrongdoing, but says that the negative media coverage has left it with no clients and mounting legal fees. "Despite Cambridge Analytica's unwavering confidence that its employees have acted ethically and lawfully, the siege of media coverage has driven away virtually all of the Company's customers and suppliers," said the company in a statement, which also revealed that SCL Elections Ltd, the UK entity affiliated with Cambridge Analytica, would also close and start insolvency proceedings. "As a result, it has been determined that it is no longer viable to continue operating the business, which left Cambridge Analytica with no realistic alternative to placing the company into administration."

When Mark Zuckerberg told Congress Facebook would use artificial intelligence to detect fake news posted on the social media site, he wasn't particularly specific about what that meant. Given my own work using image and video analytics, I suggest the company should be careful. Despite some basic potential flaws, AI can be a useful tool for spotting online propaganda – but it can also be startlingly good at creating misleading material. Researchers already know that online fake news spreads much more quickly and more widely than real news. My research has similarly found that online posts with fake medical information get more views, comments and likes than those with accurate medical content.

In the last couple of years, we have heard a lot about chatbots. They are being adopted rapidly by businesses in many industries including banking, entertainment, finance, healthcare and media, to name a few. A study by Business Insider shows 80% of the businesses want chatbots by 2020. This blog aims to answer some common questions about chatbots. A chatbot is a computer program that mimics conversation with people, using artificial intelligence (AI), and are revolutionizing the way we interact with the internet.

The strategy will require Facebook to make progress on some of the biggest challenges in computing. During two congressional sessions last month, CEO Mark Zuckerberg referenced AI more than 30 times in explaining how the company would better police activity on its platform. The man tasked with delivering on those promises, CTO Mike Schroepfer, picked up that theme in a keynote and interview at Facebook's annual developer conference Wednesday. Schroepfer told thousands of developers and journalists that "AI is the best tool we have to keep our community safe at scale." After the congressional hearings, critics accused Zuckerberg of invoking AI to mislead people into thinking the company's challenges are simply technological.

This week's IRL heads to the theater, where the latest Marvel flick is setting box-office records. Find out how several of our editors felt about it (spoiler-free, although we can't guarantee anything about the comments section below), as well as our thoughts on a few new series from Netflix, Hulu and HBO. It's been quite a while since I watched the original Lost in Space, but something tells me its weird mashup of sixties-style drama and campy sci-fi doesn't hold up too well. When I was a kid devouring as many reruns of the show as possible, I identified with Robbie the Robot, because, well, we had the same name. In the current reboot of the show, currently streaming on Netflix, I connect more with the parents.

Journalists who write about Tesla's Autopilot system kill people. That, at any rate, seemed to be the contention of Tesla CEO Elon Musk in an extraordinary corporate earnings call on Wednesday. The quarterly conference call with investors--a routine affair for most public companies--took a left turn when Musk cut short the Q&A, deeming their questions about Tesla's finances "boring" and "bonehead" and taking questions instead from a 25-year-old YouTuber who had tweeted at him on Monday. As Tesla's stock plunged in after-hours trading, Musk detoured into a Trumpian rant--yes, another one--against the dishonest media. The visionary entrepreneur–turned–press critic blasted journalists for publishing stories about accidents involving Tesla's autonomous driving software, calling such coverage "outrageous" and "fundamentally misleading."

It's a problem many are familiar with. There's a huge amount of content available on TV; so much so that many struggles to find something to watch. Consumers are spoilt for choice and it's made more difficult by the fact that it's hard to seek out shows they might like among the many unsuitable ones. The average viewer spends almost an hour every day trying to find something to watch. But there's a way that Artificial Intelligence (AI) can help with content discovery, and it's a function that's already available on Samsung TVs.

When Mark Zuckerberg told Congress Facebook would use artificial intelligence to detect fake news posted on the social media site, he wasn't particularly specific about what that meant. Given my own work using image and video analytics, I suggest the company should be careful. Despite some basic potential flaws, AI can be a useful tool for spotting online propaganda – but it can also be startlingly good at creating misleading material. Researchers already know that online fake news spreads much more quickly and more widely than real news. My research has similarly found that online posts with fake medical information get more views, comments and likes than those with accurate medical content.