WASHINGTON, DC (March 8, 2017)--Interventional radiologists at the University of California at Los Angeles (UCLA) are using technology found in self-driving cars to power a machine learning application that helps guide patients' interventional radiology care, according to research presented today at the Society of Interventional Radiology's 2017 Annual Scientific Meeting. The researchers used cutting-edge artificial intelligence to create a "chatbot" interventional radiologist that can automatically communicate with referring clinicians and quickly provide evidence-based answers to frequently asked questions. This allows the referring physician to provide real-time information to the patient about the next phase of treatment, or basic information about an interventional radiology treatment. "We theorized that artificial intelligence could be used in a low-cost, automated way in interventional radiology as a way to improve patient care," said Edward W. Lee, M.D., Ph.D., assistant professor of radiology at UCLA's David Geffen School of Medicine and one of the authors of the study. "Because artificial intelligence has already begun transforming many industries, it has great potential to also transform health care."

Think you know everything about Tinder? The company is testing a secret version of its app called Tinder Select, a invitation-only service reserved for some of the app's more influential users: the really rich, really famous, really successful and, yes, the really hot. Rather than separate app, the service, which was first reported by TechCrunch, is a section of the existing Tinder app that's activated when a user is invited to join. Sort of like an exclusive club within Tinder, users who are part of Select are matched with each other, though they continue to see matches from "normal" Tinder users as well, according to someone who has seen Select. It's not clear what exactly qualifies someone to be part of Select, but it seems Tinder has curated the group pretty closely.

Artificial intelligence has rapidly become an integral part of wealth management, so that computers and algorithms analyze massive amounts of financial data to spit out stock recommendations and other types of advice. Yet it's taken a bit longer to figure out a way to apply this technology to consumers' day-to-day spending and saving habits. But over the past year there's been an explosion in the number of artificially intelligent personal finance chatbots, suggesting that we're entering a new phase of digital assistance. Some of these digital assistants have human names like Olivia and Abe, while others go for succinct monosyllables like Plum and Trim. Financial institutions have started rolling out their own apps, too, like Bank of America's Erica that's purported to help customers make smarter financial decisions.

We propose stochastic rank-$1$ bandits, a class of online learning problems where at each step a learning agent chooses a pair of row and column arms, and receives the product of their values as a reward. The main challenge of the problem is that the individual values of the row and column are unobserved. We assume that these values are stochastic and drawn independently. We propose a computationally-efficient algorithm for solving our problem, which we call Rank1Elim. We derive a $O((K + L) (1 / \Delta) \log n)$ upper bound on its $n$-step regret, where $K$ is the number of rows, $L$ is the number of columns, and $\Delta$ is the minimum of the row and column gaps; under the assumption that the mean row and column rewards are bounded away from zero. To the best of our knowledge, we present the first bandit algorithm that finds the maximum entry of a rank-$1$ matrix whose regret is linear in $K + L$, $1 / \Delta$, and $\log n$. We also derive a nearly matching lower bound. Finally, we evaluate Rank1Elim empirically on multiple problems. We observe that it leverages the structure of our problems and can learn near-optimal solutions even if our modeling assumptions are mildly violated.

This paper is a tutorial on Formal Concept Analysis (FCA) and its applications. FCA is an applied branch of Lattice Theory, a mathematical discipline which enables formalisation of concepts as basic units of human thinking and analysing data in the object-attribute form. Originated in early 80s, during the last three decades, it became a popular human-centred tool for knowledge representation and data analysis with numerous applications. Since the tutorial was specially prepared for RuSSIR 2014, the covered FCA topics include Information Retrieval with a focus on visualisation aspects, Machine Learning, Data Mining and Knowledge Discovery, Text Mining and several others.

Too busy to call your doctor's office? Well now you can simply shout at Alexa from the comfort of your couch for slightly similar results. Amazon's helpful, know-it-all bot is working with WebMD, aka a hypochondriac's BFF, to deliver users a variety of answers to their burning health-related questions, like "how to treat a sore throat." Starting Tuesday, in addition to tens of thousands of existing commands, Echo owners will be able to hear physician-reviewed answers to questions about certain medical conditions and symptoms, drugs, side effects, tests and treatments by simply asking Alexa about them. So now it'll be even easier to spend hours on end trying to determine whether that teeny, tiny, probably harmless splinter you got in your pinky finger could lead to your imminent demise.

New technologies are emerging and showing up in our everyday lives at a rapid rate. Voice-recognition, like Apple's Siri or Amazon's Alexa, and image recognition in our Facebook and Google accounts are just two mainstream applications that leverage artificial intelligence (AI)--one of the newest technologies gaining widespread momentum today. Artificial intelligence (AI) is defined by the Association for the Advancement of Artificial Intelligence as "the scientific understanding of the mechanism underlying thought and intelligent behavior and their embodiment in machines." It is one of the Top 10 Emerging Technologies of 2016 chosen by the World Economic Forum, based on the power to improve lives, transform industries, and safeguard the planet. Combining artificial intelligence with the advancements in natural language processing (NLP), social awareness algorithms, and big data enables its many applications.

Jesus may be your lord and savior, but could he also be your next boyfriend? Just swipe right on his amazing Tinder profile to find out. A dating app doesn't seem like the most obvious place to find the son of God, but this is 2017. And Jesus has recently been popping up on Tinder, where he displays his characteristic modesty by listing his profession as carpenter. So if you're into beards, draped robes and much older men -- he's listed as 21 but he reminds you in his profile that he's actually several thousand years old -- this could be a match made in, er, heaven.

Google Assistant is having a great month. Just last week Google started rolling out its voice-activated aide to all Android phones running Marshmallow and Nougat, and now it's showing off a new trick that will make it much more useful: integration with our messages. For whatever reason, Google Assistant struggled when it came to reading our messages before. Even though it had a second home in Allo and could send messages on command, Assistant balked when asked basic questions about our incoming texts, as it was unable to read and interact with them. As first spotted by 9to5Google, that's changed in the latest update. Now you can ask Assistant to hear the last message you received, dictate the one you just sent, and even read the most recent message from a specific person.

You didn't have to be following the coverage from this year's CES too closely to notice one name repeatedly cropping up, across what felt like every announcement at the show: Alexa. Alexa is an artificial intelligence, a virtual personal assistant developed by Amazon. And right now, it looks likely to be remembered as AI's first major foray into the mainstream. Developed by Amazon's Lab126 R&D division, and building on the eCommerce giant's acquisition of Cambridge AI startup Evi in 2012, Alexa made its debut – there's a temptation to say'her debut', given the gendered name and voice, but let's stay neutral for the moment – back in November 2014. It was first sold as part of the Amazon Echo, a smart speaker that enabled users to ask questions, select and listen to music and, of course, order shopping using just their voice. It wasn't until last year, however, that Alexa made a real splash.