How can we attribute the behaviors of machine learning models to their training data? While the classic influence function sheds light on the impact of individual samples, it often fails to capture the more complex and pronounced collective influence of a set of samples. To tackle this challenge, we study the Most Influential Subset Selection (MISS) problem, which aims to identify a subset of training samples with the greatest collective influence. We conduct a comprehensive analysis of the prevailing approaches in MISS, elucidating their strengths and weaknesses. Our findings reveal that influence-based greedy heuristics, a dominant class of algorithms in MISS, can provably fail even in linear regression.

You can almost smell the drying fish as you step into Ubisoft's latest Discovery Tour, Viking Age, a free add-on for Assassin's Creed: Valhalla (also available to play stand-alone, without the game). Previous Discovery Tour titles have allowed players to take a combat-free jaunt through Ptolemaic Egypt, Greece during the Peloponnesian War, and now early Viking Britain--all "curated by historians and experts," according to Ubisoft. In partnership with UKIE, the British gaming trade association, the game developer wants to introduce Discovery Tour to 52 schools across the UK. But this isn't the first time someone has deployed a video game for education. As early as 1971, when Paul Dillenberger showed The Oregon Trail to his students, the effect gaming could have in a learning environment was apparent.

If LG has its way, the company's robots will soon be serving you breakfast, carrying your luggage, and cleaning your floors. Well, assuming they can overcome some pretty basic problems like not working, that is. The promise of a connected-robot future was made repeatedly Monday morning at CES in Las Vegas, with LG's vice president of US marketing, David VanderWaal, taking the stage to show off a line of AI-powered robots that are intended to both integrate with a smart home and work in commercial settings. Unfortunately for all The Jetsons fanboys out there, the biggest impression was made by what was left unsaid. VanderWaal first introduced CLOi, a small robot designed for the home, with an attempt at humanization.

A basic promise of AI research is that what we observe as human intelligence is in fact a computation either directly or as an emergent effect. An attempt at classifying and distinguishing types of AI researchers was to call them all either scruffy (those that wrote code and implemented systems) or neat (those that base AI on some formalism like first order predicate calculus). Out of necessity, researchers tend to focus on a particular aspect of intelligence to simulate. When this is done, the effect is to restrict the class of computations that are being considered. The goal is build pieces of intelligence.

Research on mixed-initiative interaction and assistance is still in its infancy but is poised to blossom into a wellspring of innovation that promise to change the way we work with computing systems--and the way that computing systems work with us. I share reflections about the opportunities ahead for developing computational systems with the ability to engage people in a deeply collaborative manner, founded on their ability to support fluid mixed-initiative problem solving. Such collaborative intelligence sits at the veritable heart of human civilization. In the course of daily life, we assume and rely on a rich interleaving of efforts to achieve goals while immersed in shared context. We continue to engage one another in efficient, tightly woven collaborations, reasoning with remarkable efficiency about the beliefs, preferences, intentions, and skills of potential collaborators. The inferences underlying successful collaborations typically stream in such an effortless and subconscious manner that we often fail to recognize the elegance and sophistication of these capabilities. The magic of human collaborative competency comes to the foreground with attempts to extend these skills to computational systems. Developing a better understanding of the core aspects of intelligence that enable people to collaborate with fluidity promises to enable new kinds of human-computer collaboration. The nascent area of research on mixed-initiative interaction centers on developing methods that enable computing systems to support an efficient, natural interleaving of contributions by people and computers, aimed at converging on solutions to problems. In mixed-initiative interaction, people and computers take initiatives to contribute to solving a problem, achieving a goal, or coming to a joint understanding. Conversational dialogue is an oft-cited example of mixed-initiative interaction, referring to the ability of each participant in a dialogue to take initiative to guide or add to a discussion. Endowing an automated dialogue system with the ability both to take initiative ("What city do you wish a flight to?") and to allow people to take conversational initiative ("Wait, I'd like to add a side trip.") However, mixed-initiative interaction extends beyond spoken conversations to include a broad spectrum of collaborative problem solving marked by an interleaving of contributions by different participants. Mastering mixed-initiative interaction poses a constellation of fascinating challenges and opportunities for AI researchers. Figure 1 highlights the core challenge of seeking mutual understanding or grounding of joint activity. Joint activity describes the behavior displayed by people working together to solve a mutual goal.

Scientists Look at Promise, Peril of Technology. "Scientists meeting in Los Angeles say technology offers the hope of a better world, but presents hazards if mishandled.... [T]he University of Southern California and the journal'Science' convened a panel of scientific innovators to look at the promise and the perils of technology.--There'You've got to ask, do we now have the scientific literacy in the public to be able to have informed dialogues about what these issues are really going to mean to civilization, to mankind itself,' he said. 'If we don't have the right kind of scientific literacy, all scientific debate becomes ideological.' The panelists say promoting scientific literacy is a challenge but a necessary goal, as new technologies change our society.... Raymond Kurzweil is a researcher in the field of artificial intelligence....

Simply put, machine learning and AI, in general, will become commonplace in our lives because we need them to be. Alongside development of the capability to process massive amounts of data in innovative ways, there exists another technical revolution whose time has also most definitely come -- the internet of things (IoT). If AI offers the promise of processing immense quantities of data in ways that we can't, then IoT provides the very tangible mechanism for generating that raw data in ways we might not expect. Perhaps more telling, there is already an emerging trend of AI development "following the data" in order to accelerate the capability to deliver human-machine interactions and insight based on the availability of more of those very same interactions.

Apple showed off those features as the centrepiece of the introduction of the new watch, and charges people $70 extra for the feature as well as a charge that must be paid to phone networks. "It became apparent after my first full day using the Apple Watch Series 3 with LTE that something wasn't right," wrote Lauren Goode on the Verge. By 11:42 that morning, after 60 minutes of working out with LTE, multiple attempts to use Siri, and two seven-minute phone calls, the Watch's battery had drained to 27 percent." "We have discovered that when Apple Watch Series 3 joins unauthenticated Wi-Fi networks without connectivity, it may at times prevent the watch from using cellular," the company said in a statement provided to The Verge.