We study the problem of online learning to re-rank, where users provide feedback to improve the quality of displayed lists. Learning to rank has been traditionally studied in two settings. In the offline setting, rankers are typically learned from relevance labels of judges. These approaches have become the industry standard. However, they lack exploration, and thus are limited by the information content of offline data. In the online setting, an algorithm can propose a list and learn from the feedback on it in a sequential fashion. Bandit algorithms developed for this setting actively experiment, and in this way overcome the biases of offline data. But they also tend to ignore offline data, which results in a high initial cost of exploration. We propose BubbleRank, a bandit algorithm for re-ranking that combines the strengths of both settings. The algorithm starts with an initial base list and improves it gradually by swapping higher-ranked less attractive items for lower-ranked more attractive items. We prove an upper bound on the n-step regret of BubbleRank that degrades gracefully with the quality of the initial base list. Our theoretical findings are supported by extensive numerical experiments on a large real-world click dataset.

Sorcero, a Washington DC-based AI learning solutions startup, has announced that its co-founder, Dr. Ken Haase has been invited to give the keynote address at the Automation & AI for Good forum on June 12 in San Francisco. Sorcero was one of ten early-stage AI companies selected to participate in the forum. The forum, sponsored by Village Capital and Autodesk Foundation, is showcasing the leading startups using AI, automation, or robotics to benefit society and create new jobs in emerging industries. "Usually, when we hear about automation and artificial intelligence (AI) in the workforce, it's in negative terms: robots coming for our jobs, millions of displaced workers, and so on," said Ken Haase, Ph.D., Sorcero co-founder and Chief AI Officer. "At Sorcero, we approach AI very differently," said Dr. Haase."Our goal is not to replace people but to empower them for new and emerging opportunities."

Our oceans are in trouble. Climate change, plastic waste and overfishing are all causing tremendous damage to underwater life around the world. Inspired by the BBC's Blue Planet II series, developer E-Line Media is making a video game that focuses on the scientists who are trying to understand our impact. It's called Beyond Blue and will put you in charge of a research team with stunning technology designed to unlock new insights about the sea. Your task is simply to gather information and learn what you can about these fast-changing, human-made threats to the sea.

DISCOVERING and harnessing fire unlocked more nutrition from food, feeding the bigger brains and bodies that are the hallmarks of modern humans. Google's chief executive, Sundar Pichai, thinks his company's development of artificial intelligence trumps that. "AI is one of the most important things that humanity is working on," he told an event in California earlier this year. "It's more profound than, I don't know, electricity or fire." Hyperbolic analogies aside, Google's AI techniques are becoming more powerful and more important to its business.

As someone whose work crosses so many disciplines, I spend a fair bit of my days skimming new developments across not only computer science, but the humanities, social sciences, arts and many other fields, looking for connections and unexpected new approaches that might benefit my own work. The intensely siloed nature of academia is well known, but equally striking is just how rapidly citation standards are falling in a Google Scholar world filled with explosive growth in available knowledge, in which scholars seem genuinely unaware of developments across the rest of their own field, not to mention the rest of academia. Could machine learning approaches dramatically reform the "related work" and citation review component of peer review and academic publishing? Perhaps the most striking element of modern scholarship is that in an era when much of our modern scholarship is available through web and academic database searches, it takes only a few mouse clicks to compile a cross-section of the recent developments in a given space. Yet, peruse the "related work" or "background" section of a typical academic paper and it is amazing just how discipline-specific and artificially circumscribed the set of references are.

There's no shortage of odes to the ability of athletes and high school sports teams to be the center and glue of a small town. But what if someone paid similar attention to the brainpower -- in this case the high school robotics competition and its ability to capture the hearts of that small town? In a profile in the Star Tribune in April, the Star Tribune's John Reinan said the kids are "Hoosiers with robots," a reference to the movie of an Indiana small town high school team that won a state championship. Greenbush has two of those, winning one in 2016, finishing second last year, and winning the second last month. The film will premiere at the Roso Theater in Roseau, Minn., in August.

However, be warned, it has a lot of maths If you are able to get through it, you will get a very good foundational knowledge on ML. If theory is not your cup of tea, another way to approach ML is to just implement it and learn as you go. You don't need to get a PhD in ML to start implementing it. This is the philosophy behind Jeremy Howard's and Rachel Thomas's http://www.fast.ai. They take you through the implementation steps and introduce you to the theory on a need to know basis, in essence you are doing a top down approach. I am still a few lessons away from finishing the fast.ai

The trash truck rumbles down the street, and its cameras pour video into the city's data lake. An AI-powered application mines that image data looking for graffiti--and advises whether to dispatch a fully equipped paint crew or a squad with just soap and brushes. Meanwhile, cameras on other city vehicles could feed the same data lake so another application detects piles of trash that should be collected. That information is used by an application to send the right clean-up squad. Citizens, too, can get into the act, by sending cell phone pictures of graffiti or litter to the city for AI-driven processing.

"I don't think Homo sapiens-type people will exist in 10 or 20 years' time," Getnet Assefa, 31, speculates as he gazes into the reconstructed eye sockets of Lucy, one of the oldest and most famous hominid skeletons known, at the National Museum of Ethiopia. "Slowly the biological species will disappear and then we will become a fully synthetic species," Assefa says. "Perception, memory, emotion, intelligence, dreams--everything that we value now--will not be there," he adds. Assefa is a computer scientist, a futurist, and a utopian--but a pragmatic one at that. He is founder and chief executive of iCog, the first artificial intelligence (AI) lab in Ethiopia, and a stone's throw from the home of Lucy. Their desks are cluttered with electronic components and dismembered robot body parts, from a soccer-playing bot called Abebe to a miniature robo-Einstein.

It is almost certain that the sub-field of machine learning/artificial intelligence has progressively gained more fame in recent years. As Big Data is in fashion in the tech industry right now, machine learning is staggeringly effective to make predictions or computed recommendations with lot of information. Probably the most well-known cases of machine learning are Netflix or Amazon's algorithms. Machine learning is a type of artificial intelligence (AI) that enables programming applications to be exact in anticipating results without being explicitly modified. The fundamental preface of machine learning is to build algorithms that can get input information and utilize statistical analysis to predict an output value within a worthy range.