We have just taken another momentous step in the journey to unveil the hidden wonders of our own planet! Since the launch of the Shell Ocean Discovery XPRIZE at the American Geophysical Union Fall Meeting in San Francisco in December 2015, individuals from around the world have been racing to form Teams and develop a range of groundbreaking technologies to access the deep-sea. Registration closed at the end of September 2016 with 32 bold Teams stepping forward to take on the challenge of mapping and imaging our ocean as never before. Today, we announce the 21 semi-finalists Teams advancing in the Ocean Discovery XPRIZE. These innovative semi-finalist Teams, consisting of almost 350 individuals from 25 countries, represent a broad, impressive diversity of backgrounds and expertise, including middle and high school students, university students, maker-movement enthusiasts, and water and ocean industry professionals.

Alison Gopnik is an internationally recognized expert in children's learning and development. Her new book The Gardener and the Carpenter is a response to the fact that "parenting" has become a verb, a powerful middle class trend, a lucrative self-help industry, and sometimes a kind of bloodsport. Meanwhile developmental science paints a very different picture of how children grow and learn, and what it means to be a good parent. As Gopnik puts it, "It's easy to say'just chill,' but the advice is, basically, just chill!" On this week's episode of Think Again–a Big Think Podcast, Alison Gopnik and host Jason Gots discuss play, artificial intelligence, and the trouble with "parenting" as a verb.

Lawyers spend years in school learning how to sift through millions of cases looking for the exact language that will help their clients win. What if a computer could do it for them? It's not the kind of question many lawyers would dignify with an answer. But Jimoh Ovbiagele isn't a lawyer, and as a budding computer scientist studying at the University of Toronto a couple of years ago the idea made perfect sense. He and his colleagues combined it with IBM's Watson artificial intelligence platform to co-found ROSS Intelligence, a multimillion-dollar legal research firm that has lined up global law giant Dentons as a backer and customer, along with other prominent customers including Baker & Hostetler and Latham & Watkins.

Can artificial intelligence replace teachers in near future? Artificial intelligence applications are changing our lives. While a lot of examples show how AI is already being used, there is still a lot of room for innovation and new applications. Will artificial intelligence and machine learning replace teachers in near future? Automation has affected nearly every industry.

Could machines, in time, write software themselves, and take programmers' jobs? At the very least, they might well provide the same boon automation has for many other fields: Remove some of the drudgery, and leave developers to do more creative work. A recently released research paper co-authored by Microsoft Research and the University of Cambridge discusses how a machine learning system called DeepCoder could learn to write small programs by using routines from other programs as raw material. It uses small snippets of code, only a few lines each, written in a custom, DSL (domain-specific language) to make it easier to analyze the input and output of each snippet. The better a match each snippet is to solving a particular problem, the more likely it'll end up as part of the solution.

Some enterprises use Clarabridge to mine customer data, manage customer experience, and see sentiment analysis. While Clarabridge provides an intelligence platform, Signals is a more powerful solution platform in unifying customer voice. While sentiment analysis is a key function of Signals, its deep machine learning capability allows you to do something more organic. It enables you to listen to your customer data from the ground up and identify trends and patterns as they emerge. Signals results are displayed directly in front of the user.

After completing Part 1 of Jeremy Howard's awesome deep learning course, I took a look at my AWS bill and found I was spending nearly $200/month running GPUs. It's not necessary to spend that much to complete his course, but I started working on a few extracurricular datasets in parallel and I was eager to get results. After talking with fellow students and reading a number of blog posts, I decided to try building my own box. Technology and hardware change so rapidly that I'm afraid much of post will become outdated soon, but I hope my general approach will still be useful for at least a little while. I started by reading a bunch of blogs to get the current consensus on which parts to buy.

The use of artificial intelligence and the "next-generation" of virtual learning environments (VLEs) are two areas of technology that have been forecast to have a major impact on higher education in the future, according to the expert panel of a major new report. The NMC Horizon Report: 2017 Higher Education Edition is produced by the New Media Consortium – a community of hundreds of universities, colleges, museums and research organisations driving innovation across their campuses – and is the flagship publication of the NMC Horizon Project, which analyses emerging technology uptake in education. Artificial intelligence, the report notes, has the "potential to enhance online learning, adaptive learning software, and research processes in ways that more intuitively respond to and engage with students". Samantha Adams Becker, senior director of publications and communications at NMC and the report's editor, said that the higher education world was already seeing the initial benefits of AI, which was "very much driving" the adaptive learning field.

For many internet businesses, presenting a given list of items in an order that maximizes a certain metric of interest (e.g., click-through-rate, average engagement time etc.) is crucial. We approach the aforementioned task from a learning-to-rank perspective which reveals a new problem setup. In traditional learning-to-rank literature, it is implicitly assumed that during the training data generation one has access to the \emph{best or desired} order for the given list of items. In this work, we consider a problem setup where we do not observe the desired ranking. We present two novel solutions: the first solution is an extension of already existing listwise learning-to-rank technique--Listwise maximum likelihood estimation (ListMLE)--while the second one is a generic machine learning based framework that tackles the problem in its entire generality. We discuss several challenges associated with this generic framework, and propose a simple \emph{item-payoff} and \emph{positional-gain} model that addresses these challenges. We provide training algorithms, inference procedures, and demonstrate the effectiveness of the two approaches over traditional ListMLE on synthetic as well as on real-life setting of ranking news articles for increased dwell time.

On site two days a week, she participates in project-based learning that ranges in topics from physics to fashion while her homeschool curriculum is culturally relevant and uses the Black365 calendar to introduce historical figures and events,