Facebook's chatbots are learning the art of the deal, bartering and deceiving their way to better terms in negotiations with humans and other bots. Artificial intelligences that can negotiate effectively would make useful virtual assistants, says Mike Lewis at Facebook's research lab. Bots could be left to arrange appointments for people, sorting out calendar clashes by themselves. Or they could negotiate with several agents at once to book a holiday or make a purchase on your behalf. Most existing bots – such as Apple's Siri or those built into chat apps like Facebook Messenger – may be able to get you a taxi or order a pizza but they can't engage in complex negotiations, says Lewis.

Something quite spectacular happened during the week: Students have achieved an astounding level of score improvement on a highly complicated machine learning problem - in just three afternoons. They achieved scores that improved more than 70% over the initial solution that were built by a team of experienced domain specialists and senior data scientists (figure 1). Considering that roughly half of the students had no prior exposure to machine learning, and that the other half were mostly beginners, these improvements are impressive. In fact, this is not the first time we observed this kind of results: every time we ran a data challenge using RAMP (rapid analytics and model prototyping) platform, major improvements have been made over the initial solution. So, how does this happen?

A business school in Paris will soon begin using artificial intelligence and facial analysis to determine whether students are paying attention in class. The software, called Nestor, will be used two online classes at the ESG business school beginning in September. LCA Learning, the company that created Nestor, presented the technology at an event at the United Nations in New York last week. Source: This French school is using facial recognition to find out when students aren't paying attention – The Verge This system will be used during videos to create quizzes based on when a student isn't paying attention. I don't understand the purpose since if they aren't paying attention a quiz isn't going to help them learn the material.

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Every new technology goes through an adoption cycle, either in a society or inside one particular organization. Whether the process goes smoothly or not, depends on company's readiness to accept radical change. The transformation may seem complicated and unclear at first. However, it becomes substantially easier to adapt to change pursuing a number of preconditioned rules. InData Labs experts have a lot of experience and many years of practice in guiding companies within different industries through the process of AI-transformation.

Lecturer Steffen Pfiffner of University of Weingarten in Germany is teaching ROS to 26 students at the same time at a very fast pace. They connect to a web page containing the lessons, a ROS development environment and several ROS based simulated robots. Using the browser, Pfiffner and his colleague Benjamin Stähle, are able to teach how to program with ROS quickly and to many students. This is what Robot Ignite Academy is made for. "With Ignite Academy our students can jump right into ROS without all the hardware and software setup problems.

Organizations around the world are producing accurate data-based predictions and benefitting from insightful analysis in a fraction of the time required by conventional tools and methods. This is the power of machine learning automation. In this webinar, learn how DataRobot automates predictive modeling, and how our platform can deliver these same types of insights and a substantial productivity boost to your machine learning endeavors. Built for speed and scalability, DataRobot radically reduces the time required to complete a data science project. From data to deployment, with DataRobot you can deliver highly-accurated predictions faster, react quickly to rapidly changing market conditions, and speed the transformation of your business.

Our world is generating more and more data, which people and businesses want to turn into something useful. This naturally attracts many data scientists – or sometimes called data analysts, data miners, and many other fancier names – who aim to help with this extraction of information from data. A lot of data scientists around me graduated in statistics, mathematics, physics or biology. During their studies they focused on individual modelling techniques or nice visualizations for the papers they wrote. Nobody had ever taken a proper computer science course that would help them tame the programming language completely and allow them to produce a nice and professional code that is easy to read, can be re-used, runs fast and with reasonable memory requirements, is easy to collaborate on and most importantly gives reliable results.

Apple's about to hold its Worldwide Developers Conference – the event where it shows off the future of the company, and of all its products. The event is one of the biggest company in the world's biggest events. While there won't be a new iPhone revealed – that gets saved for its own event in September – there will be new iPhone software, and plenty of glimpses at where the handset might be headed. Here's everything we're expecting when Apple takes the stage for its big keynote presentation on 5 June. But with Apple the most reliable expectation is that there'll be a surprise, so while a lot has leaked it's sure not to be everything.

For a more technical explanation, this and this article can be read. Here you can find a good explanation as well as a list of the mostly used Kernel functions.