Beginning in fall 2017, some students and educators at the University of Michigan may be getting help on writing assignments from computers. Campus Technology reports that a team of educators developed a writing-to-learn tool called M-Write, which uses automated text analysis (ATA) to identify the strengths of a writing submission. Developed by two professors, the tool was initially meant to help students grow their conceptual learning skills in large courses and to help streamline the grading process, reports a UMich article. ATA works by "using a variety of text analysis techniques, such as vocabulary matching or topic matching, which the algorithm detects." Using M-Write also lets educators identify the students who are going to need help.

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. If we want bots to help us with more complex tasks they need to become dealmakers, especially if the task involves cooperation or compromise, like negotiating the purchase or sale of a property, for example.

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?

Everyone who gets going in Machine Learning (and Deep Learning) gets overwhelmed by the plethora of MOOCs available. Here, I try to give a comprehensive survey of such courses available freely on the internet. You can take this post as an complementary to this and this previous posts. I will try to highlight some important pointers such as the difficulty of the courses, the correct order in which these should to be completed, the right audience for these courses. You will get a feel of how these courses give you a stack of skills in your arsenal and how you can use them to develop practical machine learning systems.

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.

The Robot Academy is a new learning resource from Professor Peter Corke and the Queensland University of Technology (QUT), the team behind the award-winning Introduction to Robotics and Robotic Vision courses. There are over 200 lessons available, all for free. The lessons were created in 2015 for the Introduction to Robotics and Robotic Vision courses. We describe our approach to creating the original courses in the article, An Innovative Educational Change: Massive Open Online Courses in Robotics and Robotic Vision. The courses were designed for university undergraduate students but many lessons are suitable for anybody, as you can easily see the difficulty rating for each lesson.

We propose techniques for efficiently determining optimal solutions to large logistics planning domain problems. We map a problem instance to a directed graph and show that no more than one vehicle per weakly connected component of the graph is needed for an optimal solution. We propose techniques for efficiently finding the vehicles which must be employed for an optimal solution. Also we develop a strong admissible heuristic based on the analysis of a directed graph, the cycles of which represent situations in the problem state in which a vehicle must visit a location more than once. To the best of our knowledge, ours is the first method that determines optimal solutions for large logistics instances (including the largest instances in the IPC 1998 and IPC 2000 problem sets).

Don't know where or how to start learning? But learning more about artificial intelligence, and the myriad overlapping and related fields and application domains does not require a PhD. Getting started can be intimidating, but don't be discouraged; check out this motivating and inspirational post, the author of which went from little understanding of machine learning to actively and effectively utilizing techniques in their job within a year. With more and more institutes of higher learning today making the decision to allow course materials to be openly accessible to non-students via the magic of the web, all of a sudden a pseudo-university course experience can be had by almost anyone, anywhere. Have a look at the following free course materials, all of which are appropriate for an introductory level of AI understanding, some of which also cover niche application concepts and material.

Cluster analysis is a staple of unsupervised machine learning and data science. It is very useful for data mining and big data because it automatically finds patterns in the data, without the need for labels, unlike supervised machine learning. In a real-world environment, you can imagine that a robot or an artificial intelligence won't always have access to the optimal answer, or maybe there isn't an optimal correct answer. You'd want that robot to be able to explore the world on its own, and learn things just by looking for patterns. Do you ever wonder how we get the data that we use in our supervised machine learning algorithms?

But you don't know where to start, or perhaps you have read some theory, but don't know how to implement what you have learned. This tutorial will help you break the ice, and walk you through the complete process from importing and analysing a dataset to implementing and training a few different well known classification algorithms and assessing their performance. I'll be using a minimal amount of discrete mathematics, and aim to express details using intuition, and concrete examples instead of dense mathematical formulas. We will be classifying flower-species based on their sepal and petal characteristics using the Iris flower dataset . Kaggle, if you haven't heard of it, has a ton of cool open datasets, and is a place where data scientists share their work which can be a valuable resource when learning. The Iris flower dataset is rather small (consisting of only 150 evenly distributed samples), and is well behaved which makes it ideal for this project.