But researchers in AI, and related fields such as learning analytics, are also thinking about how AI can provide more effective feedback to students and teachers. This is the use of technology – including AI – to provide people with information that helps them make better decisions and learn more effectively. So, for instance, rather than focusing on automating the grading of student essays, some researchers are focusing on how they can provide intelligent feedback to students that helps them better assess their own writing. Intelligence amplification helps counteract these concerns by keeping people in the loop.
The latest is artificial intelligence education tech (AI Ed), and only time will tell what impact it will ultimately have. But for something as important as education, now is the time to start talking about the benefits and challenges created by AI-powered personalized learning systems as they make their way into classrooms. Entefy covered this topic in previous articles: Old school no more: AI disrupts the classroom, which focused on teachers; and Artificial intelligence may transform education, but are parents ready?, which focused on parents. To help you form your own opinion, here are 14 unprecedented choices and challenges created by AI's use in education: AI has the potential to change the quality, delivery, and nature of education.
On 1st of August 2017, we will start a free online training program for Machine Learning, called Machine Learning Army Camp. We will integrate the knowledge from 20 books into a single big knowledge network, and you will get to see how it will grow over time. In ML Army Camp we will build one big network specifically for Machine Learning. Short info about me: Machine Learning is my primary interest in life, and in my book called "Machine Learning God" I explain why I chose Machine Learning, and then of course I continue with technical treatment of the subject matter.
In this guest post, Jacqueline M. Kory Westlund, a researcher in the Personal Robots Group at the MIT Media Lab describes her projects and explorations to understand children's relationships with social robots. What design features of the robots affect children's learning--like the expressivity of the robot's voice, the robot's social contingency, or whether it provides personalized feedback? When I tell people about the Media Lab's work with robots for children's education, a common question is: "Are you trying to replace teachers?" Despite all the research that seems to point to the conclusion "robots can be like people," there are also studies showing that children learn more from human tutors than from robot tutors.
Chances are, you've already encountered artificial intelligence today. Did your email spam filter keep junk out of your inbox? We constantly hear that we're on the verge of an AI revolution, but the technology is already everywhere. And Coursera co-founder Andrew Ng predicts that smart technology will help humans do even more.
Good teachers meet their students where they are, and they adapt their methods accordingly. Tutoring systems, language learning apps, and educational games are all designed to change our mental abilities. It's when we consider what it takes to change mental abilities or behaviors that things start to get interesting. It isn't just that people adapt to technology, and that technology adapts to people.
But it raises huge questions for companies and workers who face the challenges – and opportunities – of digital disruption. Just look at WoWooHR, a Chinese company that started offering social insurance management through an online HR platform – and serves enterprises with a highly efficient and high quality "Internet-Plus" HR Service. Using mobile online technology, big data management and cloud platforms, WoWooHR aims to provide any employee, in any company in even the remotest corners of China, with professional, efficient and high quality HR services. Of course, the opportunities provided by digital sustainability go much further.
About this course: This 1-week accelerated on-demand course introduces participants to the Big Data and Machine Learning capabilities of Google Cloud Platform (GCP). It provides a quick overview of the Google Cloud Platform and a deeper dive of the data processing capabilities. At the end of this course, participants will be able to: • Identify the purpose and value of the key Big Data and Machine Learning products in the Google Cloud Platform • Use CloudSQL and Cloud Dataproc to migrate existing MySQL and Hadoop/Pig/Spark/Hive workloads to Google Cloud Platform • Employ BigQuery and Cloud Datalab to carry out interactive data analysis • Choose between Cloud SQL, BigTable and Datastore • Train and use a neural network using TensorFlow • Choose between different data processing products on the Google Cloud Platform Before enrolling in this course, participants should have roughly one (1) year of experience with one or more of the following: • A common query language such as SQL • Extract, transform, load activities • Data modeling • Machine learning and/or statistics • Programming in Python Google Account Notes: • You'll need a Google/Gmail account and a credit card or bank account to sign up for the Google Cloud Platform free trial (Google is currently blocked in China).
There's no doubt about it - Data Science is big news right now. We decided to share this resource with you, and so here are Udemy's top selling courses. These are the courses that are in Udemy's top selling list over the past 90 days, but also mostly make the list in every update. These are the new courses (only a couple of months old) that have made it into Udemy's top selling list of the past 90 days.
Massive Open Online Courses (MOOCs) are a good starting point, with a lot to offer. The article entitled "Top Machine Learning MOOCs and Online Lectures: A Comprehensive Survey" lists a number of good resources. For example, the MXNet website lists a number of data set sources for CNNs and RNNs. Intel's Python-based Neon framework from Nervana, now an Intel company, supports platforms like Apache Spark, TensorFlow, Caffe, and Theano.