Multi-fingered dexterous manipulators are crucial for robots to function in human-centric environments, due to their versatility and potential to enable a large variety of contact-rich tasks, such as in-hand manipulation, complex grasping, and tool use. However, this versatility comes at the price of high dimensional observation and action spaces, complex and discontinuous contact patterns, and under-actuation during nonprehensile manipulation. This makes dexterous manipulation with multi-fingered hands a challenging problem. Dexterous manipulation behaviors with multi-fingered hands have previously been obtained using model-based trajectory optimization methods [31], [24]. However, these methods typically rely on accurate dynamics models and state estimates, which are often difficult to obtain for contact rich manipulation tasks, especially in the real world. Reinforcement learning provides a model agnostic approach that circumvents these issues. Indeed, model-free methods have been used for acquiring manipulation skills [52], [13], but so far have been limited to simpler behaviors with 2-3 finger hands or wholearm manipulators, which do not capture the challenges of highdimensional multi-fingered hands.

Volkswagen has shown off the sporty side of its electric technology by setting an all-time record in the annual Pikes Peak International Hill Climb in Colorado. Former Le Mans winner Romain Dumas took the I.D. R Pikes Peak prototype up in a time of seven minutes 57.148 seconds on the 19.9 km mountain road on Sunday. That was 16 seconds quicker than the 2013 record set by fellow-Frenchman Sebastien Loeb in a 3.2 litre V6 engined Peugeot 208. The radical car was fuelled by glycerol, a sugar alcohol often used as a sweetener in food. Former Le Mans winner Romain Dumas took the I.D. R Pikes Peak prototype up in a time of seven minutes 57.148 seconds on the 19.9 km mountain road on Sunday.

Carnegie Mellon University will become the first U.S. college to offer an undergraduate degree in artificial intelligence (AI) this fall, following careful consideration about where the fledgling field is going and how the institution can use this opportunity to promote social responsibility around AI. The Pittsburgh-based university already offers nearly two dozen courses in AI and related fields, said Reid Simmons, a Carnegie Mellon research professor who is currently on leave for the year while he works at the National Science Foundation. Simmons, who will be teaching classes in the AI degree program this fall, cited the university's existing educational and research focus on AI as the reason Carnegie Mellon has decided to offer a major in the burgeoning field. "Students have come here interested in learning more about artificial intelligence, and there really wasn't any structured way for them to do it," Simmons told EdScoop. "They could take courses here and there, they could take certain concentrations, they could do an additional major in robotics or a minor in machine learning, but the full-fledged curriculum wasn't there."

This is a collection of free resources beyond the regularly shared books, MOOCs, and courses, mostly from over the past year. They start from zero and progress accordingly, and are suitable for individuals looking to pick up some of the basic ideas, before hopefully branching out further (see the final 2 resources listed below for more on that). These resources are not presented in any particular order, so feel free to pursue those which look most enticing to you. All credit goes the the individual authors of the respective materials, without whose hard work we would not have the benefit of learning from such great content. For many good reasons, much of the highest quality machine learning educational resources tend to have a very strong focus on theory, especially at the beginning.

Machine learning and artificial intelligence are technologies that have evolved rapidly in the last decade. Most people today are familiar with intelligent voice assistants, streaming video platforms that recommend personalized content, and vehicle navigation systems that suggest best routes in real time to avoid traffic, all examples of artificial intelligence and machine learning in the consumer world. While consumer-facing machine learning often has a narrow focus, enterprise machine learning solutions must cater to many different types of businesses, all of which measure success differently. A consumer can expect Netflix to learn her movie preferences by tracking what she and people similar to her click on, a solution that can be built to be fairly generic. However, enterprise machine learning solutions rarely work as seamlessly right out of the box.

While teachers may always be the best line of defense for students falling behind, busy schedules don't always permit the special attention and feedback that students need. That's where artificial intelligence–powered teaching assistants might come in handy. "These intelligent tools can adapt pacing based on the student's ability … and provide targeted, corrective feedback in case the student makes mistakes, so that the student can learn from them," states an eSchool News report released earlier this year. "These tools also gather actionable insights and information about a student's progress and report the data back to the teacher." Understandably, there is still some hesitation at the idea of using this technology, as education professionals fear the day robots will replace teachers.

When we encounter artificial intelligence in the media, it's often discussed at extremes. At one end, there are films, books, games and even news commentary that paint a picture of a world-ending intelligence. At the other end, people picture algorithms so powerful they can solve every major problem facing mankind. In reality, the capabilities for AI lie somewhere in between. For example, some of Elsevier's products use machine-learning driven image identification to better diagnose life-threatening illnesses – but these are tools are designed to aid the deductive work of human experts, not replace them.

My name is Zurab Murvanidze, I am 1st year computer science student at UCL. I love learning about technology and have deep interest in machine learning, data science, quantum computing and artificial intelligence. I like developing applications and games in my spare time and in this article would love to share my experience in ML.NET. This article will cover basics of machine learning, will introduce you to ML.NET and teach you how to create and train machine learning models. It will also demonstrate how can we implement machine learning in ASP.NET Core Web Application.

I first met John Havens at an Aspen Institute Roundtable to discuss the future of artificial intelligence. I had always pictured IEEE as a place where engineers hammered out practical technical standards and published rigorous academic journals so I was surprised -- and excited -- to find him advocating the importance of ethics in autonomous and intelligent systems in such a nuanced and inclusive way. Soon, we had drafted the beginning of the Global Council on Extended Intelligence (CXI) and its mandate: to ensure that these tools benefit people and the planet, make our systems more robust and resilient, and don't reinforce negative systemic biases. The MIT Media Lab has a long-standing history with the discipline of machine learning and AI, beginning with the work of founding faculty member Marvin Minsky. But we're a long way from 1985 and the ideals and optimism that the field once held.

TL;DR - You can take the ML course on Coursera and you're magically a data scientist, because three really intelligent people did it. I'm not claiming the people referenced in this article are not data scientists who score high in Kaggle competitions. They're probably really intelligent people who picked up a new skill and excelled at it (although one was already an actuary, so he is basically doing machine learning in some form already). Here is my problem with it - being a data scientist usually requires a much larger skill set than a basic understanding of a few learning algorithms. I'm taking the Coursera ML course right now, and I think it is great!