Robots can rapidly acquire new skills from demonstrations. However, during generalisation of skills or transitioning across fundamentally different skills, it is unclear whether the robot has the necessary knowledge to perform the task. Failing to detect missing information often leads to abrupt movements or to collisions with the environment. Active learning can quantify the uncertainty of performing the task and, in general, locate regions of missing information. We introduce a novel algorithm for active learning and demonstrate its utility for generating smooth trajectories. Our approach is based on deep generative models and metric learning in latent spaces. It relies on the Jacobian of the likelihood to detect non-smooth transitions in the latent space, i.e., transitions that lead to abrupt changes in the movement of the robot. When non-smooth transitions are detected, our algorithm asks for an additional demonstration from that specific region. The newly acquired knowledge modifies the data manifold and allows for learning a latent representation for generating smooth movements. We demonstrate the efficacy of our approach on generalising elementary skills, transitioning across different skills, and implicitly avoiding collisions with the environment. For our experiments, we use a simulated pendulum where we observe its motion from images and a 7-DoF anthropomorphic arm.

I'm currently on vacation after completing my 1st year of computer science and i wanted to get into Artificial Intelligence (mainly with neural nets) but i was overwhelmed with the information there is online. Which is the best way for me to fully comprehend AI and learn how to apply it in real situations, for example coding an AI to pratice playing chess or some other simple game? Should i do online courses? Any help is much appreciated, thanks!

In Machine Learning one of the biggest problem faced by the practitioners in the process is choosing the correct set of hyper-parameters. And it takes a lot of time in tuning them accordingly, to stretch the accuracy numbers. For instance lets take, SVC from well known library Scikit-Learn, sklearn.svm.SVC class implements the Support Vector Machine algorithm for classification which contains more than 10 hyperparameters, now adjusting all ten to minimize the loss is very difficult just by using hit and trial. Though Scikit-Learn provides Grid Search and Random Search, but the algorithms are brute force and exhaustive, however hyperopt implements distributed asynchronous algorithm for hyperparameter optimization. Introducing SMBO- Sequential Model Based Global Optimization.

In this live stream, i'll build a real-time translation app from scratch using Node.JS and Tensorflow.js. We'll learn how machine learning can be used to help translate languages theoretically and programmatically. We'll also learn about how Node Tensorflow work together and what the modern web development workflow that includes machine learning looks like. That's what keeps me going. Sign up for the next course at The School of AI: https://www.theschool.ai

Bloomberg presents "Foundations of Machine Learning," a training course that was initially delivered internally to the company's software engineers as part of its "Machine Learning EDU" initiative. This course covers a wide variety of topics in machine learning and statistical modeling. The primary goal of the class is to help participants gain a deep understanding of the concepts, techniques and mathematical frameworks used by experts in machine learning. It is designed to make valuable machine learning skills more accessible to individuals with a strong math background, including software developers, experimental scientists, engineers and financial professionals. The 30 lectures in the course are embedded below, but may also be viewed in this YouTube playlist.

"It's very easy to get intimidated," says Hamayal Choudhry, the robotics engineer who co-created the smartARM, a robotic hand prosthetic that uses a camera to analyze and manipulate objects. "You have this idea for a project, then think, I don't know a thing about this." Here's how Choudhry and his partner Samin Khan, who programmed the smartARM's machine learning algorithm, used code libraries, college assignments, and sponsored hackathons to find and execute a meaningful project at age 20. The smartARM works by integrating the two fields of machine learning and mechatronics (robotics). A camera in the palm detects objects, and an algorithm analyzes the video feed (this is called computer vision) and tells the robotic hand how to manipulate the objects.

Sure you must have read my last article on which I treated how blockchain integration to Internet of Things will open the way to a new smart world. Now, let's discuss what the intersection of blockchain and the trending Artificial Intelligence could mean. For space and time constraint, I will refer you to read more on Blockchain technology. Apparently, the word Artificial means something not natural, a man-made entity or item. For ages now, technology has kept on feeding us with interestingly awesome innovations classified under this term.

The world around us is getting "smarter." Artificial intelligence (AI), data, and natural language processing have enabled Alexa, Siri, Pandora, Netflix, Facebook, Google, Amazon, Waze, and other platforms to become part of our lives. Both AI and robotics are projected to have a massive impact on the global economy. While anticipated improvements in GDP and efficiency are positive, some fear that jobs will be lost through automation. What will AI and robotics mean for higher education?

A new artificial intelligence approach can design drugs from scratch and is expected to help bring new drugs to patients quicker. Researchers from the University of North Carolina's (UNC) Eshelman School of Pharmacy have developed a new artificial intelligence system that can teach itself to create new drug candidates from scratch. Called Reinforcement Learning for Structural Evolution (ReLeaSE), the new approach is expected to potentially expedite the design and development of new drugs. The algorithm and computer programme features two neural networks, one of which carries knowledge on chemical structures for nearly 1.7 million biologically active molecules. The second network learns from the first one, over time, and proposes molecules with potential as new therapeutics.

Note: The popularity of this post has inspired me to write a machine learning test library. Over the past year, I've spent most of my working time doing deep learning research and internships. And a lot of that year was making very big mistakes that helped me learn not just about ML, but about how to engineer these systems correctly and soundly. One of the main principles I learned during my time at Google Brain was that unit tests can make or break your algorithm and can save you weeks of debugging and training time. However, there doesn't seem to be a solid tutorial online on how to actually write unit tests for neural network code.