An important property for lifelong-learning agents is the ability to combine existing skills to solve unseen tasks. In general, however, it is unclear how to compose skills in a principled way. We provide a "recipe" for optimal value function composition in entropy-regularised reinforcement learning (RL) and then extend this to the standard RL setting. Composition is demonstrated in a video game environment, where an agent with an existing library of policies is able to solve new tasks without the need for further learning.

Artificial intelligence technology in education is no more the future, it is the present. We are already witnessing the impact of technology in the education sector. Increased uses of digital devices, adaptive learning platforms, and engaging lessons have transformed the learning as well as teaching processes. With the help of artificial intelligence technology, the educators today are working on creating activities that can not only ignite the curiosities among the students, but can make their learning experience a memorable one. Although AI can never replace the human teachers, it has successfully given a whole new meaning to the roles and responsibilities of a teacher.

There are lots of education options available online, provided you're a self-starter with the discipline to do a lot of coursework on your own. For example, Microsoft's AI School offers a variety of lessons for developers in everything from text analytics and object recognition to custom neural-network models. The content is angled toward data scientists and developers, and heavily emphasizes the use of Microsoft products (of course) in addition to "universal" A.I. skills. It's also free, although those who want Verified Certificates will need to pay a fee. Microsoft, of course, is far from your only option when it comes to learning about A.I. online, particularly with regard to beginner-level material.

This paper introduces a fully automatic method for generating video game tutorials. The AtDELFI system (AuTomatically DEsigning Legible, Full Instructions for games) was created to investigate procedural generation of instructions that teach players how to play video games. We present a representation of game rules and mechanics using a graph system as well as a tutorial generation method that uses said graph representation. We demonstrate the concept by testing it on games within the General Video Game Artificial Intelligence (GVG-AI) framework; the paper discusses tutorials generated for eight different games. Our findings suggest that a graph representation scheme works well for simple arcade style games such as Space Invaders and Pacman, but it appears that tutorials for more complex games might require higher-level understanding of the game than just single mechanics.

Open source software has never been more valuable than it is today. But, don't take my word for it -- just look at Microsoft's most recent move. Why else would this tech titan spend $7.5 billion to acquire GitHub, the world's most popular software development platform? The Internet of Things (IoT) world may be exciting, but there are serious technical challenges that need to be addressed, especially by developers. In this handbook, learn how to meet the security, analytics, and testing requirements for IoT applications.

When talking about Artificial Intelligence, some people think about the destruction of the world and killer robots, but Artificial Intelligence is already playing a major role in our lives, non-destructively. We all are familiar with programs such as Siri and Google Now, which are improving our way of life. Similarly, you must have played chess against the computer, in which most people get beat atrociously. These programs are nothing but artificial intelligence, which is designed to assist us with a set of protocols. Similar programs are self-driving cars, or motion and reflex detecting video games, which evolve as time goes along.

We will create a simple tool that recognizes drawings and outputs the names of the current drawing. This app will run directly on the browser without any installations. We will use Google Colab for training the model, and we will deploy it on the browser using TensorFlow.js. Find the live demo and the code on GitHub. Also make sure to test the notebook on Google Colab here.

To operate robotic arms, students are required to know a small amount of coding. STEM Education Works has traveled to the Lafayette, Indiana, CoderDojo several times to teach students how to program the Dobot Magician robotic arms. The lessons involve real-world actions that make sense to young students. When using the visual programming language Blockly in May, students directed the robots to run bases in a mock baseball game. At Subaru of Indiana Automotive, the students programed the scaled-down robotic arms to write their names before learning about the larger, industrial robots, providing a real-world application for the activity they completed.

As part of our Fusions release, we have already demonstrated a use case and walked through an example using the BigML Dashboard. Our fourth of six blog posts on Fusions will demonstrate how to utilize Fusions by directly calling the BigML REST API. As a reminder, Fusions can be used for both classification and regression supervised Machine Learning problems, and function by aggregating the results of multiple models (decision trees, ensembles, logistic regressions, and/or deepnets), often achieving better performance as result. Using the BigML API, requires that you first set up the correct environment variables. Additionally, the BIGML_API_KEY can be found on the Dashboard by clicking on your username to pull up the account page, and then clicking on'API Key'.

The world is evolving faster than ever thanks to the proliferation of technology brought about by an increasingly connected global market. And while the changes in education system was slow a few decades ago, the course curriculum was not required to be updated frequently. However, with today's fast pace changes in market needs, new curriculum is required almost every few months. This is primarily due to the fact that while technology, knowledge, and the job market can and do change almost overnight, the education system is rigid, often bureaucratic, and in many cases even resistant to change. This ever expanding gap between academia and the real world has resulted in a whole generation of students who while undoubtedly talented, intelligent, and hardworking simply do not have the skillsets required for employment.