Deep reinforcement learning has demonstrated increasing capabilities for continuous control problems, including agents that can move with skill and agility through their environment. An open problem in this setting is that of developing good strategies for integrating or merging policies for multiple skills, where each individual skill is a specialist in a specific skill and its associated state distribution. We extend policy distillation methods to the continuous action setting and leverage this technique to combine expert policies, as evaluated in the domain of simulated bipedal locomotion across different classes of terrain. We also introduce an input injection method for augmenting an existing policy network to exploit new input features. Lastly, our method uses transfer learning to assist in the efficient acquisition of new skills. The combination of these methods allows a policy to be incrementally augmented with new skills. We compare our progressive learning and integration via distillation (PLAID) method against three alternative baselines.

In recent years, the importance of deep learning has significantly increased in pattern recognition, computer vision, and artificial intelligence research, as well as in industry. However, despite the existence of multiple deep learning frameworks, there is a lack of comprehensible and easy-to-use high-level tools for the design, training, and testing of deep neural networks (DNNs). In this paper, we introduce Barista, an open-source graphical high-level interface for the Caffe deep learning framework. While Caffe is one of the most popular frameworks for training DNNs, editing prototext files in order to specify the net architecture and hyper parameters can become a cumbersome and error-prone task. Instead, Barista offers a fully graphical user interface with a graph-based net topology editor and provides an end-to-end training facility for DNNs, which allows researchers to focus on solving their problems without having to write code, edit text files, or manually parse logged data.

Recently, increased computational power and data availability, as well as algorithmic advances, have led machine learning techniques to impressive results in regression, classification, data-generation and reinforcement learning tasks. Despite these successes, the proximity to the physical limits of chip fabrication alongside the increasing size of datasets are motivating a growing number of researchers to explore the possibility of harnessing the power of quantum computation to speed-up classical machine learning algorithms. Here we review the literature in quantum machine learning and discuss perspectives for a mixed readership of classical machine learning and quantum computation experts. Particular emphasis will be placed on clarifying the limitations of quantum algorithms, how they compare with their best classical counterparts and why quantum resources are expected to provide advantages for learning problems. Learning in the presence of noise and certain computationally hard problems in machine learning are identified as promising directions for the field. Practical questions, like how to upload classical data into quantum form, will also be addressed.

We propose a meta-learning approach for learning gradient-based reinforcement learning (RL) algorithms. The idea is to evolve a differentiable loss function, such that an agent, which optimizes its policy to minimize this loss, will achieve high rewards. The loss is parametrized via temporal convolutions over the agent's experience. Because this loss is highly flexible in its ability to take into account the agent's history, it enables fast task learning and eliminates the need for reward shaping at test time. Empirical results show that our evolved policy gradient algorithm achieves faster learning on several randomized environments compared to an off-the-shelf policy gradient method. Moreover, at test time, our learner optimizes only its learned loss function, and requires no explicit reward signal. In effect, the agent internalizes the reward structure, suggesting a direction toward agents that learn to solve new tasks simply from intrinsic motivation.

Subscribe to receive updates on Industry 4.0 The industrialization of the world began in the late 18th century with the advent of steam power and the invention of the power loom, radically changing how goods were manufactured. A century later, electricity and assembly lines made mass production possible. In the 1970s, the third industrial revolution began when advances in computing-powered automation enabled us to program machines and networks. Today, a fourth industrial revolution is transforming economies, jobs, and even society itself. Under the broad title Industry 4.0, many physical and digital technologies are combining through analytics, artificial intelligence, cognitive technologies, and the Internet of Things (IoT) to create digital enterprises that are both interconnected and capable of more informed decision-making. Digital enterprises can communicate, analyze, and use data to drive intelligent action in the physical world.

In the field of Machine Learning, data visualization is not just making fancy graphics for reports; it is used extensively in day-to-day work for all phases of a project. To start with, visual exploration of data is the first thing one tends to do when dealing with a new task. We do preliminary checks and analysis using graphics and tables to summarize the data and leave out the less important details. It is much more convenient for us, humans, to grasp the main points this way than by reading many lines of raw data. It is amazing how much insight can be gained from seemingly simple charts created with available visualization tools. Next, when we analyze the performance of a model or report results, we also often use charts and images.

No one can say how well these coding bots teach kids, or even whether learning to code is the essential life skill that so many techies claim. After all, by the time today's elementary-school kids are entering the workforce, computers may well be programming themselves.

Machine learning is one of the hottest topics in tech today. It is a must-have organizational competency in the data-driven era of digital transformation. Despite the unprecedented speed and ease of creating predictive models today, the human mind is still essential for generating good machine learning models. In this fast-paced introductory class, participants will be introduced to fundamental concepts and walk-through the entire machine learning lifecycle with optional hands-on exercises using open source tools. From selecting the right problem to solve to preventing algorithm bias, machine learning is still an art and a science.

For many, artificial intelligence (AI) and the human workforce are at odds. These people are concerned that intelligent machines powered by increasingly sophisticated AI will take over human jobs, leaving some people with no source of income. Even more frightening is the prospect of a complete AI labor takeover if/when we reach the technological singularity. According to Sebastian Thurn, co-founder of Google's secret X laboratory, they may be worrying over nothing. During a talk at the ongoing World Government Summit in Dubai, Thurn said he believes AI will make humans into "superhuman workers" capable of doing more with the help of technology than without it, reported CNBC.

"AI is transforming everything about the way the world does business, so any aspiring business leader will be better prepared by understanding where we're headed," Josh Tyler, executive vice president of engineering and design at Course Hero, an online learning platform, said via email.