Model-Based Reinforcement Learning (MBRL) offers a promising direction for sample efficient learning, often achieving state of the art results for continuous control tasks. However, many existing MBRL methods rely on combining greedy policies with exploration heuristics, and even those which utilize principled exploration bonuses construct dual objectives in an ad hoc fashion. In this paper we introduce Ready Policy One (RP1), a framework that views MBRL as an active learning problem, where we aim to improve the world model in the fewest samples possible. RP1 achieves this by utilizing a hybrid objective function, which crucially adapts during optimization, allowing the algorithm to trade off reward v.s. exploration at different stages of learning. In addition, we introduce a principled mechanism to terminate sample collection once we have a rich enough trajectory batch to improve the model. We rigorously evaluate our method on a variety of continuous control tasks, and demonstrate statistically significant gains over existing approaches.

The literature on machine teaching, machine education, and curriculum design for machines is in its infancy with sparse papers on the topic primarily focusing on data and model engineering factors to improve machine learning. In this paper, we first discuss selected attempts to date on machine teaching and education. We then bring theories and methodologies together from human education to structure and mathematically define the core problems in lesson design for machine education and the modelling approaches required to support the steps for machine education. Last, but not least, we offer an ontology-based methodology to guide the development of lesson plans to produce transparent and explainable modular learning machines, including neural networks.

Reinforcement learning has shown great promise in the training of robot behavior due to the sequential decision making characteristics. However, the required enormous amount of interactive and informative training data provides the major stumbling block for progress. In this study, we focus on accelerating reinforcement learning (RL) training and improving the performance of multi-goal reaching tasks. Specifically, we propose a precision-based continuous curriculum learning (PCCL) method in which the requirements are gradually adjusted during the training process, instead of fixing the parameter in a static schedule. To this end, we explore various continuous curriculum strategies for controlling a training process. This approach is tested using a Universal Robot 5e in both simulation and real-world multi-goal reach experiments. Experimental results support the hypothesis that a static training schedule is suboptimal, and using an appropriate decay function for curriculum learning provides superior results in a faster way.

Most organizations are well aware of what economists are calling the Fourth Industrial Revolution1 and what it could mean for the future of work.2 Up to an estimated 47 percent of US jobs face potential automation over the next 20 years, driven primarily by rapid advances in AI, cognitive computing, and automation of repetitive, rule-based tasks.3 Other disruptive forces seem to be shaping the future of work as well--many organizations are shifting to more team-based structures; workplaces are increasingly virtual, flexible, and geographically agnostic; the overall workforce is becoming more diverse, multigenerational, and dispersed; and most careers are morphing from following predictable road maps to constant reinvention. In the face of this, various leaders across industries are reimagining their workforce models to explore how they can use technology, expanded work settings, and alternative talent to address these disruptive forces. In addition, many are reevaluating their talent profiles, including how they measure the skill sets required for success in the future.

Many of us are fascinated by science fiction pop-culture. Be it movies like Star Wars and Star Trek or Marvel and DC characters, we all have grown up watching them. And in the process, we loved the futuristic plot lines that were riddled with subjects like artificial intelligence, robotics, automation, quantum physics, and many more, that explained the existence of the universe. While many are fortunate to pursue such technical subjects in real life, it remains a distant dream for children from lower economic backgrounds. Fourteen-year-old Ravi Patel from Pune is one of them, whose father couldn't support his dreams to get him a better education, as he earns a meagre Rs 15,000 salary to take care of his entire family.

TED Talks are influential videos from expert speakers in a variety of verticals. TED began in 1984 as a conference where Technology, Entertainment and Design converged, and today covers almost all topics -- from business to technology to global issues -- in more than 110 languages. TED is building a clearinghouse of free knowledge from the world's top thinkers, and their library of videos is expansive and rapidly growing. Solutions Review has curated this list of AI and machine learning TED talks to watch if you are a practitioner in the field. Talks were selected based on relevance, ability to add business value, and individual speaker expertise.

Could we please start teaching that a couple of online #AI courses, bootcamps, tools, algorithms *DO NOT* make anyone an #AI expert? There is a false belief that they do. The lack of ground knowledge in Computer Science is appalling, nor to mention in areas where AI is used.

For much of the previous decade, advocates of education technology imagined a classroom where computer algorithms would differentiate instruction for each student, delivering just the right lessons at the right time, like a personal tutor. The evidence that students learn better this way has not been strong and, instead, we're reading reports that technology use at school sometimes hurts student achievement. So it was interesting to see McKinsey & Co., an elite consulting firm, reframe the argument for buying education technology away from computerized instruction to something more pedestrian: saving teachers time. A January 2020 report by the firm estimated that between 20 and 40 percent of the 50 hours that a typical teacher currently works a week could be saved through existing automation technology, often enabled by artificial intelligence (AI). That adds up to 13 saved hours a week, hours of freedom that could help relieve teacher burnout.

Teachers spend about 20% to 40% of their time--or about 13 hours a week--on activities that could be automated using technology, according to a new report on artificial intelligence by the management consulting firm McKinsey & Company. Preparation time has the biggest potential for automation, making teachers more effective and efficient in lesson planning. For instance, adaptive math software lets teachers more quickly and accurately assess student performance, place learners in groups and provide the next assignments. Collaboration platforms, meanwhile, allow teachers to share relevant materials. "Technology has the least potential to save teacher time in areas where teachers are directly engaging with students: direct instruction and engagement, coaching and advisement, and behavioral-, social-, and emotional-skill development," the report found.

With strong roots in statistics(data), Machine Learning is becoming among the very fascinating and quick-paced computer science areas to work in. There is an unending source of businesses and software machine learning could be implemented to make them more wise and skillful. Chatbots, spam filtering, advertising serving, search engines, and fraud detection, are one of just a few examples of machine learning versions encourage everyday day to day life. Machine Learning is what allows us find patterns and create mathematical models for matters that would at times be unthinkable for individuals to perform. Not at all like informatics courses which include topics such as methods of exploratory data analysis, data, communication, and visualization, machine learning courses only focus on teaching machine learning algorithms the way they are numerically A programming language, and how to use them.