A BSTRACT Reinforcement learning algorithms use correlations between policies and rewards to improve agent performance. But in dynamic or sparsely rewarding environments these correlations are often too small, or rewarding events are too infrequent to make learning feasible. Human education instead relies on curricula-the breakdown of tasks into simpler, static challenges with dense rewards-to build up to complex behaviors. While curricula are also useful for artificial agents, handcrafting them is time consuming. This has lead researchers to explore automatic curriculum generation. Here we explore automatic curriculum generation in rich, dynamic environments. Using a setter-solver paradigm we show the importance of considering goal validity, goal feasibility, and goal coverage to construct useful curricula. We demonstrate the success of our approach in rich but sparsely rewarding 2D and 3D environments, where an agent is tasked to achieve a single goal selected from a set of possible goals that varies between episodes, and identify challenges for future work. Finally, we demonstrate the value of a novel technique that guides agents towards a desired goal distribution. Altogether, these results represent a substantial step towards applying automatic task curricula to learn complex, otherwise unlearnable goals, and to our knowledge are the first to demonstrate automated curriculum generation for goal-conditioned agents in environments where the possible goals vary between episodes. 1 I NTRODUCTION Reinforcement learning (RL) algorithms use correlations between policies and environmental rewards to reinforce and improve agent performance. But such correlation-based learning may struggle in dynamic environments with constantly changing settings or goals, because policies that correlate with rewards in one episode may fail to correlate with rewards in a subsequent episode. Correlation-based learning may also struggle in sparsely rewarding environments since by definition there are fewer rewards, and hence fewer instances when policy-reward correlations can be measured and learned from. In the most problematic tasks, agents may fail to begin learning at all. While RL has been used to achieve expert-level performance in some sparsely rewarding games (Silver et al., 2016; OpenAI, 2018; Vinyals et al., 2019), success has often required carefully engineered curricula to bootstrap learning, such as learning from millions of expert games or handcrafted shaping rewards. In some cases self-play between agents as they improve can serve as a powerful automatic curriculum for achieving expert or superhuman performance (Silver et al., 2018; Vinyals et al., 2019).

Cooperative learning in heterogeneous teams refers to learning methods in which teams are organised both to accomplish academic tasks and for individuals to gain knowledge. Competencies, personality and the gender of team members are key factors that influence team performance. Here, we introduce a team composition problem, the so-called synergistic team composition problem (STCP), which incorporates such key factors when arranging teams. Thus, the goal of the STCP is to partition a set of individuals into a set of synergistic teams: teams that are diverse in personality and gender and whose members cover all required competencies to complete a task. Furthermore, the STCP requires that all teams are balanced in that they are expected to exhibit similar performances when completing the task. We propose two efficient algorithms to solve the STCP . Our first algorithm is based on a linear programming formulation and is appropriate to solve small instances of the problem. Our second algorithm is an anytime heuristic that is effective for large instances of the STCP . Finally, we thoroughly study the computational properties of both algorithms in an educational context when grouping students in a classroom into teams using actual-world data. Keywords: team composition, exact algorithms, heuristic algorithms, optimisation, coalition formation 1. Introduction Active learning refers to a broad range of teaching techniques that engage students to participate in all learning activities in the classes. Typically, active learning strategies involve a substantial amount of students working together within teams. They do not only acquire and retain the information better but also are more content with their classes [2]. Nevertheless, not all teams facilitate learning. For team-based learning to be effective, every team composed in the classroom needs to be heterogeneous, i.e. diverse in individuals' characteristics. Furthermore, having some significantly weaker teams and some significantly stronger teams is undesirable. Hence, the distribution of teams in a classroom must be balanced in the sense that all teams are more or less equally strong. Even though much research in the industrial, organisational, and educational psychology fields investigated what are the predictors of team success, to the best of our knowledge, there are no computational models to build teams for a given task that are broadly used in the classrooms. Frequently studied individual characteristics that influence team performance are competencies, personality traits, and gender [3, 4, 5, 6]. Some of those characteristics were also acknowledged by multiagent systems (MAS) research. The most studied characteristic in MAS research are competencies [7, 8, 9, 10, 11].

A BSTRACT Current deep neural networks can achieve remarkable performance on a single task. However, when the deep neural network is continually trained on a sequence of tasks, it seems to gradually forget the previous learned knowledge. This phenomenon is referred to as catastrophic forgetting and motivates the field called lifelong learning. The central question in lifelong learning is how to enable deep neural networks to maintain performance on old tasks while learning a new task. In this paper, we introduce a novel and effective lifelong learning algorithm, called MixEd stochastic GrAdient (MEGA), which allows deep neural networks to acquire the ability of retaining performance on old tasks while learning new tasks. Extensive experimental results show that the proposed MEGA algorithm significantly advances the state-of-the-art on all four commonly used lifelong learning benchmarks, reducing the error by up to 18%. 1 I NTRODUCTION A significant step towards artificial general intelligence (AGI) is to enable the learning agent to acquire the ability of remembering past experiences while being trained on a continuum of tasks. Current deep neural networks are capable of achieving remarkable performance on a single task (Goodfellow et al., 2016). However when the network is retrained on a new task, its performance drops drastically on previously trained tasks, a phenomenon which is referred to as catastrophic forgetting (Ratcliff, 1990; Robins, 1995; French, 1999; Kirkpatrick et al., 2017).

Today, education doesn't stop after students graduate; many continue learning throughout their life. And in addition to adult learning courses at colleges and universities, a lot of courses are now offered online – ranging from MOOCs ("massive open online course") to many apps. At the Global Education & Skills Forum in March, two of the 10 finalists were lifelong learning startups. Here are four of the most promising EdTech startups around. Don't have time to read a book a day?

TMLS consists of a community comprised of over 6,000 ML researchers, professionals and entrepreneurs. We'd like to welcome you to join us in celebrating the top achievements in AI Research, AI companies, and applications in industry. Expect 1 day of workshops and 2-days of quality networking, food, drinks, workshops, breakouts, keynotes and Exhibitors. Come expand your network with machine learning experts and further your own personal & professional development in this exciting and rewarding field. We believe these events should be as accessible as possible and set our ticket passes accordingly.

Every student success is dependent on an accommodating, secure, challenging and academically robust learning environment. Life Learners E-Store is one the leading online store for the latest ICT Gadgets. At Life Learners, our Training program and courses are aimed at increasing knowledge in various ICT fields so as to develop a community of certified professionals. Life Learners eLearning platform offers up-to-date practice tests on UTME, NECO & WAEC as well as professional job interviews.

In the interview, he talks about 5 big ideas in AI. For more information about ReadyAI, please go to https://www.ReadyAI.org For more information about online AI Courses, please go to https://edu.ReadyAI.org For more information about WAICY(World Artificial Intelligence Competition for Youth), please go to https://www.WAICY.org

Kerry is Vice Chair of the globally leadiing #AIinPR panel looking at the implications of AI and in the PR and communications industry and also advising the industry and business on ethical AI implementation and PR - the public's adoption of good AI to realise its benefits rests on the shoulders of ethical PR practitioners. Kerry is a member of the Chartered Institute of Public Relations Council and the International Data Science Foundation, and is a graduate of Dame Wendy Hall's Fundamentals of Data, AI and Machine Learning for Business university course. Kerry will take you through the CIPR's #AIinPR Humans Still Needed globally acclaimed research and what data and artificial intelligence means for the PR and communications industry and what you can do to upskill as a practitioner to remain relevant and have the competitive edge.

The Nittany AI Alliance awarded three Penn State student teams a combined total of $25,000 on Tuesday, September 10 at the Nittany AI Challenge Celebration event. Students Christina Warren and Mathew Mancini developed Revu, a product designed to keep students engaged during reading assignments. Revu works by generating multiple-choice quizzes and other tests to measure comprehension of key concepts in order to keep students focused. Warren and Mancini won $15,000 for their product. In the future, they hope to add features that will generate flashcards, save quizzes and notes, and design a mode specifically for instructors.

With modern technology, such questions are no longer bound to creative conjecture. You have just found Keras. Today i will give a brief introduction over this topic which created headache for me when i was learning this. All video and text tutorials are free. I use Anaconda package that almost wraps up all the Python packages including Jupyter notebook.