Federated distillation (FD) paradigm has been recently proposed as a promising alternative to federated learning (FL) especially in wireless sensor networks with limited communication resources. However, all state-of-the art FD algorithms are designed for only classification tasks and less attention has been given to regression tasks. In this work, we propose an FD framework that properly operates on regression learning problems. Afterwards, we present a use-case implementation by proposing an indoor localization system that shows a good trade-off communication load vs. accuracy compared to federated learning (FL) based indoor localization. With our proposed framework, we reduce the number of transmitted bits by up to 98%. Moreover, we show that the proposed framework is much more scalable than FL, thus more likely to cope with the expansion of wireless networks.

Our work aims at efficiently leveraging ambiguous demonstrations for the training of a reinforcement learning (RL) agent. An ambiguous demonstration can usually be interpreted in multiple ways, which severely hinders the RL-Agent from learning stably and efficiently. Since an optimal demonstration may also suffer from being ambiguous, previous works that combine RL and learning from demonstration (RLfD works) may not work well. Inspired by how humans handle such situations, we propose to use self-explanation (an agent generates explanations for itself) to recognize valuable high-level relational features as an interpretation of why a successful trajectory is successful. This way, the agent can provide some guidance for its RL learning. Our main contribution is to propose the Self-Explanation for RL from Demonstrations (SERLfD) framework, which can overcome the limitations of traditional RLfD works. Our experimental results show that an RLfD model can be improved by using our SERLfD framework in terms of training stability and performance.

From the earliest years of our lives, humans use language to express our beliefs and desires. Being able to talk to artificial agents about our preferences would thus fulfill a central goal of value alignment. Yet today, we lack computational models explaining such language use. To address this challenge, we formalize learning from language in a contextual bandit setting and ask how a human might communicate preferences over behaviors. We study two distinct types of language: $\textit{instructions}$, which provide information about the desired policy, and $\textit{descriptions}$, which provide information about the reward function. We show that the agent's degree of autonomy determines which form of language is optimal: instructions are better in low-autonomy settings, but descriptions are better when the agent will need to act independently. We then define a pragmatic listener agent that robustly infers the speaker's reward function by reasoning about $\textit{how}$ the speaker expresses themselves. We validate our models with a behavioral experiment, demonstrating that (1) our speaker model predicts human behavior, and (2) our pragmatic listener successfully recovers humans' reward functions. Finally, we show that this form of social learning can integrate with and reduce regret in traditional reinforcement learning. We hope these insights facilitate a shift from developing agents that $\textit{obey}$ language to agents that $\textit{learn}$ from it.

Algorithm configuration (AC) is concerned with the automated search of the most suitable parameter configuration of a parametrized algorithm. There is currently a wide variety of AC problem variants and methods proposed in the literature. Existing reviews do not take into account all derivatives of the AC problem, nor do they offer a complete classification scheme. To this end, we introduce taxonomies to describe the AC problem and features of configuration methods, respectively. We review existing AC literature within the lens of our taxonomies, outline relevant design choices of configuration approaches, contrast methods and problem variants against each other, and describe the state of AC in industry. Finally, our review provides researchers and practitioners with a look at future research directions in the field of AC.

When we think about artificial intelligence (AI), humans are rarely what springs to mind. And understandably so, as AI is all about machine intelligence and automation. AI has become an essential business tool, so we often commend the pioneering work of AI companies to support businesses as they digitally evolve. To shed a light on the importance of people in the creation of intelligent machines, we take a look at the best-in-class executives in the AI field who continue to push the technology – and its boundaries – forward. With a passion for AI, Andrej Karpathy is interested in training deep neural nets on large datasets.

Udemy is the biggest website in the world that offer courses in many categories, all the skills that you would be looking for are offered in Udemy, including languages, design, marketing and a lot of other categories, so when you ever want to buy a courses and pay for a new skills, Udemy would be the best forum for you. You can find payment courses, 100 free courses From Udemy and coupons also, more than 12 categories are offered, and that what makes sure you will find the domain and the skill you are looking for. Our duty is to search for 100 off courses and free coupons. An autonomous database is a cloud database that uses machine learning to automate database tuning, security, backups, updates, and other routine management tasks traditionally performed by DBAs. Unlike a conventional database, an autonomous database performs all these tasks and more without human intervention.

A comprehensive progress report of students studying under the Central Board of Secondary Education, which will be based on Artificial Intelligence, will soon be tested in a pilot project in 74 schools, officials aware of the development said. Dubbed the Holistic Progress Card, the initiative is envisaged under the National Education Policy 2020 for a "multidimensional progress monitoring" of school students. Once it is successfully tested, the initiative will be eventually rolled out to include all student from classes 1 to 12, an education ministry official said, requesting anonymity. The decision on the pilot project was taken during the CBSE's general body meeting in August. The board has developed a prototype for classes 1-3, which will "track the key developmental goals and competencies, as specified by the National Initiative for Proficiency in Reading with Understanding and Numeracy (NIPUN) Bharat guidelines", according to the minutes of the CBSE meeting.

A detailed article on the Georgia Tech and Facebook partnership aimed at building diversity in the AI field appears in the January issue of University-Industry Engagement Advisor. In the initial steps of a program of collaboration with U.S. universities "that serve significant populations of Black and Latino students," Facebook has partnered with Georgia Tech to develop, co-teach, and fund graduate-level online deep learning courses. The program will be expanded in 2021 to include additional institutions. The collaboration at Georgia Tech came about through discussions between Facebook and the university's Machine Learning Center and the School of Interactive Computing, says Zsolt Kira, PhD, associate director of the Machine Learning Center at Georgia Tech and an assistant professor in the School of Interactive Computing. This is not the first collaboration between the two partners, says Paco Guzmán, research scientist manager at Facebook and a lecturer for Facebook's Co-teaching AI Program.

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Will AI be capable of overpowering humanity? Any sci-fi fan can tell you that a good apocalypse begins and ends with artificial intelligence and a god complex. But the main question is how close we really are to bringing aspects like Skynet from the Terminator franchise or Brainiac from DC Comics into the world, and if it is possible to control a being whose intelligence far exceeds that of the brightest and smartest humanity has to offer. A number of scientists, philosophers and technology experts at international educational institutions published an article in which they analyzed the danger, stating among other things that if such an entity were to arise, it would not be possible to stop it. The authors of the study, who come from academic institutions and technological bodies in the US, Australia and Madrid, published their findings in the Journal of Artificial Intelligence Research.