The challenge of language grounding is to fully understand natural language by grounding language in real-world referents. While AI techniques are available, the widespread adoption and effectiveness of such technologies for human-robot teams relies critically on user trust. This survey provides three contributions relating to the newly emerging field of trust in language grounding, including a) an overview of language grounding research in terms of AI technologies, data sets, and user interfaces; b) six hypothesised trust factors relevant to language grounding, which are tested empirically on a human-robot cleaning team; and c) future research directions for trust in language grounding.

Traditional policy gradient methods are fundamentally flawed. Natural gradients converge quicker and better, forming the foundation of contemporary Reinforcement Learning such as Trust Region Policy Optimization (TRPO) and Proximal Policy Optimization (PPO). This lecture note aims to clarify the intuition behind natural policy gradients, focusing on the thought process and the key mathematical constructs.

This is Data Science Prerequisites - Numpy - Pandas- Seaborn course. An excellent choice for both beginners and experts looking to expand their knowledge on one of the most popular Python libraries in the world! If you've spent time in a spreadsheet software like MS Excel or Google Sheets and want to take your data analysis skills to the next level, this course is for you! Pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with "relational" or "labeled" data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real-world data analysis in Python.

Adversarial continual learning is effective for continual learning problems because of the presence of feature alignment process generating task-invariant features having low susceptibility to the catastrophic forgetting problem. Nevertheless, the ACL method imposes considerable complexities because it relies on task-specific networks and discriminators. It also goes through an iterative training process which does not fit for online (one-epoch) continual learning problems. This paper proposes a scalable adversarial continual learning (SCALE) method putting forward a parameter generator transforming common features into task-specific features and a single discriminator in the adversarial game to induce common features. The training process is carried out in meta-learning fashions using a new combination of three loss functions. SCALE outperforms prominent baselines with noticeable margins in both accuracy and execution time.

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AlphaZero, Leela Chess Zero and Stockfish NNUE revolutionized Computer Chess. This book gives a complete introduction into the technical inner workings of such engines. The book is split into four main chapters -- excluding chapter 1 (introduction) and chapter 6 (conclusion): Chapter 2 introduces neural networks and covers all the basic building blocks that are used to build deep networks such as those used by AlphaZero. Contents include the perceptron, back-propagation and gradient descent, classification, regression, multilayer perceptron, vectorization techniques, convolutional networks, squeeze and excitation networks, fully connected networks, batch normalization and rectified linear units, residual layers, overfitting and underfitting. Chapter 3 introduces classical search techniques used for chess engines as well as those used by AlphaZero. Contents include minimax, alpha-beta search, and Monte Carlo tree search. Chapter 4 shows how modern chess engines are designed. Aside from the ground-breaking AlphaGo, AlphaGo Zero and AlphaZero we cover Leela Chess Zero, Fat Fritz, Fat Fritz 2 and Efficiently Updatable Neural Networks (NNUE) as well as Maia. Chapter 5 is about implementing a miniaturized AlphaZero. Hexapawn, a minimalistic version of chess, is used as an example for that. Hexapawn is solved by minimax search and training positions for supervised learning are generated. Then as a comparison, an AlphaZero-like training loop is implemented where training is done via self-play combined with reinforcement learning. Finally, AlphaZero-like training and supervised training are compared.

During the 2017-18 academic year we carried out a series of coding activities, lasting about 3 months, in a third of the Giulia Falletti primary school in Barolo in Turin (Gena et a., 2020). These activities aimed to teach students not only the basics of programming, but also to introduce a new language and a new way of thinking and solving problems: computational thinking. The class consisted of 25 pupils: 14 males and 11 females, which we then operationally divided into two working groups (13 + 12) to make coding lessons more manageable and provide better childcare. The lessons lasted one hour and were conducted, in the presence of one of the teachers, by a computer teacher assisted by a student / facilitator. At the end of the three months of this positive experience, we realized that having an educational robot that can perform the same kind of actions that virtual robots do, like those of code.org, is very useful for children, especially to help them to solve orientation problems. Therefore, since no commercial robot had the characteristics we wanted, we decided to create an educational robot from scratch, equipped with social, interactive and emotional skills, able to involve children and establish an emotional bond with them in order to increase their learning and involvement. We decided from the beginning to design the robot as an open source project, made at a low cost, proposing a kit that can be easily reproduced and improved by anyone who wishes.

SHANGHAI, Sept 1 (Reuters) - A U.S. order to ban exports of some advanced chips to China is likely to hit almost any major tech company running public clouds or advanced artificial intelligence training modules in the country, experts said. Chip designer Nvidia Corp (NVDA.O) said on Wednesday that U.S. officials told it to stop exporting two top computing chips for AI work to China. Advanced Micro Devices (AMD.O) also said it had received new license requirements that will stop its advanced AI chip called MI250 from being exported to China. Shu Jueting, a Chinese Commerce Ministry spokesperson, said on Thursday that Beijing opposes the measures, saying they undermine the rights of Chinese companies and threaten to disrupt global supply chains. The orders underscore deepening U.S.-China tensions over access to advanced chip technology.