Encoding facts as representations of entities and binary relationships between them, as learned by knowledge graph representation models, is useful for various tasks, including predicting new facts, question answering, fact checking and information retrieval. The focus of this thesis is on (i) improving knowledge graph representation with the aim of tackling the link prediction task; and (ii) devising a theory on how semantics can be captured in the geometry of relation representations. Most knowledge graphs are very incomplete and manually adding new information is costly, which drives the development of methods which can automatically infer missing facts. The first contribution of this thesis is HypER, a convolutional model which simplifies and improves upon the link prediction performance of the existing convolutional state-of-the-art model ConvE and can be mathematically explained in terms of constrained tensor factorisation. The second contribution is TuckER, a relatively straightforward linear model, which, at the time of its introduction, obtained state-of-the-art link prediction performance across standard datasets. The third contribution is MuRP, first multi-relational graph representation model embedded in hyperbolic space. MuRP outperforms all existing models and its Euclidean counterpart MuRE in link prediction on hierarchical knowledge graph relations whilst requiring far fewer dimensions. Despite the development of a large number of knowledge graph representation models with gradually increasing predictive performance, relatively little is known of the latent structure they learn. We generalise recent theoretical understanding of how semantic relations of similarity, paraphrase and analogy are encoded in the geometric interactions of word embeddings to how more general relations, as found in knowledge graphs, can be encoded in their representations.

Commonsense causality reasoning (CCR) aims at identifying plausible causes and effects in natural language descriptions that are deemed reasonable by an average person. Although being of great academic and practical interest, this problem is still shadowed by the lack of a well-posed theoretical framework; existing work usually relies on deep language models wholeheartedly, and is potentially susceptible to confounding co-occurrences. Motivated by classical causal principles, we articulate the central question of CCR and draw parallels between human subjects in observational studies and natural languages to adopt CCR to the potential-outcomes framework, which is the first such attempt for commonsense tasks. We propose a novel framework, ROCK, to Reason O(A)bout Commonsense K(C)ausality, which utilizes temporal signals as incidental supervision, and balances confounding effects using temporal propensities that are analogous to propensity scores. The ROCK implementation is modular and zero-shot, and demonstrates good CCR capabilities on various datasets.

We introduce Contrastive Intrinsic Control (CIC), an algorithm for unsupervised skill discovery that maximizes the mutual information between skills and state transitions. In contrast to most prior approaches, CIC uses a decomposition of the mutual information that explicitly incentivizes diverse behaviors by maximizing state entropy. We derive a novel lower bound estimate for the mutual information which combines a particle estimator for state entropy to generate diverse behaviors and contrastive learning to distill these behaviors into distinct skills. We evaluate our algorithm on the Unsupervised Reinforcement Learning Benchmark, which consists of a long reward-free pre-training phase followed by a short adaptation phase to downstream tasks with extrinsic rewards. We find that CIC substantially improves over prior unsupervised skill discovery methods and outperforms the next leading overall exploration algorithm in terms of downstream task performance.

In this manuscript, we offer a gentle review of submodularity and supermodularity and their properties. We offer a plethora of submodular definitions; a full description of a number of example submodular functions and their generalizations; example discrete constraints; a discussion of basic algorithms for maximization, minimization, and other operations; a brief overview of continuous submodular extensions; and some historical applications. We then turn to how submodularity is useful in machine learning and artificial intelligence. This includes summarization, and we offer a complete account of the differences between and commonalities amongst sketching, coresets, extractive and abstractive summarization in NLP, data distillation and condensation, and data subset selection and feature selection. We discuss a variety of ways to produce a submodular function useful for machine learning, including heuristic hand-crafting, learning or approximately learning a submodular function or aspects thereof, and some advantages of the use of a submodular function as a coreset producer. We discuss submodular combinatorial information functions, and how submodularity is useful for clustering, data partitioning, parallel machine learning, active and semi-supervised learning, probabilistic modeling, and structured norms and loss functions.

Although reinforcement learning has seen remarkable progress over the last years, solving robust dexterous object-manipulation tasks in multi-object settings remains a challenge. In this paper, we focus on models that can learn manipulation tasks in fixed multi-object settings and extrapolate this skill zero-shot without any drop in performance when the number of objects changes. We consider the generic task of bringing a specific cube out of a set to a goal position. We find that previous approaches, which primarily leverage attention and graph neural network-based architectures, do not generalize their skills when the number of input objects changes while scaling as $K^2$. We propose an alternative plug-and-play module based on relational inductive biases to overcome these limitations. Besides exceeding performances in their training environment, we show that our approach, which scales linearly in $K$, allows agents to extrapolate and generalize zero-shot to any new object number.

A robotics bootcamp is an opportunity for people to build engineer robots while learning AI, visual coding, and machine learning. Robotic bootcamps are a fun-based yet informative and tactic-learning camp. In today's era, technology has led to huge progress in our society in both economic and social ways. From children to teens, to elders every individual is dependent on technology in some or the other way. Few learn it to become engineers whereas few for fun and knowledge.

TEHRAN – Iran will be placed among the top 10 countries in artificial intelligence (AI) by 2032 based on the national document on artificial intelligence strategy, Shahram Moein, head of innovation and development center of artificial intelligence at the Research Institute of Information and Communication Technology, said. The study of the national artificial intelligence development roadmap started a year ago at the Research Institute of Information and Communication Technology, which was officially completed by the end of November 2021. "To draft this document, strategic documents of 23 countries in the field of artificial intelligence were evaluated so that areas such as environment, health, transportation, online education, energy, robotics, industry, agriculture, and security development are among the priority areas of AI. Among the goals of this document are 80 percent of research to meet the needs of the country, use of 45 percent of artificial intelligence in industries, $8 billion investment in artificial intelligence and a 12 percent share of AI in the GDP," he explained. Stating that this document contains 14 macro policies, he said that supporting the AI products, laying the groundwork for the development of this technology, strengthening companies active in this field, and using artificial intelligence in solving super challenges are among these macro policies.

Hello guys, if you are learning Python or Data Science and want to learn the NumPy library and look for the best resources, you have come to the right place. Earlier, I have shared the best Python courses and the best courses to learn Data Science. In this article, I will share the best courses to learn NumPy library, one of the most popular Python libraries for numerical calculation. If you don't know, Python is almost the popular programming language and has dominated every business sector from web development to making artificial intelligent models and IoT devices and is considered the most-loved language among data scientists and analyzers. Learning this language is straightforward, but you also need to know its packages, which allow you to visualize your data using matplotlib, for example, or create a deep neural network using Keras or TensorFlow or PyTorch and maybe build a web application using Flask or Django.

The Visual Artificial Intelligence Laboratory https://lnkd.in/e_mABMr is a fast-growing research unit currently running on a budget of £3 million from nine live projects funded by #horizon2020 (2), #innovateuk (2), #Leverhulme, @Huawei, the British Council and others. We are leaders in the field of #deeplearning for #actiondetection and event detection, but our research interests span artificial intelligence, #uncertainty theory, #machinelearning, #computervision, #autonomousdriving, surgical and mobile #robotics, #aihealthcare. The Lab is currently pioneering frontier topics in AI such as #machine theory of mind, self-supervised learning, continual learning and future event prediction. You will join a vibrant and fast growing team foreseen to comprise 35 people in 2022. You will support Prof Cuzzolin in the overall management of the project. You will be working on a network of cutting edge 4-GPU and 8-GPU workstations, mentor #phd and #msc students and seek additional external #funding to complement the Lab's existing activities. You are encouraged to contact Prof Cuzzolin at fabio.cuzzolin@brookes.ac.uk for more information and an informal feedback on your application. To apply, please follow the instructions you can find here: https://lnkd.in/g_esfDmp

The DONUts platform may look like a collection of bronze-colored, futuristic coffee cups, but everything becomes clearer as they begin to move. The group of modular robots dance in a well-choreographed symphony as magnets turn on and off allowing the modules to pull or push their neighbors. Using these simple interactions, the modular robots can achieve complex tasks such as energy harvesting [1]. Nialah Wilson is one of the key roboticists who helped bring these modular robots to life. Taking advantage of the right message, passed at the right time, is also one of the things that led to Nialah's career in robotics.