A major challenge in the Deep RL (DRL) community is to train agents able to generalize over unseen situations, which is often approached by training them on a diversity of tasks (or environments). A powerful method to foster diversity is to procedurally generate tasks by sampling their parameters from a multi-dimensional distribution, enabling in particular to propose a different task for each training episode. In practice, to get the high diversity of training tasks necessary for generalization, one has to use complex procedural generation systems. With such generators, it is hard to get prior knowledge on the subset of tasks that are actually learnable at all (many generated tasks may be unlearnable), what is their relative difficulty and what is the most efficient task distribution ordering for training. A typical solution in such cases is to rely on some form of Automated Curriculum Learning (ACL) to adapt the sampling distribution. One limit of current approaches is their need to explore the task space to detect progress niches over time, which leads to a loss of time. Additionally, we hypothesize that the induced noise in the training data may impair the performances of brittle DRL learners. We address this problem by proposing a two stage ACL approach where 1) a teacher algorithm first learns to train a DRL agent with a high-exploration curriculum, and then 2) distills learned priors from the first run to generate an "expert curriculum" to re-train the same agent from scratch. Besides demonstrating 50% improvements on average over the current state of the art, the objective of this work is to give a first example of a new research direction oriented towards refining ACL techniques over multiple learners, which we call Classroom Teaching.

Thanks to AHRC Collaborative Doctoral Partnership funding held jointly by Historic Environment Scotland (HES) and the University of Glasgow, we are offering a 45 month (3.75 years) PhD scholarship on developing approaches to integrate automation-led detection routines into workflows used in the professional practice of archaeological prospection and landscape archaeology, notably for large scale heritage management. The supervisors will be Dr Rachel Opitz (Archaeology) and Dr Jan Paul Siebert (Computer Science) at University of Glasgow, and Dr Lukasz Banaszek and Mr David Cowley (HES). Automated detection routines have been viewed as potentially useful or even transformative for several decades, and recent progress in artificial intelligence (AI) based in machine learning and computer vision has moved these approaches from potentially interesting to practically implementable across a variety of applications. Within archaeology, the potential of AI-led approaches and heavily automated image processing for partially automating the identification of archaeological features and landscape changes has been demonstrated in several studies. Their implementation has brought measurable benefits, leading to increased investment in their development. While the technologies themselves are being pursued, less attention has been paid to the analytical and interpretive frameworks within which semi-automated computational approaches to feature identification, notably AIs, are integrated into practices of archaeological landscape interpretation, particularly within heritage management bodies.

Artificial intelligence (AI) is no longer the realm of science fiction. AI is quickly becoming a powerful new technology and is set to disrupt any sector that deals with large amounts of data, and education is no different. The academic world is still considered one of the most human sectors -- the most human of the humanities -- but that doesn't mean there aren't ways in which teachers and school workers can benefit from implementing artificial intelligence. Just like any other industry, teachers deal with a huge amount of admin, and they are often having to spread their finite time between an ever-growing student body. As a result, the quality and relevance of education is becoming difficult to maintain.

The latest research from Cisco says that global internet traffic will reach 4.8 zetabytes a year in 2022, or 150,700 gigabytes a second. That research was published before the current coronavirus pandemic, which may well have a dramatic change in the shape and per-type breakdown of global internet traffic as face-to-face meetings are being overwhelmingly replaced with video conference calls and live video streaming. For example, NAB, the biggest event of the year in media production and distribution, has recently announced it will switch to a virtual conference for the 2020 year, with live presentations and meetings taking place via video streamed over the web. We are all aware of the bandwidth issues and contention that can pose a risk for internet connections. This is particularly true for video transfer over IP because of its need for sustained and consistent data rates and for low-latency packet delivery.

Open Domain Question Answering requires systems to retrieve external knowledge and perform multi-hop reasoning by composing knowledge spread over multiple sentences. In the recently introduced open domain question answering challenge datasets, QASC and OpenBookQA, we need to perform retrieval of facts and compose facts to correctly answer questions. In our work, we learn a semantic knowledge ranking model to re-rank knowledge retrieved through Lucene based information retrieval systems. We further propose a ``knowledge fusion model'' which leverages knowledge in BERT-based language models with externally retrieved knowledge and improves the knowledge understanding of the BERT-based language models. On both OpenBookQA and QASC datasets, the knowledge fusion model with semantically re-ranked knowledge outperforms previous attempts.

The connection between messaging and action is fundamental both to web applications, such as web search and sentiment analysis, and to economics. However, while prominent online applications exploit messaging in natural (human) language in order to predict non-strategic action selection, the economics literature focuses on the connection between structured stylized messaging to strategic decisions in games and multi-agent encounters. This paper aims to connect these two strands of research, which we consider highly timely and important due to the vast online textual communication on the web. Particularly, we introduce the following question: can free text expressed in natural language serve for the prediction of action selection in an economic context, modeled as a game? In order to initiate the research on this question, we introduce the study of an individual's action prediction in a one-shot game based on free text he/she provides, while being unaware of the game to be played. We approach the problem by attributing commonsensical personality attributes via crowd-sourcing to free texts written by individuals, and employing transductive learning to predict actions taken by these individuals in one-shot games based on these attributes. Our approach allows us to train a single classifier that can make predictions with respect to actions taken in multiple games. In experiments with three well-studied games, our algorithm compares favorably with strong alternative approaches. In ablation analysis, we demonstrate the importance of our modeling choices -- the representation of the text with the commonsensical personality attributes and our classifier -- to the predictive power of our model.

One of the main reasons to integrate AI in the current school curriculum is to make the upcoming generation familiar with technology. The Government of India and the educational board have been pushing for more artificial intelligence to be integrated into the education system, not from the perspective of enhancing it, but also with the intention of making young minds more aware and skilled when it comes to artificial intelligence. Today, children are curious about the smart conversational devices and AI used in applications like Siri and Alexa; some of them even wonder how Netflix gives them precise recommendations. Gradually, they will grow curious and try to learn what algorithms are, what a neural network is, and how they work. The Government of India and the educational board have been taking measures to make the existing school curriculum more AI-centric with a firm belief that the students will learn about AI, have fun and also take India forward.

Working at Royal Dutch Shell's Deepwater division in New Orleans gives Barbara Waelde a front-row seat to how the right data can unlock crucial information for the oil giant. So when her supervisor asked her last year if she was interested in a program that could sharpen her digital and data science capabilities, Waelde, 55, jumped at the chance. Since she began her online coursework, the seven-year Shell veteran has learned Python programming, supervised learning algorithms and data modeling, among other skills. Shell began making these online courses available to U.S. employees long before COVID-19 upended daily life. And according to the oil giant, there are no plans to halt or cancel any of them, despite the fact that on March 23 it announced plans to slash operating costs by $9 billion.

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This research work deals with Natural Language Processing (NLP) and extraction of essential information in an explicit form. The most common among the information management strategies is Document Retrieval (DR) and Information Filtering. DR systems may work as combine harvesters, which bring back useful material from the vast fields of raw material. With large amount of potentially useful information in hand, an Information Extraction (IE) system can then transform the raw material by refining and reducing it to a germ of original text. A Document Retrieval system collects the relevant documents carrying the required information, from the repository of texts. An IE system then transforms them into information that is more readily digested and analyzed. It isolates relevant text fragments, extracts relevant information from the fragments, and then arranges together the targeted information in a coherent framework. The thesis presents a new approach for Word Sense Disambiguation using thesaurus. The illustrative examples supports the effectiveness of this approach for speedy and effective disambiguation. A Document Retrieval method, based on Fuzzy Logic has been described and its application is illustrated. A question-answering system describes the operation of information extraction from the retrieved text documents. The process of information extraction for answering a query is considerably simplified by using a Structured Description Language (SDL) which is based on cardinals of queries in the form of who, what, when, where and why. The thesis concludes with the presentation of a novel strategy based on Dempster-Shafer theory of evidential reasoning, for document retrieval and information extraction. This strategy permits relaxation of many limitations, which are inherent in Bayesian probabilistic approach.