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Can we 'see' photons, black holes, curved spacetime, quantum jumps, the expansion of the universe, or quanta of space? Physics challenges appearances, showing convincingly that our everyday vision of reality is limited, approximate and badly incomplete. Established theories such as quantum theory and general relativity and investigations like loop quantum gravity have a reputation of obscurity. Many suggest that science is forcing us into a counterintuitive and purely mathematical understanding of reality. I disagree. I think that there is a visionary core at the root of the best science. Where 'visionary' truly means formed by visual images. Our mind, even when dealing with abstract and difficult notions, relies on images, metaphors and, ultimately, vision. Contrary to what is sometimes claimed, science is not just about making predictions: it is about understanding, and, for this, developing new eyes to see. I shall illustrate this point with some concrete cases, including the birth of quantum theory in Einstein’s intuition, curved spacetimes and quanta of space.
Sophie Hackford explores the idea that the way that computers see the world is becoming our dominant reality. The idea that a physical object, and its data ‘exhaust’, are in constant dialogue with each other. As machine autonomy creeps into our everyday lives, we are creating a physical internet, where people, objects, vehicles move as seamlessly in the real world as data moves around the internet. Digital bots or ‘agents’ might represent us in interactions with our banks, friends, colleagues. Autonomous companies might soon be big players in the economy. Hackford will explore a world where human and machine ‘vision’ will collaborate, compete and even merge together.
When Turner daubed a red buoy in his seascape Helvoetsluys, what did he mean? In nature, red may repel or attract, signalling toxicity or ripeness, anger, ruddy health or sexual readiness. For Turner, the red created contrast, and in making that mark, he meant to generate salience and arouse interest, to dominate his rivals and draw in his admirers. Colour has long excited emotions and intellectual debate, not only for visual art, but also in philosophy, psychology and physiology. In contemporary vision science studies, colour helps people find objects faster, discern material properties, learn, conceptualise and memorise. Yet colour is made in the mind, not out there in the world. It is a subjective phenomenon, a personal possession, one that varies between individual eyes, and one that people cling to with ardour when challenged: witness the public divide over the 'blue/black', 'white/gold' dress. So the question is not only what does colour mean, in life and in art, but how does it mean anything? How does the human brain create colour, stabilise it, and make its meaning? And why does it evoke emotion and aesthetic appreciation?
Eyes abound in the animal kingdom. Some are large as basketballs and others are just fractions of a millimetre. Eyes also come in many different types, such as the compound eyes of insects, the mirror eyes of scallops or our own camera-like eyes. Common to all animal eyes is that they serve the same fundamental role of collecting external information for guiding the animal’s behaviour. But behaviours vary tremendously across the animal kingdom, and it turns out this is the key to understanding how eyes evolved. In the lecture we will take a tour from the first animals that could only sense the presence of light, to those that saw the first crude image of the world and finally to animals that use acute vision for interacting with other animals. Amazingly, all these stages of eye evolution still exist in animals living today, and this is how we can unravel the evolution of behaviours that has been the driving force behind eye evolution.
Argument mining (AM) aims to explain how individual argumentative discourse units (e.g. sentences or clauses) relate to each other and what roles they play in the overall argumentation. The automatic recognition of argumentative structure is attractive as it benefits various downstream tasks, such as text assessment, text generation, text improvement, and summarization. Existing studies focused on analyzing well-written texts provided by proficient authors. However, most English speakers in the world are non-native, and their texts are often poorly structured, particularly if they are still in the learning phase. Yet, there is no specific prior study on argumentative structure in non-native texts. In this article, we present the first corpus containing argumentative structure annotation for English-as-a-foreign-language (EFL) essays, together with a specially designed annotation scheme. The annotated corpus resulting from this work is called “ICNALE-AS” and contains 434 essays written by EFL learners from various Asian countries. The corpus presented here is particularly useful for the education domain. On the basis of the analysis of argumentation-related problems in EFL essays, educators can formulate ways to improve them so that they more closely resemble native-level productions. Our argument annotation scheme is demonstrably stable, achieving good inter-annotator agreement and near-perfect intra-annotator agreement. We also propose a set of novel document-level agreement metrics that are able to quantify structural agreement from various argumentation aspects, thus providing a more holistic analysis of the quality of the argumentative structure annotation. The metrics are evaluated in a crowd-sourced meta-evaluation experiment, achieving moderate to good correlation with human judgments.
The 34th Darwin College Lecture Series, held in 2019, addressed Vision. The aim of these lectures, as with all the Darwin College Lectures, was to provide an interdisciplinary study. The lectures range widely: they survey the mechanisms of visual perception, and the evolution of eyes; they address the mental processes underpinning vision, and the nature and significance of private visions and hallucinations; they explore the vision and imagery of artists and of scientists in their endeavours to elucidate the world. The discussions encompass astronomical observation, which enables us to look back over the evolution of the Universe to the earliest epochs, and they extend to foresight, with a vision of a digital future. We conclude this volume with a review of the current developments of computer vision, which increasingly underpin our day-to-day experience of surveillance and of automation.
The brain strives to become a model of the world in which it must survive. It is often more important for it to be functional and efficient than it is to be factually correct. Indeed, there are numerous instances in which it seems to favour usefulness over accuracy, expectation over actuality. This has led many to conclude that even normal perception has a constructive or hallucinatory quality. In extremis, under the influence of fatigue, fear, illness or drugs, an entire reality may be created, one that seems to conflict with the reality accepted by those around us. This condition, known as psychosis, offers us important glimpses into the mechanisms of the mind and the many ways in which they may be altered.
Language resources and computational models are becoming increasingly important for the study of language variation. A main challenge of this interdisciplinary field is that linguistics researchers may not be familiar with these helpful computational tools and many NLP researchers are often not familiar with language variation phenomena. This essential reference introduces researchers to the necessary computational models for processing similar languages, varieties, and dialects. In this book, leading experts tackle the inherent challenges of the field by balancing a thorough discussion of the theoretical background with a meaningful overview of state-of-the-art language technology. The book can be used in a graduate course, or as a supplementary text for courses on language variation, dialectology, and sociolinguistics or on computational linguistics and NLP. Part 1 covers the linguistic fundamentals of the field such as the question of status and language variation. Part 2 discusses data collection and pre-processing methods. Finally, Part 3 presents NLP applications such as speech processing, machine translation, and language-specific issues in Arabic and Chinese.
We give an in-depth account of compositional matrix-space models (CMSMs), a type of generic models for natural language, wherein compositionality is realized via matrix multiplication. We argue for the structural plausibility of this model and show that it is able to cover and combine various common compositional natural language processing approaches. Then, we consider efficient task-specific learning methods for training CMSMs and evaluate their performance in compositionality prediction and sentiment analysis.
The wide usage of multiple spoken Arabic dialects on social networking sites stimulates increasing interest in Natural Language Processing (NLP) for dialectal Arabic (DA). Arabic dialects represent true linguistic diversity and differ from modern standard Arabic (MSA). In fact, the complexity and variety of these dialects make it insufficient to build one NLP system that is suitable for all of them. In comparison with MSA, the available datasets for various dialects are generally limited in terms of size, genre and scope. In this article, we present a novel approach that automatically develops an annotated country-level dialectal Arabic corpus and builds lists of words that encompass 15 Arabic dialects. The algorithm uses an iterative procedure consisting of two main components: automatic creation of lists for dialectal words and automatic creation of annotated Arabic dialect identification corpus. To our knowledge, our study is the first of its kind to examine and analyse the poor performance of the MSA part-of-speech tagger on dialectal Arabic contents and to exploit that in order to extract the dialectal words. The pointwise mutual information association measure and the geographical frequency of word occurrence online are used to classify dialectal words. The annotated dialectal Arabic corpus (Twt15DA), built using our algorithm, is collected from Twitter and consists of 311,785 tweets containing 3,858,459 words in total. We randomly selected a sample of 75 tweets per country, 1125 tweets in total, and conducted a manual dialect identification task by native speakers. The results show an average inter-annotator agreement score equal to 64%, which reflects satisfactory agreement considering the overlapping features of the 15 Arabic dialects.
Automated writing assistance – a category that encompasses a variety of computer-based tools that help with writing – has been around in one form or another for 60 years, although it’s always been a relatively minor part of the NLP landscape. But the category has been given a substantial boost from recent advances in deep learning. We review some history, look at where things stand today, and consider where things might be going.