Ten years on, Brexit's economic impact is becoming clearer Not long after the UK left the EU in 2020, a Bristol-based firm called Eskimo started selling a new kind of high-fashion and energy-efficient electric radiator, based on new technology developed by academics in the city. They planned to send them around Europe using the Channel Tunnel. It was a timely product given Europe's green ambitions, and with orders flowing, its Birmingham factory was being kept busy. The boss Phil Ward tells me his start-up has continued to grow, but that in his view it could have been so much more without what he calls the Long Brexit effect: in 2020, 40% of his exports went to the European Union, and by 2025 it was just 5%. The post-Brexit deal agreed with the EU by then-Prime Minister Boris Johnson in December 2020 guaranteed zero tariffs on exports to the EU, but Ward says that despite this, red tape and paperwork not directly related to tariffs were enough to create delays, costs and the expectation of hassle for prospective customers.

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Has Brexit achieved its objectives six years on? Money Works Has Brexit achieved its objectives six years on? Brexit aimed to achieve greater national sovereignty and an economic revival. Six years after leaving the EU, has it achieved its objectives? Are we overestimating the value of AI? Can Syria's new banknotes rebuild the economy?

In this work, we explore the capability of Large Language Models (LLMs) to annotate hate speech and abusiveness while considering predefined annotator personas within the strong-to-weak data perspectivism spectra. We evaluated LLM-generated annotations against existing annotator modeling techniques for perspective modeling. Our findings show that LLMs selectively use demographic attributes from the personas. We identified prototypical annotators, with persona features that show varying degrees of alignment with the original human annotators. Within the data perspectivism paradigm, annotator modeling techniques that do not explicitly rely on annotator information performed better under weak data perspectivism compared to both strong data perspectivism and human annotations, suggesting LLM-generated views tend towards aggregation despite subjective prompting. However, for more personalized datasets tailored to strong perspectivism, the performance of LLM annotator modeling approached, but did not exceed, human annotators.

Behavioural analytics provides insights into individual and crowd behaviour, enabling analysis of what previously happened and predictions for how people may be likely to act in the future. In defence and security, this analysis allows organisations to achieve tactical and strategic advantage through influence campaigns, a key counterpart to physical activities. Before action can be taken, online and real-world behaviour must be analysed to determine the level of threat. Huge data volumes mean that automated processes are required to attain an accurate understanding of risk. We describe the mathematical basis of technologies to analyse quotes in multiple languages. These include a Bayesian network to understand behavioural factors, state estimation algorithms for time series analysis, and machine learning algorithms for classification. We present results from studies of quotes in English, French, and Arabic, from anti-violence campaigners, politicians, extremists, and terrorists. The algorithms correctly identify extreme statements; and analysis at individual, group, and population levels detects both trends over time and sharp changes attributed to major geopolitical events. Group analysis shows that additional population characteristics can be determined, such as polarisation over particular issues and large-scale shifts in attitude. Finally, MP voting behaviour and statements from publicly-available records are analysed to determine the level of correlation between what people say and what they do.

Paraphrasing is the task of re-writing an input text using other words, without altering the meaning of the original content. Conversational systems can exploit automatic paraphrasing to make the conversation more natural, e.g., talking about a certain topic using different paraphrases in different time instants. Recently, the task of automatically generating paraphrases has been approached in the context of Natural Language Generation (NLG). While many existing systems simply consist in rule-based models, the recent success of the Deep Neural Networks in several NLG tasks naturally suggests the possibility of exploiting such networks for generating paraphrases. However, the main obstacle toward neural-network-based paraphrasing is the lack of large datasets with aligned pairs of sentences and paraphrases, that are needed to efficiently train the neural models. In this paper we present a method for the automatic generation of large aligned corpora, that is based on the assumption that news and blog websites talk about the same events using different narrative styles. We propose a similarity search procedure with linguistic constraints that, given a reference sentence, is able to locate the most similar candidate paraphrases out from millions of indexed sentences. The data generation process is evaluated in the case of the Italian language, performing experiments using pointer-based deep neural architectures.

For a prolific art collector, Nicolai Tangen is remarkably relaxed about the prospect of masterpieces created by robots. The threat of AI-made paintings, impossible to distinguish from human brushstrokes, has sparked soul-searching and paranoia in the art world, but not with Tangen. "Hey, if it creates better art that's fantastic," says the Norwegian philanthropist, art historian and boss of the world's biggest sovereign wealth fund. "If you create something which is even more aesthetically pleasing, what's wrong about that?" Tangen's own gallery, a converted grain silo in the Norwegian seaside resort of Kristiansand, will open later this year to display one of the world's biggest collections of Nordic modernist art. Tangen has amassed more than 5,000 works by 300 artists.

Finding a best response policy is a central objective in game theory and multi-agent learning, with modern population-based training approaches employing reinforcement learning algorithms as best-response oracles to improve play against candidate opponents (typically previously learnt policies). We propose Best Response Expert Iteration (BRExIt), which accelerates learning in games by incorporating opponent models into the state-of-the-art learning algorithm Expert Iteration (ExIt). BRExIt aims to (1) improve feature shaping in the apprentice, with a policy head predicting opponent policies as an auxiliary task, and (2) bias opponent moves in planning towards the given or learnt opponent model, to generate apprentice targets that better approximate a best response. In an empirical ablation on BRExIt's algorithmic variants against a set of fixed test agents, we provide statistical evidence that BRExIt learns better performing policies than ExIt.

Is it going to take over the world? All questions a novice like myself is thinking whenever someone far more clued-up on the ever changing advancement of technology turns the conversation onto the dreaded topic of Artificial Intelligence (AI). Usually I let them dribble on and myself stay silent in the hope that our chat comes to an end, however, you illustrators and writers out there may want to be paying close attention to the recent craze sweeping through twitter boards and reddit threads. Open AI (based in San Francisco) has been growing in popularity recently on account of its new Playground and Dall-E 2 systems. The Playground system is a new predictive language tool in which you input a question or a command and in a matter of seconds an AI responds with cohesive and calculated language.

Polarization and echo chambers are often studied in the context of explicitly political events such as elections, and little scholarship has examined the mixing of political groups in non-political contexts. A major obstacle to studying political polarization in non-political contexts is that political leaning (i.e., left vs right orientation) is often unknown. Nonetheless, political leaning is known to correlate (sometimes quite strongly) with many lifestyle choices leading to stereotypes such as the "latte-drinking liberal." We develop a machine learning classifier to infer political leaning from non-political text and, optionally, the accounts a user follows on social media. We use Voter Advice Application results shared on Twitter as our groundtruth and train and test our classifier on a Twitter dataset comprising the 3,200 most recent tweets of each user after removing any tweets with political text. We correctly classify the political leaning of most users (F1 scores range from 0.70 to 0.85 depending on coverage). We find no relationship between the level of political activity and our classification results. We apply our classifier to a case study of news sharing in the UK and discover that, in general, the sharing of political news exhibits a distinctive left-right divide while sports news does not.