Showing papers by "Nello Cristianini published in 2021"
TL;DR: The authors used a word embedding method (GloVe) to extract gender and valence biases for blue, pink, and red, as well as for the remaining basic colour terms from a large English-language corpus containing six billion words.
Abstract: In Western societies, the stereotype prevails that pink is for girls and blue is for boys. A third possible gendered colour is red. While liked by women, it represents power, stereotypically a masculine characteristic. Empirical studies confirmed such gendered connotations when testing colour-emotion associations or colour preferences in males and females. Furthermore, empirical studies demonstrated that pink is a positive colour, blue is mainly a positive colour, and red is both a positive and a negative colour. Here, we assessed if the same valence and gender connotations appear in widely available written texts (Wikipedia and newswire articles). Using a word embedding method (GloVe), we extracted gender and valence biases for blue, pink, and red, as well as for the remaining basic colour terms from a large English-language corpus containing six billion words. We found and confirmed that pink was biased towards femininity and positivity, and blue was biased towards positivity. We found no strong gender bias for blue, and no strong gender or valence biases for red. For the remaining colour terms, we only found that green, white, and brown were positively biased. Our finding on pink shows that writers of widely available English texts use this colour term to convey femininity. This gendered communication reinforces the notion that results from research studies find their analogue in real word phenomena. Other findings were either consistent or inconsistent with results from research studies. We argue that widely available written texts have biases on their own, because they have been filtered according to context, time, and what is appropriate to be reported.
8 citations
TL;DR: It is argued that autonomous social machines provide a new paradigm for the design of intelligent systems, marking a new phase in AI.
Abstract: Social machines are systems formed by material and human elements interacting in a structured way. The use of digital platforms as mediators allows large numbers of humans to participate in such machines, which have interconnected AI and human components operating as a single system capable of highly sophisticated behaviour. Under certain conditions, such systems can be understood as autonomous goal-driven agents. Many popular online platforms can be regarded as instances of this class of agent. We argue that autonomous social machines provide a new paradigm for the design of intelligent systems, marking a new phase in AI. After describing the characteristics of goal-driven social machines, we discuss the consequences of their adoption, for the practice of artificial intelligence as well as for its regulation.
5 citations
TL;DR: In this article, the authors analyzed the aggregated Twitter content of 54 UK cities in the 9 weeks of complete lockdown, comparing them with the 10 weeks that preceded them, as well as with the corresponding weeks of 2019.
Abstract: Diurnal variation in psychometric indicators of emotion found in Twitter content has been known for many years. The degree to which this pattern depends upon different environmental zeitgebers has been difficult to determine. The nationwide lockdown in the United Kingdom in spring 2020 provided a unique government-mandated experiment to observe the temporal variation of psychometric indicators in the absence of certain specific social rhythms related to commuting and workplace social activities as well as many normal home-based social activities. We therefore analyzed the aggregated Twitter content of 54 UK cities in the 9 weeks of complete lockdown, comparing them with the 10 weeks that preceded them (as well as with the corresponding weeks of 2019). We observed that the key indicators of emotion retained their diurnal behavior. This suggests that even during lockdown there are still sufficient zeitgebers to maintain this diurnal variation in indicators of emotion.
1 citations
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TL;DR: The authors developed an Artificial Intelligence tool that can be used to perform various processing tasks such as detection of equivalence between questions, detection of topic and type, and answering of the question.
Abstract: Every day people ask short questions through smart devices or online forums to seek answers to all kinds of queries. With the increasing number of questions collected it becomes difficult to provide answers to each of them, which is one of the reasons behind the growing interest in automated question answering. Some questions are similar to existing ones that have already been answered, while others could be answered by an external knowledge source such as Wikipedia. An important question is what can be revealed by analysing a large set of questions. In 2017, "We the Curious" science centre in Bristol started a project to capture the curiosity of Bristolians: the project collected more than 10,000 questions on various topics. As no rules were given during collection, the questions are truly open-domain, and ranged across a variety of topics. One important aim for the science centre was to understand what concerns its visitors had beyond science, particularly on societal and cultural issues. We addressed this question by developing an Artificial Intelligence tool that can be used to perform various processing tasks: detection of equivalence between questions; detection of topic and type; and answering of the question. As we focused on the creation of a "generalist" tool, we trained it with labelled data from different datasets. We called the resulting model QBERT. This paper describes what information we extracted from the automated analysis of the WTC corpus of open-domain questions.