In the book, Dan argues that Artificial Intelligence (AI) is everywhere, yet it causes damage to society in ways that can’t be fixed. Instead of helping to address our current crises, AI causes divisions that limit people’s life chances, and even suggests fascistic solutions to social problems. This book provides an analysis of AI’s deep learning technology and its political effects and traces the ways that it resonates with contemporary political and social currents, from global austerity to the rise of the far right.
“Resisting AI” argues that AI is harmful by nature and has already become a kind of algorithmic violence. Not only that, but its application to crises like austerity and climate change moves the needle quickly to far right and even fascistic solutions. The book argues for a different approach based on a bottom up politics of people’s councils that can not only resist AI but radically restructure it. The only acceptable future for advanced computation is to support socially useful production and the common good.
«The effect of AI on work & social life isn’t the alleviation of routine labour but the amplification of precarity. Our most advanced computation helps turn the clock back on 100 years of hard won rights & protections.»
With “Resisting AI”, Dr Dan McQuillan calls for us to resist AI as we know it and restructure it by prioritising the common good over algorithmic optimisation. He sets out an anti-fascist approach to AI that replaces exclusions with caring, proposes people’s councils as a way to restructure AI through mutual aid and outlines new mechanisms that would adapt to changing times by supporting collective freedom.
«AI advocates talk about Artificial General Intelligence without acknowledging its roots in Victorian eugenics. Meanwhile, real world deep learning is entangled with operations that come to decide who’s life is disposable.»
Academically rigorous, yet accessible to a socially engaged readership, this unique book will be of interest to all who wish to challenge the social logic of AI by reasserting the importance of the common good.
Dan developed his ideas after a lifetime spent studying how technology impacts impacts our lives from a social and ethical point of view. After a degree in Physics from Oxford and a PhD in Experimental Particle Physics from Imperial College London, his career focused on supporting and advocating for people’s right.
In blind people, the visual cortex takes on higher cognitive functions, including language. Why this functional reorganisation mechanistically emerges at the neuronal circuit level is still unclear. Here, we use a biologically constrained network model implementing features of anatomical structure, neurophysiological function and connectivity of fronto-temporal-occipital areas to simulate word-meaning acquisition in visually deprived and undeprived brains. We observed that, only under visual deprivation, distributed word-related neural circuits ‘grew into’ the deprived visual areas, which therefore adopted a linguistic-semantic role. Three factors are crucial for explaining this deprivation-related growth: changes in the network’s activity balance brought about by the absence of uncorrelated sensory input, the connectivity structure of the network, and Hebbian correlation learning. In addition, the blind model revealed long-lasting spiking neural activity compared to the sighted model during word recognition, which is a neural correlate of enhanced verbal working memory. The present neurocomputational model offers a neurobiological account for neural changes following sensory deprivation, thus closing the gap between cellular-level mechanisms, system-level linguistic and semantic function.
Dynamic new interactive technology which visualises the 3D structures inside DNA has been launched by a team of computational artists, game developers and scientists, working together to help the public better understand the cause of diseases.
CSynth is a software platform created by researchers at Goldsmiths, University of London and Oxford University. Described by its designers as ‘bio-visualisation made interactive’, it shows how cell machinery physically interacts with a structure as complex and compact as the genome.
Viewers can watch and explore the 3D models on a screen, or use a Virtual Reality headset to immerse themselves in genetic material and manipulate it themselves.
Traditionally, scientists have only been able to visualise and understand the genome – the complete set of genetic material present in a cell – in 2D presentations, on a screen or through graphs or histograms.
But as researchers gather more data about how cells work it is clear that a 3D structure is extremely important for gene regulation and how cells differentiate. For example, a white blood cell looks and behaves differently to a red blood cell even though its genome is exactly the same.
Subtle differences in the way the genome is folded can impact on whether genes can be switched on and off at particular times, which then dictates what a cell can do. Changes in the way chromatin is folded can cause rare blood diseases, for example, because it impacts on how genetic code is read by a cell.
Understanding this process is vital for seeking the cause of diseases such as diabetes or anaemia, and for the development of treatments for them.
Thanks to advances in genetic techniques, researchers are able to harness more information than ever before from biological data provided by patients and volunteers.
The CSynth software then integrates data from genome sequencing and computer modelling and presents it in an attractive and engaging way, using computer game technology.
The team have launched a complete software package that will also allow the import of public data, and help both the public and medical researchers gain a better understanding of how the genome is folded in a cell, and the complex mechanisms involved.
Professor Frederic Fol Leymarie and Professor William Latham from the Department of Computing at Goldsmiths are the computer artists and software designers behind CSynth, working with Steve Taylor, Head of Analysis, Visualisation and Informatics at the WIMM Centre of Computational Biology, and Professor Jim Hughes at the MRC Weatherall Institute of Molecular Medicine, University of Oxford. They are joined by Professor Stephen Todd, lead software architect at London Geometry Ltd and Visiting Professor in Computing at Goldsmiths, and Peter Todd, senior developer, London Geometry Ltd.
Steve Taylor said: “We have made a web-based interface where any researcher can load in the data from their experiments. Previously the software had to be installed and all the parameters were adjusted in text files by us behind the scenes. Now you can upload or drag and drop the data into a web page, and it will build a model allowing investigators to really get a handle on their data. You also get a fantastic user interface to interact with the model and overlay other data, such as genes and enhancers. We get asked a lot about making CSynth available for teaching and and now we can do this easily.”
Professor Fol Leymarie said: “Our body is made of trillions of cells, each one containing chromatin tightly folded. This very long molecular strand is not static, but rather keeps moving, vibrating, unfolding and refolding locally, more like a molecular dance.
“Furthermore, it keeps interacting with other molecular structures present in the cell and with itself. It is this dynamic nature that CSynth makes visible and interactive, so that a user – a researcher, student or even a curious member of the public – can load different data sequences, try out various parameters, compare various situations, to eventually get a much better, intuitive understanding, which we hope may help lead to new discoveries.”
People create their own ‘secret languages’ by attaching lasting alternative meanings to emoji unrelated to what they are designed to represent, according to a study from Goldsmiths Computing.
In people’s secret languages emoji of pizza or wedges of cheese mean ‘I love you’ (because these were foods people love), a bathtub emoji means a coffin (because it was the closest to a coffin shape), and a thinking face means ‘lesbian’ (because the position of the thumb and forefinger on the chin means ‘lesbian’ in American Sign Language).
These alternative meanings can be assigned randomly but become permanent and are used consistently over time between partners, friends, or family members, the research found.
The study, by researchers from Goldsmiths and the University of Birmingham, is due to be presented at the Computer Human Interaction 2018 conference in Montreal, Canada (21-26 April 2018).
In 2016 there was a furious customer backlash against Apple for changing the rendering of its peach emoji to look smoother. Researchers found that most Apple users were using this emoji to refer to buttocks, with only 7% referring to the foodstuff, and were angry the redrawn emoji did not fit this alternative meaning.
The Goldsmiths-led team launched an online survey to investigate how individuals personalise emoji to create ‘secret’ meanings. Those responding reported repurposing 69 different emoji for secret communication with the most common emoji chosen being an octopus, the most common emoji for an affectionate name being a penguin, and the most common category of emoji used ‘Animals & Nature’.
Dr Sarah Wiseman, lecturer in Computer Science at Goldsmiths and co-author of the study, said: “While we know some fruit and vegetable emoji have been repurposed by many people to mean something else, we were intrigued to find out about personal instances of this – examples of emoji that have a special meaning for just two people. Often this was about more than just typing something more quickly: people found that by using emoji they could convey very complex meanings and thoughts with them that could not be described in words.”
Of the survey’s 72 respondents (134 participants in total) who reported repurposing emoji:
47% exchanged them with partners and 28% exchanged with friends
21% used the emoji to express some form of affection
19% used them to symbolise a particular person or pet
7% used them to refer to sex
6% used them to be covert while referring to sex or illegal activity
Dr Sarah Wiseman said: “Our study shows that people use emoji in a similar way to nicknames or slang, as a handy shortcut to what they mean, which through consistent use creates an intimate ‘secret language’ others don’t understand. Creators of emoji need to bear in mind the subtle way that people repurpose them and the impact even small visual changes to them could have on these alternative meanings.”
A new dissertation by MSc Data Science student Caroline Butler highlights the relationship between health and politics in the USA.
MSc Data Science student Caroline Butler has been investigating whether there is a relationship between mortality among middle-aged white Americans, social and economic well-being, and the 2016 presidential primary election outcomes at county-level.
Her research suggests that middle-aged white Americans living in counties with higher death rates are more cautious voters. That is, they are more likely to vote for a safe bet over a wildcard such as Trump.
After analysing data from the United States Center for Disease Control’s WONDER tool, the United States Census Bureau’s County QuickFacts, and the Kaggle forum, 2016 US Election, Caroline discovered a pattern connecting death rates to voting.
Contrary to expectations, a one unit increase in the all-cause mortality rate increased log odds of Hillary Clinton winning in that county’s Democratic presidential election primary by 1.5693 compared to Bernie Sanders. However, this result could have been skewed by Bernie Sanders’ younger fan base.
To Caroline’s surprise, a one unit increase in the all-cause mortality rate decreased log odds of Donald Trump winning his primary in a county by 1.4371.
The project was inspired by recent evidence that drug and alcohol poisoning, suicide and chronic liver diseases have caused the mortality rate among middle-aged white people in the United States to increase. At the same time, anti-establishment candidates, such as Donald Trump and Bernie Sanders, have achieved unexpected success.
In a follow-up investigation to her project, Caroline ran her data on mortality, socio-economic status of a county, and which state the counties were in through the CHAID machine learning algorithm, and found that with 85-89% accuracy, you could predict who would win the primary for each political party.
Her results suggest that for both white people and all races combined, the social and economic well-being of a county is as much related to the outcomes of the 2016 primary election as the mortality rates of middle aged Americans is.
“Understanding whether mortality data for middle-aged white Americans is associated with political viewpoints is important not only from a political perspective, but also for purposes of developing appropriate public health directives,” Caroline explains.
“I was surprised to find that in areas with higher mortality rates, people were more likely to vote for Clinton over Sanders in the primaries – but I’d suggest this could be because Sanders had a high number of young, so generally more healthy, voters.
“A similar study should definitely be done for the United States Presidential Election so we can compare the voting patterns from the Democratic Party to the votes from the Republican Party.”
Professor of Computing William Latham recently reviewed the book Mathematics and Art for New Scientist magazine. We reprint his review here, with added hyperlinks and images.
From Renaissance painters’ first use of perspective to artistic algorithms shaping 21st-century works, mathematics and art have a long, rich history. “Cells and tissues, shell and bone, leaf and flower, are so many portions of matter, and it is their obedience to the laws of physics that their particles have been moved, molded and conformed. Their problems of form are in the first instance mathematical problems,” wrote the Scottish polymath D’Arcy Wentworth Thompson in his influential 1917 book, On Growth and Form.
This is a text that the author of the excellent new book, Mathematics and Art, has taken to heart and built on. In 500-plus, sumptuously illustrated pages, Lynn Gamwell has interleaved mathematics and culture (art, in particular) from 3000 BC to the present day, as she works to show how artists have harnessed maths for their own creative goals and how the arts, albeit to a lesser extent, have influenced maths.
There are many telling examples. Take Piero della Francesca’s 1455 painting The Flagellation of Christ, in which he positioned Jesus in a three-dimensional, naturalistic scene rather than an out-of-scale figure on a flat, 2D plane as his early Renaissance predecessors such as Giotto had done. This was a radical and daring innovation. What made it possible was the painter’s use of a set of new mathematical rules, which we now call linear perspective, that had been invented by mathematician and architect Filippo Brunelleschi.
Brunelleschi had himself been influenced by an 11th-century Islamic treatise on optics and visual distortion that had helped shape his ideas on perspective. This single mathematical step was to influence the whole of Western art, as exemplified in works by Leonardo da Vinci, Hans Holbein, Albrecht Dürer, Salvador Dali and, of course, M. C. Escher.
“Early Renaissance artists no longer painted saints floating in a golden mist in a faraway place; linear perspective gave them the tool to depict Jesus and the apostles existing right here, right now before their eyes in the natural world,” writes Gamwell.
There have been many examples of these mathematical cross-overs: think of Mandelbrot’s fractal maths translated into psychedelic-style computer art in the 1980s, or the influence of quantum mechanics on post-modernist painting and sculpture. They may not all be of the same magnitude as Francesca’s use of perspective but they are significant, and it’s illuminating to discover the background to these innovations.
It’s also important to recognise how many mathematical fields inform art. Crystallography, celestial geometry, phyllotaxis, differential calculus – all helped to shape Renaissance art and movements such as surrealism, constructivism, pop art and minimalism.
Mathematics and Art is split in two, with the first section bringing us up to about 1900, and serving as a handbook for readers who want to choose specific topics. Among the mathematical gems and anecdotes, Gamwell cites conversations between da Vinci and Franciscan friar and mathematician Luca Pacioli discussing what would become Pacioli’s book, On The Divine Proportion. There are also reproductions of John Dalton’s rough but extraordinary diagrams of atomic elements from 1806.
The second half, post-1900, has fewer diagrams and works less well as a mathematical handbook. Instead, its strong suit is the presentation of the philosophical relationship between the arts and maths – as when Gamwell discusses the detail of quantum mechanics, taking Antony Gormley’s Quantum Cloud V sculpture as her hook.
Gamwell also dives into the compelling area of how we measure aesthetic value, citing George D. Birkhoff’s attempts in the 1930s to reduce aesthetics to a mathematical formula, M=O:C, or the amount of aesthetic pleasure produced by an object (M) equals the ratio of the object’s order (O) to its complexity (C).
“George D. Birkhoff attempted to reduce aesthetics to a single mathematical formula”
This is particularly relevant to the emerging field of creative robotics, where the goal is, apparently, to create a robot that will create art for its own aesthetic enjoyment, emulating the human creative process.
Gamwell must have had her work cut out deciding what to include and exclude in what aims to be a comprehensive tome. There are casualties. In the computation section, for example, it was right to make much of fractal mathematics, Alan Turing, John Conway’s Game of Life and computer artworks by Roman Verostko, Manfred Mohr and Yoichiro Kawaguchi. But some classic computer graphic algorithms are missing, such as Ken Perlin’s noise texture algorithm or the Blinn-Phong reflection model, which have had a major impact across the arts and in film.
And we really do need more than a brief reference to artist Robert Rauschenberg, composer John Cage and the Experiments in Art and Technology group’s show in 1966 at The Armory in New York. The group was set up to foster collaborations between artists and engineers through direct personal contact rather than through any kind of formal process. The creative talents that came together then helped define the work of a generation – and generations to come.
Overall this is a comprehensive, valuable and detailed book. It is written in an accessible style, with enough mathematics to interest the technical reader without overwhelming one with an arts background. It doesn’t quite rival Douglas Hofstadter’s hugely influential Gödel, Escher, Bach from 1979, but its rich anthology is particularly relevant today, given the explosion of interest in the digital arts and the need for digital artists to use maths creatively. I will definitely be keeping it close at hand.
Postgraduate degrees at Goldsmiths Computing include: