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"Highfield, Roger"
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The Science Museum Stephen Hawking genius at work : explore his life, mind and science through the objects in his study
In 2021, the Science Museum made a once-in-a-lifetime acquisition of the contents of Stephen Hawking's office. This book delves into that remarkable collection, using the seminal papers, items and curiosities in his office to explain his theories and reveal more about one of the greatest minds in modern science.
BOOK
Big data need big theory too
The current interest in big data, machine learning and data analytics has generated the widespread impression that such methods are capable of solving most problems without the need for conventional scientific methods of inquiry. Interest in these methods is intensifying, accelerated by the ease with which digitized data can be acquired in virtually all fields of endeavour, from science, healthcare and cybersecurity to economics, social sciences and the humanities. In multiscale modelling, machine learning appears to provide a shortcut to reveal correlations of arbitrary complexity between processes at the atomic, molecular, meso- and macroscales. Here, we point out the weaknesses of pure big data approaches with particular focus on biology and medicine, which fail to provide conceptual accounts for the processes to which they are applied. No matter their 'depth' and the sophistication of data-driven methods, such as artificial neural nets, in the end they merely fit curves to existing data. Not only do these methods invariably require far larger quantities of data than anticipated by big data aficionados in order to produce statistically reliable results, but they can also fail in circumstances beyond the range of the data used to train them because they are not designed to model the structural characteristics of the underlying system. We argue that it is vital to use theory as a guide to experimental design for maximal efficiency of data collection and to produce reliable predictive models and conceptual knowledge. Rather than continuing to fund, pursue and promote 'blind' big data projects with massive budgets, we call for more funding to be allocated to the elucidation of the multiscale and stochastic processes controlling the behaviour of complex systems, including those of life, medicine and healthcare.
This article is part of the themed issue ‘Multiscale modelling at the physics–chemistry–biology interface’.
Journal Article
Supercooperators : altruism, evolution, and why we need each other to succeed
Looks at the importance of cooperation in human beings and in nature, arguing that this social tool is as important an aspect of evolution as mutation and natural selection.
Book
When we can trust computers (and when we can’t)
With the relentless rise of computer power, there is a widespread expectation that computers can solve the most pressing problems of science, and even more besides. We explore the limits of computational modelling and conclude that, in the domains of science and engineering which are relatively simple and firmly grounded in theory, these methods are indeed powerful. Even so, the availability of code, data and documentation, along with a range of techniques for validation, verification and uncertainty quantification, are essential for building trust in computer-generated findings. When it comes to complex systems in domains of science that are less firmly grounded in theory, notably biology and medicine, to say nothing of the social sciences and humanities, computers can create the illusion of objectivity, not least because the rise of big data and machine-learning pose new challenges to reproducibility, while lacking true explanatory power. We also discuss important aspects of the natural world which cannot be solved by digital means. In the long term, renewed emphasis on analogue methods will be necessary to temper the excessive faith currently placed in digital computation.
This article is part of the theme issue ‘Reliability and reproducibility in computational science: implementing verification, validation and uncertainty quantification in silico’.
Journal Article
Characterizing the cognitive and mental health benefits of exercise and video game playing
Two of the most actively studied modifiable lifestyle factors, exercise and video gaming, are regularly touted as easy and effective ways to enhance brain function and/or protect it from age-related decline. However, some critical lingering questions and methodological inconsistencies leave it unclear what aspects of brain health are affected by exercise and video gaming. In this cross-sectional global online study, we recruited over 1000 people and collected data about participants’ physical activity levels, time spent playing video games, mental health, and cognitive performance using tests of short-term memory, verbal abilities, and reasoning skills from the Creyos battery. The amount of regular physical activity was not significantly related to any measure of cognitive performance; however, more physical activity was associated with better mental health as indexed using the Patient Health Questionnaire (PHQ-2) and Generalized Anxiety Disorder (GAD-2) screeners for depression and anxiety. Conversely, we found that more time spent playing video games was associated with better cognitive performance but was unrelated to mental health. We conclude that exercise and video gaming have differential effects on the brain, which may help individuals tailor their lifestyle choices to promote mental and cognitive health, respectively, across the lifespan.
Journal Article
بعد دوللي
يعرض الكتاب لحدث علمى شغل العالم كله بإعلان استنساخ النعجة دوللى من خلية ضرع لأنثى بالغة وهو الأمر الذى كان يعد مستحيلا ولقد اتسمت الضجة التى أعقبته بمعالجات غير علمية وعناوين إعلامية مثيرة ولذلك كان من الضرورى طرح الجوانب العلمية بموضوعية ووضوح وبيان الآفاق المحتملة لتطبيق تقنيات الاستنساخ فى الطب وإنتاج الغذاء وفى ضوء ذلك يتم التطرق إلى المحاذير ومناقشة القضايا الأخلاقية والقانونية والاجتماعية المختلفة.
BOOK
Digital twins and Big AI: the future of truly individualised healthcare
The integration of physics-based digital twins with data-driven artificial intelligence—termed “Big AI”—can advance truly personalised medicine. While digital twins offer individual ‘healthcasts,’ accuracy and interpretability, and AI delivers speed and flexibility, each has limitations. Big AI combines their strengths, enabling faster, more reliable and individualised predictions, with applications from diagnostics to drug discovery. Above all, Big AI restores mechanistic insights to AI and complies with the scientific method.
Journal Article