Tag Archives: reading

Shaping the mind during COVID-19

Books on a window sillIf you looked closely at our holiday bookshelf in my post on August 12th 2020, you might have spotted ‘The Living Mountain‘ by Nan Shepherd [1893-1981] which a review in the Guardian newspaper described as ‘The finest book ever written on nature and landscape in Britain’.  It is an account of the author’s journeys in the Cairngorm mountains of Scotland.  Although it is  short, only 108 pages, I have to admit that it did not resonate with me and I did not finish it.  However, I did enjoy the Introduction by Robert MacFarlane and the Afterword by Jeanette Winterson, which together make up about a third of the book. MacFarlane draws parallels between Shepherd’s writing and one of her contemporaries, the French philosopher,  Maurice Merleau-Ponty [1908-1961] who was a leading proponent of existentialism and phenomenology.  Existentialists believe that the nature of our existence is based on our experiences, not just what we think but what we do and feel; while phenomenology is about the connections between experience and consciousness.  Echoing Shepherd and in the spirit of Merleau-Ponty, MacFarlane wrote in 2011 in his introduction that ‘we have come increasingly to forget that our minds are shaped by the bodily experience of being in the world’.  It made me think that as the COVID-19 pandemic pushes most university teaching on-line we need to remember that sitting at a computer screen day after day in the same room will shape the mind rather differently to the diverse experiences of the university education of previous generations.  I find it hard to imagine how we can develop the minds of the next generation of engineers and scientists without providing them with real, as opposed to virtual, experiences in the field, design studio, workshop and laboratory.

Source:

Nan Shepherd, The Living Mountain, Edinburgh: Canongate Books Ltd, 2014 (first published in 1977 by Aberdeen University Press)

 

35 years later and still working on a PhD thesis

It is about 35 years since I graduated with my PhD.  It was not ground-breaking although, together with my supervisor, I did publish about half a dozen technical papers based on it and some of those papers are still being cited, including one this month which surprises me.  I performed experiments and computer modelling on the load and stress distribution in threaded fasteners, or nuts and bolts.  There were no digital cameras and no computer tomography; so, the experiments involved making and sectioning models of nuts and bolts in transparent plastic using three-dimensional photoelasticity [see ‘Art and Experimental Mechanics‘ on July 17th, 2012].  I took hundreds of photographs of the sections and scanned the negatives in a microdensitometer.  The computer modelling was equally slow and laborious because there were no graphical user interfaces (GUI); instead, I had to type strings of numbers into a terminal, wait overnight while the calculations were performed, and then study reams of numbers printed out on long rolls of paper.  The tedium of the experimental work inspired me to work on utilising digital technology to revolutionise the field of experimental mechanics over the following 15 to 20 years.  In the past 15 to 20 years, I have moved back towards computer modelling and focused on transforming the way in which measurement data are used to improve the fidelity of computer models and to establish confidence in their predictions [see ‘Establishing fidelity and credibility in tests and simulations‘ on July 25th, 2018].  Since completing my PhD, I have supervised 32 students to successful completion of their PhDs.  You might think that was a straightforward process of an initial three years for the first one to complete their research and write their thesis, followed by one graduating every year.  But that is not how it worked out, instead I have had fallow years as well as productive years.  At the moment, I am in a productive period, having graduated two PhD students per year since 2017 – that’s a lot of reading and I have spent much of the last two weekends reviewing a thesis which is why PhD theses are the topic of this post!

Footnote: the most cited paper from my thesis is ‘Kenny B, Patterson EA. Load and stress distribution in screw threads. Experimental Mechanics. 1985 Sep 1;25(3):208-13‘ and this month it was cited by ‘Zhang D, Wang G, Huang F, Zhang K. Load-transferring mechanism and calculation theory along engaged threads of high-strength bolts under axial tension. Journal of Constructional Steel Research. 2020 Sep 1;172:106153‘.

Where is AI on the hype curve?

I suspect that artificial intelligence is somewhere near the top of the ‘Hype Curve’ [see ‘Hype cycle’ on September 23rd, 2015].  At the beginning of the year, I read Max Tegmark’s book, ‘Life 3.0 – being a human in the age of artificial intelligence’ in which he discusses the prospects for artificial general intelligence and its likely impact on life for humans.  Artificial intelligence means non-biological intelligence and artificial general intelligence is the ability to accomplish any cognitive task at least as well as humans.  Predictions vary about when we might develop artificial general intelligence but developments in machine learning and robotics have energised people in both science and the arts.  Machine learning consists of algorithms that use training data to build a mathematical model and make predictions or decisions without being explicitly programmed for the task.  Three of the books that I read while on vacation last month featured or discussed artificial intelligence which stimulated my opening remark about its position on the hype curve.  Jeanette Winterson in her novel, ‘Frankissstein‘ foresees a world in which humanoid robots can be bought by mail order; while Ian McEwan in his novel, ‘Machines Like Me‘, goes back to the early 1980s and describes a world in which robots with a level of consciousness close to or equal to humans are just being introduced to the market the place.  However, John Kay and Mervyn King in their recently published book, ‘Radical Uncertainty – decision-making beyond numbers‘, suggest that artificial intelligence will only ever enhance rather replace human intelligence because it will not be able to handle non-stationary ill-defined problems, i.e. problems for which there no objectively correct solution and that change with time.  I think I am with Kay & King and that we will shortly slide down into the trough of the hype curve before we start to see the true potential of artificial general intelligence implemented in robots.

The picture shows our holiday bookshelf.

Success is to have made people wriggle to another tune

Shortly before the pandemic started to have an impact in the UK, I went to our local second-hand bookshop and bought a pile of old paperbacks to read.  One of them was ‘Daisy Miller and Other Stories’ by Henry James (published in 1983 as Penguin Modern Classic).  The title of this post is a quote from one of the ‘other stories’, ‘The Lesson of the Master’, which was first published in 1888.  ‘Success is to have made people wriggle to another tune’ is said by the successful fictional novelist, Henry St George as words of encouragement to the young novelist Paul Ovett.  It struck a chord with me because I think it sums up academic life. Success in teaching is to inspire a new level of insight and way of thinking amongst our students; while, success in research is to change the way in which society, or at least a section of it, thinks or operates, i.e. to have made people wriggle to another tune.