Photo by Sarah
Best wishes during the holiday season to all my readers. I’m in digital detox over the Christmas and New Year holidays. So no post today. If you’re having withdrawal symptoms or want to know more about digital detox then read ‘Digital detox with a deep vacation‘ posted on August 10th, 2016. Otherwise switch off and do something utterly absorbing, such as deep reading or painting [see ‘You can only go there in your head‘ on November 5th, 2022].
I photographed this infographic at Killerton Hall in Devon this summer at the entrance to an exhibition called ‘Thirsty for fashion – circular fashion, past and present‘ which was about how clothes have been altered and repurposed through the centuries. I hear many references to the circular economy at the moment but I suspect many people do not really know the difference between a recycling and circular economy, and I thought this infographic elegantly illustrated it. Just a short post this week as we begin to slow down for the end of the year [see ‘Slow down, breathe your own air‘ on December 23rd, 2015].
It is sometimes suggested that materialism and greed are key drivers of our social and political system that largely refuses to acknowledge that we live in a world of limits. However, Rowan Williams has proposed that we have a ‘culture that is resentful about material reality, hungry for anything and everything that distances us from the constraints of being a physical animal subject to temporal processes, to uncontrollable changes and to sheer accident.’ In other words, it is our desire to be in control of our lives and surroundings that drives us to accumulate wealth and build our strongholds. Education and learning lead to an understanding of the complexity of the world, a realisation that we cannot control the world and perhaps to unavoidable insecurity, particularly for those people who thought they had some distance between themselves and uncontrollable events. It is more comfortable to believe that we are in control, adhere to the current out-dated paradigm, and ignore evidence to the contrary. This is equivalent to living in a virtual reality. This approach not only accelerates uncontrollable changes to the planet but also leads to economic disparities because, as Williams states, economic justice will only arrive when everyone recognises a shared vulnerability and limitation in a world that is not infinite.
Source: Rowan Williams, Faith in the public square, London: Bloomsbury Publishing, 2015.
Image: a pile of carved stones in the cloisters of Hereford cathedral where I bought a second-hand copy of ‘Faith in the public square’ while on holiday [see ‘Personale mappa mundi‘ on November 1st, 2023].
A couple of weeks ago I read about Google’s new weather forecasting algorithm, GraphCast. It takes a radical new approach to forecasting by using machine learning rather than modelling the weather using the laws of physics [see ‘Storm in a computer‘ on November 16th, 2022]. GraphCast uses a graph neural network that has been trained on 39 years (1979 -2017) of historical data from the European Centre for Medium-Range Weather Forecasts (ECMWF). It requires two inputs: the current state of the weather and the state six hours ago; then it predicts the weather six hours ahead with a 0.25 degree latitude-longitude resolution (about 17 miles) at 38 vertical levels. This compares to ECMWF’s high resolution forecasts which have 0.1 degree resolution (about 7 miles), 137 levels and 1 hour timesteps. Although the training of the neural network took about four weeks on 32 Cloud TPU v4 devices (Tensor Processing Units), the forecast requires less than a minute on a single device whereas the ECMWF’s high resolution forecast requires a couple of hours on a supercomputer. Within a day or so of reading about GraphCast, we watched ‘The Day After Tomorrow’, a movie in which a superstorm suddenly plunges the entire northern hemisphere into an ice age with dramatic consequences. Part of the movie’s message is that humanity’s disregard for the state of the planet could lead to existential consequences. It occurred to me that the traditional approach to weather forecasting using the laws of physics might predict the onset of such a superstorm and avoid it becoming a black swan event; however, it is very unlikely forecasts based on machine learning would predict it because there is nothing like it in the historical record used to train the neural network. So for the moment we should continue to use the laws of physics to model and predict the weather since climate change appears to be making superstorms more likely [see ‘More violent storms‘ on March 1st 2017].
Sources:
Blum A, The weather forecast may show AI storms ahead, FT Weekend, 18/19 November 2023.
Lam R, Sanchez-Gonzalez A, Willson M, Wirnsberger P, Fortunato M, Alet F, Ravuri S, Ewalds T, Eaton-Rosen Z, Hu W, Merose A. Learning skillful medium-range global weather forecasting. Science. 10.1126/science.adi2336, 2023.
Image: Painting by Sarah Evans owned by the author.
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