Author Archives: Eann Patterson

Do you know RIO?

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Infrared image of group of people in meetingDuring the pandemic many political leaders have been heard to justify their decisions by telling us that they were following advice from scientists.  I think it was Thomas Kuhn who proposed that the views of a group of scientists will be normally distributed if the group is large enough, i.e., a bell-shaped curve with a few scientists providing outlying opinions on either end and the majority in the middle of the distribution [see ‘Uncertainty about Bayesian methods’ on June 7th, 2017].  So, it depends which scientist you consult as to what advice you will receive.  Of course, you can consult a group of experts in order to identify the full range of advice and seek a consensus; however, this is notoriously difficult because some voices will be louder than others and some experts will be very certain about their predictions of the future while others will be very cautious about predicting anything.  This is often because the former group are suffering from meta-ignorance, i.e., failing to even consider the possibility of being wrong, while the latter are so aware of the ontological or deep uncertainties that they prefer to surround their statements with caveats that render them difficult or impossible to interpret or employ in decision-making [see ‘Deep uncertainty and meta ignorance’ on July 21st 2021].  Politicians prefer a simple message that they can explain to the media and tend to listen to the clear but usually inaccurate message from the confident forecasters [see ‘Forecasts and chimpanzees throwing darts’ on September 2nd, 2020].  However, with time and effort, it is possible to make rational decisions based on expert opinion even when the opinions appear to diverge.  There are several recognised protocols for expert elicitation which are used in a wide range of engineering and scientific activities to support decision-making in the absence of comprehensive information.  I frequently use a form of the Sheffield protocol developed originally to elicit a probability distribution for an unknown uncertainty from a group of experts.  Initially, the group of experts are asked individually to provide private, written, independent advice on the issue of concern.  Subsequently, their advice is shared with the group and a discussion to reach a consensus is led by a facilitator. This can be difficult if the initial advice is divergent and individuals hold strong views.  This is when RIO can help.  RIO stands for Rational Impartial Observer and an expert group often rapidly reach a consensus when they are asked to consider what RIO might reasonably believe after reading their independent advice and listening to their discussion.

Source:

Anthony O’Hagan, Expert knowledge elicitation: subjective but scientific, The American Statistician, 73:Sup.1, 69-81, 2019.

Follow your gut

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Decorative image of a fruit fly nervous system Albert Cardona HHMI Janelia Research Campus Welcome Image Awards 2015Data centres worldwide consume about 1% of global electricity generation, that’s 200-250 TWh (Masenet et al, 2020), and if you add in mining of cryptocurrencies then consumption jumps by about 50% (Gallersdörfer et al, 2020). Data transmission consumes about 260-340 TWh or at least another 1% of global energy consumption (IEA, 2020).  The energy efficiency of modern computers has been improving; however, their consumption is still many millions times greater than the theoretical limit defined by Landauer’s principle which was verified in 2012 by Bérut et al.  According to Landauer’s principle, a computer operating at room temperature would only need 3 zJ (300 billion billionths of a Joule) to erase a bit of information.  The quantity of energy used by modern computers is many millions times the Landauer limit.  Of course, progress is being made almost continuously, for example a team at EPFL in Lausanne and ETH Zurich recently described a new technology that uses only a tenth of the energy of current transistors (Oliva et al 2020).  Perhaps we need turn to biomimetics because Escherichia Coli, which are bacteria that live in our gut and have to process information to reproduce, have been found to use ten thousand times less energy to process a bit of information than the average human-built device for processing information (Zhirnov & Cavin, 2013).  So, E.coli are still some way from the Landauer limit but demonstrate that there is considerable potential for improvement in engineered devices.

References

Bérut A, Arakelyan A, Petrosyan A, Ciliberto S, Dillenschneider R & Lutz E. Experimental verification of Landauer’s principle linking information and thermodynamics. Nature, 483: 187–189, 2012.

IEA (2021), Data Centres and Data Transmission Networks, IEA, Paris https://www.iea.org/reports/data-centres-and-data-transmission-networks

Gallersdörfer U, Klaaßen L, Stoll C. Energy consumption of cryptocurrencies beyond bitcoin. Joule. 4(9):1843-6, 2020.

Masanet E, Shehabi A, Lei N, Smith S, Koomey J. Recalibrating global data center energy-use estimates. Science. 367(6481):984-6, 2020.

Oliva N, Backman J, Capua L, Cavalieri M, Luisier M, Ionescu AM. WSe 2/SnSe 2 vdW heterojunction Tunnel FET with subthermionic characteristic and MOSFET co-integrated on same WSe 2 flake. npj 2D Materials and Applications. 4(1):1-8, 2020.

Zhirnov VV, Cavin RK. Future microsystems for information processing: limits and lessons from the living systems. IEEE Journal of the Electron Devices Society. 1(2):29-47, 2013.

It is hard to remain positive

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Frequent readers of this blog will have noticed that I am regular reader of the FT Weekend pages.  I particularly like the ‘Life & Arts’ section for its balance of opinion and reviews.  However, one weekend last month I was depressed by two articles I read in quick succession.  Shannon Vallor described life as an ageing roller coaster with failed brakes and ‘accelerating climate change, a deadly pandemic and unravelling global supply chains’.  While on the facing page Nilanjana Roy wrote that the ‘past few decades have brought humankind and most other species on Earth to the brink of destruction’.  I was depressed because I agree with their analysis and our leaders seem either unaware of the impending crash of the roller coaster or unable to construct a global strategy to avert the looming destruction.  However, spiralling into negativity does not help because negativity tends to promote fight-or-flight survival mechanisms that can lead to narrow-mindedness, a lack of creativity and limiting one’s options to the tried and tested actions which are unlikely to avert destruction.  Whereas a positive outlook broadens your repertoire of options and builds physical, social and psychological resources.  Positive psychological capital, associated with hope, efficacy, resilience and optimism, leads to higher positive outcomes including commitment, successful outcomes, satisfaction and well-being.  In the face of apparently insurmountable challenges it is difficult to remain positive whether you are leading a small team, a department, an organisation or a country; nevertheless it is important to remain positive because research shows that the ‘happier and smarter’ approach works better than the ‘sadder but wiser’ style of leadership.  Of course, extreme positivity is usually delusional or irresponsible and can lead to complacency; so, you need to dodge that too.

Sources

Kelloway EK, Weigand H, McKee MC & Das H, 2013. Positive leadership and employee well-being. J. Leadership & Organizational Studies, 20(1), pp.107-117.

Nel T, Stander MW & Latif J. 2015, Investigating positive leadership, psychological empowerment, work engagement and satisfaction with life in a chemical industry. SA Journal of Industrial Psychology/SA Tydskrif vir Bedryfsielkunde, 41(1):1243.

Nilanjana Roy, Lessons from 1971 for eco-activists today, in FT Weekend 9 October / 10 October 2021.

Shannon Vallor, Tech’s future shocks, in FT Weekend 9 October / 10 October 2021.

Youssef-Morgan CM, Luthans F. Positive leadership: Meaning and application across cultures. Organizational Dynamics 42:3:198–208, 2013

Jigsaw puzzling without a picture

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A350 XWB passes Maximum Wing Bending test

A350 XWB passes Maximum Wing Bending test

Research sometimes feels like putting together a jigsaw puzzle without the picture or being sure you have all of the pieces.  The pieces we are trying to fit together at the moment are (i) image decomposition of strain fields [see ‘Recognising strain’ on October 28th 2015] that allows fields containing millions of data values to be represented by a feature vector with only tens of elements which is useful for comparing maps or fields of predictions from a computational model with measurements made in the real-world; (ii) evaluation of the variation in measurement uncertainty over a field of view of measured displacements or strains in a large structure [see ‘Industrial uncertainty’ on December 12th 2018] which provides information about the quality of the measurements; and (iii) a probabilistic validation metric that provides a measure of how well predictions from a computational model represent measurements made in the real world [see ‘Million to one’ on November 21st 2018].  We have found some of the missing pieces of the jigsaw, for example we have established how to represent the distribution of measurement uncertainty in the feature vector domain [see ‘From strain measurements to assessing El Niño events’ on March 17th 2021] so that it can be used to assess the significance of differences between measurements and predictions represented by their feature vectors – this connects (i) and (ii) together.  Very recently we have demonstrated a generic technique for performing image decomposition of irregularly shaped fields of data or data fields with holes [see Christian et al, 2021] which extends the applicability of our method for comparing measurements and predictions to real-world objects rather than idealised shapes.  This allows (i) to be used in industrial applications but we still have to work out how to connect this to the probabilistic metric in (iii) while also incorporating spatially-varying uncertainty.  These techniques can be used in a wide range of applications, as demonstrated in our recent work on El Niño events [see Alexiadis et al, 2021], because, by treating all fields of data as images, the techniques are agnostic about the source and format of the data.  However, at the moment, our main focus is on their application to ground tests on aircraft structures as part of the Smarter Testing project in collaboration with Airbus, Centre for Modelling & Simulation, Dassault Systèmes, GOM UK Ltd, and the National Physical Laboratory with funding from the Aerospace Technology Institute.  Together we are working towards digital continuity across virtual and physical testing of aircraft structures to provide live data fusion and enable condition-led inspections, test control and validation of computational models.  We anticipate these advances will reduce time and costs for physical tests and accelerate the development of new designs of aircraft that will contribute to global sustainability targets (the aerospace industry has committed to reduce CO2 emissions to 50% of 2005 levels by 2050).  The Smarter Testing project has an ambitious goal which reveals that our pieces of the jigsaw puzzle belong to a small section of a much larger one.

For more on the Smarter Testing project see:

https://www.aerospacetestinginternational.com/news/structural-testing/smarter-testing-research-program-to-link-virtual-and-physical-aerospace-testing.html

https://www.aerospacetestinginternational.com/opinion/how-integrating-the-virtual-and-physical-will-make-aerospace-testing-and-certification-smarter.html

References

Alexiadis A, Ferson S, Patterson EA. Transformation of measurement uncertainties into low-dimensional feature vector space. Royal Society open science. 8(3):201086, 2021.

Christian WJ, Dean AD, Dvurecenska K, Middleton CA, Patterson EA. Comparing full-field data from structural components with complicated geometries. Royal Society open science. 8(9):210916, 2021.

Image: http://www.airbus.com/galleries/photo-gallery