Tag Archives: uncertainty

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.

The blind leading the blind

Three years after it started, the MOTIVATE project has come to an end [see ‘Getting smarter’ on June 21st, 2017].  The focus of the project has been about improving the quality of validation for predictions of structural behaviour in aircraft using fewer, better physical tests.  We have developed an enhanced flowchart for model validation [see ‘Spontaneously MOTIVATEd’ on June 27th, 2018], a method for quantifying uncertainty in measurements of deformation in an industrial environment [see ‘Industrial uncertainty’ on December 12th, 2018] and a toolbox for quantifying the extent to which predictions from computational models represent measurements made in the real-world [see ‘Alleviating industrial uncertainty’ on May 13th, 2020].  In the last phase of the project, we demonstrated all of these innovations on the fuselage nose section of an aircraft.  The region of interest was the fuselage skin behind the cockpit window for which the out-of-plane displacements resulting from an internal pressurisation load were predicted using a finite element model [see ‘Did cubism inspire engineering analysis?’ on January 25th, 2017].  The computational model was provided by Airbus and is shown on the left in the top graphic with the predictions for the region of interest on the right.  We used a stereoscopic imaging system  to record images of a speckle pattern on the fuselage before and after pressurization; and from these images, we evaluated the out-of-plane displacements using digital image correlation (DIC) [see ‘256 shades of grey‘ on January 22, 2014 for a brief explanation of DIC].  The bottom graphic shows the measurements being made with assistance from an Airbus contractor, Strain Solutions Limited.  We compared the predictions quantitatively against the measurements in a double-blind process which meant that the modellers and experimenters had no access to one another’s results.  The predictions were made by one MOTIVATE partner, Athena Research Centre; the measurements were made by another partner, Dantec Dynamics GmbH supported by Strain Solutions Limited; and the quantitative comparison was made by the project coordinator, the University of Liverpool.  We found that the level of agreement between the predictions and measurements changed with the level of pressurisation; however, the main outcome was the demonstration that it was possible to perform a double-blind validation process to quantify the extent to which the predictions represented the real-world behaviour for a full-scale aerospace structure.

The content of this post is taken from a paper that was to be given at a conference later this summer; however, the conference has been postponed due to the pandemic.  The details of the paper are: Patterson EA, Diamantakos I, Dvurecenska K, Greene RJ, Hack E, Lampeas G, Lomnitz M & Siebert T, Application of a model validation protocol to an aircraft cockpit panel, submitted to the International Conference on Advances in Experimental Mechanics to be held in Oxford in September 2021.  I would like to thank the authors for permission to write about the results in this post and Linden Harris of Airbus SAS for enabling the study and to him and Eszter Szigeti for providing technical advice.

For more on the validation flowchart see: Hack E, Burguete R, Dvurecenska K, Lampeas G, Patterson E, Siebert T & Szigeti, Steps towards industrial validation experiments, In Multidisciplinary Digital Publishing Institute Proceedings (Vol. 2, No. 8, p. 391) https://www.mdpi.com/2504-3900/2/8/391

For more posts on the MOTIVATE project: https://realizeengineering.blog/category/myresearch/motivate-project/

The MOTIVATE project has received funding from the Clean Sky 2 Joint Undertaking under the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 754660 and the Swiss State Secretariat for Education, Research and Innovation under contract number 17.00064.

The opinions expressed in this blog post reflect only the author’s view and the Clean Sky 2 Joint Undertaking is not responsible for any use that may be made of the information it contains.

Alleviating industrial uncertainty

Want to know how to assess the quality of predictions of structural deformation from a computational model and how to diagnose the causes of differences between measurements and predictions?  The MOTIVATE project has the answers; that might seem like an over-assertive claim but read on and make your own judgment.  Eighteen months ago, I reported on a new method for quantifying the uncertainty present in measurements of deformation made in an industrial environment [see ‘Industrial uncertainty’ on December 12th, 2018] that we were trialling on a 1 m square panel of an aircraft fuselage.  Recently, we have used the measurement uncertainty we found to make judgments about the quality of predictions from computer models of the panel under compressive loading.  The top graphic shows the outside surface of the panel (left) with a speckle pattern to allow measurements of its deformation using digital image correlation (DIC) [see ‘256 shades of grey‘ on January 22, 2014 for a brief explanation of DIC]; and the inside surface (right) with stringers and ribs.  The bottom graphic shows our results for two load cases: a 50 kN compression (top row) and a 50 kN compression and 1 degree of torsion (bottom row).  The left column shows the out-of-plane deformation measured using a stereoscopic DIC system and the middle row shows the corresponding predictions from a computational model using finite element analysis [see ‘Did cubism inspire engineering analysis?’ on January 25th, 2017].  We have described these deformation fields in a reduced form using feature vectors by applying image decomposition [see ‘Recognizing strain’ on October 28th, 2015 for a brief explanation of image decomposition].  The elements of the feature vectors are known as shape descriptors and corresponding pairs of them, from the measurements and predictions, are plotted in the graphs on the right in the bottom graphic for each load case.  If the predictions were in perfect agreement with measurements then all of the points on these graphs would lie on the line equality [y=x] which is the solid line on each graph.  However, perfect agreement is unobtainable because there will always be uncertainty present; so, the question arises, how much deviation from the solid line is acceptable?  One answer is that the deviation should be less than the uncertainty present in the measurements that we evaluated with our new method and is shown by the dashed lines.  Hence, when all of the points fall inside the dashed lines then the predictions are at least as good as the measurements.  If some points lie outside of the dashed lines, then we can look at the form of the corresponding shape descriptors to start diagnosing why we have significant differences between our model and experiment.  The forms of these outlying shape descriptors are shown as insets on the plots.  However, busy, or non-technical decision-makers are often not interested in this level of detailed analysis and instead just want to know how good the predictions are.  To answer this question, we have implemented a validation metric (VM) that we developed [see ‘Million to one’ on November 21st, 2018] which allows us to state the probability that the predictions and measurements are from the same population given the known uncertainty in the measurements – these probabilities are shown in the black boxes superimposed on the graphs.

These novel methods create a toolbox for alleviating uncertainty about predictions of structural behaviour in industrial contexts.  Please get in touch if you want more information in order to test these tools yourself.

The MOTIVATE project has received funding from the Clean Sky 2 Joint Undertaking under the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 754660 and the Swiss State Secretariat for Education, Research and Innovation under contract number 17.00064.

The opinions expressed in this blog post reflect only the author’s view and the Clean Sky 2 Joint Undertaking is not responsible for any use that may be made of the information it contains.

Do you believe in an afterlife?

‘I believe that energy can’t be destroyed, it can only be changed from one form to another.  There’s more to life than we can conceive of.’ The quote is from the singer and songwriter, Corinne Bailey Rae’s answer to the question: do you believe in an afterlife? [see Inventory in the FT Magazine, October 26/27 2019].  However, the first part of her answer is the first law of thermodynamics while the second part resonates with Erwin Schrödinger’s view on life and consciousness [see ‘Digital hive mind‘ on November 30th, 2016]. The garden writer and broadcaster, Monty Don gave a similar answer to the same question: ‘Absolutely.  I believe that the energy lives on and is connected to place.  I do have this idea of re-joining all of my past dogs and family on a summer’s day, like a Stanley Spencer painting.’ [see Inventory in the FT Magazine, January 18/19 2020].  The boundary between energy and mass is blurry because matter is constructed from atoms and atoms from sub-atomic particles, such as electrons that can behave as particles or waves of energy [see ‘More uncertainty about matter and energy‘ on August 3rd 2016].  Hence, the concept that after death our body reverts to a cloud of energy as the complex molecules of our anatomy are broken down into elemental particles is completely consistent with modern physics.  However, I suspect Rae and Don were going further and suggesting that our consciousness lives on in some form. Perhaps through some kind of unified mind that Schrödinger thought might exist as a consequence of our individual minds networking together to create emergent behaviour.  Schrödinger found it utterly impossible to form an idea about how this might happen and it seems unlikely that an individual mind could ever do so; however, perhaps the more percipient amongst us occasionally gets a hint of the existence of something beyond our individual consciousness.

Reference: Erwin Schrodinger, What is life? with Mind and Matter and Autobiographical Sketches, Cambridge University Press, 1992.

Image: ‘Sunflower and dog worship’ by Stanley Spencer, 1937 @ https://www.bbc.co.uk/news/entertainment-arts-13789029