Tag Archives: innovation

All arms and legs

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Imagine building a humanoid robot with head, arms and legs from different organisations that will not share the technical details of the products, probably because they are worried about their intellectual property being stolen.  The likely result would be a robot that moves in slow uncoordinated jerky steps, unlike those seen at the recent World Humanoid Robot Games in China where a humanoid robot beat Usain Bolt’s 100m record.  We have encountered this problem in attempting to build an industrial metaverse consisting of a connected series of digital twins [see ‘Digital twins that thrive in the real-world’ on June 9th, 2021] that represent different parts of a complex engineering system, e.g., a power station.  The organisations that have developed the digital twins or computer simulations are reluctant to share technical information because they want to protect their intellectual property.  Our solution has been to develop an apex software architecture that seamlessly and securely connects computer models of all of the parts of the system with high-speed two-way exchange of information – the equivalent of the central nervous system in the humanoid robot.  Our architecture reduced the processing times for simulations of a nuclear reactor sixteen-fold compared to current approaches and required one-third of the number of analysts to perform the simulations.  The architecture is readily adaptable too; you can plug n’ play with different computational models – the equivalent to changing a leg on a humanoid robot using a snap-fit connector.  Read our 2022 paper if you would like to know more or get in touch if you would like to use our apex architecture.

Image: Figure 9b from Bowman et al, 2022, showing simulated power distribution and control rod position overlayed on a model of a PWR reactor.

Reference: Bowman D, Dwyer L, Levers A, Patterson EA, Purdie S, Vikhorev K. A unified approach to digital twin architecture—proof-of-concept activity in the nuclear sector. IEEE Access, 10:44691-709, 2022

Mapping the road between digital twins and NDE

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RoadmapMichael Grieves, who probably first formulated the concept of digital twins in 2002, stated that all information about a physical entity should be present in its cyber or digital twin with data flow from the physical entity to the digital twin, and information flow from the digital twin to the physical entity throughout its lifecycle [1] [see ‘Digital twins that thrive in the real-world’ on June 9th, 2021].  The realisation of data flows between cyber-physical twins has been slow and there are relatively few cases where this definition has been fully implemented.  Non-destructive evaluation (NDE) is a well-established set of techniques used to assess the properties, integrity and condition of engineering components and structures that could play a significant role in supplying data to digital twins.  At the moment NDE is commonly used in quality control to check for manufacturing defects, in integrity assessments to investigate damage in safety critical components during maintenance inspections, and for determining the remaining life expectancy of engineering parts.  The integration of NDE in digital engineering has been slow; so, in a recent study, involving representatives from a wide range of industries, we have defined in a roadmap [2] the potential opportunity and the route to its realisation.  This work represents a different approach to research for my group in that we have explored the need or the ‘pull’ from industry rather than delivering innovative technology which can result in a ‘push’ to industry [see for example, ‘Our last DIMES’ on September 22nd, 2021].  My regular readers might also detect the influence of social science, which also surfaced in our recent work on knowledge management [see ‘Opportunities lost in knowledge management using digital technology’ on October 25th, 2023 and ‘Evolutionary model of knowledge management’ on March 6th 2024].  We have used the same methods, based on semi-structured interviews and thematic analysis, across all of these studies.

Image: roadmap [© University of Liverpool] which should be read in conjunction with [2].

References:

  1. Grieves, M., Vickers, J. (2017). Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems. In: Kahlen, J., Flumerfelt, S., Alves, A. (eds) Transdisciplinary Perspectives on Complex Systems. Springer, Cham.
  2. Middleton CA, Nguyen T & Patterson EA, NDE4.0 and digital twins in industry: current user experiences and future development, J. Nondestructive Evaluation, 45:107, 2026.

Beyond language with stochastic parrots

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Decorative image of a summer flowerSome months ago, I wrote in unflattering terms about artificial intelligence applications (AI apps) and large language models (LLMs), (see ‘Ancient models and stochastic parrots‘ on October 1st, 2025).  My view is changing, probably as AI apps develop and my user skills improve.  I have started using a couple of different free AI apps as research assistants in three ways.  First, when I am writing administrative documents, such as a job description for a Coordinator of AI in Education, for which a job title was sufficient for the app to generate a first draft that only required light editing and tailoring to the specific context.  Second, using a different AI app, to answer questions about phenomena which have allowed me to construct explanations for observations made of new and, or, complex systems – I could have delved into textbooks and monographs or searched research articles but AI does this much more quickly.  The third way I have used AI apps is to identify gaps in knowledge that could be fruitful topics for research.  This is a more difficult task because AI apps only know about stuff they can find on the internet in the form of language or text.  Hence, I have to ask questions with answers that reveal something unknown or not understood.  This is not straightforward because LLMs are fundamentally constrained by language.  In ‘The Years’, Annie Ernaux wrote that ‘language will continue to put the world into words’.  Yann LeCun, Meta’s former chief scientist, has suggested that to understand how the world works, a model would need to learn from videos and spatial data, not just language, and that without this type of learning human-level intelligence is impossible.  He has set up a new company, Advanced Machine Intelligence Labs, to do just that.  Language is used by people to describe the world from their perspective which might be inaccurate, incomplete or distorted and that can mislead LLMs.  However, using AI apps we can also ‘distort’ videos of the world, so that machine intelligence will have to be based on direct observation of the real-world, which after all is the approach that science attempts to use.

Source:

Yann LeCun, Intelligence is really about learning. FT Weekend, 3-4 January 2026

Annie Ernaux, The Years, Fitzcarraldo Editions, London, 2018.

Extra! Extra!

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Extra! Extra! Read all about it!  As newspaper vendors used to shout.  The Pint of Science Festival is happening across the UK in the week beginning Monday 18th May for three evenings in venues in 43 locations.  I am talking on the first evening, May 18th, in Lime Street Social (51 Lime Street, L1 1JQ) on ‘I Sell Here, Sir, What all World Desires to have – POWER’.  My title is a quote from Matthew Boulton, who with James Watt, set up a factory in Birmingham to produce steam engines in the 18th century.  I am going to talk about producing nuclear power units in a factory (see ‘Commoditization of civil nuclear power’ on June 5th, 2024).  If you would like to come to the event and hear three other speakers besides me and have a pint or two then please register at https://pintofscience.co.uk/events/liverpool/.