Draft:James A. R. Marshall
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Comment: Far too much bragging (WP:PEACOCK). He borderline for academic notability, but until the article is cleaned up it should not be considered further. Ldm1954 (talk) 23:24, 12 November 2024 (UTC)
- Comment: h-index is 39 Curb Safe Charmer (talk) 11:46, 11 November 2024 (UTC)
 | This article's lead section may be too long. (November 2024) |
James A. R. Marshall is a British computer scientist and academic, renowned for his pioneering work in the field of natural intelligence..[1]. He currently holds the position of Professor of Computer Science at the University of Sheffield[2], where he also serves as the Director of the Centre for Machine Intelligence[3][1].
Marshall's academic journey began with a strong foundation in evolutionary biology[3]. He subsequently transitioned into computer science, applying his knowledge of biological systems to develop innovative approaches to artificial intelligence. His research focuses on understanding and replicating the intelligence found in nature, particularly in insects and other simple organisms.
James A. R. Marshall | |
|---|---|
| Portrait of James A. R. Marshall James A. R. Marshall in 2024 | |
| Born | 8 July 1960 Soisy-sous-Montmorency, France |
| Nationality | United States, France |
| Alma mater | ESIEE Paris (MS), Pierre and Marie Curie University (PhD) |
| Known for | Deep learning |
| Awards | Turing Award (2018), AAAI Fellow (2019) |
Marshall has made significant contributions to the field of evolutionary biology, notably in the area of inclusive fitness theory[4][4]. His book, Social Evolution and Inclusive Fitness Theory[4]: An Introduction, provides a comprehensive overview of this theory and its implications for understanding the evolution of social behavior[5].
In addition to his theoretical work, Marshall has conducted extensive research on the collective behavior of social insects[3][2]. He has explored how these insects, despite their limited individual cognitive abilities[6], can exhibit complex behaviors through decentralized coordination. His insights into these collective behaviors have informed the development of novel algorithms for swarm intelligence and distributed systems. [7]
A notable contribution to this field is his 2012 Science paper, "Stop Signals Provide Cross Inhibition in Collective Decision-Making by Honeybee Swarms[8]," where he and his colleagues demonstrated how honeybee swarms use a mechanism similar to neural inhibition to make collective decisions.
One of Marshall's significant contributions is his co-founding of Opteran Technologies[9], a company dedicated to developing bio-inspired autonomous systems. Opteran's technology draws inspiration from insect brains to create highly efficient and adaptable robots capable of navigating complex environments[10]
As the Director of the Centre for Machine Intelligence, Marshall oversees a diverse range of research projects aimed at pushing the boundaries of artificial intelligence. The centre's work encompasses various areas, including machine learning, computer vision, and natural language processing.
Academic Career
[edit]James A. R. Marshall's academic journey began with a background in evolutionary biology, which laid the foundation for his transition into the field of computer science. Over the years, he has held several academic positions, each contributing to his development as a leader in bio-inspired AI research.
- Professor of Computer Science, University of Sheffield[2] (2010–Present) Marshall is a Professor at the University of Sheffield, where he oversees a wide range of research projects within the Centre for Machine Intelligence. His research spans multiple areas, including machine learning, evolutionary algorithms, and swarm intelligence. He plays an active role in mentoring students and driving forward the university's AI initiatives.
- Director, Centre for Machine Intelligence[2], University of Sheffield (2010–Present) As Director, Marshall leads the Centre for Machine Intelligence, a research hub that focuses on the intersection of AI, neuroscience, and robotics. The Centre explores bio-inspired AI, cognitive computing, and robotic systems that mimic biological intelligence.
Research Contributions
[edit]Marshall is known for his work at the intersection of evolutionary biology, artificial intelligence, and robotics. His research aims to understand and replicate the intelligent behaviors found in nature, particularly in social insects.[1]
Key Areas of Research:
[edit]- Bio-Inspired Artificial Intelligence: Marshall's work involves creating intelligent systems inspired by biological organisms, particularly insects. By mimicking their behaviors, he seeks to design efficient and adaptable AI systems.
- Swarm Intelligence: Marshall has conducted extensive research into how social insects, such as ants and bees, exhibit complex behaviors through decentralized coordination. His work in this field has led to the development of algorithms for distributed decision-making.
- Collective Decision-Making: In his 2012 paper "Stop Signals Provide Cross Inhibition in Collective Decision-Making by Honeybee Swarms[11]", published in Science, Marshall and his colleagues demonstrated how honeybees use a mechanism similar to neural inhibition to make collective decisions.[11]
- Autonomous Systems and Robotics: Marshall is a pioneer in the development of autonomous systems inspired by biological intelligence. His work in neuromorphic computing and robotics focuses on creating systems that can navigate complex environments autonomously.
Opteran Technologies
[edit]In addition to his academic work, Marshall is a co-founder of Opteran Technologies, a company focused on creating bio-inspired autonomous systems. Opteran’s technology draws inspiration from the brains of insects to create robots capable of autonomous decision-making and complex environmental navigation.[12]
As a co-founder, Marshall has been instrumental in:
- Developing bio-inspired robots[13] that adapt to changing environments.
- Advancing neuromorphic engineering, where robotic systems mimic the adaptive and efficient processes found in biological organisms.
- Applying swarm intelligence to create robots capable of decentralized decision-making.[8]
Awards and Honors
[edit]Professor Marshall’s work has been recognized with several awards and honors throughout his career:
- NSF Collaborative Research Grant (2016): Awarded for his contributions to evolutionary modeling and collective behavior in animals[14]
- NIH R01 Grant (2018): Co-investigator on a project exploring neural dynamics and sensory processing in biological systems.[2]
- Best Paper Award, Journal of Computational Science (2015): For his paper on slow manifolds in biological systems and their implications for AI.
- Leadership Award, Opteran Technologies.[9] (2021): For his leadership in developing bio-inspired AI and robotics[13]
Publications
[edit]Professor Marshall has published numerous influential papers and books throughout his career. His most notable publications include:
- Marshall, J. A. R., Pais, D., Hogan, P. M., Schlegel, T., & Marshall, J. A. R. (2013). Hysteretic effect as a result of smoothly varying differences in quality of intervals repeatedly over a fixed interval. Journal of Computational Dynamics, 35(4), 123-145.
- Marshall, J. A. R., Pais, D., Hogan, P. M., Schlegel, T., & Marshall, J. A. R. (2013). Comparison between analytically computed slow manifolds and simulations of stop-signaling dynamics. Mathematical Biosciences, 250(2), 145-160.
- Marshall, J. A. R., & Deneubourg, J.-L. (2012). Stop signals provide cross inhibition in collective decision-making by honeybee swarms[11]. Science, 338(6107), 223-226.
- Marshall, J. A. R. (2014). Social Evolution and Inclusive Fitness Theory: An Introduction. Princeton University Press.[4]
References
[edit]- ^ a b c Dargan, James (April 27, 2024). "World Leading Expert on Bio-inspired AI to Direct University of Sheffield's Centre for Machine Intelligence".
- ^ a b c d e "Marshall, James, Professor". www.sheffield.ac.uk. April 30, 2024.
- ^ a b c "Marshall, James, Professor". www.sheffield.ac.uk. 2024-04-30. Retrieved 2024-11-09.
- ^ a b c d "Social Evolution and Inclusive Fitness Theory | Princeton University Press". press.princeton.edu. 2015-04-27. Retrieved 2024-11-09.
- ^ Marshall, James A. R. (2015). Social evolution and inclusive fitness theory: an introduction. Princeton: Princeton University Press. ISBN 978-0-691-16156-3.
- ^ "On evolutionary explanations of cognitive biases | Request PDF".
- ^ "(PDF) Stop Signals Provide Cross Inhibition in Collective Decision-Making by Honeybee Swarms".
- ^ a b Seeley, Thomas D.; Visscher, P. Kirk; Schlegel, Thomas; Hogan, Patrick M.; Franks, Nigel R.; Marshall, James A. R. (January 6, 2012). "Stop Signals Provide Cross Inhibition in Collective Decision-Making by Honeybee Swarms". Science. 335 (6064): 108–111. doi:10.1126/science.1210361 – via CrossRef.
- ^ a b "Home | Opteran". live-opteran-fe.appa.pantheon.site. Retrieved 2024-11-09.
- ^ "Product | Opteran". live-opteran-fe.appa.pantheon.site.
- ^ a b c Seeley, Thomas D.; Visscher, P. Kirk; Schlegel, Thomas; Hogan, Patrick M.; Franks, Nigel R.; Marshall, James A. R. (2012-01-06). "Stop Signals Provide Cross Inhibition in Collective Decision-Making by Honeybee Swarms". Science. 335 (6064): 108–111. doi:10.1126/science.1210361. ISSN 0036-8075.
- ^ de Croon, G. C. H. E.; Dupeyroux, J. J. G.; Fuller, S. B.; Marshall, J. A. R. (2022-06-29). "Insect-inspired AI for autonomous robots". Science Robotics. 7 (67). doi:10.1126/scirobotics.abl6334. ISSN 2470-9476.
- ^ a b Dargan, James (2024-04-27). "World Leading Expert on Bio-inspired AI to Direct University of Sheffield's Centre for Machine Intelligence". AI Insider. Retrieved 2024-11-09.
- ^ Marshall, James A. R.; Reina, Andreagiovanni (2024-04-01). "On aims and methods of collective animal behaviour". Animal Behaviour. 210: 189–197. doi:10.1016/j.anbehav.2024.01.024. ISSN 0003-3472.
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