About

Donald Patterson is a Professor Emeritus in the Department of Mechanical Engineering at the University of Michigan. His research interests include internal combustion engines and emissions. He is associated with the G.G. Brown Laboratory in Ann Arbor, Michigan, and can be contacted at (734) 764-2694. His work focuses on advancing understanding and technology related to engine performance and environmental impact within the field of mechanical engineering.

Research topics

• Computer science
• Automotive engineering
• Environmental science
• Engineering
• Chemistry

Selected publications

• The carbon emissions of writing and illustrating are lower for AI than for humans

  Scientific Reports · 2024-02-14 · 61 citations

  articleOpen access

  As AI systems proliferate, their greenhouse gas emissions are an increasingly important concern for human societies. In this article, we present a comparative analysis of the carbon emissions associated with AI systems (ChatGPT, BLOOM, DALL-E2, Midjourney) and human individuals performing equivalent writing and illustrating tasks. Our findings reveal that AI systems emit between 130 and 1500 times less CO2e per page of text generated compared to human writers, while AI illustration systems emit between 310 and 2900 times less CO2e per image than their human counterparts. Emissions analyses do not account for social impacts such as professional displacement, legality, and rebound effects. In addition, AI is not a substitute for all human tasks. Nevertheless, at present, the use of AI holds the potential to carry out several major activities at much lower emission levels than can humans.

  PublisherOA PDFDOI

• 526 Non-surgical management of urinary incontinence in women with cystic fibrosis

  Journal of Cystic Fibrosis · 2024-09-01

  article
  PublisherDOI

• Xylem: An Energy-efficient, Globally Redistributive, Financial Infrastructure Using Proof-by-Location

  Distributed Ledger Technologies Research and Practice · 2024-03-22 · 1 citations

  articleOpen access1st authorCorresponding

  The Proof-of-Work algorithm that underlies Bitcoin, Ethereum w , 1 and many other cryptocurrencies is well known for its energy-intensive requirements. The Proof-of-Stake algorithm that underlies Ethereum and various other cryptocurrencies is less impactful environmentally, but it has a second, looming issue: the problem of wealth inequality. We have developed an alternative to Proof-of-Work and Proof-of-Stake, called Proof-by-Location, that has the potential to address both of these issues. This article describes Proof-by-Location and a financial platform called Xylem that is based on it. This platform seeks to distribute transaction fees to billions of cryptocurrency “Notaries” around the world (essentially, anyone with a smartphone), who work together to establish a distributed consensus about financial transactions. In this article, we demonstrate that this platform can scale to more than 3.9 trillion transactions per year (more than triple the number of digital payments per year currently occurring). We show a reduction of electricity usage per transaction of 99.9999914% compared to Bitcoin, 99.999905% compared to Ethereum w , 99.83% compared to Ethereum, and 95.9% compared to the Visa financial services company. We demonstrate that this platform would have a redistributive rather than consolidatory effect on wealth compared to any of these platforms, leading to a source of income for more than 1 billion people around the world, including more than 110 million in the bottom 10th to 20th percentile by income, with income for that group equivalent to 8.8 million full-time jobs. Finally, this currency provides a positive, non-compulsory mechanism for shaping human habitation patterns in ways that can slow global biodiversity loss and enable ecological restoration. Using Xylem as a global financial infrastructure could lead to significantly better social and environmental outcomes than existing financial platforms. 2

  PublisherOA PDFDOI

• The Carbon Emissions of Writing and Illustrating Are Lower for AI than for Humans

  Research Square · 2023-03-29 · 2 citations

  preprintOpen access

  Abstract As AI systems proliferate, their greenhouse gas emissions are an increasingly important concern for human societies. In this article, we present a comparative analysis of the carbon emissions associated with AI systems (ChatGPT, BLOOM, DALL-E2, Midjourney) and human individuals performing equivalent writing and illustrating tasks. Our findings reveal that AI systems emit between 130 and 1500 times less CO2e per page of text generated compared to human writers, while AI illustration systems emit between 310 to 2900 times less CO2e per image than their human counterparts. Emissions analyses do not account for social impacts such as professional displacement, legality, and rebound effects. In addition, AI is not a substitute for all human tasks. Nevertheless, at present, the use of AI holds the potential to carry out several major activities at much lower emission levels than can humans.

  PublisherOA PDFDOI

• The Carbon Emissions of Writing and Illustrating Are Lower for AI than for Humans

  SSRN Electronic Journal · 2023-01-01 · 7 citations

  articleOpen access
  PublisherDOI

• Late-Binding Scholarship in the Age of AI: Navigating Legal and Normative Challenges of a New Form of Knowledge Production

  arXiv (Cornell University) · 2023-05-04

  preprintOpen accessSenior author

  Artificial Intelligence (AI) is poised to enable a new leap in the creation of scholarly content. New forms of engagement with AI systems, such as collaborations with large language models like GPT-3, offer affordances that will change the nature of both the scholarly process and the artifacts it produces. This article articulates ways in which those artifacts can be written, distributed, read, organized, and stored that are more dynamic, and potentially more effective, than current academic practices. Specifically, rather than the current "early-binding" process (that is, one in which ideas are fully reduced to a final written form before they leave an author's desk), we propose that there are substantial benefits to a "late-binding" process, in which ideas are written dynamically at the moment of reading. In fact, the paradigm of "binding" knowledge may transition to a new model in which scholarship remains ever "unbound" and evolving. An alternative form for a scholarly work could be encapsulated via several key components: a text abstract of the work's core arguments; hyperlinks to a bibliography of relevant related work; novel data that had been collected and metadata describing those data; algorithms or processes necessary for analyzing those data; a reference to a particular AI model that would serve as a "renderer" of the canonical version of the text; and specified parameters that would allow for a precise, word-for-word reconstruction of the canonical version. Such a form would enable both the rendering of the canonical version, and also the possibility of dynamic AI reimaginings of the text in light of future findings, scholarship unknown to the original authors, alternative theories, and precise tailoring to specific audiences (e.g., children, adults, professionals, amateurs).

  PublisherOA PDFDOI

• Late-Binding Scholarship in the Age of AI

  2023-06-24

  preprintOpen accessSenior author

  Scholarly processes play a pivotal role in discovering, challenging, improving, advancing, synthesizing, codifying, and disseminating knowledge. Since the 17th Century, both the quality and quantity of knowledge that scholarship has produced have increased tremendously, granting academic research a pivotal role in ensuring material and social progress. Artificial Intelligence (AI) is poised to enable a new leap in the creation of scholarly content. New forms of engagement with AI systems, such as collaborations with large language models like GPT-3, offer affordances that will change the nature of both the scholarly process and the artifacts it produces. This article articulates ways in which those artifacts can be written, distributed, read, organized, and stored that are more dynamic, and potentially more effective, than current academic practices.

  PublisherOA PDFDOI

• The Carbon Emissions of Writing and Illustrating Are Lower for AI than for Humans

  arXiv (Cornell University) · 2023-03-08 · 4 citations

  preprintOpen access

  As AI systems proliferate, their greenhouse gas emissions are an increasingly important concern for human societies. We analyze the emissions of several AI systems (ChatGPT, BLOOM, DALL-E2, Midjourney) relative to those of humans completing the same tasks. We find that an AI writing a page of text emits 130 to 1500 times less CO2e than a human doing so. Similarly, an AI creating an image emits 310 to 2900 times less. Emissions analysis do not account for social impacts such as professional displacement, legality, and rebound effects. In addition, AI is not a substitute for all human tasks. Nevertheless, at present, the use of AI holds the potential to carry out several major activities at much lower emission levels than can humans.

  PublisherOA PDFDOI

• Proof-by-Location as a Socially Responsible Financial Infrastructure

  2022-11-07 · 2 citations

  articleOpen access1st authorCorresponding

  The Proof-of-Work algorithm that underlies Bitcoin and many other cryptocurrencies is well known for its energy-intensive requirements. The Proof-of-Stake algorithm that underlies Ethereum2 and various other cryptocurrencies is less impactful environmentally, but it has a second, looming issue: the problem of wealth inequality. We have developed an alternative to Proof-of-Work and Proof-of-Stake, called Proof-by-Location, that has the potential to address both of these issues. This paper describes Proof-by-Location and a financial platform called Xylem that is based on it. This platform seeks to distribute transaction fees to billions of cryptocurrency "Notaries" around the world (essentially, anyone with a smartphone), who work together to establish a distributed consensus about financial transactions. Using Xylem as a global financial infrastructure could lead to significantly better social and environmental outcomes than existing financial platforms.

  PublisherOA PDFDOI

• Emergency Remote Teaching of English for Specific Purposes : Introduction to Rehabilitation English Course

  2021-03-31

  article
  Publisher

Recent grants

• HCC: Computing Place Context

  NSF · $450k · 2007–2012

Frequent coauthors

• Bill Tomlinson

  University of California, Irvine

  54 shared

• Andrew W. Torrance

  30 shared

• Rebecca W. Black

  University of California, Irvine

  19 shared

• E.A. Sondreal

  University of North Dakota

  17 shared

• Robert H. Kadlec

  17 shared

• M. Six Silberman

  The London College

  15 shared

• H. A. Lord

  15 shared

• Brice. Carnahan

  14 shared