AI News, Artificial Intelligence for Computational Sustainability: A Lab Companion/Preface
- On Thursday, October 4, 2018
- By Read More
Artificial Intelligence for Computational Sustainability: A Lab Companion/Preface
Long-term planet sustainability requires the intelligent use of intelligent computational systems, at least if we accept that planet-transforming technology and over-population are here to stay, taxing human abilities to plan intelligently and with the long-view.
This requires socially-engaged computational thinkers who can build and evaluate intelligent systems technology, informed by sustainability applications, and able to work across disciplinary lines.
To engage computing students on sustainability problems doesn’t simply require an AI education, leaving to hope that they will later see and act on its relevance to sustainability, but the connections are best made when they are made explicit, else the knowledge may be left inert in the student (Bransford, et al., 1990),
This text is motivated by a desire for increasing and sustaining the numbers and diversity of the computing community engaged in environmental and societal sustainability, and in particular is focused on introducing sustainability content into undergraduate courses on AI.
In addition to describing the AI and sustainability lab text generally, the chapter discusses the implications of the project for integration of research and education, and communicating science to the public;
Wikipedia’s popularity as a source for students and many others, faculty included (though typically not in their areas of expertise), make accurate and complete communication of scientific knowledge in the medium all the more critical.
for example, calls upon its members to contribute to Wikipedia, directly and through their students, to better insure completeness and accuracy, while also exercising these scientists and their associates on the critical skills of communicating science to the public.
Articles generally covering unsupervised learning, to take but one example, most notably clustering, are dominated by material from classic data clustering of statistics, with very limited coverage of uniquely machine learning and AI perspectives.
We can hope and expect that the sustainability text on Wikibooks will be a steppingstone for its contributors, largely self-selected to care substantially about educational and public outreach, to more broadly contribute to Wikipedia articles on AI.
Ideally, students who participate as Wikipedians will see their efforts as contributing to global pedagogy, both as it relates to sustainability and AI, and this will contribute to their self-image as people who can make a difference in society, in large part by working in community.
More broadly, universities are facing important questions of how to best use the World’s freely-available educational resources for a better onsite education – one that ideally fosters a commitment to place, a vital prerequisite to a commitment to sustainability.
In addition, a primary organization based on AI topics will help to highlight sustainability-related problems that share similar problem structure, but which may be in very different sustainability domains, thus encouraging abstraction and generalization on the part of students and guarding against inert knowledge.
The authors apply their methods to grizzly bear corridor design, finding paths between existing protected ecological reserves to allow for bear population mobility and increasing possibilities of genetic diversity.
Indeed, the intent of this effort is not to displace one bit of AI content from AI courses, but rather to facilitate the easy adoption by instructors of sustainability problems that can be used to motivate AI methods and concepts, while passing on sustainability knowledge in the process.
In addition, a hope is that the evolving lab text will be a resource for broader impact and education plans of research projects, such as NSF proposals and awards, in large part because this opportunity will be promoted, perhaps going so far as to provide templates for such activities on the lab book site, as well as virtual-meeting tutorial sessions on Wikibooks editing.
There are important broader impact motivations too, notably to foster integration of research and education, better communication of science to the public, and presenting opportunities to students and faculty for contributing, in community, on a global stage.
- On Tuesday, January 21, 2020
John Deere’s New AI Lab Is Designing More Sustainable Future
The acquisition of a computer vision Machine Learning startup speeds the John Deere company's goal of helping farmers grow enough food for an exploding ...
Rolls-Royce | How we’re using AI today
We're already using game-changing AI technology to transform the way our power systems are designed, maintained and operated. And with R2 Data Labs ...
AI for Marketing & Growth #0 - Applications of AI in Business
AI for Marketing & Growth #0 - Applications of AI in Business Welcome to our brand new AI for Marketing & Growth series in which we'll get you up to speed on ...
Crowdsourcing, computer vision, and data science for conservation - Tanya Berger-Wolf, IBEIS.org
This presentation was recorded at #H2OWorld 2017 in Mountain View, CA. Enjoy the slides: ...
Building the future of artificial intelligence for everyone (Google I/O '18)
In this Keynote Session, some of Google's leading minds on artificial intelligence and machine learning discuss their vision for a future where artificial ...
Machine Learning Approaches
See the full course: Follow along with the course eBook: Machine learning is a challenging area of computer science .
Microsoft & Prism Skylabs: Using AI to help organizations search visual data
Prism Skylabs is using Microsoft Cognitive Services to help businesses search, analyze and categorize their videos automatically with artificial intelligence.
Open science: Michael Nielsen at TEDxWaterloo
Michael Nielsen is one of the pioneers of quantum computation. Together with Ike Chuang of MIT, he wrote the standard text in the field, a text which is now one ...
Computer Science is Changing Everything
No matter what field you want to go into, Computer Science is changing that industry. Start learning at Special thanks to: - Elena Silenok ..
ProductAI - Fashion Color Trend Analysis & Forecasting
ProductAI - AI for Product Recognition Visual Product Search and Tagging APIs Designed for Industry Verticals ProductAI® provides state-of-the-art APIs for ...