AI News, Artificial Intelligence for Computational Sustainability: A Lab Companion/Guide for Contributors
- On Thursday, October 4, 2018
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Artificial Intelligence for Computational Sustainability: A Lab Companion/Guide for Contributors
The lab companion contains a variety of projects, assignments and exercises (of varying difficulty and length) that explore topics in artificial intelligence and sustainability.
Of course, all style and technical conventions are subject to change through discussion, on this page's Discussion page or the top level text's Discussion list (though these latter top-level discussions will focus on the appropriateness of content areas in AI and Sustainability, rather than formatting and specification conventions for projects, assignments, and exercises per se).
While this material can be briefly summarized as part of the lab companion, the expectation is that substantive treatment of the requisite AI material will be external to the lab companion, with pointers (ideally urls, but other references too) to these substantive external treatments from the lab companion.
in fact, if there is insufficient online AI material to support the assignment, then rather than adding this AI requisite material to the lab companion, editors are encouraged to consider whether that (sustainability-INdependent) AI material might be better placed on Wikipedia, and then pointed at from the lab companion.
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.
A 3-D World for Smarter AI Agents
Google DeepMind, a subsidiary of Alphabet that’s focused on making fundamental progress toward general artificial intelligence, is releasing a new 3-D virtual world today, making it available for other researchers to experiment with and modify however they wish.
Simple example tasks that will come bundled with the platform include navigating a maze, collecting fruit, and traversing narrow passages without falling off.
“We’re trying to develop these artificial intelligence agents that can learn to perform well on a wide range of tasks from looking at the environment and from observing what happens,” says Shane Legg, chief scientist and cofounder of DeepMind.
Having AI agents work inside a 3-D environment could also have benefits for developing algorithms to control systems that work in the real world such as industrial robots, Legg says.
What’s more, the idea of creating agents that learn about a simulated world from basic principles taps into key ideas about how humans learn, something Legg explored in his academic career.
Artificial Intelligence for Computational Sustainability: A Lab Companion/Machine Learning for Prediction
Machine learning for purposes of predicting properties of objects and events -- as opposed to machine learning for purposes on improving search, planning and problem solving -- is the dominant form of machine learning studied (though the latter is often usefully understood in terms of the former).
In this lab, you will examine the effects of climate and climate change on the distributions of several species of tree, and then use climate and species-range data to construct computational models of species distribution using maximum entropy modeling (also known as Maxent).
Maxent is a general method from information theory for finding the probability distribution that has maximum entropy (i.e., is the most non-committal, or closest to a uniform distribution), subject to a set of constraints that represent our partial knowledge of the target distribution.
Each location on the map, including the known samples, is characterized by a set of climate variables, such as mean annual temperature, mean diurnal temperature range, mean precipitation during the coldest quarter of the year, etc.
Overlaid on each climate map are maps of six species’ ranges: bigcone Douglas fir (Pseudotsuga macrocarpa), Bishop pine (Pinus muricata), Blue oak (Quercus douglasii), Jeffrey pine (Pinus jeffreyi), coast redwood (Sequoia sempervirens), and giant sequoia (Sequoia giganteum).
The Maxent software  for species distribution modeling was developed in a collaboration between machine learning researchers and a biologist (emphasizing the interdisciplinary nature of computational sustainability) in 2004.
To learn the species distribution models, Maxent takes two inputs: (1) a file containing exact locations where a species of interest is known to grow and (2) a file containing climate data for each of those locations.
By evaluating the climate data at each location where the species of interest is present, Maxent calculates a probability function that describes the chances of a tree location having any given climate setting.
Each output folder will contain a .html webpage that summarizes the model's information, including the predicted species distribution overlayed on a map and several performance curves, as shown in the figures below.
Cooler colors (blue/green) indicate areas where the model calculates a low probability of species presence and warmer colors (red/yellow) indicate areas where the model calculates a higher probability of species presence.
For the response curve (middle figure), the x-axis represents a variety of climate values (in this case the annual precipitation in mm) and the y-axis indicates the probability of finding the species of interest in an area with any given annual precipitation.
Notice that the ROC curve lies in the unit square, so a model with perfect (100%) accuracy would have the red line go all the way to the upper left (coordinates (0,1)) and would have area 1 (although this is seldom achieved).
This warming is expected to continue for many years to come as a result of an increase in the amount of long-wave radiation emitted towards the ground by greenhouse gas molecules like CO2, CH3, and H2O.
If evapotranspiration increases, new seedlings and mature trees growing on the lower elevation tree line between alpine forest above and desert scrub below will die more often and the lower tree line will also rise.
While real temperature change will be very spatially, seasonally, and diurnally variable (warming should be most substantial near poles, during winter, and at night), this hypothetical temperature change is applied everywhere at all times.
Few undergraduate textbooks go significantly into other forms of regression, such as polynomial regression and tree-structured regression, but these texts typically provide ample material for instructors and the lab text to get into these issues, perhaps with pointers to other online content that is created in response to the lab text’s coverage.
An important aspect of both regression (and decision) trees is that they make explicit the important principle of 'context' through the strategy of recursive decomposition – some variables may be informative in some contexts (e.g., subtrees), but not others.
Global Footprint Network, 2012) is the amount of land (e.g., in hectares) that is needed to sustain indefinitely, without degradation, a process or entity, ranging in scale from (manufacture, use, and disposal of) individual artifacts to cities, nations and the world’s human population.
In supervised learning there is (typically) one attribute or variable, called the dependent variable, that is the focus of attention -- the goal of supervised learning from labeled data is to optimize prediction performance of this one dependent variable given some or all of the values of the remaining independent variables.
In contrast, unsupervised learning can be cast as a problem in which no one variable is the exclusive focus of attention, but rather a system might be called upon to make predictions along any variables with unknown values, given known values for other variables.
Unsupervised learning, such as belief network learning and clustering, can be used to discover, represent, and exploit statistical relationships between features and objects (e.g., people, processes, artifacts) for purposes of contextualizing and predicting ecological footprints.
Stanford AI Lab's Outreach
SAIL won a number of best paper awards this year: SAIL is delighted to announce that JD.com, China’s largest retailer has agreed to establish the SAIL JD AI Research Initiative, a sponsored research program at the Stanford Artificial Intelligence Lab.
The collaboration will fund research into a range of areas including natural language processing, computer vision, robotics, machine learning, deep learning, reinforcement learning, and forecasting.
The SAIL Affiliates Program is pleased to welcome Google, the largest internet-related technology company providing advertising, search, cloud computing, software, and hardware technologies and, DiDi, a major ride-sharing company that provides transportation services for close to 400 million users across over 400 cities in China.
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