Earth & Atmospheric Science

Applications of AI to Geology and Atmospheric Science

The driving force behind the development of AI-meteorology systems is the need to deal more effectively with the immense stream of data that forecasting depends upon. For example, we receive about 10 Megabytes per second of remotely sensed data from satellites. --- Bjarne Kristian Hansen

Applications

Contents

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General Readings

Publication

'Mushroom' robot to plumb the depths

After a successful deep-water test in Mexico, a NASA-funded autonomous robot is a step closer to proving its ability to carry out a mission to seek life on Europa, an ice-covered moon of Jupiter.

The robot, the Deep Phreatic Thermal Explorer (DEPTHX), which resembles a 2.5m-wide, 1.2-tonne orange mushroom, has the tools and intelligence to swim to the depths of flooded caverns, create maps of the area, then bring back scientific samples
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Source

Mar 13 2007
Link

A Growing Intelligence Around Earth

Science@NASA (October 26, 2006). "EO-1 [Earth Observing 1] is a new breed of satellite that can think for itself. 'We programmed it to notice things that change (like the plume of a volcano) and take appropriate action,' [Steve] Chien explains. EO-1 can re-organize its own priorities to study volcanic eruptions, flash-floods, forest fires, disintegrating sea-ice---in short, anything unexpected. Is this real intelligence? 'Absolutely,' he says. EO-1 passes the basic test: 'If you put the system in a box and look at it from the outside, without knowing how the decisions are made, would you say the system is intelligent?' Chien thinks so. And now the intelligence is growing. 'We're teaching EO-1 to use sensors on other satellites....'"

Source

2006
Publication

AI systems may blow weathermen away

"Weather forecasters could find themselves pushed out of a job by an artificial intelligence system designed to write clearer, less ambiguous reports. Computer scientists at the University of Aberdeen, UK, were asked to generate an 'artificial weatherperson' by operators of offshore oil rigs, who wanted more clarity in their forecasts.... To remove such uncertainties, the team programmed a natural language generation (NLG) software package to transform data on the forecast weather into an unambiguous written bulletin (Artificial Intelligence, vol 167, p 137)."

Full text available to subscribers

New Scientist (September 24, 2005; Issue 2518: page 27).

Source

2005
Link

CASPER: NASA's Continuous Activity Scheduling Planning Software

An autonomous spacecraft must balance long-term and short-term considerations. It must perform purposeful activities that ensure long-term science and engineering goals are achieved and ensure that it maintains positive resource margins. This requires planning in advance to avoid a series of shortsighted decisions that can lead to failure. However, it must also respond in a timely fashion to a somewhat dynamic and unpredictable environment. Thus, spacecraft plans must often be modified due to fortuitous events such as early completion of observations and setbacks such as failure to acquire a guidestar for a science observation.

CASPER (Continuous Activity Scheduling Planning Execution and Replanning) uses iterative repair to support continuous modification and updating of a current working plan in light of changing operating context

See also:  "Artificial Intelligence Software to Command Mission". Space Daily (May 29, 2001). "NASA software that thinks for itself and makes decisions without help from ground controllers will fly as the brains of triplet satellites in 2002. The software builds on previous efforts to use artificial intelligence to control a spacecraft (such as NASA's Remote Agent experiment, which controlled the Deep Space 1 spacecraft during portions of several days in 1999). However, this new software uses more advanced technology to respond more quickly to events and will command a mission continuously for a period of approximately three months. The Continuous Activity Scheduling, Planning Execution and Replanning (CASPER) software will guide a constellation of three identical miniature satellites, each weighing less than 15 kilograms (33 pounds). "

Source

2000
JPL, nasa
Link

PI-in-a-Box: A Knowledge-Based System for Space Science Experimentation

By Richard Franier, Nicholas Groleau, Lyman Hazelton, Silvano Colombano, Michael Compton, Irving Statler, Peter Szolovits, and Laurence Young. AI Magazine 15(1): Spring 1994, 39-56. "The principal investigator (PI)-IN-A-BOX knowledge based system helps astronauts perform science experiments in space.... The system is in use on the ground for mission training and was used in flight during the October 1993 space life sciences 2 (SLS-2) shuttle mission."

Source

1994
Publication

Application of the PROSPECTOR system to geological exploration problems

A practical criterion for the success of a knowledge-based problem-solving system is its usefulness as a tool to those working in its specialized domain of expertise.  

This paper describes an evaluation and several applications of a knowledge-based system, the PROSPECTOR consultant for mineral exploration. PROSPECTOR is a rule-based judgmental reasoning system that evaluates the mineral potential of a site or region with respect to inference network models of specific classes of ore deposits. Knowledge about a particular type of ore deposit is encoded in a computational model representing observable geological features and the relative significance thereof.

Source

By Gaschnig, J., 1982
classic, mineral exploration

News

AI in the News

Opportunity Rover Begins Tenth (!) Year On Mars

... the fact that we've got robots on Mars right now is something that is, or should be, a continual source of wonder for every single human on Earth. As a species, we're taking the first tentative steps towards exploring and understanding our solar system, and these robots are leading the way. There will certainly be more, probably lotsmore, but Opportunity will always be among the first, and it's already made itself into a legend.

Source

Jan 29 2013, By Ackerman, Evan
mars, opportunity
AI in the News

Dangerous side effect of common drug combination discovered by data mining

A widely used combination of two common medications may cause unexpected increases in blood glucose levels, according to a study conducted at the Stanford University School of Medicine, Vanderbilt University and Harvard Medical School. ... “It’s very exciting because we were led to this conclusion by mining data that already exists, but of which many people were skeptical,” said [Prof. Russ] Altman. “Physicians tend to think of electronic medical records as ways to better track data about single patients, but there’s another really important component to them — their utility in looking at population effects. The information is there to change health-care practice in a meaningful, substantial way.” Altman, the Guidant Professor for Applied Biomedical Engineering and the chair of Stanford’s bioengineering department, is the senior author of the study, published online May 25 in Clinical Pharmacology and Therapeutics.

Source

May 25 2011

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