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AI Can Help Improve Understanding of Earth Science

Besides, the requirement for data processing and storage capacity is very high

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Artificial Intelligence Bot
Artificial Intelligence Bot. Pixabay

Artificial Intelligence (AI) can significantly improve our understanding of the climate and Earth science, says a study by German scientists.

AI can be applied to data related to extreme events such as fire spreads or hurricanes, which are very complex processes influenced by local conditions.

It can also be applied to atmospheric and ocean transport, soil movement and vegetation dynamics data — some of the classic topics of Earth system science.

“From a plethora of sensors, a deluge of Earth system data has become available, but so far we’ve been lagging behind in analysis and interpretation,” said Markus Reichstein of the Max Planck Institute for Biogeochemistry in Jena, Germany.

“This is where deep learning techniques become a promising tool, beyond classical machine learning applications such as image recognition, natural language processing or AlphaGo,” added co-author Joachim Denzler, from the Friedrich Schiller University in Jena (FSU).

Artificial Intelligence can help to improve understanding of Earth Science.

However, deep learning approaches are difficult. All data-driven and statistical approaches do not guarantee physical consistency per se, are highly dependent on data quality, and may experience difficulties with extrapolations, according to the study published in the journal Nature.

Besides, the requirement for data processing and storage capacity is very high.

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If both techniques are brought together, so-called hybrid models are created. They can, for example, be used for modelling the motion of ocean water to predict sea surface temperature. While the temperatures are modelled physically, the ocean water movement is represented by a machine learning approach.

“The idea is to combine the best of two worlds, the consistency of physical models with the versatility of machine learning, to obtain greatly improved models,” Reichstein explained. (IANS)

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Researchers Develop AI Algorithm That can Solve Rubik’s Cube in Less Than a Second

According to the researchers, the ultimate goal of projects such as this one is to build the next generation of AI systems

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Researchers have developed an AI algorithm that can solve a Rubiks Cube in a fraction of a second, faster than most humans. The work is a step toward making AI systems that can think, reason, plan and make decisions.

The study, published in the journal Nature Machine Intelligence, shows DeepCubeA — a deep reinforcement learning algorithm programmed by University of California computer scientists and mathematicians — can solve the Rubik’s Cube in a fraction of a second, without any specific domain knowledge or in-game coaching from humans.

This is no simple task considering that the cube has completion paths numbering in the billions but only one goal state – each of six sides displaying a solid colour – which apparently can not be found through random moves.

“Artificial Intelligence can defeat the world’s best human chess and Go players, but some of the more difficult puzzles, such as the Rubik’s Cube, had not been solved by computers, so we thought they were open for AI approaches,” said study author Pierre Baldi, Professor at the University of California.

“The solution to the Rubik’s Cube involves more symbolic, mathematical and abstract thinking, so a deep learning machine that can crack such a puzzle is getting closer to becoming a system that can think, reason, plan and make decisions,” Baldi said.

artificial intelligence, nobel prize
“Artificial intelligence is now one of the fastest-growing areas in all of science and one of the most talked-about topics in society.” VOA

For the study, the researchers demonstrated that DeepCubeA solved 100 percent of all test configurations, finding the shortest path to the goal state about 60 per cent of the time.

The algorithm also works on other combinatorial games such as the sliding tile puzzle, Lights Out and Sokoban.

The researchers were interested in understanding how and why the Artificial Intelligence (AI) made its moves and how long it took to perfect its method.

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“It learned on its own, our AI takes about 20 moves, most of the time solving it in the minimum number of steps,” Baldi said.

“Right there, you can see the strategy is different, so my best guess is that the AI’s form of reasoning is completely different from a human’s,” he added.

According to the researchers, the ultimate goal of projects such as this one is to build the next generation of AI systems. (IANS)