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Researchers Develop Artificial Intelligence Method that can Help Crops Cope with Climate Changes

This analysis illustrates the connection between the molecular components and the knowledge gained in basic chemistry

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In this method, instructions are given to the companies staff members to perform transactions such as money transfers, as well as malicious activity on the company's network. Pixabay

Israeli and US researchers have developed an artificial intelligence (AI) method that can help crops cope with climate changes, the Ben Gurion University (BGU) reported on Sunday.

In the study published in the journal of Communications Biology, researchers at BGU and the University of California developed a method to identify metabolic pathways which are chemical reactions in the cell, allowing it to grow and multiply, Xinhua news agency reported.

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The researchers used machine learning techniques, in which systems learn to identify patterns and make decisions in conjunction with correlation-based network analysis. Pixabay

“The world is facing the loss of crop yields because of climate changes, insects, and more. The identification of metabolic pathways that helps the plant deal with such problems will allow farmers to grow significantly stronger crops,” the study said.

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The researchers used machine learning techniques, in which systems learn to identify patterns and make decisions in conjunction with correlation-based network analysis. This analysis illustrates the connection between the molecular components and the knowledge gained in basic chemistry. Thus, the researchers collected data on known metabolic pathways from public databases and built correlation-based networks of tomato metabolites.  (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.

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“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)