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Researchers Develop New Programming System for Artificial Intelligence Applications

"Gen" models are black boxes called generative functions (GF), that provide an interface (GFI), exposing capabilities required by inference, researchers said

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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

Researchers have developed a novel system named “Gen” that can be used for Artificial Intelligence applications such as computer vision, robotics, and statistics without having to deal with equations or manually writing high-performance codes.

“Gen” includes a number of novel language constructs such as a generative function interface to encapsulate probabilistic models, combinators to create new generative functions from existing ones and an inference library providing high-level inference algorithms.

In the study published in the journal PLDI 2019, researchers from the Massachusetts Institute of Technology demonstrated the probabilistic programming system that aims to be both expressive at the modelling level and efficient at the algorithmic level.

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Researchers have developed a novel system named “Gen” that can be used for Artificial Intelligence applications. Pixabay

“Gen” has already showed better performance than existing probabilistic programming systems for a number of different problems such as tracking objects in space, estimating 3D body pose from a depth image, and inferring the structure of a time series, researchers said.

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Based on Julia – a language specialised in numerical analysis and which aims to allow users to express models and create inference algorithms using high-level programming constructs, “Gen” models can be expressed in a number of different ways, each striking a different flexibility/efficiency trade-off. “Gen” provides a built-in modelling language that extends Julia’s syntax for function definition.

“Gen” models are black boxes called generative functions (GF), that provide an interface (GFI), exposing capabilities required by inference, researchers said. The generative function approach is key to making “Gen” suitable for application to a wide range of problems and enables it to use models created in TensorFlow as algorithms written in a programming language or as a result of simulations. (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)