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Microsoft Says Building Tool to Spot Bias in AI Algorithms

The issue of bias will become crucial as more customers make use of these algorithms to take important decisions

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Microsoft's beta Android launcher has digital health feature. Pixabay

After Facebook announced its own tool to detect bias in an algorithm earlier this month, a new report suggests that Microsoft is also building a tool to automate the identification of bias in a range of different Artificial Intelligence (AI) algorithms.

The Microsoft tool has the potential to help businesses make use of AI without inadvertently discriminating against certain groups of people, MIT Technology Review reported on Friday.

Representational image (AI)
Representational image (AI). Pixabay

Although Microsoft’s new tool may not eliminate the problem of bias that may creep into Machine-Learning models altogether, it will help AI researchers catch more instances of unfairness, Rich Caruna, a senior researcher at Microsoft who is working on the bias-detection dashboard, was quoted as saying.

“Of course, we can’t expect perfection — there’s always going to be some bias undetected or that can’t be eliminated — the goal is to do as well as we can,” he said.

Also Read: Microsoft Also Has an AI Bot That Makes Phone Calls to Humans

The issue of bias will become crucial as more customers make use of these algorithms to take important decisions.

At its annual developer conference on May 2, Facebook announced its own bias-catching tool, called Fairness Flow, as the social network has found that the number of people using AI to make important decisions is increasing at the company. (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.

Also Read: Amazon Alexa May Come to Windows 10’s Lock Screen

“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)