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Summary: A new machine-learning algorithm could help practitioners identify autism in children more effectively.

Source: USC

For children with autism spectrum disorder (ASD), receiving an early diagnosis can make a huge difference in improving behavior, skills and language development. But despite being one of the most common developmental disabilities, impacting 1 in 54 children in the U.S., it’s not that easy to diagnose.

Games have a long history of serving as a benchmark for progress in artificial intelligence. Recently, approaches using search and learning have shown strong performance across a set of perfect information games, and approaches using game-theoretic reasoning and learning have shown strong performance for specific imperfect information poker variants. We introduce, a general-purpose algorithm that unifies previous approaches, combining guided search, self-play… See more.

Games have a long history of serving as a benchmark for progress in.

Artificial intelligence. Recently, approaches using search and learning have.

Continue reading “Player of Games” »

The idiom “actions speak louder than words” first appeared in print almost 300 years ago. A new study echoes this view, arguing that combining self-supervised and offline reinforcement learning (RL) could lead to a new class of algorithms that understand the world through actions and enable scalable representation learning.

Machine learning (ML) systems have achieved outstanding performance in domains ranging from computer vision to speech recognition and natural language processing, yet still struggle to match the flexibility and generality of human reasoning. This has led ML researchers to search for the “missing ingredient” that might boost these systems’ ability to understand, reason and generalize.

In the paper Understanding the World Through Action, UC Berkeley assistant professor in the department of electrical engineering and computer sciences Sergey Levine suggests that a general, principled, and powerful framework for utilizing unlabelled data could be derived from RL to enable ML systems leveraging large datasets to better understand the real world.

To set some benchmarks for their simulator, the researchers tried out three different design algorithms working in conjunction with a deep reinforcement learning algorithm that learned to control the robots through many rounds of trial and error.

The co-designed bots performed well on the simpler tasks, like walking or carrying things, but struggled with tougher challenges, like catching and lifting, suggesting there’s plenty of scope for advances in co-design algorithms. Nonetheless, the AI-designed bots outperformed ones design by humans on almost every task.

Continue reading “Robots Evolve Bodies and Brains Like Animals in MIT’s New AI Training Simulator” »

In recent years, computational tools based on reinforcement learning have achieved remarkable results in numerous tasks, including image classification and robotic object manipulation. Meanwhile, computer scientists have also been training reinforcement learning models to play specific human games and videogames.

To challenge research teams working on reinforcement learning techniques, the Neural Information Processing Systems (NeurIPS) annual conference introduced the MineRL competition, a contest in which different algorithms are tested on the same task in Minecraft, the renowned computer game developed by Mojang Studios. More specifically, contestants are asked to create algorithms that will need to obtain a diamond from raw pixels in the Minecraft game.

The algorithms can only be trained for four days and on 8,000,000 samples created by the MineRL simulator, using a single GPU machine. In addition to the training dataset, participants are also provided with a large collection of human demonstrations (i.e., video frames in which the task is solved by human players).

Working with two teams of mathematicians, DeepMind engineered an algorithm that can look across different mathematical fields and spot connections that previously escaped the human mind. The AI doesn’t do all the work—when fed sufficient data, it finds patterns. These patterns are then passed on to human mathematicians to guide their intuition and creativity towards new laws of nature.

“I was not expecting to have some of my preconceptions turned on their head,” said Dr. Marc Lackenby at the University of Oxford, one of the scientists collaborating with DeepMind, to Nature, where the study was published.

The AI comes just a few months after DeepMind’s previous triumph in solving a 50-year-old challenge in biology. This is different. For the first time, machine learning is aiming at the core of mathematics—a science for spotting patterns that eventually leads to formally-proven ideas, or theorems, about how our world works. It also emphasized collaboration between machine and man in bridging observations to working theorems.

Machine learning projects are much more complicated and bigger than machine learning model algorithms.

Meta, the company formerly known as Facebook is pulling the plug on its facial recognition program. The company is planning to delete more than one billion people’s individual facial recognition templates, and will no longer automatically recognize people’s faces in photos or videos as a result of this change, according to its own post. The use of facial recognition technology has a disparate impact on people of color, disenfranchising a group who already face inequality, and Facebook seems to be acknowledging this inherent harm. The Breakdown You Need to Know.

CultureBanx reported that Meta seems to always be embroiled in corporate drama and with intense scrutiny. When you add that to the growing concern from users and regulators that facial recognition space remains complicated, an exit makes sense. More than 600 million daily active users on Facebook had opted into the use of the face recognition technology.

Research shows commercial artificial intelligence systems tend to have higher error rates for women and black people. Some facial recognition systems would only confuse light-skin men 0.8% of the time and would have an error rate of 34.7% for dark-skin women. Just imagine surveillance being used with these flawed algorithms. A 2018 IDC report noted it expects worldwide spending on cognitive and AI systems to reach $77.6 billion in 2022.

Blackholes are a breakdown in the equations of spacetime. This means both space and time no longer behave the way we would expect of them.
Today we explore the breakdown in time around blackholes and what it means to interact with the event horizon, or the place where time appears to stand still.

Further Reading/Consumption:

Continue reading “Why Time ‘Stops’ in a Black Hole” »