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Category: robotics/AI – Page 1510

At this point i think the US government is going to get stuck paying to develop human level robotic hands.

Over the past few decades, roboticists and computer scientists have developed a variety of data-based techniques for teaching robots how to complete different tasks. To achieve satisfactory results, however, these techniques should be trained on reliable and large datasets, preferably labeled with information related to the task they are learning to complete.

For instance, when trying to teach robots to complete tasks that involve the manipulation of objects, these techniques could be trained on videos of humans manipulating objects, which should ideally include information about the types of grasps they are using. This allows the robots to easily identify the strategies they should employ to grasp or manipulate specific objects.

Researchers at University of Pisa, Istituto Italiano di Tecnologia, Alpen-Adria-Universitat Klagenfurt and TU Delft recently developed a new taxonomy to label videos of humans manipulating objects. This grasp classification method, introduced in a paper published in IEEE Robotics and Automation Letters, accounts for movements prior to the grasping of objects, for bi-manual grasps and for non-prehensile strategies.

Continue reading “A new taxonomy to characterize human grasp types in videos” | >

Researchers at the University of Sydney and quantum control startup Q-CTRL today announced a way to identify sources of error in quantum computers through machine learning, providing hardware developers the ability to pinpoint performance degradation with unprecedented accuracy and accelerate paths to useful quantum computers.

A joint scientific paper detailing the research, titled “Quantum Oscillator Noise Spectroscopy via Displaced Cat States,” has been published in the Physical Review Letters, the world’s premier physical science research journal and flagship publication of the American Physical Society (APS Physics).

Focused on reducing errors caused by environmental “noise”—the Achilles’ heel of —the University of Sydney team developed a technique to detect the tiniest deviations from the precise conditions needed to execute quantum algorithms using trapped ion and superconducting quantum computing hardware. These are the core technologies used by world-leading industrial quantum computing efforts at IBM, Google, Honeywell, IonQ, and others.

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The predicted shapes still need to be confirmed in the lab, Ellis told Technology Review. If the results hold up, they will rapidly push forward the study of the proteome, or the proteins in a given organism. DeepMind researchers published their open-source code and laid out the method in two peer-reviewed papers published in Nature last week.

And in 20 other animals often studied by science, too.

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CORVALLIS, Ore. – A two-legged robot invented at Oregon State University completed a 5K in just over 52 minutes. Cassie the robot, created by OSU spinout company Agility Robotics, made history with the successful trot. “Cassie, the first bipedal robot to use machine learning to control a running gait on outdoor terrain, completed the 5K on Oregon State’s campus untethered and on a single battery charge,” according to OSU. But it didn’t go off without a hitch.

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AlphaFold 2 paper and code is finally released. This post aims to inspire new generations of Machine Learning (ML) engineers to focus on foundational biological problems.

This post is a collection of core concepts to finally grasp AlphaFold2-like stuff. Our goal is to make this blog post as self-complete as possible in terms of biology. Thus in this article, you will learn about:

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A transformative artificial intelligence (AI) tool called AlphaFold, which has been developed by Google’s sister company DeepMind in London, has predicted the structure of nearly the entire human proteome (the full complement of proteins expressed by an organism). In addition, the tool has predicted almost complete proteomes for various other organisms, ranging from mice and maize (corn) to the malaria parasite.

The more than 350000 protein structures, which are available through a public database, vary in their accuracy. But researchers say the resource — which is set to grow to 130 million structures by the end of the year — has the potential to revolutionize the life sciences.

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“Killer Robots” may seem far fetched, but as @AlexGatopoulos explains, the use of autonomous machines and other military applications of artificial intelligence are a growing reality of modern warfare.

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Experimental facilities around the globe are facing a challenge: their instruments are becoming increasingly powerful, leading to a steady increase in the volume and complexity of the scientific data they collect. At the same time, these tools demand new, advanced algorithms to take advantage of these capabilities and enable ever-more intricate scientific questions to be asked—and answered. For example, the ALS-U project to upgrade the Advanced Light Source facility at Lawrence Berkeley National Laboratory (Berkeley Lab) will result in 100 times brighter soft X-ray light and feature superfast detectors that will lead to a vast increase in data-collection rates.

To make full use of modern instruments and facilities, researchers need new ways to decrease the amount of data required for and address data acquisition rates humans can no longer keep pace with. A promising route lies in an emerging field known as autonomous discovery, where algorithms learn from a comparatively little amount of input data and decide themselves on the next steps to take, allowing multi-dimensional parameter spaces to be explored more quickly, efficiently, and with minimal human intervention.

“More and more experimental fields are taking advantage of this new optimal and autonomous data acquisition because, when it comes down to it, it’s always about approximating some function, given noisy data,” said Marcus Noack, a research scientist in the Center for Advanced Mathematics for Energy Research Applications (CAMERA) at Berkeley Lab and lead author on a new paper on Gaussian processes for autonomous data acquisition published July 28 in Nature Reviews Physics. The paper is the culmination of a multi-year, multinational effort led by CAMERA to introduce innovative autonomous discovery techniques across a broad scientific community.

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