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Category Archives: Machine Learning

New study uses machine learning to bridge the reality gap in quantum devices – University of Oxford

A study led by the University of Oxford has used the power of machine learning to overcome a key challenge affecting quantum devices. For the first time, the findings reveal a way to close the reality gap: the difference between predicted and observed behaviour from quantum devices. The results have been published in Physical Review X.

Functional variability is presumed to be caused by nanoscale imperfections in the materials that quantum devices are made from. Since there is no way to measure these directly, this internal disorder cannot be captured in simulations, leading to the gap in predicted and observed outcomes.

To address this, the research group used a physics-informed machine learning approach to infer these disorder characteristics indirectly. This was based on how the internal disorder affected the flow of electrons through the device.

Lead researcher Associate Professor Natalia Ares (Department of Engineering Science, University of Oxford) said: As an analogy, when we play crazy golf the ball may enter a tunnel and exit with a speed or direction that doesnt match our predictions. But with a few more shots, a crazy golf simulator, and some machine learning, we might get better at predicting the balls movements and narrow the reality gap.

Associate Professor Ares added: In the crazy golf analogy, it would be equivalent to placing a series of sensors along the tunnel, so that we could take measurements of the balls speed at different points. Although we still cant see inside the tunnel, we can use the data to inform better predictions of how the ball will behave when we take the shot.

Not only did the new model find suitable internal disorder profiles to describe the measured current values, it was also able to accurately predict voltage settings required for specific device operating regimes.

Co-author David Craig, a PhD student at the Department of Materials, University of Oxford, added, Similar to how we cannot observe black holes directly but we infer their presence from their effect on surrounding matter, we have used simple measurements as a proxy for the internal variability of nanoscale quantum devices. Although the real device still has greater complexity than the model can capture, our study has demonstrated the utility of using physics-aware machine learning to narrow the reality gap.

The study 'Bridging the reality gap in quantum devices with physics-aware machine learning has been published in Physical Review X.

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New study uses machine learning to bridge the reality gap in quantum devices - University of Oxford

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Toyota’s Robots Are Learning to Do HouseworkBy Copying Humans – WIRED

As someone who quite enjoys the Zen of tidying up, I was only too happy to grab a dustpan and brush and sweep up some beans spilled on a tabletop while visiting the Toyota Research Lab in Cambridge, Massachusetts last year. The chore was more challenging than usual because I had to do it using a teleoperated pair of robotic arms with two-fingered pincers for hands.

Courtesy of Toyota Research Institute

As I sat before the table, using a pair of controllers like bike handles with extra buttons and levers, I could feel the sensation of grabbing solid items, and also sense their heft as I lifted them, but it still took some getting used to.

After several minutes tidying, I continued my tour of the lab and forgot about my brief stint as a teacher of robots. A few days later, Toyota sent me a video of the robot Id operated sweeping up a similar mess on its own, using what it had learned from my demonstrations combined with a few more demos and several more hours of practice sweeping inside a simulated world.

Autonomous sweeping behavior. Courtesy of Toyota Research Institute

Most robotsand especially those doing valuable labor in warehouses or factoriescan only follow preprogrammed routines that require technical expertise to plan out. This makes them very precise and reliable but wholly unsuited to handling work that requires adaptation, improvisation, and flexibilitylike sweeping or most other chores in the home. Having robots learn to do things for themselves has proven challenging because of the complexity and variability of the physical world and human environments, and the difficulty of obtaining enough training data to teach them to cope with all eventualities.

There are signs that this could be changing. The dramatic improvements weve seen in AI chatbots over the past year or so have prompted many roboticists to wonder if similar leaps might be attainable in their own field. The algorithms that have given us impressive chatbots and image generators are also already helping robots learn more efficiently.

The sweeping robot I trained uses a machine-learning system called a diffusion policy, similar to the ones that power some AI image generators, to come up with the right action to take next in a fraction of a second, based on the many possibilities and multiple sources of data. The technique was developed by Toyota in collaboration with researchers led by Shuran Song, a professor at Columbia University who now leads a robot lab at Stanford.

Toyota is trying to combine that approach with the kind of language models that underpin ChatGPT and its rivals. The goal is to make it possible to have robots learn how to perform tasks by watching videos, potentially turning resources like YouTube into powerful robot training resources. Presumably they will be shown clips of people doing sensible things, not the dubious or dangerous stunts often found on social media.

If you've never touched anything in the real world, it's hard to get that understanding from just watching YouTube videos, Russ Tedrake, vice president of Robotics Research at Toyota Research Institute and a professor at MIT, says. The hope, Tedrake says, is that some basic understanding of the physical world combined with data generated in simulation, will enable robots to learn physical actions from watching YouTube clips. The diffusion approach is able to absorb the data in a much more scalable way, he says.

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Toyota's Robots Are Learning to Do HouseworkBy Copying Humans - WIRED

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Daily AI Roundup: Biggest Machine Learning, Robotic And Automation Updates – AiThority

This is our AI Daily Roundup. We are covering the top updates from around the world. The updates will feature state-of-the-art capabilities inartificial intelligence (AI),Machine Learning, Robotic Process Automation, Fintech, and human-system interactions.

We cover the role of AI Daily Roundup and its application in various industries and daily lives.

Ahead ofNRF2024, the retail industrys largest event, Google Cloud debuted several new AI andgenerative AI-powered technologies to help retailers personalize online shopping, modernize operations, and transform in-store technology rollouts.

Quantiphi, a leading AI-first digital engineering company andLambda, the GPU cloud and AI infrastructure company founded by deep learning engineers, have partnered to provide tailored AI solutions to enterprise customers and digital AI natives across multiple industries.

Quanta Computer Inc., a trailblazer in advanced technology solutions, andAmbarella, Inc., an edge AI semiconductor company,announced duringCESthe expansion of their strategic partnership. This collaboration is being broadened to include development with Ambarellas CV3-AD, CV7 and new N1 series AI systems-on-chip (SoCs), marking a significant capabilities advancement for cutting-edge AI products.

Patronus AI announced it is partnering with MongoDB to bring automated LLM evaluation and testing to enterprise customers. The joint offering will combine Patronus AIs capabilities with MongoDBs Atlas Vector Search product.

In a strategic move that anticipates the imminent shift indigital advertising,ZeotapData, the leading provider of people-based digital audiences, has announced a partnership with Illuma, the leader in AI-powered expansion and optimisation. This collaboration offers a new tactic in the face of third-party cookie deprecation.

[To share your insights with us, please write tosghosh@martechseries.com]

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Daily AI Roundup: Biggest Machine Learning, Robotic And Automation Updates - AiThority

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Researchers from UT Austin Propose a New Machine Learning Approach to Generating Synthetic Functional Training Data that does not Require Solving a…

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Researchers from UT Austin Propose a New Machine Learning Approach to Generating Synthetic Functional Training Data that does not Require Solving a...

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Use of Non-invasive Machine Learning to Help Predict the Chronic Degree of Lupus Nephritis – Lupus Foundation of America

Using a non-invasive machine learning model based on ultrasound radiomic imaging to analyze features of the kidneys, such as shape and texture, researchers were able to predict the degree of kidney injury in people with lupus nephritis, (LN, lupus-related kidney disease). Currently, a renal biopsy, an invasive test which can cause bleeding, pain and other outcomes, is the most common form of assessing a persons chronic degree of LN.

Using radiomics, the ultrasound images of 136 people with LN who had renal biopsies were examined. The images were divided into two groups, a training set and a validation set, and seven machine learning models were constructed based on five ultrasound-based radiomics to establish prediction models. The Xgboost model performed the best in the training and test sets.

Knowing the degree of kidney injury in people with LN can be useful to clinicians as they develop an individuals treatment plan. Learn more about lupus and the kidneys.

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Deep fake, AI and face swap in video edit. Deepfake and machine learning. Facial tracking, detection and recognition … – Frederick News Post

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Deep fake, AI and face swap in video edit. Deepfake and machine learning. Facial tracking, detection and recognition ... - Frederick News Post

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