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Category: robotics/AI – Page 1,348

Early detection and identification of pathogenic bacteria in food and water samples are essential to public health. Bacterial infections cause millions of deaths worldwide and bring a heavy economic burden, costing more than 4 billion dollars annually in the United States alone. Among pathogenic bacteria, Escherichia coli (E. coli) and other coliform bacteria are among the most common ones, and they indicate fecal contamination in food and water samples. The most conventional and frequently used method for detecting these bacteria involves culturing of the samples, which usually takes 24 hours for the final read-out and needs expert visual examination. Although some methods based on, for example, the amplification of nucleic acids, can reduce the detection time to a few hours, they cannot differentiate live and dead bacteria and present low sensitivity at low concentrations of bacteria. That is why the U.S. Environmental Protection Agency (EPA) approves no nucleic acid-based bacteria sensing method for screening water samples.

In an article recently published in ACS Photonics, a journal of the American Chemical Society (ACS), a team of scientists, led by Professor Aydogan Ozcan from the Electrical and Computer Engineering Department at the University of California, Los Angeles (UCLA), and co-workers have developed an AI-powered smart bacterial colony detection system using a thin-film transistor (TFT) array, which is a widely used technology in mobile phones and other displays.

The ultra-large imaging area of the TFT array (27 mm × 26 mm) manufactured by researchers at Japan Display Inc. enabled the system to rapidly capture the growth patterns of bacterial colonies without the need for scanning, which significantly simplified both the hardware and software design. This system achieved ~12-hour time savings compared to gold-standard culture-based methods approved by EPA. By analyzing the microscopic images captured by the TFT array as a function of time, the AI-based system could rapidly and automatically detect colony growth with a deep neural network. Following the detection of each colony, a second neural network is used to classify the species.

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Using reinforcement learning (RL) to train robots directly in real-world environments has been considered impractical due to the huge amount of trial and error operations typically required before the agent finally gets it right. The use of deep RL in simulated environments has thus become the go-to alternative, but this approach is far from ideal, as it requires designing simulated tasks and collecting expert demonstrations. Moreover, simulations can fail to capture the complexities of real-world environments, are prone to inaccuracies, and the resulting robot behaviours will not adapt to real-world environmental changes.

The Dreamer algorithm proposed by Hafner et al. at ICLR 2020 introduced an RL agent capable of solving long-horizon tasks purely via latent imagination. Although Dreamer has demonstrated its potential for learning from small amounts of interaction in the compact state space of a learned world model, learning accurate real-world models remains challenging, and it was unknown whether Dreamer could enable faster learning on physical robots.

In the new paper DayDreamer: World Models for Physical Robot Learning, Hafner and a research team from the University of California, Berkeley leverage recent advances in the Dreamer world model to enable online RL for robot training without simulators or demonstrations. The novel approach achieves promising results and establishes a strong baseline for efficient real-world robot training.

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Research in the field of machine learning and AI, now a key technology in practically every industry and company, is far too voluminous for anyone to read it all. This column, Perceptron, aims to collect some of the most relevant recent discoveries and papers — particularly in, but not limited to, artificial intelligence — and explain why they matter.

In this batch of recent research, Meta open-sourced a language system that it claims is the first capable of translating 200 different languages with “state-of-the-art” results. Not to be outdone, Google detailed a machine learning model, Minerva, that can solve quantitative reasoning problems including mathematical and scientific questions. And Microsoft released a language model, Godel, for generating “realistic” conversations that’s along the lines of Google’s widely publicized Lamda. And then we have some new text-to-image generators with a twist.

Meta’s new model, NLLB-200, is a part of the company’s No Language Left Behind initiative to develop machine-powered translation capabilities for most of the world’s languages. Trained to understand languages such as Kamba (spoken by the Bantu ethnic group) and Lao (the official language of Laos), as well as over 540 African languages not supported well or at all by previous translation systems, NLLB-200 will be used to translate languages on the Facebook News Feed and Instagram in addition to the Wikimedia Foundation’s Content Translation Tool, Meta recently announced.

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Multiple angles of Booster 7 experiencing an unexpected ignition during Raptor engine testing.

Video and Pictures from the NSF Robots. Edited by Jack (@theJackBeyer).

All content copyright to NSF. Not to be used elsewhere without explicit permission from NSF.

Click “Join” for access to early fast turnaround clips, exclusive discord access with the NSF team, etc — to support the channel.

Rolling Updates and Discussion: https://forum.nasaspaceflight.com/index.php?board=72.

Articles: https://www.nasaspaceflight.com/?s=Starship.

Continue reading “SpaceX Booster 7 Experiences Explosion” | >

A new language model similar in scale to GPT-3 is being made freely available and could help to democratise access to AI.

BLOOM (which stands for BigScience Large Open-science Open-access Multilingual Language Model) has been developed by 1,000 volunteer researchers from over 70 countries and 250 institutions, supported by ethicists, philosophers, and legal experts, in a collaboration called BigScience. The project, coordinated by New York-based startup Hugging Face, used funding from the French government.

The new AI took more than a year of planning and training, which included a final run of 117 days (11th March – 6th July) using the Jean Zay, one of Europe’s most powerful supercomputers, located in the south of Paris, France.

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The question of how the chemical composition of a protein—the amino acid sequence—determines its 3D structure has been one of the biggest challenges in biophysics for more than half a century. This knowledge about the so-called “folding” of proteins is in great demand, as it contributes significantly to the understanding of various diseases and their treatment, among other things. For these reasons, Google’s DeepMind research team has developed AlphaFold, an artificial intelligence that predicts 3D structures.

A team consisting of researchers from Johannes Gutenberg University Mainz (JGU) and the University of California, Los Angeles, has now taken a closer look at these structures and examined them with respect to knots. We know knots primarily from shoelaces and cables, but they also occur on the nanoscale in our cells. Knotted proteins can not only be used to assess the quality of structure but also raise important questions about folding mechanisms and the evolution of proteins.

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Researchers at the Massachusetts Institute of Technology (MIT) Media Lab have created a novel fabrication process to produce smart textiles that comfortabl | Technology.

Using 3DKnITS, the research team created a “smart” shoe and mat, followed by building a hardware and software system capable of measuring and interpreting real-time data from the pressure sensors. An individual then performed yoga poses on the smart textile mat while the machine-learning system was able to accurately predict the individual’s motions and poses 99 percent of the time.

“Some of the early pioneering work on smart fabrics happened at the Media Lab in the late ’90s. The materials, embeddable electronics, and fabrication machines have advanced enormously since then,” said co-author Jospeh A. Paradiso, an Alexander W. Dreyfoos Professor and Director of the Responsive Environments group within the Media Lab. “It’s a great time to see our research returning to this area, for example through projects like Irmandy’s — they point at an exciting future where sensing and functions diffuse more fluidly into materials and open up enormous possibilities.”

Wicaksono now plans to refine the circuit and machine learning model since the fabrication technique has been deemed a success. This refinement involves the removing a time-consuming calibration step which currently needs to be done to each individual before the system can classify actions. Once this is done, 3DKnITS will be easier to use. Along with that, the researchers also hope to conduct tests on smart shoes outside of the lab to test how the accuracy of the sensors are affected by environmental conditions such as temperature and humidity.

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The story of future video games starts when artificial intelligence takes over building the games for players — while they play them. And human brains are mapped by virtual reality headsets.

This sci fi documentary also covers A.I. npc characters, Metaverse scoreboards, brain to computer chips and gaming, Elon Musk and Neuralink, and the simulation hypothesis.

Taking inspiration from the likes of Westworld, Ready Player One, Squid Game, and Inception.

A future gaming sci-fi documentary, and a timelapse look into the future.
See more of Venture City at: https://vx-c.com.

Book recommendations by Elon Musk on A.I,. future technology and innovations, and sci-fi stories (affiliate links):

• Superintelligence: Paths, Dangers, Strategies https://amzn.to/3j28WkP

Continue reading “Timelapse of Future Gaming Worlds (2030 & Beyond)” | >

In today’s business world, machine-learning algorithms are increasingly being applied to decision-making processes, which affects employment, education, and access to credit. But firms usually keep algorithms secret, citing concerns over gaming by users that can harm the predictive power of algorithms. Amid growing calls to require firms to make their algorithms transparent, a new study developed an analytical model to compare the profit of firms with and without such transparency. The study concluded that there are benefits but also risks in algorithmic transparency.

Conducted by researchers at Carnegie Mellon University (CMU) and the University of Michigan, the study appears in Management Science.

“As managers face calls to boost , our findings can help them make decisions to benefit their firms,” says Param Vir Singh, Professor of Business Technologies and Marketing at CMU’s Tepper School of Business, who coauthored the study.

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