Tag Archives: outperforms

Carbon Nanotube-Woven Fabric Outperforms Kevlar in Strength

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Bulletproof fabric is lightweight and strong

Peking University Jinzhang Group

The innovative material is remarkably strong, capable of stopping bullets with a 1.8-millimeter-thick sheet, surpassing Kevlar and potentially setting a new standard for fabric strength.

Bulletproof vests functionality relies on dissipating the energy of projectiles through an intricate network of interconnected fibers. Kevlar’s composition consists of aramid fibers, which are polymers recognized for their exceptional strength. However, under extreme pressure, these chains can slip, which limits their protective capabilities.

For the last six years, Jin Chang and researchers from Peking University in China have focused on creating materials that outperform Kevlar and Dyneema, another renowned polyethylene fiber claimed to be the strongest fabric in the world.

“Extremely high dynamic strength and toughness are essential for textile materials used in impact protection applications,” notes Zhang. “This includes ballistic armor, vehicles, and aircraft.”

His team has pioneered a technique to align carbon nanotubes with aramid polymer chains to prevent molecular slippage. “Our new fiber surpasses all previously noted high-performance polymer fibers,” asserts Zhang. “Our fabric is entirely superior to Kevlar.”

The new invention is described as an “engineered carbon nanotube/heterocyclic aramid composite,” according to Zhang, who aims to create a catchy name similar to Kevlar in the future.

This material outperforms Kevlar, achieving the same ballistic protection with significantly less fabric. Zhang explains that each layer is roughly 0.6 millimeters thick and can slow a bullet’s speed from 300 meters per second to 220 meters per second. “Based on energy absorption calculations, about three fabric layers can halt a bullet,” resulting in a total thickness of 1.8 mm. In contrast, Kevlar would need to be at least 4 mm thick for equivalent protection.

Julie Cairney and her team at the University of Sydney in Australia have called the combination of aramid fibers and aligned carbon nanotubes revolutionary.

“This strategy could lead to the development of other innovative composite materials,” Cairney states, also highlighting that this manufacturing approach is compatible with existing industrial methods, indicating promise for scalable production and practical implementation.

“For personal and military protection, these materials have the potential to create lighter and more effective body armor, enhancing safety while maintaining mobility,” she adds.

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Source: www.newscientist.com

British AI Startup Outperforms Humans in Global Forecasting Competition

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The artificial intelligence system has outperformed numerous prediction enthusiasts, including a number of experts. A competition focused on event predictions spanned events from the fallout between Donald Trump and Elon Musk to Kemi Badenok being dismissed as a potential Conservative leader.

The UK-based AI startup, established by former Google DeepMind researchers, ranks among the top 10 in international forecasting competitions, with participants tasked with predicting the probabilities of 60 events occurring over the summer.

Manticai secured 8th place in the Metaculus Cup, operated by a forecasting firm based in San Francisco aiming to predict the futures of investment funds and corporations.

While AI performance still lags behind the top human predictors, some contend that it could surpass human capabilities sooner than anticipated.

“It feels odd to be outperformed by a few bots at this stage,” remarked Ben Sindel, one of the professional predictors who ended up behind the AI during the competition, eventually finishing on Mantic’s team. “We’ve made significant progress compared to a year ago when the best bots were ranked around 300.”

The Metaculus Cup included questions like which party would win the most seats in the Samoan general election, and how many acres of the US would be affected by fires from January to August. Contestants were graded based on their predictions as of September 1st.

“What Munch achieved is remarkable,” stated Degar Turan, CEO of Metaculus.

Turan estimated that AI would perform at par or even surpass top human predictors by 2029, but also acknowledged that “human predictors currently outshine AI predictors.”

In complex predictions reliant on interrelated events, AI systems tend to struggle with logical validation checks when interpreting knowledge into final forecasts.

Mantic effectively dissects prediction challenges into distinct tasks and assigns them to various machine learning models such as OpenAI, Google, and DeepSeek based on their capabilities.

Co-founder Toby Shevlane indicated that their achievements mark a significant milestone for the AI community, utilizing large language models for predictive analytics.

“Some argue that LLMs merely replicate training data, but we can’t predict such futures,” he noted. “We require genuine inference. We can assert that our system’s forecasts are more original than those of most human contenders, as individuals often compile average community predictions. AI systems frequently differ from these averages.”

Mantic’s systems deploy a range of AI agents to evaluate current events, conduct historical analyses, simulate scenarios, and make future predictions. The strength of AI prediction lies in its capacity for hard work and endurance, vital for effective forecasting.

AI can simultaneously tackle numerous complex challenges, revisiting each daily to adapt based on evolving information. Human predictors also leverage intuition, but Sindel suggests this may emerge in AI as well.

“Intuition is crucial, but I don’t think it’s inherently human,” he commented.

Top-tier human super forecasters assert their superiority. Philip Tetlock, co-author of the bestseller SuperForecasting, recently published research indicating that, on average, experts continue to outperform the best bots.

Turan reiterated that AI systems face challenges in complex predictions involving interdependent events, struggling to identify logical inconsistencies in output during validation checks.

“We’ve witnessed substantial effort and investment,” remarked Warren Hatch, CEO of Good Judgement, a forecasting firm co-founded by Tetlock. “We anticipate AI excelling in specific question categories, such as monthly inflation.

Or, as Lubos Saloky, the human forecaster who placed third in the Metaculus Cup, expressed, “I’m not retiring. If you can’t beat them, I’ll collaborate with them.”

Source: www.theguardian.com

AI from DeepMind outperforms current weather predictions in accuracy

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Weather forecasting today relies on simulations that require large amounts of computing power.

Petrovich9/Getty Images/iStockphoto

Google DeepMind claims its latest weather forecasting AI can predict faster and more accurately than existing physics-based simulations.

GenCast is the latest in DeepMind's ongoing research project to improve weather forecasts using artificial intelligence. The model was trained on 40 years of historical data from the European Center for Medium-Range Weather Forecasts (ECMWF). ERA5 ArchiveThis includes regular measurements of temperature, wind speed, and barometric pressure at various altitudes around the world.

Data up to 2018 was used to train the model, and then 2019 data was used to test predictions against known weather conditions. The company found that it outperformed ECMWF's industry standard ENS forecasts 97.4% of the time, and 99.8% of the time when forecasting more than 36 hours ahead.

Last year, DeepMind collaborated with ECMWF to create an AI that outperformed the “gold standard” high-resolution HRES 10-day forecast by more than 90%. Previously, he developed a “nowcasting” model that used five minutes of radar data to predict the probability of rain over a given one square kilometer area from five to 90 minutes in advance. Google is also working on ways to use AI to replace small parts of deterministic models to speed up calculations while maintaining accuracy.

Existing weather forecasts are based on physical simulations run on powerful supercomputers to deterministically model and estimate weather patterns as accurately as possible. Forecasters typically run dozens of simulations with slightly different inputs in groups called ensembles to better capture the variety of possible outcomes. These increasingly complex and large numbers of simulations are computationally intensive and require ever more powerful and energy-consuming machines to operate.

AI has the potential to provide lower-cost solutions. For example, GenCast uses an ensemble of 50 possible futures to create predictions. Using custom-built, AI-focused Google Cloud TPU v5 chips, each prediction takes just 8 minutes.

GenCast operates at a cell resolution of approximately 28 square kilometers near the equator. Since the data used in this study were collected, ECMWF's ENS has been upgraded to a resolution of just 9 kilometers.

Yilan price DeepMind says AI doesn't have to follow, and could provide a way forward without collecting more detailed data or performing more intensive calculations. “If you have a traditional physics-based model, that's a necessary requirement to solve the physical equations more accurately, and therefore to get more accurate predictions,” Price says. “[With] machine learning, [it] It is not always necessary to go to higher resolution to get more accurate simulations and predictions from your model. ”

david schultz Researchers at the University of Manchester in the UK say AI models offer an opportunity to make weather forecasts more efficient, but they are often over-hyped and rely heavily on training data from traditional physically-based models. states that it is important to remember that

“is that so [GenCast] Will it revolutionize numerical weather forecasting? No, because in order to train a model, you first have to run a numerical weather prediction model,” says Schulz. “These AI tools wouldn't exist if ECMWF didn't exist in the first place and without creating the ERA5 reanalysis and all the investment that went into it. It's like, 'I can beat Garry Kasparov at chess. But only after studying every move he's ever played.''

Sergey Frolov Researchers at the National Oceanic and Atmospheric Administration (NOAA) believe that further advances in AI will require higher-resolution training data. “What we're basically seeing is that all of these approaches are being thwarted.” [from advancing] “It depends on the fidelity of the training data,” he says. “And the training data comes from operational centers like ECMWF and NOAA. To move this field forward, we need to generate more training data using higher-fidelity physically-based models. .”

But for now, GenCast offers a faster way to perform predictions at lower computational costs. kieran hunt A professor at the University of Reading in the UK believes ensembles can improve the accuracy of AI predictions, just as a collection of physics-based predictions can produce better results than a single prediction. states.

Mr Hunt points to the UK's record temperature of 40C (104C) in 2022 as an example. A week or two ago, there was only one member of the ensemble who was predicting it, and they were considered an anomaly. Then, as the heat wave approached, the predictions became more accurate, providing early warning that something unusual was about to happen.

“You can get away with it a little bit if you have one member who shows something really extreme. That might happen, but it probably won't happen,” Hunt says. “I don’t think it’s necessarily a step change; it’s a combination of new AI approaches with tools we’ve been using in weather forecasting for a while to ensure the quality of AI weather forecasts. There is no doubt that this will yield better results than the first wave of AI weather forecasting.”

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Source: www.newscientist.com

DeepMind AI outperforms top weather forecasts, with one caveat

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Will the AI ​​tell me if I need an umbrella?

Sebastien Bozon/AFP via Getty Images

AI company Google DeepMind says AI can more accurately predict the weather 10 days out than current state-of-the-art simulations, but meteorologists are still trying to build weather models based on actual physical principles. It warns against abandoning and relying solely on patterns in the data. Expose the shortcomings of AI approaches.

Existing weather forecasts are based on mathematical models, which use physics and powerful supercomputers to definitively predict what will happen in the future. These models have gradually become more accurate by adding more details, but this requires more calculations, more powerful computers, and higher energy demands.

Rémi Lam and his colleagues at Google DeepMind took a different approach. The company’s GraphCast AI model is trained on his 40 years of historical weather data from satellites, radar, and ground-based measurements to identify patterns that even Google DeepMind can’t understand. “As with many machine learning AI models, it’s not that easy to interpret how the model works,” Lamb says.

To make the predictions, actual weather measurements taken at two points six hours apart from more than one million locations around the globe are used to predict the weather six hours into the future. These predictions can be used as input for another round that predicts another 6 hours into the future.

DeepMind researchers carried out this process using data Produces a 10-day forecast from the European Center for Medium-Range Weather Forecasts (ECMWF). They say it outperformed ECMWF’s “gold standard” High Resolution Forecasting (HRES) by providing more accurate forecasts on more than 90 per cent of the data points tested. At some altitudes, this accuracy increased to 99.7%.

Matthew Chantry He, who worked with Google DeepMind at ECMWF, said his organization had previously seen AI as a tool to complement existing mathematical models, but in the past 18 months it has seen a shift in the way AI can actually provide predictions on its own. He said that he has become able to do so.

“We at ECMWF believe this is a very exciting technology that has the potential not only to reduce energy costs when making forecasts, but also to improve them. Creating a reliable operational product “Probably more work is needed, but this is likely the beginning of a revolution in the way weather forecasts are made, and this is our assessment,” he says. According to Google DeepMind, using GraphCast to make a 10-day forecast takes him less than a minute on a high-end PC, but with HRES it can take several hours on a supercomputer.

But some meteorologists are wary of trusting weather forecasting to AI. Ian Renfrew According to researchers at the University of East Anglia in the UK, GraphCast currently lack the ability to marshal data into its own starting state, a process known as data assimilation. In traditional predictions, this data is carefully incorporated into simulations after thorough checks on physics and chemistry calculations to ensure accuracy and consistency. Currently, GraphCast must use a starting state prepared in the same way by ECMWF’s own tools.

“Google won’t be doing weather forecasts any time soon because they can’t assimilate the data,” Renfrew said. “And data assimilation is typically one-half to two-thirds of the computation time for these forecasting systems.”

He says there are also concerns about completely abandoning deterministic models based on chemistry and physics and relying solely on AI output.

“Even if you have the best predictive model in the world, what’s the point if the public doesn’t trust you and you don’t take action? We ordered the evacuation of 30 miles of Florida’s coastline. “If nothing happens, it will blow away the trust that has been built over decades,” he says. “The advantage of a deterministic model is that you can investigate it. If you get a bad prediction, you can investigate why that prediction is bad and target those aspects for improvement.”

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Source: www.newscientist.com