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Computer Modeling

The Hidden Environmental Cost of Thinking AI Models

Every query typed into a large language model (LLM), such as ChatGPT, requires energy and produces CO2 emissions. Emissions, however, depend on the model, the subject matter, and the user. Researchers have now compared 14 models and found that complex answers cause more emissions than simple answers, and that models that provide more accurate answers produce more emissions. Users can, however, to an extent, control the amount of CO2 emissions caused by AI by adjusting their personal use of the technology, the researchers said.

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The article “Thinking AI models emit 50x more CO2—and often for nothing” reveals a shocking truth about the environmental cost of using thinking AI models. These models, which are capable of generating elaborate responses to complex questions, have a significant carbon footprint due to the computing processes involved in producing these answers. Researchers in Germany have measured and compared the CO2 emissions of different LLMs (Large Language Models) using standardized questions, and their findings are eye-opening.

The study found that reasoning-enabled models produced up to 50 times more CO2 emissions than concise response models. This is because reasoning models generate additional tokens, which are words or parts of words converted into a string of numbers that can be processed by the LLM. These tokens require significant computational power and energy consumption, resulting in substantial carbon emissions.

The researchers evaluated 14 LLMs with varying parameters (7-72 billion) on 1,000 benchmark questions across diverse subjects. The results showed that reasoning models created an average of 543.5 “thinking” tokens per question, whereas concise models required just 37.7 tokens per question. This significant difference in token footprint resulted in higher CO2 emissions.

The study also highlighted the accuracy-sustainability trade-off inherent in LLM technologies. None of the models that kept emissions below 500 grams of CO2 equivalent achieved higher than 80% accuracy on answering the 1,000 questions correctly. The researchers concluded that users can significantly reduce emissions by prompting AI to generate concise answers or limiting the use of high-capacity models to tasks that genuinely require that power.

The findings of this study are crucial for individuals who use AI technologies daily. By understanding the environmental cost of their AI usage, they can make more informed decisions about when and how they use these technologies. The choice of model, subject matter, and even hardware used in the study can make a significant difference in CO2 emissions.

In conclusion, the hidden environmental cost of thinking AI models is a pressing concern that requires attention from both researchers and users. By being more thoughtful and selective in our AI usage, we can reduce the carbon footprint associated with these technologies and promote sustainability in the long run.

Computational Biology

A Quantum Leap Forward – New Amplifier Boosts Efficiency of Quantum Computers 10x

Chalmers engineers built a pulse-driven qubit amplifier that’s ten times more efficient, stays cool, and safeguards quantum states—key for bigger, better quantum machines.

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Quantum computers have long been touted as revolutionary machines capable of solving complex problems that stymie conventional supercomputers. However, their full potential has been hindered by the limitations of qubit amplifiers – essential components required to read and interpret quantum information. Researchers at Chalmers University of Technology in Sweden have taken a significant step forward with the development of an ultra-efficient amplifier that reduces power consumption by 90%, paving the way for more powerful quantum computers with enhanced performance.

The new amplifier is pulse-operated, meaning it’s activated only when needed to amplify qubit signals, minimizing heat generation and decoherence. This innovation has far-reaching implications for scaling up quantum computers, as larger systems require more amplifiers, leading to increased power consumption and decreased accuracy. The Chalmers team’s breakthrough offers a solution to this challenge, enabling the development of more accurate readout systems for future generations of quantum computers.

One of the key challenges in developing pulse-operated amplifiers is ensuring they respond quickly enough to keep pace with qubit readout. To address this, the researchers employed genetic programming to develop a smart control system that enables rapid response times – just 35 nanoseconds. This achievement has significant implications for the future of quantum computing, as it paves the way for more accurate and powerful calculations.

The new amplifier was developed in collaboration with industry partners Low Noise Factory AB and utilizes the expertise of researchers at Chalmers’ Terahertz and Millimeter Wave Technology Laboratory. The study, published in IEEE Transactions on Microwave Theory and Techniques, demonstrates a novel approach to developing ultra-efficient amplifiers for qubit readout and offers promising prospects for future research.

In conclusion, the development of this highly efficient amplifier represents a significant leap forward for quantum computing. By reducing power consumption by 90%, researchers have opened doors to more powerful and accurate calculations, unlocking new possibilities in fields such as drug development, encryption, AI, and logistics. As the field continues to evolve, it will be exciting to see how this innovation shapes the future of quantum computing.

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Artificial Intelligence

AI Uncovers Hidden Heart Risks in CT Scans: A Game-Changer for Cardiovascular Care

What if your old chest scans—taken years ago for something unrelated—held a secret warning about your heart? A new AI tool called AI-CAC, developed by Mass General Brigham and the VA, can now comb through routine CT scans to detect hidden signs of heart disease before symptoms strike.

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The Massachusetts General Brigham researchers have developed an innovative artificial intelligence (AI) tool called AI-CAC to analyze previously collected CT scans and identify individuals with high coronary artery calcium (CAC) levels, indicating a greater risk for cardiovascular events. Their research, published in NEJM AI, demonstrated the high accuracy and predictive value of AI-CAC for future heart attacks and 10-year mortality.

Millions of chest CT scans are taken each year, often in healthy people, to screen for lung cancer or other conditions. However, this study reveals that these scans can also provide valuable information about cardiovascular risk, which has been going unnoticed. The researchers found that AI-CAC had a high accuracy rate (89.4%) at determining whether a scan contained CAC or not.

The gold standard for quantifying CAC uses “gated” CT scans, synchronized to the heartbeat to reduce motion during the scan. However, most chest CT scans obtained for routine clinical purposes are “nongated.” The researchers developed AI-CAC, a deep learning algorithm, to probe through these nongated scans and quantify CAC.

The AI-CAC model was 87.3% accurate at determining whether the score was higher or lower than 100, indicating a moderate cardiovascular risk. Importantly, AI-CAC was also predictive of 10-year all-cause mortality, with those having a CAC score over 400 having a 3.49 times higher risk of death over a 10-year period.

The researchers hope to conduct future studies in the general population and test whether the tool can assess the impact of lipid-lowering medications on CAC scores. This could lead to the implementation of AI-CAC in clinical practice, enabling physicians to engage with patients earlier, before their heart disease advances to a cardiac event.

As Dr. Raffi Hagopian, first author and cardiologist at the VA Long Beach Healthcare System, emphasized, “Using AI for tasks like CAC detection can help shift medicine from a reactive approach to the proactive prevention of disease, reducing long-term morbidity, mortality, and healthcare costs.”

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Computer Modeling

Harnessing True Randomness from Entangled Photons: The Colorado University Randomness Beacon (CURBy)

Scientists at NIST and the University of Colorado Boulder have created CURBy, a cutting-edge quantum randomness beacon that draws on the intrinsic unpredictability of quantum entanglement to produce true random numbers. Unlike traditional methods, CURBy is traceable, transparent, and verifiable thanks to quantum physics and blockchain-like protocols. This breakthrough has real-world applications ranging from cybersecurity to public lotteries—and it’s open source, inviting the world to use and build upon it.

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The Colorado University Randomness Beacon (CURBy) is a pioneering service that harnesses the true randomness of entangled photons to produce unguessable strings of numbers. This breakthrough was made possible by the work of scientists at the National Institute of Standards and Technology (NIST) and their colleagues at the University of Colorado Boulder.

“True randomness is something that nothing in the universe can predict in advance,” said Krister Shalm, a physicist at NIST. “If God does play dice with the universe, then you can turn that into the best random number generator that the universe allows.”

The CURBy system uses a Bell test to measure pairs of entangled photons whose properties are correlated even when separated by vast distances. When researchers measure an individual particle, the outcome is random, but the properties of the pair are more correlated than classical physics allows, enabling researchers to verify the randomness.

This is the first random number generator service to use quantum nonlocality as a source of its numbers, and the most transparent source of random numbers to date. The results are certifiable and traceable to a greater extent than ever before.

The CURBy system consists of a nonlinear crystal that generates entangled photons, which travel via optical fiber to separate labs at opposite ends of the hall. Once the photons reach the labs, their polarizations are measured. The outcomes of these measurements are truly random.

NIST passes millions of these quantum coin flips to a computer program at the University of Colorado Boulder, where special processing steps and strict protocols are used to turn the outcomes into 512 random bits of binary code (0s and 1s). The result is a set of random bits that no one, not even Einstein, could have predicted.

The CURBy system has been operational for several months now, with an impressive success rate of over 99.7%. The ability to verify the data behind each random number was made possible by the Twine protocol, a novel set of quantum-compatible blockchain technologies developed by NIST and its collaborators.

“The Twine protocol lets us weave together all these other beacons into a tapestry of trust,” said Jasper Palfree, a research assistant on the project at the University of Colorado Boulder. This allows any user to verify the data behind each random number, providing security and traceability.

The CURBy system can be used anywhere an independent, public source of random numbers would be useful, such as selecting jury candidates, making a random selection for an audit, or assigning resources through a public lottery.

“I wanted to build something that is useful. It’s this cool thing that is the cutting edge of fundamental science,” said Gautam Kavuri, a graduate student on the project. The whole process is open source and available to the public, allowing anyone to not only check their work but even build on the beacon to create their own random number generator.

The CURBy system has the potential to revolutionize fields such as cryptography, gaming, and finance, where true randomness is essential. By harnessing the power of entangled photons, scientists have created a truly independent source of random numbers that can be trusted.

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