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Friday, October 23, 2026

21ST CENTURY ALCHEMY


Could lab-made gold crash prices like diamonds? US start-up says it turned mercury into gold

FILE. Gold coins and gold bars are shown at the California Numismatic Investments, Inc. in Inglewood California, 13 March 2008
Copyright AP Photo/Nick Ut


By Quirino Mealha
Published on


Little-known San Francisco start-up Marathon Fusion says it has turned mercury into stable gold at ten times the rate of CERN's record, raising a question that has haunted the diamond trade: could lab-made gold one day sink the price of the mined metal?

Marathon Fusion, a small fusion energy company in San Francisco, announced on Wednesday that it had transmuted, or converted, mercury into stable gold last month, beating a record set by CERN tenfold. The Geneva-based organisation runs the world's largest particle physics laboratory.

The claim, which has not been independently verified, revives an alchemist's dream and a market fear that lab-made gold could do to bullion what lab-grown precious stones did to diamonds.

Founded in 2023 by Kyle Schiller and Adam Rutkowski, a former SpaceX engineer, Marathon develops fuel systems for future fusion power plants, which aim to generate electricity by fusing atoms together, as the sun does.

It lists the US Department of Energy as a partner on its website and won a 2023 award from ARPA-E, the department's agency for high-risk energy research.

Its backers include Breakthrough Energy, the climate technology network founded by Bill Gates, and Anglo American, the mining group that owns De Beers, the world's leading natural diamond company behind the "A diamond is forever" slogan.

In its announcement, the company said it had turned "mercury into real, stable gold, beating CERN's record by a factor of 10".

In 2025, CERN said its Large Hadron Collider (LHC), the world's most powerful particle accelerator, had created about 29 picograms, or 29 trillionths of a gram, of gold during its 2015–2018 run. That amounted to around 86 billion gold nuclei, or atomic cores, which existed for only a tiny fraction of a second before being destroyed in collisions with accelerator equipment.

Marathon's method fires neutrons from fusion reactions at mercury, turning it into an unstable isotope, or form of the metal, that decays into gold.

It also claims to have broken a 40-year-old Soviet record with its plasma centrifuge, a machine that spins electrically charged gas at high speed to separate isotopes used in fusion fuel.

It says it is now building a reactor in San Francisco to achieve record power density, meaning the most power produced for its size.

The gold claim has not been independently verified, and the material available does not establish how much gold was produced or at what cost.

What happened to diamonds?

Lab-grown diamonds are chemically identical to mined stones.

Although specialists can tell them apart, factories churned them out and sold them for a fraction of the price.

Wholesale prices for lab-grown stones fell 90% to 95% from their 2015 levels, diamond industry analyst Paul Zimnisky said last year, while competition from lab-grown stones also weighed on mined diamond prices.

De Beers launched its own lab-grown brand, Lightbox, in 2018 at $800 (€712) a carat. However, it shut it down in 2025 to focus on natural diamonds.

De Beers made an underlying loss of $511 million (€455mn) in 2025, and Anglo American is in talks to sell its 85% stake for about $1 billion (€890mn) to a group led by former De Beers boss Gareth Penny, according to Bloomberg, citing people familiar with the matter.

Swiss luxury group Richemont, which owns Cartier, and US jeweller Signet, which owns Kay and Zales, are reportedly also in talks to join the group.

Why gold is different

Gold is a far bigger market.

Mines produced 3,672 tonnes in 2025, and about 222,600 tonnes have been mined throughout history, according to the World Gold Council.

Central banks also hold it in their reserves as a safe store of value, and many have been buying heavily in recent years, including those of China and Poland.

Gold traded at $4,062 (€3,617) an ounce on Thursday, more than 27% below its record high of $5,598 (€4,985) in January.

Marathon's own 2025 study estimated that a fusion plant could make about 5 tonnes of gold a year per gigawatt of electricity, roughly the output of a large nuclear reactor.

On that basis, 100 such plants would add an estimated 500 tonnes a year, roughly 14% of last year's mine output.

However, no fusion plant yet sells electricity, and the first commercial ones are still years away. The gold could also contain radioactive isotopes and might need to be stored for up to 18 years before it could be sold, the company has said.

There are currently no public plans to attempt to produce and commercialise the product, but unlike lab-grown diamonds, transmuted gold, once its radioactivity fades, would be impossible to tell apart from the mined kind.

Thursday, October 08, 2026

  


AI chatbots hesitant to recommend change



Study finds that chatbots are more likely to recommend what is common or popular, and not necessarily the best options for you or the planet




University of Waterloo






A new study of multiple large language models (LLMs) found that when users asked chatbots for advice, they were more likely to make suggestions that favour the status quo over alternatives. The tendency of artificial intelligence to default to more common advice could be bad news for the climate.

Researchers from the University of Waterloo assessed 11 LLMs for their tendency to prefer existing conditions over change, known as status quo bias. Well-known in psychology research, this is the first paper to apply the concept to climate advice from LLMs. The team evaluated more than 7,500 queries spanning multiple domains, including vehicle purchases, recipes, home heating, and climate-relevant policy trade-offs. Each of these queries was tested on at least six different LLMs for a total of nearly 55,000 prompts. Where climate trade-offs are possible in policy, platforms reinforced decisions the user already made with double the frequency.

LLMs commonly provide advice that runs counter to changes needed from households and decision makers for climate action. Given the proliferation of AI, progress on climate change is at risk if the status quo favours high emissions.

“I think it's worth being aware that these models, even the new ones, have blind spots,” said Dr. Seth Wynes, professor in the Faculty of Environment. “It's good for consumers to be aware of this bias and if you're asking for advice on a topic, you could ask it to make the case for doing something new.”

While different models responded similarly during the study, the findings were most obvious in policymaking. When models were asked whether to proceed with a plan, they said "yes" 70 per cent of the time for plans that were already in place but only agreed 34 per cent of the time when the plan or policy was new. 

While researchers noted that models do take regional patterns into consideration, they still showed a surprising bias here as well. For instance, if you claim to be a user from Norway, where sales of new electric vehicles are very high, the chatbot is more likely to recommend an EV than if you are from Canada. But in both cases, the bot will recommend an EV less often than the pace at which EVs are already being sold in their respective country.

“In almost every jurisdiction, the LLMs are working at at slower pace of change than is needed, so they recommend fewer EV models than what are actually being sold today,” Wynes said. “It might be very good for AI to favour the status quo for lots of other things, such as proven medical advice, but climate is where we really do need change.”

In the future, the researchers will keep tabs on advances in LLMs and if and whether humans are relying on AI to make purchases within the AI platform, amplifying the effect of any status quo bias.

The study, Large language models exhibit status quo bias in climate-relevant advice, appears in Environmental Research Communications.

 

Top AI tools accurately identified validated home BP monitors only about 2/3 of the time



American Heart Association Hypertension Scientific Sessions 2026 – Moderated Poster #TH125



American Heart Association






Research Highlights:

  • Three of the top four popular artificial intelligence (AI) tools – OpenAI’s ChatGPT, Microsoft’s Copilot and Perplexity AI – could not consistently identify if a home blood pressure monitor had passed clinical validation testing, with accuracy only 63% to 83% of the time. Google Gemini performed better but still provided incorrect responses about 10% to 15% of the time.
  • All four tools misidentified various home blood pressure monitors as not meeting clinical standards even though the AI tool was verifying against the independent registries that list only validated blood pressure monitors.
  • Researchers advise healthcare professionals and the public to confirm validation of home blood pressure monitors according to recognized independent, free and publicly available registries.
  • Note: The studies featured in this news release are research abstracts. Abstracts presented at the American Heart Association’s scientific meetings are not peer-reviewed, and the findings are considered preliminary until published as a full manuscript in a peer-reviewed scientific journal.


Arlington, Va., Oct. 8, 2026 — Three of four leading AI-powered search tools accurately identified home blood pressure monitors that met validated clinical standards only 63%-83% of the time, according to preliminary research being presented at the American Heart Association’s Hypertension Scientific Sessions 2026. The meeting is in Arlington, Virginia, October 7-11, 2026, and is the premier scientific exchange focused on recent advances in basic and clinical research on high blood pressure and its relationship to cardiac and kidney disease, stroke, obesity and genetics.

High blood pressure is the number one risk factor for cardiovascular disease, affecting more than 125 million adults in the U.S. – about 47% – according to the American Heart Association’s 2026 Heart Disease and Stroke Statistics Update. Only about 1 in 4 of those adults have their blood pressure within the target range of less than 120 mm Hg/80 mm Hg, and high blood pressure.
The 2025 American Heart Association Guideline for the Prevention, Detection, Evaluation, and Management of High Blood Pressure in Adults recommends that people use a home blood pressure monitoring device that has been validated for accuracy. People can talk with their clinician and visit www.validatebp.org for more information.

Now that AI-powered search tools are integrated into all online platforms, people are increasingly asking AI tools for quick answers to health questions, including whether home health devices such as blood pressure monitors have been validated and meet clinical standards. A home blood pressure monitor is considered validated when independent, third-party testing has proven it gives consistently accurate blood pressure readings, and there are three primary registries that list these devices. In this analysis, researchers found AI-powered chatbots were not reliable at making this determination.

“We found that most AI tools performed only slightly better than if you had flipped a coin for each question. Even Google Gemini, which performed best, was often wrong and couldn’t find information that is easily located,” said Anna Soriano, M.D., a third-year internal medicine resident at the University of Montreal and the study’s presenting author.

“People may unknowingly think a device is validated based on the AI tool’s inaccurate responses. Using that device may result in inaccurate blood pressure readings, which could lead to an inappropriate diagnosis or treatment decisions,” she added.

What are the key findings of the study?

  • Researchers found that accuracy at identifying if a home blood pressure monitor meets clinical standards varied significantly depending on the AI-powered tool used.
  • Google Gemini scored the highest – answering correctly 86% to 91% of the time depending on how the questions were phrased.
  • Correct responses from ChatGPT, Copilot and Perplexity ranged from only about 63% to 83%.
  • All four AI tools were less accurate at identifying validated BP monitors vs. unvalidated monitors.
  • When researchers retested the AI tools’ responses for devices that had mixed accuracy results, asking the same AI tool the same questions – on a different day or from a different computer – the AI tools often produced a different answer.  

Researchers also tested a wide range of devices, including lesser-known home blood pressure monitors, though researchers noted these still appear on the same official registries as more popular ones. The authors could not determine exactly why AI tools struggled with identifying devices that had already passed validation testing.

“It is surprising, and almost counterintuitive, that AI tools had so much difficulty specifically identifying validated devices, since those devices are the ones with clear listings on official registries,” Soriano said. “In many cases, the AI tools found the blood pressure monitor was listed within an official website, however, it seems AI was not able to interpret being listed on the registry as proof of validation.”

“Artificial intelligence holds great promise to help support clinicians in areas such as cardiac imaging, electrocardiography, mobile devices and other tools,” said Keith C. Ferdinand, M.D., FAHA, an American Heart Association volunteer expert and vice chair of the Association’s 2025 High Blood Pressure Guideline. “The potential shortcomings of AI demonstrated by this study’s results should remind clinicians and the public that the use of AI for clinical decision-making requires caution. In addition, home blood pressure devices need to be both validated [listed on the national registry validatebp.org and accurate. With the proper technique and regular monitoring, readings from home BP devices are a valuable component of integrated, individual treatment plans that can improve patient care and outcomes.” Ferdinand, who was not involved in this study, is the Gerald S. Berenson Endowed Chair in Preventative Cardiology and professor of medicine at Tulane University School of Medicine in New Orleans.

A limitation of the study is that these results are likely to change as AI technology continues to improve with time. In addition, to limit each tool’s ability to learn from previous test searches, scientists used private internet browsing sessions; however, this technique could not fully prevent language model training of the AI tools.

What are the details, background and design of the study?   

  • Researchers tested 324 home blood pressure monitors in Canada during April - May 2026 – 145 validated devices and 179 not-validated devices – drawn from 3 validation registries (StrideBP, ValidateBP and Hypertension Canada); a list of known non-validated devices; and the list of top-selling blood pressure cuff monitors sold via Amazon in Canada, Australia and the U.S.
  • The authors tested each device with four AI search tools: Google Gemini, Microsoft Copilot, ChatGPT and Perplexity.
  • Researchers asked each of the AI tools three types of questions about each of the 324 blood pressure devices: a general, public-style question about the device’s validation status; a more specific question directing it to check a named validation registry; and the same question asked with more details and requiring a one-word answer.
  • To test consistency, the researchers retested 20% of devices that had produced mixed results, using three different examiners on different computers and on different days primarily in May 2026.

Note: This abstract will be presented at 5:30 p.m., Thursday, October 8, 2026.

Co-authors, their disclosures and funding sources are listed in the abstract.

Statements and conclusions of studies that are presented at the American Heart Association’s scientific meetings are solely those of the study authors and do not necessarily reflect the Association’s policy or position. The Association makes no representation or guarantee as to their accuracy or reliability. Abstracts presented at the Association’s scientific meetings are not peer-reviewed, rather, they are curated by independent review panels and are considered based on the potential to add to the diversity of scientific issues and views discussed at the meeting. The findings are considered preliminary until published as a full manuscript in a peer-reviewed scientific journal.  

The Association receives more than 85% of its revenue from sources other than corporations. These sources include contributions from individuals, foundations and estates, as well as investment earnings and revenue from the sale of our educational materials. Corporations (including pharmaceutical, device manufacturers and other companies) also make donations to the Association. The Association has strict policies to prevent any donations from influencing its science content. Overall financial information is available here.

Additional Resources:

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The American Heart Association’s Hypertension Scientific Sessions 2026 is a premier scientific conference dedicated to recent advancements in both basic and clinical research related to high blood pressure and its connections to cardiac and kidney diseases, stroke, obesity and genetics. The primary aim of the meeting is to bring together interdisciplinary researchers from around the globe and facilitate engagement with leading experts in the field of hypertension. Attendees will have the opportunity to discover the latest research findings and build lasting relationships with researchers and clinicians across various disciplines and career stages. Follow the conference on X using the hashtag #Hypertension26.

About the American Heart Association

The American Heart Association is a relentless force for a world of longer, healthier lives. Dedicated to ensuring equitable health in all communities, the organization has been a leading source of health information for more than one hundred years. Supported by more than 35 million volunteers globally, we fund groundbreaking research, advocate for the public’s health, and provide critical resources to save and improve lives affected by cardiovascular disease and stroke. By driving breakthroughs and implementing proven solutions in science, policy, and care, we work tirelessly to advance health and transform lives every day. Connect with us on heart.org, Facebook, X or by calling 1-800-AHA-USA1.


High performance computing and sustainability through artificial intelligence


New book explores the convergence of AI, high-performance computing and sustainable digital transformation




Bentham Science Publishers





Bentham Books announces the release of High Performance Computing and Sustainability Through Artificial Intelligence, a timely reference exploring how high-performance computing (HPC), artificial intelligence (AI), cloud technologies and cybersecurity can contribute to sustainable and resilient digital infrastructures.

About the Book

The rapid growth of computational technologies is creating new opportunities to address complex sustainability challenges while improving the efficiency and security of digital systems. High Performance Computing and Sustainability Through Artificial Intelligence examines the integration of HPC, AI, cloud computing and cybersecurity to support environmentally responsible and technologically resilient development.

The book explores emerging developments in big data and cloud computing, AI- and IoT-enabled sustainable agriculture, cloud security, data protection and cybersecurity frameworks. It also examines how HPC and AI can contribute to carbon reduction, smart farming and the protection of critical cyber-physical infrastructure, including smart power grids. Ethical, governance and policy considerations surrounding AI-driven computing are also addressed.

Key Highlights

• Integration of HPC, AI and cloud computing for sustainable digital transformation
• Cloud security, risk assessment, auditing and data protection strategies
• AI- and AIoT-enabled approaches for sustainable agriculture and carbon reduction
• Cybersecurity solutions for critical infrastructure and smart power grids
• Ethical, governance and policy considerations in intelligent computing systems

Readership: Researchers, graduate students, computer scientists and professionals working in artificial intelligence, high-performance computing, cloud computing, cybersecurity and sustainable computing.

About the Editors

Jagdish Chandra Patni and Nilesh Bhaskarrao Bahadure bring together research and professional perspectives on emerging computational technologies, artificial intelligence, cybersecurity and sustainable digital systems.

Book Link: https://bit.ly/4ja78sT

DOI: 10.2174/97988988171141260101