Russia irked by US, EU moves to secure Central Asia critical minerals

Russia says it is concerned by the intensity with which ‌Washington ⁠is pushing agreements on critical minerals and rare earth metals in Central Asia. / Tmy350, cc-by-sa 4.0Facebook

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By IntelliNews Eurasia desk May 20, 2026

Russia has made clear its concern about growing US and European Union moves to gain access to rare earth ​and critical mineral resources in Central Asia. Izvestia newspaper on May 20 reported the Kremlin’s displeasure, as outlined by Deputy Foreign ‌Minister Mikhail Galuzin.

G7 countries are mounting multiple efforts across Central Asia to agree rights to such deposits. The worry is that China is dominant in rare earth and other supply chains critical to the manufacturing of transition technologies including defence systems, electric vehicles (EVs) and renewables in energy. As efforts to diversify away from Chinese supplies gather pace, even US President Donald Trump’s sons, Donald Trump Jr and Eric Trump, have entered the game by taking a stake in a US group that is set to mine critical mineral tungsten, essential in the production of missiles and munitions, in Kazakhstan.

However, Russia ​considers the resource-rich ex-Soviet Central Asian states of Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan and Uzbekistan as in its sphere of interest – and these countries are already, at least economically speaking, increasingly coming under the sway of another major power, ​China. Russia's neighbour Mongolia, another country with valuable critical mineral resources, was not mentioned by Galuzin in his remarks, but the Kremlin is likely to have similar concerns relating to it, given Western exploration and mining activities there.

"We are concerned by the intensity with which ‌Washington ⁠is pushing agreements on critical minerals and rare earth metals," Galuzin told the Russian publication in an interview published on May 20.

"This is not merely about economic competition, ​but about an ​attempt to ⁠push Russia out and create a Western-controlled infrastructure in the immediate vicinity of ​our borders," he added.

Access to critical resources often seen vital to national security was very much on the agenda as President Trump last November met the five presidents of Central Asia for an unprecedented US summit with the region’s leaders in the White House. In a reference to Central Asia’s natural resources, Trump referred to the region as “an extremely wealthy region” and pledged to strengthen US relations with Central Asia “like never before”.

This map of Kazakhstan focused on critical minerals, rare earths and rare earth elements (REE), issued by AIFC as part of a presentation, gives an idea of the potential riches at stake.

Brussels, the UK and South Korea have like the US made plain their interest in helping the Central Asian countries to mine rare earth, rare metal and other mineral deposits in return for export rights, but only very infrequently is potential Russian investment in this aspect of Central Asia mentioned. Not a week goes by without a substantial feature in international media on how one or more of the G7 countries could capitalise on the riches.

On May 18, The National Interest published an article titled “How Central Asia Can Seize the Critical Minerals Moment”, by Marsha McGraw Olive, who teaches the Central Eurasia Practicum at Johns Hopkins SAIS in the US.

A former World Bank manager, and a trustee of the Eurasia Foundation and advisory member of the Caspian Policy Center, McGraw Olive referred to the Trump administration’s Project Vault, the US strategic critical minerals reserve, and FORGE, the Forum on Resource Geostrategic Engagement.

Central Asian states – particularly Kazakhstan, Kyrgyzstan, Uzbekistan and Tajikistan—are keen to contribute in these areas, she wrote.

McGraw Olive also observed: “China captures the largest share of selected critical mineral exports from Central Asia, up from approximately 50 percent in 2020 to 70 percent in 2023. China is also the largest foreign investor in Central Asian mining. Without alternative markets and investors, Central Asia inadvertently supports the Chinese production monopoly.”

Central Asians, contended McGraw Olive, “want Western investment to diversify partners and technologically upgrade mining production, from extraction to processing. The opportunities are vast, including the purported trillions of dollars in unexplored deposits recently announced in Uzbekistan. For both sides, pundits say, the time has come for implementation.”

 

New AI system improves detection of fake online reviews

Researchers test a new AI-based system that could help online platforms better identify misleading fake reviews

University of East London

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Online shoppers could one day face fewer misleading fake reviews thanks to a newly tested AI-powered detection system developed by researchers at the University of East London.

Fake reviews are a growing problem for consumers and online businesses, especially with the growth in AI generated content. According to the researchers, from the Royal Docks School of Business and Law, misleading reviews can distort competition, damage trust in online marketplaces and persuade people to buy poor-quality or even unsafe products.

The new system combines AI language analysis with behavioural clues such as whether the emotional tone of a review matches its star rating, how long the review is and other patterns linked to suspicious activity. The researchers, from the Royal Docks School of Business and Law, say this gives the model a fuller picture of whether a review is genuine or deceptive.

The new study, published in FinTech and Sustainable Innovation, describes a new “hybrid fusion” model designed to identify fraudulent reviews on platforms such as Amazon and Yelp.

Unlike older systems that mainly relied on keywords or simple patterns, the new approach is designed to understand the meaning and context behind written reviews. That helps it detect more convincing fake reviews that might otherwise appear genuine to shoppers.

In testing, the model achieved 93% accuracy on Amazon review data and 91% accuracy on Yelp reviews, outperforming several traditional detection methods examined in the study.

Co-author Dr Hisham AbouGrad said, “Fake reviews are becoming increasingly sophisticated and harder to detect. Our findings show that combining AI language understanding with behavioural signals can provide a more reliable way to identify misleading reviews and help strengthen trust in online marketplaces.”

Co-author Fiza Riaz said, “This research shows that AI systems can move beyond simply spotting suspicious words. By looking at context and behaviour together, the model can better recognise patterns linked to deceptive reviews while still supporting genuine customer feedback.”

The paper says the next stage of the research will focus on improving the system using larger and more varied datasets, exploring newer AI models and studying how the technology could eventually work in real-time on large e-commerce platforms.

AbouGrad, H, & Riaz, F (2026). Metadata-Enhanced Hybrid Fusion Architecture: Commercial Fake Reviews Detection Model Using Transformer Embeddings. FinTech and Sustainable Innovation. https://doi.org/10.47852/bonviewFSI62028859

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DOI

10.47852/bonviewFSI62028859 

Method of Research

Experimental study

Article Title

Metadata-Enhanced Hybrid Fusion Architecture: Commercial Fake Reviews Detection Model Using Transformer Embeddings

Article Publication Date

13-May-2026

New report: U.S. Government is using AI more, but still has a long way to go

Larger agencies leading the way

Brookings Institution

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As is every large organization, the U.S. government is assessing how to best integrate artificial intelligence into its procedures and workflows. While AI has undeniable risks, it also has the potential to make work significantly more efficient and effective in a broad range of ways, from automating simpler tasks to unearthing unexpected insights.

 

Over the past decade, the federal government has made the adoption of AI a priority. Both the Biden administration and the two Trump administrations have emphasized the need for federal government AI adoption to improve service delivery, foster data-driven analysis, promote national competitiveness, and strengthen national security.

 

New research from the Brookings Institution has found that while the scope and pace of this adoption have accelerated over the past three years, AI use across the federal government remains concentrated in a few large agencies More widespread adoption has been slowed by several factors, including workforce capacity constraints, a risk-averse culture, funding challenges, and a lack of trust in AI’s usefulness and safety.

 

“While the federal government has made progress on using AI, there’s still a long way to go,” says Brookings fellow Valerie Wirtschafter, the author of the report. To understand the current state of AI adoption across the federal government, she analyzed data on federal government AI use from 2023 to 2025 as well as federal jobs data. In addition, she interviewed current and former technology specialists across eight federal agencies.

 

Over the past few years, AI use by the federal government has grown. More agencies are using it, and the amount they use it has also increased. In 2023, 21 agencies, including 13 large agencies and eight midsize agencies, reported using AI; no small agencies participated. By last year, 41 agencies (13 large, 17 midsize, and 11 small) reported AI use. In 2025, 41 agencies documented more than 3,600 distinct projects that used AI, a 69% increase from the previous year and five times the number reported in 2023. While many of these cases focused on streamlining operations and facilitating back-office processes, others involved more mission-oriented work, including benefits delivery, health and medical services, and law enforcement.

 

However, there are still significant disparities among agencies. Over the past three years, five agencies accounted for over half of the total AI use. In 2025, large agencies (more than 15,000 employees) accounted for more than three-quarters of all AI use. While more small and midsize agencies are starting to experiment with AI, large agencies are scaling their efforts more aggressively. It is important to note that overall, AI-focused workers continue to represent a small fraction of the overall federal technological workforce.

 

Wirtschafter identified several key bottlenecks to adoption. Some of these apply only to certain agencies, such as those handling sensitive health or security data.[NL1]  Others stem from issues that have hindered federal adoption of technology for decades, such as outdated equipment and infrastructure.

 

Hiring challenges remain a key obstacle to integrating AI into federal agencies. Among the issues: The federal government has a slow hiring timeline, and limited pathways for career advancement for technologists. The Executive branch has rolled out efforts to improve hiring timelines, and Congress has explored possibilities for improving AI-focused hiring across agencies.

 

It is worth noting that since the second Trump administration laid off nearly 300,000 federal workers last year, the number of AI-focused federal job listings has dropped significantly, part of an overall decline in hiring. Wirtschafter argues that these layoffs may have undermined efforts to recruit AI expertise into the federal government because many recent hires were still probationary. She says that it’s likely that the layoffs led to the departure of at least some AI-focused employees.

 

Moreover, the federal government tends to have a risk-averse culture that discourages experimentation and innovation. In addition, the opaqueness of AI processes—it’s often unclear how a program came to its conclusions—can undermine trust and deter use, especially for sensitive work. Moreover, the growing politicization of some large language models (LLMs) is another challenge that could impede the adoption process. For example, Grok, developed by Elon Musk’s xAI, has a well-documented history of reflecting his political values and generating questionable content, while Anthropic’s Claude has been dubiously labeled a “supply chain risk” by the Department of Defense following contract disagreements with the agency.

 

Wirtschafter offers a series of recommendations to help the federal government more effectively adopt AI. These include:

• Streamlining the hiring process for AI-related jobs;
• Creating new job paths so AI-focused workers have a chance to advance;
• Investing in AI literacy and treating it as a core job requirement;
• Documenting and sharing AI success stories across the government;
• Increasing transparency around AI usage across agencies; and
• Focusing AI investment on high‑impact projects that clearly improve people’s lives.

Read the full report here.

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 [NL1]which agencies?

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Method of Research

Survey

Subject of Research

People

Article Title

Assessing the state of AI adoption across the federal government

 

AI system automates coding for scientific research

Empirical Research Assistance out-performs software written by experts

Harvard John A. Paulson School of Engineering and Applied Sciences

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Key Takeaways

• A new AI tool called Empirical Research Assistance (ERA) can automatically write high-performance scientific software. 

• ERA could significantly accelerate scientific discovery across many domains. 

A research team at Google co-led by Michael Brenner, Catalyst Professor of Applied Mathematics and Physics at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) and Google research scientist, has produced a new artificial intelligence system that can automatically write scientific software programs that surpass the performance of human-written programs. 

Published in Nature, the system is called Empirical Research Assistance (ERA), and the project was co-led by Brenner and Shibl Mourad from Google DeepMind. Harvard Ph.D. students Qian-Ze Zhu, Ryan Krueger, and Sarah Martinson contributed as Google student researchers while working in Brenner’s group. The research was done in Brenner's capacity as a Catalyst Professor, a position established by the University to enhance relationships between academia and the private sector by supporting senior faculty in research roles at external companies. 

Across modern science, customized software is constantly used to test specific hypotheses or interpret complex data. The authors refer to this type of computer program as “empirical software” – a program whose sole purpose is to maximize how well it does on a scientific task, like making weather predictions or forecasting hospitalizations during a disease outbreak. Any problem that can be expressed as a numerical value – its “score” — is called a scorable task. 

Empirical software for solving such scorable tasks underpins major advances across many fields, including three recent chemistry Nobel prizes. But the specialized, custom-built software to tackle these experiments is labor-intensive, requiring a human to test and sharpen code many times over.  

The new ERA system removes this bottleneck by essentially automating the full cycle of scientific software design and refinement – a process that can normally take months or even years by human experts. 

The system combined the Google Gemini large language model with a search strategy to explore and refine thousands of pieces of code – far faster and with greater breadth than a human could. 

Starting with a baseline piece of code aimed at a specific problem, the new AI system proposes modifications by adding new components or switching out algorithms, toward the goal of improving a predefined quality score – for example, how accurately can this model predict the spread of a disease, based on past hospitalization numbers? How well does this model predict the shape of proteins based on these amino acid sequences?  

The system uses a method called tree search — also used in game-playing systems like AlphaGo — to decide which promising ideas to pursue and which to discard in order to get better at the task of predicting hospitalization numbers, predicting protein shapes, etc. 

The AI does not work in isolation. In the process, it can be guided by research ideas in papers or textbooks. These ideas can be provided directly by a user or retrieved automatically and incorporated into later versions of the code.  

“This ability to integrate and recombine research ideas enables the system to find “needle-in-a-haystack” solutions that human research might never get to test,” Brenner said. 

To prove it, the Harvard and Google team applied the ERA system to a diverse set of scientific problems. Zhu’s role in the project was to use ERA to predict the activity of more than 70,000 neurons in the brain of a zebrafish and compare it against actual neural data. 

In one experiment, the team prompted ERA to use an existing neuron-modeling library to build more physically accurate simulations of neural activity. This task would have taken weeks or months for Zhu of learning a new software package, but ERA could assemble and tune the models automatically. 

“This new system is going to accelerate scientific discovery by allowing you to explore a lot of ideas at the same time,” Zhu said. “Previously it might take you a week to implement some specific methods, but now you can just run them in parallel in a few hours.” 

On one test, the ERA system generated 14 models for predicting COVID-19 hospitalizations that outperformed the best U.S. Centers for Disease Control models used during the pandemic. 

In another experiment, ERA discovered four new methods for integrating single-cell RNA sequencing datasets, beating top human-designed approaches. 

By reducing the time required for exploration of a set of ideas from months to hours or days, the new system could save significant time for scientists to focus on “truly creative and critical challenges, and to continue to define and prioritize the fundamental research questions and societal challenges that scientific research can help address,” according to a Google blog post about the breakthrough.

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Journal

Nature

DOI

10.1038/s41586-026-10658-6 

Method of Research

Computational simulation/modeling

Subject of Research

Not applicable

Article Title

An AI system to help scientists write expert-level empirical software

Article Publication Date

19-May-2026

Watching the detectors: Researchers probe efficacy – and danger – of AI detection tools

University of Florida

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Patrick Traynor, Ph.D., professor and interim chair, UF Department of Computer & Information Science & Engineering

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Credit: University of Florida

Patrick Traynor, Ph.D., has questions.  

When the professor and interim chair of the University of Florida Department of Computer & Information Science & Engineering saw reports in the media positing that scientific literature is increasingly being generated by artificial intelligence, he wondered, “How do they know?”  

Traynor knows the detectors that determine the presence of AI-generated text, known as AIGT, in publications are themselves AI systems. They use the same large language models, called LLMs, that less than honest researchers could be using to generate their text.  

How good could they be? 

Spoiler alert: They’re not very good. 

In a paper to be presented at this week’s 2026 IEEE Symposium on Security and Privacy, Traynor and his co-authors assert that current AIGT detectors are not effective or robust tools for determining the presence of AI-generated text. The results, the researchers said, indicate that commercially available AIGT detectors are “poorly suited for deployment in academic or high-stakes contexts.”  

In other words, when the results matter, the tools are not effective. The paper, “AI Wrote My Paper and All I Got Was This False Negative: Measuring the Efficacy of Commercial AI Text Detectors,” co-authored by Seth Layton, Ph.D., Bernardo B.P. Madeiros and Kevin Butler, Ph.D., examined common commercially available solutions that test for AIGT and found wildly inconsistent efficacy rates — false positive rates  between 0.05% and 68.6%, false negative rates of between 0.3% and 99.6%.  

Researchers also found that with a simple tweak to the LLM, the detectors were rendered basically useless, incapable of distinguishing AIGT from human-generated text. 

“These current tools are not reliable or robust enough to use to measure the problem,” co-author Traynor said. “We really can’t use them to adjudicate these decisions. People’s careers are on the line here.”  

A recent article in Nature asserts the problem is widespread.  

“The fear of many in the research community is that poor-quality or entirely fabricated research produced by large language models could overwhelm the ability of current quality-control systems to detect it, thereby polluting the scientific canon," Nature reporter Miryam Naddaf noted in the article.  

However, as Traynor and his team point out, this conclusion could not be reliably reached using current tools. In short, while it may feel like the use of AI in academic writing is widespread, current evidence to support that claim is inadequate.  

The issue, of course, is personal for Traynor. He pointed out that in academia, individual merit is literally measured by an author’s intellectual output and publications. Suspicion or accusations of having used AI-generated text in scholarly submissions can stain a researcher’s reputation and can adversely affect their career. 

The study’s methodology was very meta. Using a set of all papers submitted to top-tier security conferences prior to the advent of ChatGPT (about 6,000 papers), they directed LLMs to create AIGT clones of the very same papers. The combined dataset was then evaluated by the five most popular commercial AIGT detectors on the market.  

While two of the five detectors performed well, researchers found that by making trivial changes to the AIGT, the reliability fell dramatically. Researchers simply asked the LLM to generate the AI version of the papers using more complex vocabulary (researchers call this a lexical complexity attack), and the detectors were more easily fooled.  

AIGT detectors, it seems, are easily swayed by fancy words. 

And while the research concludes AIGT detectors pose high-stakes risks in academia, don’t think Traynor and his team are AI naysayers. They believe LLMs and AI have great potential to speed up science, to help us find new insights. But Traynor cautions against a prevailing notion that AI is all-knowing.  

“It’s not an oracle. It doesn’t always know the answer,” Traynor said. “It’s happy to give us answers, but whether or not those answers have value, we still need people to figure that out. This paper shows us that for as many studies as we see claiming that a certain percentage of academic work is AI-generated, we actually don’t have tools to measure any of that.” 

Co-author Layton agreed, adding that the research should remind the public to view all AI claims skeptically, just as scientists view all evidence with skepticism.  

“We demand that such claims include substantial proof that they are correct,” Traynor reiterated.

Germany’s Merz says wouldn’t advise young people to move to US


By AFP
May 15, 2026


German Chancellor Friedrich Merz cited a worsening 'social climate' in America - Copyright POOL/AFP Matthias Schrader

German Chancellor Friedrich Merz said Friday he would not advise young people in his country to move to the United States for study or work, in the latest sign of cooling ties between Berlin and Washington.

Last month Merz had a high-profile spat with US President Donald Trump after the chancellor said Iran was “humiliating” Washington at the negotiating table.

Following the comment, Trump — who suggested Merz was doing a “terrible” job as leader — abruptly announced that the United States would pull 5,000 troops from bases in Germany.

At a gathering of German Catholics in the southern city of Wuerzburg on Friday, Merz garnered applause after saying: “I would not recommend to my children today that they go to the US to get an education and to work.”

He cited “the social climate that has suddenly developed” in the United States and said that “even the best educated in America have great difficulty in finding a job”.

Merz has traditionally been a transatlanticist in the mould of most centrist German politicians but the relationship with the US has become increasingly strained under Trump’s administration.

“I am a great admirer of America’s, but right now my admiration is not increasing,” he said, to laughter from the audience.

Even before the row over Iran, Merz had said that a cultural “rift” has opened between the United States and Europe due to the culture wars embraced by Trump’s Make America Great Again (MAGA) movement.

The Trump administration has charged that Europe faces a “civilisational decline”, and has courted far-right parties on the continent.

Trump’s threats to annex Greenland, an autonomous territory of NATO member Denmark, and his cutting back of support to Ukraine have also frayed ties between the US and its traditional European allies.

Ex-Google man takes reins at under-fire BBC


ByAFP
May 18, 2026


New BBC director general Matt Brittin arrives at the BBC's central London headquarters - Copyright AFP Brook Mitchell

A former Google executive took over as the BBC’s new director-general on Monday amid proposed job cuts and a $10-billion lawsuit brought by US President Donald Trump, arguing that the world now “needs the BBC more than ever”.

Matt Brittin, 57, who has no broadcasting or journalism experience, starts the job against a background of drastic shifts in the media landscape.

The British-born executive was for over a decade president of Google’s Europe, Middle East and Africa division, which earns around a third of its revenue. He previously worked as a consultant for McKinsey.

Arriving at the BBC’s central London headquarters for his first day in charge, he was greeted by a handful of placard-waving protesters from the National Union of Journalists (NUJ).


The BBC is set to cut up to 2,000 jobs amid financial pressures and a challenging media landscape – Copyright AFP/File Susannah Ireland

The corporation has said it is looking to axe up to 2,000 jobs as it tries to reduce costs by 10 percent over the next three years.

Brittin told reporters he was “honoured” and “humbled” to be taking on the role.

But he warned that “tough choices are unavoidable as we make savings”, in a note to staff.

“We must be where audiences are, and experiment more bravely: test ideas, learn quickly and back what works,” he added.

Brittin replaces Tim Davie, who had held the BBC post since 2020. He resigned in November over the Trump lawsuit.

– Reinvention –

Trump launched his legal action over a documentary that included an edited clip of a speech he made ahead of the US Capitol riot in January 2021. The edit made it appear he explicitly urged supporters to attack the seat of Congress.

The BBC said in March it had formally asked a US federal court in Florida to dismiss the lawsuit.

Brittin also faces the politically sensitive task of renegotiating the BBC’s Royal Charter that outlines the corporation’s governance. Its current charter will end next year.

A sizeable proportion of the BBC’s income comes from the licence fee, which is payable by all UK households with a television, or whose occupants watch live screening online.

But the BBC lost more than £1.1 billion in revenues last year as fewer homes felt the need to apply for one, a parliamentary committee report said in November.

The Trump lawsuit is the latest scandal to hit the corporation.

Earlier in 2025 it was forced to issue several apologies for “serious flaws” in the making of another documentary entitled “Gaza: How To Survive A Warzone”, broadcast last February.

In October it accepted a sanction from the UK media watchdog for what was deemed a “materially misleading” programme, whose child narrator was later revealed to be the son of Hamas’s former deputy agriculture minister.

Brittin said the BBC had “proved throughout its history how quickly it can reinvent itself to serve the needs of audiences”.

“We need, collectively, to call on that sense of urgency now. That means moving with velocity and clarity,” he added in his note to staff.

Canada takes key step towards new oil pipeline


ByAFP
May 16, 2026


The Syncrude refinery in the oil sands region of Alberta in western Canada - Copyright AFP Daphné LEMELIN

Prime Minister Mark Carney and the leader of Canada’s oil‑rich Alberta province took a major step Friday toward building an oil pipeline that could substantially increase crude exports to Asia.

Expanding overseas energy exports has emerged as a key part of Carney’s strategy to reduce Canada’s economic reliance on the United States, but plans for a new pipeline are facing stiff resistance over environmental concerns.

Alberta’s conservative Premier Danielle Smith was a relentless critic of Carney’s climate‑focused predecessor, Justin Trudeau, accusing him of suffocating the province’s oil industry, but she has sought to work with Carney.

Carney and Smith cleared a key hurdle toward a new pipeline on Friday by signing a deal on industrial carbon pricing, a system that extracts a fee from large‑scale CO2 emitters.

Oil companies have been critical of the system, but Smith said Friday that the prohibitive rates set under Trudeau’s government had been “rolled back.”

Ottawa and the provincial government agreed that the rate would gradually rise to a fee of CAN$130 ($96) per tonne of CO2 emitted by 2040. Trudeau had called for a rate of CAN$170 by 2030.

At the announcement in Alberta’s capital, Edmonton, Carney said a final proposal for a pipeline that will serve Asian markets should be submitted to his major projects office by July 1.

He said Canada had earned “the trust of Asian countries who want our energy because they know that we are a safe, stable, reliable partner in a world that is anything but.”

Carney has repeatedly warned that the trade hostility ushered in by President Donald Trump is not a passing phase and Canada needs to plan for a fundamentally different economic relationship with the US, including by broadening ties with Asia.

Pipeline approval is also contingent on developing what Carney called “the largest global initiative for carbon capture and storage.”

Some Indigenous groups and First Nations have said they will oppose any pipeline that runs from Alberta to Canada’s Pacific Coast.

The leader of the left‑wing New Democratic Party, Avi Lewis, charged that Friday’s announcement “marks the Carney government’s official surrender to the oil and gas lobby.”

The announcement also comes at a fraught political moment.

Alberta separatists have built record‑high support of roughly 30 percent for the province’s independence by railing against Ottawa’s control over the provincial oil industry.

Carney, who was raised in Alberta, insists the province can thrive within a united Canada.