Hamilton claimed his first Grands Prix victory for Ferrari last weekend in the Barcelona-Catalunya Grand Prix, becoming the first driver to beat Mercedes on a Sunday this year.

Nine-time Le Mans winner Tom Kristensen joins *Beyond The Grid* to discuss his impressive career and how he came close to racing in F1.

Formula 1 has made outlined the significant steps made in its sustainability goals in the latest Impact Report.

Pierre Gasly extended his points-scoring streak with a P7 finish in the Barcelona-Catalunya Grand Prix.

Annual update of key indicators of the state of the climate system and human influence [PDF] "...This has served to highlight that many components of the observing system are under considerable threat, including key aspects such as measurements of the top-of-atmosphere radiation budget and ocean heat content that are critical to continued monitoring of the indicators presented herein."

Abstract: In a rapidly changing climate, evidence-based decision-making benefits from up-to-date and timely information. We track twelve key sets of indicators of the state of the climate system, closely following Inter-governmental Panel on Climate Change (IPCC) Sixth Assessment report (AR6) methods, to produce our fourth annual publication. One of the indicators, the Earth's energy imbalance (EEI) provides a crucial integrative measure of the overall heating of the planet and the pace of climate change – this has more than doubled since the 1976–1995 period. A newly added indicator of temperature extremes, the number of days experiencing marine heatwaves, has more than tripled between 1991 and 2025. For the 2016–2025 decade average, observed warming relative to 1850–1900 was 1.26 [1.13 to 1.36] °C, of which 1.24 [1.0 to 1.5] °C was human-induced. Human-induced warming reached 1.37 °C relative to 1850–1900 in the year 2025, increasing at a rate of 0.27 [0.2–0.4] °C per decade over 2016–2025. This high rate of warming, which matches the all-time high seen last year in the instrumental record, was caused by a combination of greenhouse gas emissions being at an all-time high of 54.6 ± 5.5 GtCO2e yr−1 over the last decade (2015–2024), as well as reductions in the strength of aerosol cooling. Despite this, there is evidence that CO2 emission growth is slowing. The continuation of these annual updates could track decreases or increases in the rate of human influence and climatic changes presented here, reflecting the outcomes of societal choices during the critical The data presented herein can provide a useful reference point for the drafting of the IPCC seventh assessment report. In total, we employ analysis from over 40 global datasets (https://doi.org/10.5281/zenodo.20499280, Smith et al., 2026a). Future monitoring of these indicators, such as ocean and satellite measurements of the Earth's energy imbalance, are threatened by geopolitical and public funding decisions. Our ability to consistently track many of the indicators requires the continuity of observation programs and coordination mechanisms, including the Global Climate Observing System (GCOS) program, that enable their effective integration and use.

On 14 April, the Trump administration quietly acknowledged the widespread use of AI to automate government processes. The office of management and budget (OMB) disclosed a staggering 3,611 active or planned use cases for AI across the federal government. The list has ballooned by 70% from the one published in the final year of the Biden administration, and includes many disturbing-seeming plans to hand over sensitive governmental functions to AI.

Scanning this list, many readers may find many causes for alarm. It represents a transfer of decision processes from human to machine on a massive scale over matters of individual freedom, public health and well-being, nuclear reactor safety and more.

Consider these examples. The Health and Human Services’ (HHS) office of administration for children and families hired the world’s “scariest AI company,” Palantir—notorious for its work on behalf of the military, the CIA and ICE—to scan all grant applications to flag those not ideologically aligned with the administration’s dictates. The Federal Bureau of Prisons is developing an AI system to assess the “potential for misconduct for newly admitted inmates,” routing people into high-security confinement before they have actually done anything wrong in their custody. These read like programs fit for a Philip K Dick or George Orwell novel.

Other use cases insert AI into life-and-death decision making. The Department of Veterans Affairs is developing an AI that will listen in on calls to the veterans crisis line, and then gather information from external databases to assess the mental state and suicide risk of the caller.

The Department of Energy is testing the use of AI to control nuclear reactors, targeting a way to autonomously respond to potential nuclear safety incidents. Here’s one that’s disturbing for its retirement, rather than its deployment: the state department has ended a program to use AI to forecast mass civilian killings, which had been intended to aid conflict prevention.

While it’s easy to raise questions about these and similar uses of AI, the reality is that any of these programs could be implemented responsibly. In some cases, like the HHS system, the AI might be enforcing alignment to a policy prescription that opponents abhor. But that concern is more about the policy itself rather than the idea that agencies should comply with executive orders.

In other cases, there may even be bipartisan agreement on the goal, like taking urgent action to help veterans at risk of self-harm. Lots of work and validation is needed to prove AI safe and effective for these use cases and convince the public it is appropriate, but the idea is plausible.

In other cases, a scary-sounding AI use may not even be new. The use of predictive methods and statistics to assign prisoner security classifications goes back decades, even if such systems are often biased and ineffective.

Using autonomous systems for model predictive control (MPC) of nuclear reactors is a well studied, and a widely applied aspect of nuclear plant management. And the recently disclosed addition of AI was initiated under the Biden administration.

But anyone reviewing the 2025 inventory could be forgiven for leaping to severe conclusions. What matters are the details of how the AI system is used, and here the inventory is severely lacking.

The disclosures carry minimal information, and lack the context necessary to understand their purpose and approach. The descriptions are typically just a sentence, and rarely more than a paragraph.

And while the process theoretically involves some form of public consultation, in reality there is generally none. It would take an eagle-eyed citizen to even come across this disclosure. Unless you read FedScoop regularly, or watch the OMB’s federal chief information officer’s GitHub account, you probably missed it.

Only one of the examples cited above (the DoJ) even proposes to involve the public. Under the administration’s policy, it’s not required for the rest because they are not classified as “high impact” use cases—a label that is applied inconsistently across agencies.

We wrote a book surveying applications of AI to democratic processes worldwide, including executive agencies as well as the courts, legislatures and politics. Our conclusion was that, while there are inappropriate applications of AI in governance that should be resisted, an urgent need to reform the economics of AI, and an imperative for renovating the democratic systems it is being unleashed on, there are also valuable and beneficial use cases for AI in government.

Machine translation is a good example. Customs and Border Protection (CBP) has deployed an AI translation system to help officers when human interpreters are not available. The idea that CBP, an agency under heavy scrutiny for reported abuses of human rights, would direct people to talk to a machine instead of a person may strike many as inhumane.

It’s true that human interpreters have very real advantages when it comes to understanding nuance from physical cues and social context. But an officer with a competent AI translator available immediately is better than one who cannot communicate with the person in front of them.

The Trump administration’s AI use case inventory has 70 such translation use cases, up from 58 in the Biden administration’s 2024 disclosure.

Disclosure of AI use cases could be a means to build public confidence and trust, but only if paired with consistent, meaningful public consultation. Washington DC and California are actively engaging the public to determine where and how it’s appropriate to use AI in government processes, or for government to regulate AI use in society.

Both have held public deliberations on this topic at a wide scale, using AI platforms. These examples demonstrate the potential for capturing broad-based public input to steer AI policy.

The international gold standard was arguably set by the French in 2016, via their Digital Republic Act. The law, itself informed by an online citizen consultation, requires all algorithms used to automate government administrative decisions to be subject to public records requests, to be appealable to a human reviewer, and to have mandatory notification of the use of automation to those affected by the decisions.

Canada offers another example of what more rigorous and participatory disclosure might look like. In 2025, they launched an AI use case registry, not unlike the US inventory. However, Canada also has a federal directive mandating a transparent risk-scoring and impact assessment process for automated systems that make administrative decisions about citizens.

That longstanding directive requires a detailed explanation of risks and benefits as well as consultation with certain stakeholders from the conception of the AI use case. The Canadian system could be improved; it could require a public comment period and an obligation for agencies to respond substantively to feedback before engaging in sensitive uses of AI.

AI offers real potential to improve the efficacy, efficiency and accessibility of government. But, equally, there is legitimate reason for public concern and distrust that can only be addressed through transparency and dialog. The US should adopt, at the federal and state level, algorithmic impact risk assessment procedures and public comment processes to facilitate a safe, trusted, equitable transformation of government agencies to take advantage of modern technology.

This essay was written with Nathan E. Sanders, and originally appeared in The Guardian.

mikeleonardvisualarts posted a photo:

The UK's Competition and Markets Authority (CMA) has imposed two new conduct requirements for Google's search services, to improve transparency and fairness in result rankings and allowing users to port their search data to third parties. The requirements follow the CMA's actions in early June that let publishers opt out of having their work appear in AI Overviews, while requiring attribution and clear links to sources. "More activity is expected over the summer," the regulator warned. The fair ranking requirement arises from complaints from UK businesses that Google's current approach is "neither fair nor transparent," as the web giant makes changes without sufficient notice and does not offer an easy way to complain. Google sees it differently. A spokesperson told The Register: "Our ranking systems are fair, transparent and show the most relevant, highest quality results. "We are committed to protecting the integrity of our systems, and will work constructively with the CMA to ensure that we can uphold the high quality of Search for our users." Be that as it may, the CMA's conduct requirements call for Google to provide businesses with more transparency into how its rankings work and to introduce "clear processes" for raising concerns about the Big G's practices. Furthermore, "organic" search results must be ranked using "objective and non-discriminatory criteria." The requirement also encompasses Google's AI Overviews, but not sponsored results. Google has six months to implement the ranking requirements. It has three months to implement a data portability requirement, but this is more about putting the voluntary processes already in place via Google's UK Data Portability API on a legal footing. According to the CMA, "the rights of UK users will now be on a par with those in the EU (under the EU's Digital Markets Act)." Businesses, unsurprisingly, are keen to get hold of that data. The CMA wrote: "Using this data would allow third parties to offer people more personalized features – like tailored travel suggestions, more relevant shopping deals, and rewards (including cashback and discounts)." Will Hayter, Executive Director for Digital Markets at the CMA, said: "These new measures will ensure search results are ranked fairly and objectively, with clearer information about changes and effective routes to raise concerns. "At the same time, innovative businesses will have the confidence that they can access search data in practice, unlocking investment and innovation in new products and services for users." The CMA slapped Google with Strategic Market Status (SMS) in general search and search advertising in October 2025. This designation was a recognition of Google's market power, although it does not, by itself, indicate the company has acted anti-competitively. It does, however, give the CMA more power to introduce interventions such as the conduct requirements above. Google is not the only company facing scrutiny. The CMA recently launched a fourth SMS investigation into Microsoft's business software ecosystem. ®

DEN HAAG (ANP) - Het Outbreak Management Team (OMT) had tijdens de coronacrisis weinig zin in de "gevraagde en ongevraagde" kritiek van het zogeheten 'Red Team', dat was opgericht om het OMT scherp te houden. Dat zei viroloog Amrish Baidjoe, een van de oprichters van het Red Team, tijdens zijn verhoor voor de parlementaire enquête over het coronavirus.

Er waren slechts "beleefdheidsgesprekken" met het OMT, waarin niet inhoudelijk gediscussieerd werd over de corona-aanpak, aldus Baidjoe. Volgens hem gaf OMT-voorzitter Jaap van Dissel het advies om kritiek vooral via de media te leveren.

Baidjoe durft niet te zeggen in hoeverre kritiek van het Red Team de adviezen van het OMT heeft beïnvloed. Wel zag hij "sommige elementen" van de feedback terug. "Maar ik weet niet of dat door ons kwam, of dat het OMT zelf tot bepaalde inzichten was gekomen."