Scotland could freeze datacentre projects in challenge to UK's AI strategy The Scottish government is about to consider a sweeping moratorium on building new datacentres, putting a key plank of the UK's AI strategy at risk. Last Sunday the Scottish National party (SNP)'s national council passed a motion to freeze all new datacentres in Scotland. That motion has been sent to the Scottish government to consider. It could apply to all datacentre projects that have not yet received planning permission - although its exact implementation is up to the Scottish government to decide. Lesley Backhouse, who attended the national council meeting, said that Scotland's current datacentre plans amounted to "overdevelopment" and were "intrusive and not keeping with the local environment".

The hosting platform wants sites to have more control over how AI companies use their content. Cloudflare has announced plans to automatically block mixed-use web crawlers that index websites for search engines and act as AI agents and trainers at the same time. The company previously offered its customers the optional ability to prevent crawlers from scraping their sites for AI chatbots, but now Cloudflare's stance is becoming more defensive by default. Now that the majority of traffic on the Internet is non-human, we must go further and act faster so that a sustainable ecosystem can emerge, Matthew Prince, Cloudflare's CEO and co-founder shared in a statement. Cloudflare's new tools and partnerships give website owners increased visibility and commercial opportunities and benefit AI companies that have bots with clear and transparent intent.

Don't get Pushing Buttons delivered to your inbox? Wed 1 Jul 2026 10.00 EDTLast modified on Wed 1 Jul 2026 10.02 EDT It was once a truth universally acknowledged that an ageing console in possession of good revenue must be in line for a price reduction. Those days may be over. In March, Sony announced a price increase of £90 for the PS5, while last month Microsoft informed gamers that it would be charging at least £75 more for the Xbox Series S and X consoles from August. All three were first released back in 2020.

Guardian Australia has been told an industry proposal has been presented to cabinet that would grant AI companies special exemptions to mine creative content. In exchange, the companies would bankroll the artists' fund and commit more than $50bn worth of investment in datacentres. Australia'sleepwalking' into AI crisis and'tech bro free-for-all', says Greens senator The independent senator David Pocock said the proposal was the "ultimate dirty deal" as he demanded the government categorically rule it out. The potential adoption of a text and data mining exemption would represent a major reversal from the federal government, which last year ruled it out after criticism from artists, authors and media groups. Amid fears the government could capitulate to big tech, a delegation of creatives staged a press conference in parliament house on Wednesday to urge the government to hold the line.

Predictive models are increasingly embedded in operational decision-making, yet standard explanation methods typically explain forecasts rather than the decisions those forecasts induce. This distinction is important in predict-then-optimize systems: large forecast changes may leave the optimizer's action unchanged, while small changes can alter the selected decision and its realized value. We propose Decision Value Attribution (DVA), a Shapley-based framework for attributing the value of a fixed prediction--optimization pipeline. The framework defines cooperative games whose payoff is the downstream decision value, allowing the players to be information sources, optimization or design parameters, or both. We present three variants: InfoDVA attributes value to features, DesignDVA attributes value to operational configurations, and Decision-Value Interactions (DVI) quantifies how information and design jointly create value. We further distinguish post-DVA, which evaluates decisions using realized outcomes, from pre-DVA, which evaluates decisions under the model's full prediction. This separation turns attribution into a decision-level diagnostic of whether the model's operational beliefs align with realized performance. The resulting attributions are expressed in the units of the operational objective and decompose the gain or loss relative to a baseline. Case studies in electricity storage arbitrage and emergency medical service coverage show that predictive explanations can be poor proxies for operational value, that DVA can guide targeted information-control interventions, and that optimization configurations determine when predictive information is decision-relevant.

'We're up against forces that have all the money in the world': Erin Brockovich on her battle against AI datacentres In 1993, she squeezed a $333m settlement from a Californian energy company in a scandal over contaminated water. Three decades later, she has a new target in her sights - and it's global When Erin Brockovich woke to find 30 emails from people from the same town, she realised something was going on. People email Brockovich all the time because of what happened in 1993, when she was instrumental in suing Pacific Gas and Electric Company (PG&E) on behalf of residents of the town of Hinkley, California, whose groundwater had been contaminated. The case resulted in a settlement of $333m - then the largest ever payout for a direct-action lawsuit. When she was immortalised by Julia Roberts in the 2000 film Erin Brockovich, she became the hero we didn't know we needed, a modern day Joan of Arc.

Google's Search history update stores media uploads from your interactions, like images used in reverse image searches, for training its AI models. A little piece of my soul shrivels up every time I get a message laying out how another company plans to use personal data in ever encroaching ways for AI training . I got one of those emails recently from Google, with the subject line: "New privacy settings for Search services." It's part of Google's global rollout happening over the next few months that will change how it handles users' Search history data. Every piece of media, from photos you upload for reverse image searches to audio of you speaking with Google Translate, may be retained in your account and used to improve Google's AI models.

Long-separated research has been conducted on two highly correlated tracks: traffic and incidents. Traffic track witnesses complicating deep learning models, e.g., to push the prediction a few percent more accurate, and the incident track only studies the incidents alone, e.g., to infer the incident risk. We, for the first time, spatiotemporally aligned the two tracks in a large-scale region (16,972 traffic nodes) from year 2022 to 2024: our TraffiDent dataset includes traffic, i.e., time-series indexes on traffic flow, lane occupancy, and average vehicle speed, and incident, whose records are spatiotemporally aligned with traffic data, with seven different incident classes. Additionally, each node includes detailed physical and policylevel meta-attributes of lanes. Previous datasets typically contain only traffic or incident data in isolation, limiting research to general forecasting tasks.

Masayoshi Son dismisses Musk's idea for orbital data centers SoftBank founder Masayoshi Son dismissed the idea of space-based data centers, arguing that the AI race will be won by computing power on Earth. SoftBank Group founder Masayoshi Son said there's little merit to building data centers in space, as championed by Elon Musk, predicting that the artificial intelligence race will be clinched by computing power on Earth. The main advantage of building data centers in space would be to slash electricity costs. But such expenses comprise a small fraction of the cost of operating data centers, compared with hardware like chips, Son said during an annual shareholder meeting for SoftBank's mobile unit on Tuesday. The tradeoff for any power cost reductions would also include higher fees to transport everything into space, maintenance and communication delays, he added.

We study the problem of approximating all-pair distances in a weighted undirected graph with differential privacy, introduced by Sealfon [Sea16]. Given a publicly known undirected graph, we treat the weights of edges as sensitive information, and two graphs are neighbors if their edge weights differ in one edge by at most one. We obtain efficient algorithms with significantly improved bounds on a broad class of graphs which we refer to as recursively separable. In particular, for any n-vertex Kh-minor-free graph, our algorithm achieve an additive error of eO(h(nW)1/3), where W represents the maximum edge weight; For grid graphs, the same algorithmic scheme achieve additive error of eO(n1/4 W). Our approach can be seen as a generalization of the celebrated binary tree mechanism for range queries, as releasing range queries is equivalent to computing all-pair distances on a path graph. In essence, our approach is based on generalizing the binary tree mechanism to graphs that are recursively separable. JL and ZZ have been supported by National Science Foundation of China under Grant No. 62472212 and the New Cornerstone Science Foundation. Supported in part by NSF award 2228995 JU's research was funded by the NSFCNS 2433628, Google Seed Fund grant, Google Research Scholar Award, Dean Research Seed Fund, and Rutgers Decanal Grant no.