Wed 3 June 2026
Daily Brief — Curated and contextualised by Best Practice AI
UK Blocks Google, AI Boosts Economy, and Publishers Feel the Pinch
TL;DR UK media sites can now block Google from using their articles in AI search summaries, addressing concerns over lost traffic and revenue. A policy brief suggests AI may already be contributing hundreds of billions to the global economy, but these gains remain invisible in official data. Meanwhile, research questions whether scientific publishing rewards merit or connections, and a study examines the challenges of coding agents taking over interrupted tasks. Finally, U.S. financial institutions' algorithmic fairness programs remain opaque despite decades of operation.
The stories that matter most
Selected and contextualised by the Best Practice AI team
UK media websites given power to block Google using their articles in AI search
Watchdog makes ruling on search summaries after publishers complain about drop in click-through traffic and revenue Business live – latest updates Online publishers and news organisations are now able to block their content from appearing in Google’s AI summaries in UK search results, the British competition watchdog has announced. The Competition and Markets Authority (CMA) said the new requirement would “put publishers, like news organisations, in a stronger position to negotiate content deals with Google”. Continue reading...
Merit or networks? What decides where research is published
arXiv:2606.03763v1 Announce Type: new Abstract: Does scientific publishing reward the quality of ideas or the advantage of connections? The question is universal to prestige-driven science, yet it has resisted decades of study because a paper's quality could not be gauged ahead of its publication fate without using that fate as the yardstick. We break this constraint by measuring a paper's idea quality directly from its text, before publication, using a discipline-trained LLM evaluator that scores the idea without seeing author names or outcomes. Using economics as a case study, we combine this text-legible idea-quality score with an execution-quality rubric, a connection index, an author-ability index, and an off-the-shelf language-model text score to estimate a five-input production function for journal placement across 6,208 economics working papers. The inputs are not rivals but a sequence along the ladder of prestige. Execution sets a meritocratic floor and is the largest input overall. Text-legible idea quality grades the rungs in between. Connections set a favoritism ceiling that bites mainly near the apex, the most selective journals. Connections work through two additive channels: connected authors write papers that score higher, and at equal scores their papers are still more likely to place better. Yet this advantage is bounded. Connections raise the odds of every rung without making the apex the typical outcome for ordinary ideas, and even the highest-scoring papers face real friction reaching the visible journal ladder. The result nests, rather than chooses between, the meritocracy and network accounts of how science is published.
Handoff Debt: The Rediscovery Cost When Coding Agents Take Over Interrupted Tasks
arXiv:2606.02875v1 Announce Type: new Abstract: Coding-agent benchmarks evaluate whether a single uninterrupted agent can resolve a repository issue. Real software work is messier: tasks are interrupted, reassigned, reviewed, and resumed from partial states left by another agent or engineer. We study this missing dimension through \emph{handoff debt}: the rediscovery cost imposed when a predecessor's work is opaque or incomplete. Our takeover protocol interrupts a coding agent at deterministic handoff points, freezes the repository, and evaluates successor agents under four handoff views: repository state only, raw trace, summary notes, and structured notes. Across 75 source tasks, the protocol generates 181 handoff-point tasks and 724 takeover runs per successor model. Across three successor models, context-bearing handoffs reduce median agent events by 20--59\% and cumulative prompt tokens by 42--63\% relative to repository-only takeover. Solved-rate effects are smaller and model-dependent, but efficiency gains are consistent. These findings suggest that coding-agent evaluation should report not only whether a task is solved, but also how costly that work is for another agent to resume.
AI may already be adding hundreds of billions to the economy—without showing up in the data | Fortune
A new policy brief argues AI may already be adding hundreds of billions to the global economy—but official statistics aren’t built to see it.
The Fair Lending Model: How the Longest-Running Algorithmic Fairness Programs Work in Practice
arXiv:2606.02957v1 Announce Type: new Abstract: U.S. financial institutions subject to fair lending laws have been running algorithmic fairness programs for decades. Despite this long history, remarkably little is known about how these requirements operate in practice. In this paper, we offer the first empirical account of how financial institutions test for and mitigate algorithmic discrimination on the ground. In doing so, we shed light on how the regulatory design of fair lending law and regulation have shaped the policies, processes, and practices of fair lending programs. Drawing on 35 semi-structured interviews with participants across the fair lending ecosystem, we find that while financial institutions have a floor of fairness practices aimed at preventing discrimination in lending largely absent in other domains, the specifics of how firms test for discrimination and search for less discriminatory algorithms varies widely. We also find that regulatory supervision via fair lending examinations has been the key driver of compliance work, but that the practical impact of fair lending programs often depends on how well they can navigate competing business incentives, perceived legal tensions, and regulatory uncertainty. Ultimately, our findings highlight the unique role that supervisory authority has played in successfully fostering fair lending practices -- a regulatory design feature that is distinct from other areas of civil rights law and almost completely absent from recent policy proposals for dealing with algorithmic discrimination.
Economics & Markets
Will the IT consulting share price rout ever end?
Accenture made a fortune from previous tech revolutions but investors think AI could kill it, not make it stronger
News Publishers Weigh Whether AI is Industry Killer or Savior
The potential effects of AI dominate a meeting of global publishers
Market-Research Firm AlphaSense Clinches $7.5 Billion Valuation in New Funding Round
Firm raises $350 million from investors including Accenture and JPMorgan’s asset-management unit.
AI Funding Boom Reaches Muni Market With Google-Tied Bond Sale
Google parent Alphabet Inc. is poised to enter the municipal-bond market’s prepaid energy space by participating in a $1 billion transaction out of California, a major development in the evolution of a booming segment.
Google’s $80bn equity raise adds to that giant AI sucking sound
Search engine’s move is a good example of how artificial intelligence has made big numbers all but meaningless
JPMorgan’s Lipikhina Sees Earnings Supercycle Driving US Stocks
An earnings “supercycle” in the US is poised to drive stocks to more record highs, according to Nataliia Lipikhina at JPMorgan Chase & Co., fueled by heavy spending from hyperscalers and advances in agentic AI.
Alphabet’s shares drop after announcing $80bn share sale, as AI threatens to drive up youth unemployment – as it happened
Rolling coverage of the latest economic and financial news Anthropic confidentially files for initial public offering on US stock market In a landmark moment, gold has overtaken US government bonds as the world’s top reserve asset, according to calculations from the European Central Bank. The ECB says that gold made up 27% of total official foreign reserves at the end of 2025, ahead of US Treasuries (22% of reserves) and the euro (15%). Forces of fragmentation are becoming more pronounced. Geopolitical tensions continue to drive strong central bank demand for gold. In nominal terms, the gold price surged by around 60% and 30% in 2025 and 2024 respectively, which mechanically increases the share of gold in total official foreign reserves. Correcting for such valuation effects by using the gold price at the end of 2023, the share of the euro (16%) remains at par with the share of gold (16%), while the share of US Treasuries continues to be markedly higher (26%). Continue reading...
Sector Snapshot: Defense Startup Funding Hits An All-Time Record As VCs Begin To Eye Exits
Already this year, more than $14.6 billion in venture investment has gone into companies in Crunchbase’s military, national security and law enforcement categories, blowing past the sector's previous annual record of $9.6 billion raised in all of 2025.
Crypto VC 2026: Where the Money Is Going and Why Consumer Apps Are Out | VaaSBlock
Crypto VC has rotated decisively into infrastructure, AI-crypto, and tokenised assets. NFTs, consumer apps, and metaverse lost funding. Here is what 2026 capital flow data reveals.
ServiceNow Surges 9% as Nvidia Endorses Enterprise Software, AI Revenue Target Tops $30B - Forex News by FX Leaders
Management increasingly describes the platform as an AI “control tower” for enterprises deploying agentic AI systems across departments.
Wiley acquires Emerald, expanding research scale and deepening proprietary content across the AI-driven knowledge economy
Press Release: Wiley acquires Emerald, expanding research scale and deepening proprietary content across the AI-driven knowledge economy. Wiley, a global leader in authoritative content and research intelligence, announced it has acquired Emerald Publishing Limited from Cambridge Information ...
Physical AI Market Size, Share, Regions, Companies & Growth Report - Global Forecast to 2032
Delray Beach, FL, June 02, 2026 (GLOBE NEWSWIRE) -- The global physical AI market was valued at USD 0.89 billion in 2025 and is projected to reach USD 15.28 billion by 2032, expanding at a CAGR of 47.2% from 2026 to 2032. This growth is driven by the rapid integration of AI intelligence into ...
Market Outlook: AI spending fuels next wave of technology gains
Michelle Connell discusses Hewlett Packard's earnings, AI spending trends and investment opportunities in technology, utilities and commodities.
Wall Street inches to more records, thanks to booming AI stocks - Los Angeles Times
The U.S. stock market inched to more records as winners of the artificial intelligence boom kept driving higher.
AI may already be adding hundreds of billions to the economy—without showing up in the data | Fortune
A new policy brief argues AI may already be adding hundreds of billions to the global economy—but official statistics aren’t built to see it.
A guide for the perplexed on AI
Should we think of this technology as boon, bane or bubble?
Sitecore Said to Acquire Scrunch for $225 Million
Sitecore A/S, the digital-experience software company, has acquired Scrunch, a customer-experience platform that helps brands improve how they appear in AI-generated search results, according to a statement reviewed by Bloomberg.
Salesforce acquires German-founded AI platform Contentful
The Information reported that the acquisition deal cost Salesforce between $1bn and $1.5bn. Read more: Salesforce acquires German-founded AI platform Contentful
Palo Alto raises annual forecasts on strong AI cybersecurity demand, shares surge
Palo Alto Networks raised its annual revenue and profit forecast on Tuesday, as enterprises increased spending on cloud, identity and AI-driven cybersecurity products amid a rising threat landscape, sending shares up 7.4% in extended trading. Here are some details on the results: • ...
Nvidia corners the AI agent stack
Nvidia is positioning itself to dominate the AI agent ecosystem by integrating its hardware and software stack.
White House advisor Sacks calls 30-day review period 'game changer' in US AI order
David Sacks stated that reducing the voluntary review period from 90 to 30 days allows AI labs to comply with the framework without delaying new model releases.
Competition From Nvidia PC Chip A 'Good Thing', Says Intel • Channels Television
On Tuesday, Intel announced upgrades to its AI data centre hardware offerings as well as new collaborations with supply chain partners such as Taiwan’s Foxconn. While several experts told AFP that Nvidia’s competitors should be worried about its new PC chip for the AI era, the RTX Spark, ...
OpenAI vs Google: 3 Pascal's Wager Strategies Reshaping AI Business Models - FourWeekMBA
The Billion-Dollar Bet: Why AI Giants Are Playing Pascal’s Wager While internet searches for “Pascal’s Wager” surge, two tech titans are embodying the 17th-century philosopher’s famous risk calculation in their AI business models. OpenAI and Google are making fundamentally different ...
X Corp. defends objection to producing Musk messages in US antitrust suit
X Corp. is fighting a court order to produce Elon Musk's emails in an antitrust case against OpenAI and Apple, arguing that Musk is not a plaintiff in the action.
‘The CGI would have cost millions. I spent $2,000.’ Is Dreams of Violets AI slop – or the future of film-making?
It should have taken years, but Ash Koosha made a drama about Iran’s anti-government protests in weeks – and now it’s the first AI-made movie to screen at a major film festival. It could transform indie film-making, claims the director Next week a breakthrough 75-minute drama about the brutal crackdown in Iran on anti-government protesters in January will premiere at the Tribeca film festival in New York. It is called Dreams of Violets and is based on journalism, video footage and eyewitness accounts. “I would say 80% of it is a recreation of events that actually happened,” says its Iranian-British director Ash Koosha. But Dreams of Violets is a work of fiction, not a documentary: a drama following a group of strangers caught up in the protests, who meet by chance in an alleyway. How on earth has Koosha managed to pull together a drama about the killings in less than six months? The answer, it turns out, is by using artificial intelligence. Every image and character in Dreams of Violets is AI-generated. Koosha says he created the characters by describing their physical appearances, using people he has known in the past as references. It would be too dangerous to base characters on living people in Iran, he says. “Because of the security issue, it would not be safe for the characters to even remotely resemble someone.” Continue reading...
Australia’s biggest bank says corporate AI is racking up bigger bills and producing ‘work slop’
CBA chief executive Matt Comyn flagged surging AI costs and “work slop” as production-stage complexity inverts the favourable per-token AI economics that defined 2024-2025.
Martin Scorsese Is Embracing A.I. - The New York Times
In a sign of Hollywood’s softening stance on artificial intelligence, the cinema icon is backing Black Forest Labs, an image and video generation start-up.
IQM raises PIPE to $146m with Finnish pension fund backing
Finnish quantum start-up IQM has bolstered its pre-listing war chest to $146m with backing from pension giant Ilmarinen. Read more: IQM raises PIPE to $146m with Finnish pension fund backing
Defense tech darling Mach Industries hits $1.8B valuation
Mach Industries has reached a $1.8 billion valuation, marking a 4x increase in just one year.
Deeptech startup funding nears 2025 levels as VC conviction strengthens | Company Business News
Venture capital funding of deeptech startups has reached almost 80% of 2025 levels. The deeptech sector raised $1.1 billion as of June 1, highlighting a shift towards supporting early-stage companies and larger capital allocations, particularly in AI and defence technologies.
Exclusive: The maths teacher who bootstrapped an AI startup to £1.6M before taking a penny of VC just raised £2M to fix AI's broken data pipeline — TFN
Poindexter Labs has raised a £2 million seed round led by Episode 1. The London-based company aims to fix what it calls the “broken” data supply chain
Edinburgh-based Wordsmith raises €60.2 million Series B to scale legal AI platform for in-house teams
Wordsmith, an Edinburgh-based legal AI startup building the legal operations platform for in-house teams, today announced a €60.2 million ($70 million) Series B funding round, bringing the company’s total funding to €86 million ($100 million). The funding was secured from Highland Europe and Index Ventures, among others. The round comes alongside an expansion of Wordsmith’s […]
Labor, Society & Culture
Bank of America Bucks AI Job Fears With 2,000 Summer Interns
Bank of America Corp. is continuing to hire even as artificial intelligence and other technology tools take over jobs and cut off many traditional career paths.
Remote work – not AI – is killing job prospects for the youth
Young professionals may be perfectly productive while working from home, says the New York Fed, but the quality of their output isn't so great, so companies don't want to hire them
15 companies, including Wix and GitLab, that have said they're doing AI-related layoffs
A number of companies, including Snap, GitLab, and Wix, have attributed recent staff reductions to AI.
The 20 New Agentic AI Jobs Box, McKinsey, And LinkedIn All See Coming
Designs autonomous multi-agent systems where models orchestrate tools and chain complex actions. The engineering core of agentic commerce, support, and operations. Owns enterprise-wide AI strategy and ethical implementation. An IBM survey found one in four companies now have one, with two-thirds ...
What’s on the mind of EEI conference attendees? Labor, AI, affordability and more. | Utility Dive
Utility Dive talked to registrants before the conference to hear how industry changes are impacting their work.
AI adoption surges, but providers worry about deskilling | Healthcare Dive
Nearly three-quarters of clinicians said losing critical thinking or decision-making skills will be one of the greatest risks of adopting artificial intelligence, according to a survey by Wolters Kluwer Health.
AI may end up creating more jobs than it replaces by 2030, says WEF’s Mirek Dusek - CNBC TV18
Artificial intelligence is moving beyond pilots and beginning to reshape real workplaces, with the World Economic Forum highlighting both productivity gains and fresh opportunities for jobs and entrepreneurship.
Office Hours: How Do We Deal With the Inevitable Loss of Good Jobs to AI?
Four possible directions
What Benchmarks Don't Measure: The Case for Evaluating Abstention Competence in Autonomous Agents
arXiv:2606.02965v1 Announce Type: new Abstract: Benchmarks for autonomous agents measure whether agents complete tasks, yet this framing is systematically blind to whether an agent should have proceeded at all. Agents trained under human-feedback objectives develop a structural tendency to proceed even when they lack the inputs, evidence, or authorization to act safely, a disposition we term compliance bias, because both the reward signal and the benchmark scoring regime treat proceeding as the correct default regardless of whether the preconditions for safe action are present. We make three contributions. We first show that compliance bias originates in reward hacking within human-feedback pipelines and is entrenched by prominent agent benchmarks, which either penalize agents for pausing or are architecturally unable to distinguish a principled pause from a silent failure. We then introduce a three-gap taxonomy of abstention-warranted scenarios, covering specification gaps where required information is absent, verification gaps where world state cannot be confirmed, and authority gaps where explicit authorization has not been given, which together provide a principled basis for constructing abstention-aware agent benchmarks. Finally, we propose abstention evaluation protocols (Safety Rate, Usability Rate, and Informed Refusal Rate) and report preliminary results across 144 enterprise agent scenarios and five model families, in which a runtime-enforced abstention mechanism achieves up to 89.2% hazardous-action blocking and 87.5% usability on authorized scenarios, demonstrating that the safety--usability tradeoff is tunable rather than inherent and that its shape varies substantially across model families. We treat this as preliminary work and offer the taxonomy and composite metrics as a starting point for further conversations.
Auditing Engagement Incentives in the Kidfluencer Ecosystem: A Multimodal Weak Supervision Approach
arXiv:2606.03173v1 Announce Type: new Abstract: The rise of `kidfluencers' on YouTube has raised ethical concerns about child digital labor and exploitation. While emerging legislation attempts to regulate this ecosystem, empirical evidence linking exploitation to engagement remains scarce, given the difficulty of operationalizing exploitation at scale. This study presents a multimodal AI audit of 5,051 videos across 79 kidfluencer channels, using weak supervision to detect exploitation signals without large-scale manual labels. We aggregate noisy labeling functions -- including LLM-based classification of titles and GPT-4 Vision analysis of thumbnails and descriptions across six literature-grounded dimensions -- to assign a probabilistic exploitation score to each video. A multi-annotator validation study (N=107) shows strong agreement with human judgment (macro-average F1 $= 0.911$) and high sensitivity for overall exploitation risk (recall $= 0.960$, F1 $= 0.793$). Our findings reveal a significant engagement premium for performative labor, emotional bait, and privacy violations. Exploitation scores correlate with view counts (Spearman $\rho = 0.229$, $p < 10^{-50}$), and mixed-effects regression controlling for channel-level variation shows that a one-unit increase in exploitation score yields a $4.4\times$ increase in views ($p < 0.001$). Within-channel analyses indicate median view boosts of $+65.6\%$ for emotional bait and $+56.0\%$ for performative content (FDR-corrected $p<0.001$), with effects holding in same-year robustness checks ($p=0.030$). Explicit commercial content (product placement), by contrast, shows no premium ($-3.8\%$, n.s.), suggesting the platform rewards commodification of the child's identity and labor over traditional advertising. These findings challenge policy frameworks focused solely on financial trusts, showing that engagement is systematically tied to the intensive, performative labor of children.
Effect of Demographic Bias on Skin Lesion Classification
arXiv:2606.03214v1 Announce Type: cross Abstract: In this study, we evaluate the performance of skin lesion classification using ResNet-based convolutional models, focusing on the impact of demographic bias in training data, particularly variations in patient sex and age. We use linear programming to generate datasets with controlled demographic characteristics, allowing systematic investigation of bias effects. Three learning strategies are evaluated: a single-task model, a reinforcing multi-task model, and an adversarial learning scheme. Our sex-based analysis indicates that sex-specific training datasets optimise model performance. Notably, including male patients in the training data improved performance for the male subgroup, even in female-majority cases. Reinforcing and adversarial learning schemes narrowed or eliminated bias gaps in balanced and female-majority datasets. However, these strategies proved less effective in male-majority settings, where models continued to perform better for males than females. The two learning schemes showed marginal bias reduction compared to the baseline model in predominantly male patient populations. Age-based analysis demonstrates comparable baseline performance across the three model approaches, with performance declining across age categories. Younger groups consistently achieve the highest performance, regardless of training data distribution. Although balanced training yields optimal results for the youngest age category, performance decreases in older categories. We find that sex biases arise mainly from data imbalances, while age biases consistently favour younger groups regardless of distribution. These distinct mechanisms require targeted mitigation strategies. Additionally, cross-dataset validation on two external datasets revealed that domain shifts notably affect performance and patterns of demographic bias.
Fairness Definitions and Metrics in Deep Reinforcement Learning for Drug Discovery in Healthcare: A Rapid Evidence Review
arXiv:2606.02902v1 Announce Type: new Abstract: Deep reinforcement learning (DRL) is increasingly applied to de novo molecular design, but choices in data, rewards, and evaluation can yield uneven performance across disease areas and chemotypes. Despite this, there is no concise synthesis of how fairness is defined, measured, and tested in DRL-based drug discovery. In this rapid evidence review, we synthesize fairness definitions and metrics for DRL-driven molecule generation in healthcare. We focus on three questions: (i) how dataset composition and split strategies, especially scaffold versus random splits, affect evaluation and distribution shift; (ii) how reward design (e.g., QED, docking, toxicity, synthetic accessibility) can create or mitigate bias, with emphasis on cancer targets; and (iii) which measurable metrics best capture fairness. This includes parity across cancer versus non-cancer indications and across cancer subtypes. It also includes distributional balance in key physicochemical descriptors, scaffold/chemotype diversity, groupwise validity, toxicity, and synthetic accessibility. From 2017 onward, we searched major biomedical, computer science, and engineering literature databases and used arXiv for horizon scanning. Records were screened using PRISMA-style procedures and analyzed via content coding to link reported parity outcomes to dataset and reward choices. Our review provides a concise set of fairness definitions and metrics for DRL molecule generation. It offers practical guidance for reporting distribution parity and outcome parity. It also summarizes how dataset and reward choices relate to observed parity effects and identifies open gaps relevant to trustworthy, cancer-relevant DRL generation.
Greener Than Humans? Environmental Attitudes in Large Language Models
arXiv:2606.02741v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used in sustainability-related decision support, reporting, and public communication, yet little systematic evidence exists on the environmental attitudes embedded in their outputs. This paper develops a benchmark for evaluating environmental cognition, affect, and behavioural recommendations in LLMs and applies it to 31 widely used proprietary and open-weight models. Drawing on questions from established environmental awareness surveys and additional sustainability-related behavioural measures, we compare LLM responses 1) among models and 2) between models and human survey benchmarks from Germany. We assess their robustness across prompting conditions. We find that many LLMs align more closely with environmentally progressive attitudes than the average survey respondent, exhibiting higher levels of environmental affect and cognition and recommending behaviours associated with substantial potential CO2 reductions. At the same time, we observe no systematic relationship between sustainability-oriented responses and model origin, size, or release context. However, models exhibit contextual sensitivity, controlled by persona-based prompting and show sycophantic shifts mirroring user-specified ideological positions, which raises concerns about steerability and normative reliability in real-world deployments. Our findings provide a reusable evaluation framework for assessing sustainability-related value alignment in LLMs and highlight the importance of governance, transparency, and critical oversight as AI systems become increasingly embedded in sustainability transformations and public decision-making.
Can autonomous AI-powered killer drones take morality onboard?
While the technology is set to play a growing role in modern warfare, there remains an unresolved ethical challenge Should the AI-powered drones of the future have a licence to kill? The question is becoming ever more pressing as governments and the defence industry acknowledge that drone systems will play an increasingly crucial role in future warfare. With drones being deployed in huge numbers in the Ukraine war and AI being used to assist bombing missions in the Iran conflict, there is an expectation among some observers that weapons will have to operate with increased operational autonomy, which means they will need something approximating a moral framework. Continue reading...
The Dynamic and Endogenous Behavior of Re-Offense Risk: An Agent-Based Simulation Study of Treatment Allocation in Incarceration Diversion Programs
arXiv:2601.12441v2 Announce Type: replace-cross Abstract: Incarceration-diversion treatment programs aim to improve societal reintegration and reduce recidivism, but limited capacity forces policymakers to make prioritization decisions that often rely on risk assessment tools. While predictive, these tools typically treat risk as a static, individual attribute, which overlooks how risk evolves over time and how treatment decisions shape outcomes through social interactions. In this paper, we develop a new framework that models reoffending risk as a human-system interaction, linking individual behavior with system-level dynamics and endogenous community feedback. Using an agent-based simulation calibrated to U.S. probation data, we evaluate treatment allocation policies under different capacity constraints and incarceration settings. Our results show that no single prioritization policy dominates. Instead, policy effectiveness depends on temporal windows and system parameters: prioritizing low-risk individuals performs better when long-term trajectories matter, while prioritizing high-risk individuals becomes more effective in the short term or when incarceration leads to shorter monitoring periods. These findings highlight the need to evaluate risk-based decision systems as sociotechnical systems with long-term accountability, rather than as isolated predictive tools.
Meta Expands Safety Features for Teenagers
The changes, after Meta’s legal losses in two child safety cases, are aimed at limiting harmful content shown to teenagers on Instagram, Facebook and Messenger.
5 Strategies Protect Human Dignity During Organizational AI Adoption
Discover strategic insights for leaders to integrate artificial intelligence while protecting human dignity, preventing burnout, and driving true business growth.
Solipsistic Superintelligence is Unlikely to be Cooperative
arXiv:2606.03237v1 Announce Type: cross Abstract: AI's central challenge is shifting from capability to coexistence. The dominant paradigm in AI research focuses on developing powerful agents that treat the world as an exogenous and stationary source of feedback. We contend that superintelligence, an extremely capable task solver, born out of such a solipsistic approach to AI design, is unlikely to be cooperative. Deploying AI systems induces endogenous non-stationarity, resulting in a train-test-deploy gap where historical distributions diverge from the deployment context. We refer to this as the self-undermining property of unilateral optimization. Closing this gap requires AI that participates in cooperation: the equilibrium-selection process through which multiple actors navigate their interdependence. We call for a non-solipsistic research paradigm that treats this interdependence as a core design principle rather than approaching cooperation as a task to solve. This entails building dynamic evaluation testbeds involving adaptive counterparties, treating institutions as design primitives, and preserving human agency as a structural feature of the systems we build.
AI-Generated Traces for Novice Programmers: Learning Effects and Learner Differences in a Multi-Institutional Study
arXiv:2606.03288v1 Announce Type: new Abstract: Introductory programming (CS1) courses often struggle to support students' understanding of program execution. While visualizations can make execution processes explicit, their effectiveness depends on design and context, and empirical evidence for AI-generated visualizations remains limited. We propose Generated Animated Traces (GATs), AI-generated, analogy-based, narrated animations that coordinate source code, execution state, and conceptual analogies. We conduct a study at two institutions in CS1 courses (Python, N=961; Java N=151) comparing GATs to textual explanations. We measure immediate learning performance and experience, end-of-course engagement and exam performance. Results show that GATs can yield selective benefits for immediate learning, but benefits are context-dependent and short-term. We observe that GATs' influence on performance is moderated by learner engagement profiles. This finding underscores the importance of personalized approaches.
Designing a Hardware Reverse Engineering Course: Lessons from Eight Years in a Rapidly Evolving Tech Domain
arXiv:2606.03697v1 Announce Type: new Abstract: Integrated Circuits (ICs) are omnipresent, yet their globalized manufacturing process remains vulnerable to supply chain threats. Hardware Reverse Engineering (HRE) is essential for detecting such threats and re-establishing trust; however domain experts remain scarce due to a lack of educational programs. To contribute educational insights in this critical and rapidly evolving technology domain, we present our HRE course focusing on digital circuit analysis and digital circuit extraction from ICs. The course targets junior-level undergraduates at a major European research university. The curriculum has been refined over nine iterations (2017-2025), with several alumni subsequently pursuing careers in the HRE field. By reflecting on the evolution of the course organization, content, and assignments, we derive key lessons learned. We further distill these insights into actionable design priorities for educators developing courses in rapidly evolving technological domains, emphasizing iterative growth and sustainable workload management for both students and instructors.
Technology & Infrastructure
AI alone won't change your business. The system running it will. - The Official Microsoft Blog
At the same time, a new class of ... era of business. The winners won’t be those with the most demos, but those that turn AI into a governed, continuously improving system for running real work. This isn’t just about chatbots, either. Those experiences are useful, but they don’t transform how large organizations operate. The real opportunity is teams of agents executing long running work across functions like software delivery, support, finance, HR, and operations ...
Handoff Debt: The Rediscovery Cost When Coding Agents Take Over Interrupted Tasks
arXiv:2606.02875v1 Announce Type: new Abstract: Coding-agent benchmarks evaluate whether a single uninterrupted agent can resolve a repository issue. Real software work is messier: tasks are interrupted, reassigned, reviewed, and resumed from partial states left by another agent or engineer. We study this missing dimension through \emph{handoff debt}: the rediscovery cost imposed when a predecessor's work is opaque or incomplete. Our takeover protocol interrupts a coding agent at deterministic handoff points, freezes the repository, and evaluates successor agents under four handoff views: repository state only, raw trace, summary notes, and structured notes. Across 75 source tasks, the protocol generates 181 handoff-point tasks and 724 takeover runs per successor model. Across three successor models, context-bearing handoffs reduce median agent events by 20--59\% and cumulative prompt tokens by 42--63\% relative to repository-only takeover. Solved-rate effects are smaller and model-dependent, but efficiency gains are consistent. These findings suggest that coding-agent evaluation should report not only whether a task is solved, but also how costly that work is for another agent to resume.
Traj-Evolve: A Self-Evolving Multi-Agent System for Patient Trajectory Modeling in Lung Cancer Early Detection
arXiv:2606.02812v1 Announce Type: new Abstract: Modeling patient trajectories from longitudinal electronic health records (EHRs) requires reasoning over sparse, noisy, and long-context multimodal sequences. Existing LLM-based multi-agent systems address context length but process patients in isolation, failing to mirror how clinicians leverage accumulated experience from similar prior cases. We present Traj-Evolve, a self-evolving multi-agent system with two complementary evolving mechanisms. First, an Experience Pool (ExPool) acts as a non-parametric memory, indexing rejection-sampled reasoning traces to retrieve similar patients as few-shot contexts. Second, multi-agent reinforcement learning (MARL) via reward-ranked fine-tuning parametrically optimizes inter-agent and agent-memory collaboration. A leave-one-out cross-retrieval strategy unifies the two, aligning training- and inference-time behavior under retrieval augmentation. On a lung cancer prediction task utilizing up to five years of multimodal EHRs, Traj-Evolve outperforms 9 strong baselines on the overall population and a challenging never-smoker population. Analysis of the evolving dynamics highlights three key findings: (1) expanding the ExPool shifts optimal retrieval from diverse to specific samples; (2) under MARL, the manager agent's prediction loss converges quickly while the worker agents' temporal reasoning continues to benefit from more verified patients; and (3) the two mechanisms are complementary on the predicted risk, where ExPool improves specificity while MARL improves sensitivity.
Rehumanizing global health care with agentic AI
The global health care sector is under increasing strain. Decades of chronic underinvestment and constraints in recruitment have coincided with a surge in demand for services for aging populations. Gaps in provision are already taking a toll, with fragmented access to care and high rates of stress and burnout among staff. And it’s getting worse.…
As AI Agents Scale, Enterprises Demand Execution Control— Devenex Takes Control
Las Vegas, USA, June 02, 2026 (GLOBE ... AI agents are no longer experimental. They are executing in production — modifying financial records, triggering payments, approving workflows, and acting with the full operational authority of the enterprises that deployed them. By every credible estimate, the volume and consequence of these actions will increase by an order of magnitude within 24 months. Yet no infrastructure layer exists to govern what these agents actually do. No systematic policy enforcement ...
Berlin’s INXM emerges from stealth with €5.7 million to build AI process execution engine for enterprises
INXM, a Berlin-based startup developing an AI process execution engine for enterprise and Mittelstand operations, announced it has closed a €5.7 million pre-Seed funding round as it exits stealth mode. The round was led by Cherry Ventures and Redstone, with participation from Angel Invest and other business angels such as Linden Capital. With this funding, […]
Do Matching Mechanisms Work with LLM Agents?
arXiv:2606.03030v1 Announce Type: cross Abstract: This study examines whether standard matching mechanisms function as intended in LLM-agent markets, where LLM agents make allocation-related decisions as delegated decision-makers. We compare decentralized free-negotiation markets with centralized mechanism-based markets including several representative mechanisms. Across controlled one-to-one matching environments, mechanism-based markets generally outperform free negotiation in terms of stability and efficiency. We also find that LLM agents report preferences truthfully at substantially higher rates than human subjects in comparable DA and EADA environments. However, truth-telling is not uniformly aligned with formal strategy-proofness across all mechanisms: TTC, despite being strategy-proof, does not always elicit higher truth-telling than EADA. These results suggest that matching theory provides a useful but incomplete guide for designing institutions in LLM-agent markets.
Microsoft debuts an expansion of its model families and agentic AI intelligence for developers - SiliconANGLE
For developers building code with agents and models, Microsoft launched Codename MDASH – a joke about how some AI systems tend to add additional em dashes to generated text. The new multimodel agentic-security system deploys more than 100 agents to find exploitable bugs in code by reasoning ...
Reuters AI News | Latest Headlines and Developments | Reuters
Cisco Systems on Tuesday announced a new suite of software tools that businesses can use to build their own armies of bots known as AI agents, to protect their IT infrastructure against cybersecurity threats.
Resistance is futile, says Qualcomm CEO. AI agents will be become invisible, inescapable, follow you across devices
Qualcomm's CEO suggests that AI agents will soon become invisible and inescapable, following users across all their devices.
Microsoft bets on AI agents, not apps, and dynamic UI with Project Solara | Tech News - Business Standard
According to Microsoft’s official ... “run AI agents instead of traditional applications,” shifting the core unit of computing from apps to tasks. Unlike a conventional operating system, Solara is structured as a chip-to-cloud platform. It combines an Android Open Source Project (AOSP) base with Microsoft’s enterprise stack, including ...
Microsoft reveals new quantum chip made with AI, says it will have systems by 2029 | Reuters
Microsoft made the switch with the help of AI tools that it developed for use in materials science, and the result was a 1,000-fold improvement in some aspects of Majorana 2's performance, said Jason Zander, an executive vice president at Microsoft who oversees the firm's quantum efforts.
CEA-Leti and CNRS spin-off Quobly raises €115 million to industrialise silicon-based quantum computers
Quobly, a Grenoble-based quantum computing company, today announced the closing of a €115 million Series A financing to accelerate the industrialisation of its silicon-based quantum computers and launch its first commercial product by the end of 2026. The round was led by Bpifrance, SEALSQ and STMicroelectronics, with participation from the European Innovation Council (EIC), Blast, […]
Chinese Photonics Finds its Moment with the AI Boom | Cloud News
The race for artificial intelligence is pushing data center infrastructure to the limit. Models are growing, clusters are getting larger, and the movement of
VinFast, Autobrains, NVIDIA Launch Level 4 Autonomous Driving
The companies are collaborating to develop a Level 4 autonomous driving system for Southeast Asia, leveraging NVIDIA DRIVE Hyperion for scalable robotaxi operations.
AURA: Action-Gated Memory for Robot Policies at Constant VRAM
arXiv:2606.02775v1 Announce Type: new Abstract: The KV-cache is the right memory for datacenters but the wrong memory for robots. Datacenter inference batches many short requests and resets them, amortizing an attention cache across a crowd. Embodied agents instead run one long, non-resetting episode on bandwidth-limited edge hardware, where high-bandwidth memory and flash are scarce, flash has finite write endurance, and memory writes rather than compute can become the binding constraint. AURA-Mem (Action-Utility Recurrent Adaptive Memory) targets this regime. It wraps a frozen vision-language-action backbone with a constant-size recurrent memory and a learned gate that writes only when the current observation would change the next action: memory that knows when to stay silent. Unlike reconstruction-based memory, the gate is trained directly against a closed-loop action-error signal. Its inference state is fixed at 4,224 bytes regardless of horizon, while a KV-cache grows to 6,061 times larger at 100,000 steps. On a controlled synthetic benchmark, AURA-Mem matches the best O(1) baseline in accuracy while using 5.19-6.13 times fewer writes, and up to 9.19 times fewer writes on easier configurations. Budget-matched random and periodic schedules do not recover this gain, isolating the benefit to the action-surprise signal. On a trained closed-loop OpenVLA-OFT 7B panel on LIBERO-Long (n=60 episodes per arm), the gate does not hurt success: AURA-Mem matches the ungated base policy (0.233) and slightly exceeds an always-write KV arm (0.217), while using 7.0 times fewer writes and constant memory. We also instantiate an approximate-information-state value-loss bound as a methodology demonstration; at this scale, the bound is vacuous rather than a guarantee.
Marvell enters the AI network fray with 102.4 Tbps switch silicon
High radix, low latency and low power is what AI datacenters crave, the chipmaker says
Toward a Modular Architecture for Embedded AI Agent Systems at the Edge
arXiv:2606.02862v1 Announce Type: new Abstract: The rise of Large Language Models (LLMs) has enabled agentic AI capable of complex reasoning and tool use; however, deploying such autonomy in pervasive computing environments remains challenging due to the strict memory and energy constraints of embedded microcontrollers. Existing frameworks typically assume server-class resources or continuous connectivity, leaving a gap for deeply embedded systems. This paper proposes a modular reference architecture for Embedded Agent Systems that bridges the divide between deterministic real-time control and agentic intelligence. We introduce a tiered design that decouples On-Device Agents - executing highly compressed neural networks and rule-based logic for low-latency, privacy-critical tasks - from Cloud-Augmented Agents that leverage Small Language Models (SLMs) for higher-level reasoning and planning. A key contribution is the integration of a cross-cutting Governance Layer, ensuring observability, policy enforcement, and safety across distributed fleets of autonomous devices. Rather than presenting purely empirical benchmarks, we analyze architectural design principles and trade-offs regarding latency, energy, and reliable execution in resource-constrained environments.
Perplexity AI unveils hybrid local-cloud inference system at Computex 2026
Perplexity AI, the fast-growing search startup now valued at $20 billion, unveiled what it calls the first hybrid local-server inference orchestrator at Computex 2026 on Monday night, demonstrating software that autonomously decides — in real time and mid-task — which AI workloads stay on a user's device and which get routed to frontier models in the cloud. CEO Aravind Srinivas demonstrated the system onstage alongside Intel CEO Lip-Bu Tan during Intel's keynote address, using Perplexity's "Personal Computer" agent to process confidential deal materials. In the demonstration, local models running on Intel Core Ultra Series 3 determined which information should remain on the device and which information could be sent to cloud-based models. Srinivas said the approach balances intelligence, accuracy, privacy, and cost. The key claim is not that a model can run locally — dozens of tools already do that. It is that Perplexity's system makes the routing decision itself, task by task, without requiring the user to choose in advance. Sensitive data like financial records or health information stays on the local machine; the heavier reasoning tasks that require frontier-scale models get sent to the cloud. One task, multiple execution locations, automatic orchestration. "No product has done this before," a Perplexity spokesperson said in an email to VentureBeat. The product is not yet available to users; according to the company, the hybrid inference feature will launch in the coming weeks. Perplexity's road from cloud-only agents to on-device AI orchestration To understand why the Computex demonstration matters, it helps to trace the product arc Perplexity has been building since early this year. On February 25, Perplexity launched Computer, a multi-model AI agent that orchestrates 19 different AI models to complete complex, long-running tasks on behalf of users. The system ran entirely in the cloud, breaking goals into subtasks and routing each to whichever model — Claude, Gemini, GPT, Grok, or others — was best suited for the job. Perplexity Computer unified every current AI capability into a single system, functioning as a general-purpose digital worker that operates the same interfaces a user does. Then, in March, Perplexity introduced Personal Computer at its inaugural Ask 2026 developer conference. That product launched as a new Mac app with support for a hybrid local-cloud AI agent, which Perplexity described as a "personal orchestrator" that hybridizes local and server environments for security and productivity. Personal Computer could access the Mac's file system and native Mac apps to create and execute entire workflows, with files created in a secure sandbox and all actions auditable and reversible. What Srinivas demonstrated at Computex extends this architecture in a fundamental way. Previously, even the Personal Computer product divided labor along relatively clear lines: local file access on the device, heavy computation on Perplexity's servers. The new hybrid inference orchestrator gives the system itself the ability to reason about where each piece of a task should execute — not just which model to use, but which physical location should process it. The system reportedly asks for user permission before sending sensitive tasks to the cloud, a design choice that addresses one of the central anxieties enterprises have about agentic AI: data governance. Why Nvidia’s RTX Spark and Intel's new silicon make the timing strategic The timing of the demonstration is not coincidental. Computex 2026 has been dominated by a single theme: on-device AI. Just hours before the Intel keynote, Nvidia CEO Jensen Huang unveiled the RTX Spark, a new Arm-based superchip that the company positions as the foundation for a new generation of AI-native Windows PCs. At full strength, the RTX Spark Superchip offers up to 20 Arm CPU cores, a Blackwell GPU with 6,144 CUDA cores, 128GB of LPDDR5X RAM, and up to 300 GB/s of memory bandwidth — enough power and memory for AI agents and 120-billion-parameter models with context lengths stretching to a million tokens. RTX Spark systems will begin arriving in the fall. Intel, not to be outdone, used its keynote to showcase Xeon 6+ processors with 288 efficiency cores built on 18A technology for the data center, and positioned its Core Ultra Series 3 as the client silicon that makes hybrid inference possible on the PC. Perplexity's hybrid orchestrator sits at the intersection of both strategies. If the system performs as advertised, it creates a direct economic incentive for users — and eventually enterprises — to invest in more powerful local silicon. The more capable the on-device chip, the more inference can run locally, reducing cloud costs and improving latency for sensitive workloads. That dynamic benefits Nvidia, Intel, and every other chipmaker competing for AI PC sockets. The implications extend well beyond chip economics. "As chips become more powerful, more intelligence moves onto a person's machine, alongside server inference for the complex tasks that still need frontier models," a Perplexity spokesperson told VentureBeat. "Sensitive and sovereign work can stay local, which changes the need for massive country-level infrastructure." That last claim — about sovereign infrastructure — is the most provocative. Nations from the UAE to France to India have been investing billions in domestic AI compute capacity partly on the assumption that sensitive data must stay within their borders, which means building or buying access to local data centers. If meaningful inference can run on an end user's device with no data leaving the machine, the calculus changes. It does not eliminate the need for data centers, but it could soften the urgency of the buildout. The model-agnostic architecture that makes hybrid inference possible Perplexity's hybrid inference play rests on the same architectural bet the company has been making all year: that the orchestration layer matters more than any individual model. For AI engineers, this signals a fundamental shift — the orchestration layer may matter more than the models themselves. The key insight is separation of concerns: the orchestration layer handles task decomposition, state management, and tool coordination, while the model layer handles specific computations. This decoupling means teams can swap models as better alternatives emerge without redesigning the entire system. Perplexity has leaned heavily into this philosophy. The company is doubling down on packaging frontier models in a consumer-friendly user experience, arguing that there is value in orchestrating multiple third-party LLMs to obtain the most cost-effective and accurate answers to queries. Models, in Perplexity's view, are specializing, not commoditizing. The hybrid inference extension takes that logic one step further. Perplexity is now orchestrating not just across models but across physical compute locations — choosing which model runs where. A lightweight local model might handle a privacy-sensitive document summarization task while a frontier cloud model tackles the complex reasoning required to analyze that summary against a broader market landscape. The orchestrator manages the handoff. This is a technically ambitious claim. Making it work reliably in production will require the orchestrator to accurately assess the complexity of each subtask, understand the sensitivity of the data involved, know the capabilities and latency characteristics of whatever local hardware the user has, and manage the state of a task that may be bouncing between environments mid-execution. It is easy to imagine edge cases where the routing logic fails, sends something sensitive to the cloud, or degrades performance by assigning a task to an underpowered local model. Perplexity says the system will be chip-agnostic, though the initial Computex demo ran on Intel silicon. The company expressed enthusiasm in its communications about the new AI chips announced at Computex this week, suggesting it intends to optimize across vendors. A $20 billion valuation, nine lawsuits, and the pressure to deliver The hybrid inference announcement arrives at a complicated moment for Perplexity. The company has been on a remarkable growth trajectory: It secured $200 million in new capital at a $20 billion valuation, just two months after raising $100 million at an $18 billion valuation. Since its founding three years ago, the rapidly growing AI company has raised $1.5 billion in total funding, according to PitchBook data. But the company also faces a mounting stack of legal challenges. Nine organizations have filed active suits against Perplexity for alleged copyright and trademark infringement as of May 31, 2026: CNN, the New York Times, News Corp and Dow Jones, the New York Post, the Chicago Tribune, Encyclopedia Britannica, Merriam-Webster, Reddit, and Japan's Yomiuri Shimbun. The CNN lawsuit, filed just days ago on May 28, is the most recent, accusing Perplexity of scraping more than 17,000 CNN stories, photos, videos, and other content and using that material to train its products. Perplexity has responded with a consistent message. "You can't copyright facts," the company's chief communications officer Jesse Dwyer said in a statement. Other publishers have opted for partnership over litigation. Time, Gannett, Le Monde, and Der Spiegel have signed licensing arrangements with Perplexity. The company launched a Publishers Program in mid-2024 in which participating outlets receive a share of revenue generated when their content is cited in Perplexity answers. According to CNBC, Perplexity's chief business officer Dmitry Shevelenko confirmed at the time that the flat rate was a double-digit percentage but declined to share specifics. As TechCrunch reported in December 2024, additional publishers including the LA Times, Adweek, The Independent, and Lee Enterprises subsequently joined the program, though not without internal controversy — reporters at some outlets told TechCrunch they were not informed of the deals before they were announced publicly. The legal risk is not existential, but it is material, and with enterprises increasingly evaluating Perplexity's tools for sensitive workflows — precisely the use case the hybrid inference system is designed to serve — unresolved intellectual property questions could dampen adoption. How hybrid inference sharpens Perplexity's enterprise ambitions The hybrid inference demo should be read alongside Perplexity's broader push into enterprise software, a transformation that accelerated dramatically this year. At the Ask 2026 developer conference in March, VentureBeat reported that Perplexity announced Computer for Enterprise, positioning the three-year-old startup as a direct competitor to Microsoft, Salesforce, and the legacy enterprise software stack. Beyond Computer's existing 100-plus integrations, enterprise customers gained access to business-grade connectors for Snowflake, Datadog, Salesforce, SharePoint, and HubSpot, with administrators able to install custom connectors via the Model Context Protocol. The package also includes purpose-built workflow templates for legal contract review, finance audit support, sales call preparation, and customer support ticket triage, alongside SOC 2 Type II certification and the option for zero data retention. Hybrid inference deepens this enterprise pitch considerably. For regulated industries — financial services, healthcare, defense, legal — the ability to keep sensitive data on a local device while still accessing the reasoning power of frontier cloud models is not a nice-to-have. It is a potential compliance requirement. An investment bank parsing confidential deal documents, for instance, might be unable to send those materials to a third-party cloud under existing data handling agreements. A system that can run the sensitive parsing locally while routing non-sensitive analytical tasks to the cloud offers a middle path. IDC forecasts a tenfold increase in agent usage and a thousandfold growth in inference demands by 2027, and security and governance rank as the top evaluation factor for enterprise agentic platforms, according to a CrewAI survey. Hybrid inference speaks directly to that priority. The race to decide where AI actually runs is just getting started Several questions will determine whether Perplexity's Computex demonstration becomes a landmark product or a compelling prototype. The actual performance characteristics remain untested outside a controlled stage environment — how the routing logic handles varied hardware configurations, unreliable network connections, and ambiguous data sensitivity classifications is an open question. The competitive response matters too: Google, Microsoft, Apple, and OpenAI are all building their own local-cloud AI architectures. Apple Intelligence already routes some tasks locally and some to Private Cloud Compute servers, Google's Gemini Nano runs on-device, and Microsoft's Copilot+ PCs are designed around local inference capabilities. None of these systems, however, currently offer the kind of dynamic, autonomous task-level routing Perplexity demonstrated on stage. Then there is the business itself. Perplexity's annualized recurring revenue surged past $450 million in March 2026, up from roughly $200 million six months earlier — rapid growth, but at a valuation north of $20 billion, the company still trades at a premium that demands the technology translate into sustained enterprise adoption. Perplexity has built its business on a bet that the future belongs not to any single model but to the system that orchestrates all of them. At Computex, it extended that bet from the software layer to the physical layer — from which model to which machine. In the AI industry's relentless race to build bigger data centers and train larger models, Perplexity just argued that the most important computer in the stack might be the one already sitting on your desk.
Microsoft Build: Surface RTX Spark Dev Box, Coreutils for Windows, air-gapped GitHub and more
Execution Containers provide safe environment for running AI agents, while Windows Developer Config aims to make Windows less unpleasant for developers
UK’s Oxford Quantum Circuits raises €301 million Series C to scale Quantum-AI data-centre platform
Oxford Quantum Circuits (OQC), a UK-based company building quantum computers and a secure, scalable Quantum-AI data-centre platform for enterprise and government customers, has closed an oversubscribed €301 million (£260 million/$350 million) Series C funding round. OQC claims this to be Europe’s largest ever private funding round for a quantum computing company. The round was led […]
DOE’s Agora Simulates AI Data Center Power Challenges on the Grid
A new government-developed platform analyzes volatile AI campus power demands to help utilities and regulators ensure grid stability.
Enhanced performance for server consolidation with Intel Xeon 6+
SPONSORED POST: How Intel’s first 18A data center CPU delivers efficiency and TCO gains, with Intel's Kira Boyko
Nutanix Achieves NVIDIA Certification For AI Storage | Security News
Nutanix Unified Storage now NVIDIA-Certified, offering enterprises scalable, interoperable solutions to maximize GPU utilization and streamline AI workloads, ensuring high-performance storage and data efficiency.
When Helping Hurts and How to Fix It: Multi-Agent Debate for Data Cleaning
arXiv:2606.02866v1 Announce Type: new Abstract: When does multi-agent debate help data cleaning, and when does it hurt? Across three benchmarks, four model families, and over 6,000 task-condition pairs, we find debate's effect reverses sign: it degrades generation across all four models (-1.6 to -15.5pp) through critique-induced confusion (CIC), hallucinated Critic feedback that the Generator accepts uncritically, yet improves error detection (+27.4pp F1, d=1.0). We derive a debate benefit condition: debate helps when the probability of rescuing a wrong output (Critic verification odds weighted by fixability) exceeds the probability of destroying a correct one. A factorial experiment proves adversarial separation is essential: self-verification with identical tools fails, while a separate Critic with code-execution grounding and evidence-gated generation produces the first debate configuration to significantly exceed single-agent on a generative task (+5.3pp, p<0.05). The condition correctly predicts all nine task types and generalizes with zero false positives across 19 published comparisons in seven domains.
Position: Prioritize Identifying Structure, Not Complex Models, for Scientific Discovery
arXiv:2606.02632v1 Announce Type: cross Abstract: Modern Machine Learning (ML) and Artificial Intelligence (AI) models, especially large language models (LLMs), are increasingly used to generate scientific hypotheses and mechanistic explanations from observational data. This position paper argues that in the high-dimensional proxy regimes where modern ML excels, mechanistic learning is generically underdetermined: many incompatible mechanisms induce essentially the same observational relationships on the support of the data, so predictive success and coherent explanations are insufficient evidence of mechanism discovery. This underdetermination becomes uniquely hazardous with large language models (LLMs), which tend to collapse large equivalence classes of explanations into a single fluent narrative. This paper proposes concrete standards for ``mechanistic ML,'' and argues these norms are necessary if LLM-centered workflows are to support science rather than merely simulate it.
‘Close to the Terminator narrative’: the dawn of self-improving AI
Industry chiefs say a looming tech breakthrough might deliver superintelligence. Safety experts say we’re not ready
Visual Graph Scaffolds for Structural Reasoning in Large Language Models
arXiv:2606.02673v1 Announce Type: new Abstract: Graphs have been used to enhance large language models (LLMs) for structured reasoning, mostly as external knowledge sources are provided to models at test time. In this paper, we take a different view: the value of graphs for LLMs lie not only in supplying information, but also in organizing reasoning. Inspired by how humans use graph-structured mind maps to organize branching and converging thoughts, we ask whether graphs can serve as an internal form of reasoning assistance. We study this question on multi-hop question answering tasks, where teacher-provided reasoning traces are rewritten as graph mind maps and used to guide a student model. Our experiments reveal a clear modality gap. When graph structures are flattened into text, their benefits become limited once direct answer hints are removed. Under this abstract guidance setting, both reasoning efficiency and answer quality degrade substantially. In contrast, visual graph guidance remains effective without direct answer clues, and its advantage persists after supervised fine-tuning and KL-based distillation. The above findings support the claim that graphs should be studied not only as external knowledge structures for LLMs, but also as visual scaffolds for organizing reasoning.
Microsoft debuts efficient reasoning model
Microsoft introduced its first internally developed reasoning model, MAI-Thinking-1, and a personal AI agent called Scout at its Build developer conference.
Beyond Semantics: Modeling Factual and Affective Perceptual Experiences from Vision-Language Data
arXiv:2606.03345v1 Announce Type: cross Abstract: We present P-Topics (Perception Topics) modeling, a novel problem for understanding how images are perceived affectively and across cultures. The goal is to (1) discover and model the different perception experiences in a dataset of images and captions, where each experience is defined by an objective factual and a subjective affective aspect, and (2) associate images to their relevant perception experiences. We introduce **PercepT** (**Percep**tion topic **T**ransformer), a two-stage architecture that tackles P-Topics modeling. In the formation stage, percepT discovers *P-Topics* as visual-textual clusters using an unsupervised training objective, and dynamically selects the number of clusters to match the perceptual richness of the dataset. In the mapping stage, it learns *P-Topic mapping functions* via attention pooling to associate images to their respective clusters. On ArtELingo, PercepT achieves a silhouette score of **0.97** compared to **0.37** from the closest baseline reflecting better perceptual clusters. PercepT also achieves an AUC score of **0.94** compared to **0.77** showing better mapping to perceptual clusters. Human evaluation confirms that PercepT captures semantically meaningful perception experiences and significantly outperforms existing methods. Our implementation will be made public.
Hybrid agentic inference is coming soon to Perplexity Computer: What is it | Tech News - Business Standard
According to Perplexity, its upcoming hybrid AI system can automatically route tasks between on-device and cloud models, aiming to improve privacy, efficiency, and performance
Develop Physical AI Reasoning, World, and Action Models with NVIDIA Cosmos 3
NVIDIA Cosmos 3 is an open physical AI model designed for robotics and autonomous systems, featuring open weights and deployment paths via NIM.
WISE-HAR: A Generalizable Ensemble Deep Learning Framework for WiFi-Based Human Activity Recognition
arXiv:2606.02974v1 Announce Type: new Abstract: Human Activity Recognition (HAR) using WiFi signals has emerged as a transformative technology for smart homes, healthcare monitoring, security systems, and ambient assisted living. Unlike traditional camera-based systems that raise significant privacy concerns and fail in low-light conditions, or wearable sensors that require user compliance, WiFi-based HAR is non-intrusive, privacy-preserving, cost-effective, and works seamlessly in any lighting condition. This paper presents a comprehensive approach to recognize three distinct human activities: "No Presence" (empty room), "Walking", and "Walking + Arm-waving" using the Wallhack1.8k WiFi spectrogram dataset. We propose three key improvements to address the main challenges in WiFi-based HAR. First, to address high performance variance, we implement ensemble learning with five different CNN architectures (Deep CNN, Wide CNN, MobileNetV2, ResNet50V2, and EfficientNetB0). Second, to address the small dataset size limitation, we apply aggressive data augmentation techniques including time-warping, frequency masking, and noise addition. Third, to evaluate real-world generalization capability, we perform cross-scenario evaluation (training on Line-of-Sight and testing on Non-Line-of-Sight) and cross-antenna evaluation (training on Biquad antenna and testing on PIFA antenna). Our ensemble model achieved a test accuracy of 94.87% on the LOS scenario with Biquad antenna, outperforming the best individual model by 0.66%. Data augmentation improved Random Forest performance from 60% to 95%. Cross-scenario evaluation showed minimal accuracy drops of only 1.37% and 2.07%, demonstrating strong generalization capabilities. The results indicate that the proposed approach is robust, reliable, and suitable for real-world deployment in diverse environments with different hardware configurations.
Don't Gamble, GAMBLe: An Analytical Framework for AI-Driven Research Systems
arXiv:2606.02863v1 Announce Type: new Abstract: AI-Driven Research Systems (ADRS) -- systems coupling LLMs with automated evaluation to discover algorithms, proofs, and designs -- are being optimized and adopted across domains, but the tools to analyze them have not kept pace. ADRS performance depends on component interactions that are poorly understood, expensive to explore, and (as we show) not well captured by standard convergence guarantees. These guarantees rely on structural assumptions that do not hold under the ADRS process we formalize. We introduce GAMBLe, a framework that decomposes ADRS behavior into four parameters (generator $G$, assessor $\mathcal{A}$, discovery mechanism $\mathcal{M}$, budget $B$) and one compositional object, the effective landscape $L_{\text{eff}} = \mathcal{A} \circ G$, which reveals that distinct generator-assessor pairs induce structurally different per-problem optimization landscapes. We exercise the framework on 760+ replicated runs (>46,000 iterations) spanning generators from single LLMs to dynamically-adaptive ensembles, mechanisms from greedy selection to co-evolutionary meta-search, and three NP-hard problems whose assessors range from continuous scoring to cliff functions. The experiments reveal no total ordering of generators or mechanisms: frontier models can underperform open-source alternatives and the simplest mechanism sometimes outperforms state-of-the-art meta-search. Results show that even under limited budgets (60 iterations per run), the right component choices can improve performance by 13-67% and search efficiency by 6-39x.
Merit or networks? What decides where research is published
arXiv:2606.03763v1 Announce Type: new Abstract: Does scientific publishing reward the quality of ideas or the advantage of connections? The question is universal to prestige-driven science, yet it has resisted decades of study because a paper's quality could not be gauged ahead of its publication fate without using that fate as the yardstick. We break this constraint by measuring a paper's idea quality directly from its text, before publication, using a discipline-trained LLM evaluator that scores the idea without seeing author names or outcomes. Using economics as a case study, we combine this text-legible idea-quality score with an execution-quality rubric, a connection index, an author-ability index, and an off-the-shelf language-model text score to estimate a five-input production function for journal placement across 6,208 economics working papers. The inputs are not rivals but a sequence along the ladder of prestige. Execution sets a meritocratic floor and is the largest input overall. Text-legible idea quality grades the rungs in between. Connections set a favoritism ceiling that bites mainly near the apex, the most selective journals. Connections work through two additive channels: connected authors write papers that score higher, and at equal scores their papers are still more likely to place better. Yet this advantage is bounded. Connections raise the odds of every rung without making the apex the typical outcome for ordinary ideas, and even the highest-scoring papers face real friction reaching the visible journal ladder. The result nests, rather than chooses between, the meritocracy and network accounts of how science is published.
Scaling AI With Adaptive Governance
Christian Gralingen The Research From 2022 to 2025, the authors conducted in-depth, semistructured interviews with senior leaders and practitioners responsible for AI governance, risk, compliance, data, and product decisions. Core interviews were conducted at Microsoft, Barclays, Kyriba, Nasdaq, Lloyds Bank, Danske Bank, and the Abu Dhabi Department of Finance. The interviews focused on how governance […]
Microsoft launches MXC, an OS-level sandbox for AI agents, with OpenAI and Nvidia already on board
For the past two years, the technology industry has raced to make AI agents more capable — teaching them to write code, navigate software interfaces, manage files, and orchestrate multi-step workflows with increasing autonomy. What the industry has not done, at least not with any consistency, is answer the question that keeps chief information security officers awake at night: what happens when an agent goes wrong? On Tuesday at its annual Build developer conference, Microsoft offered what may become the definitive answer. The company introduced Microsoft Execution Containers, or MXC — a policy-driven execution layer, built into the Windows operating system itself, that lets developers and IT administrators declare exactly what an AI agent can and cannot access, with those boundaries enforced at runtime by the OS kernel. The announcement, buried within a sweeping set of developer-focused updates, is arguably the most consequential platform move Microsoft made at Build this year, and it has the potential to reshape how every enterprise on Earth thinks about deploying autonomous AI software. MXC is not a product you buy. It is an SDK and a policy model — a foundational primitive embedded in Windows and the Windows Subsystem for Linux — that provides what Microsoft calls a "composable sandbox spectrum." That spectrum ranges from lightweight process isolation, already adopted by GitHub Copilot's command-line interface, all the way up to micro-virtual machines, Linux containers, and full cloud instances running on Windows 365. The system separates an agent's execution from the user's desktop, clipboard, user interface, and input devices. Critically, it binds every agent to a strong identity — either a local ID or a cloud-provisioned identity backed by Microsoft Entra — so that every action the agent takes can be attributed, audited, and governed. The implications are enormous. Until now, the enterprise deployment of AI agents has been stuck in a paradox: the more autonomous and useful an agent becomes, the more dangerous it is to let it operate on a corporate network without guardrails. MXC is Microsoft's attempt to break that paradox — not by making agents less capable, but by making the environment they operate in fundamentally more controlled. Why every autonomous AI agent is a security incident waiting to happen To understand why MXC matters, consider what an AI agent actually does when it runs on your computer. Unlike a traditional application, which operates within well-understood boundaries — a word processor reads and writes documents, a browser fetches web pages — an AI agent is, by design, unpredictable. It receives a goal in natural language, reasons about how to achieve it, and then takes actions: opening files, executing code, calling APIs, browsing the web, interacting with other software. Each of those interactions creates what security professionals call "attack surface." Microsoft's own blog post framed the challenge in stark terms. The company wrote that "as agents become more capable and autonomous, they're delivering material productivity gains. But they're also introducing new risk, and the issue isn't just the agent. It's the entire system the agent operates across." Every interaction between agents and humans, tools, applications, models, and other agents "exposes new attack surface and introduces different failure modes." Microsoft characterized this as "a multi-layer systems problem." This is not a theoretical concern. In the months leading up to Build, security researchers demonstrated numerous ways that AI agents could be manipulated — through prompt injection, through malicious tool calls, through data exfiltration disguised as normal workflow. For enterprises that handle sensitive data, proprietary models, and regulated information, the absence of a trusted execution environment has been the single biggest barrier to moving agents from demo to deployment. Microsoft's answer is a sandbox that scales from a single process to a full virtual machine MXC operates on a deceptively simple principle: declare what the agent can do before it runs, and let the operating system enforce those declarations at runtime. A developer or an IT administrator writes a policy that specifies which files, directories, and network resources an agent is allowed to access. MXC then creates a contained execution environment — a sandbox — that enforces those boundaries regardless of what the agent attempts to do. What makes MXC unusual, and potentially very powerful, is the breadth of its isolation options. Microsoft designed the system so that a single SDK and policy model can map to the appropriate isolation construct for any given workload. For a lightweight coding assistant that just needs to read the current project directory, fast process isolation may be sufficient. For an autonomous agent that executes arbitrary code downloaded from the internet, a full micro-VM may be required. The system is designed to be "dynamically composable based on intent and risk," meaning that the level of isolation can be adjusted based on what the agent is actually doing, not just what category it falls into. Session isolation is a particularly important feature. MXC separates the agent's execution from the user's desktop, clipboard, UI, and input devices. This directly mitigates several classes of attacks that security researchers have identified as particularly dangerous for AI agents: UI spoofing, where an agent manipulates what the user sees to trick them into approving a malicious action; input injection, where an agent sends keystrokes or mouse clicks to other applications; and cross-session data leakage, where information from one user's session bleeds into another. A live demo showed an AI agent trying to delete files — and failing, because the OS wouldn't let it During a pre-briefing with VentureBeat the night before the announcement, a Microsoft developer offered a vivid demonstration of the technology in action. He had set up the open-source agent framework OpenClaw running inside MXC's sandbox on his personal development machine. He then instructed the agent to delete all the files on his desktop. The agent attempted to comply — but the sandbox prevented it. "If you look at my desktop here, you see how clean my desktop is," the developer said during the demo. "That's a lie." The files, he explained, were completely safe because "the container won't allow it." The demonstration went further, showcasing the granularity of MXC's controls. Users can mark specific files as read-only for the agent, restrict access to the browser and screen capture, control whether the agent can see location data, and have all of those permissions managed centrally by an enterprise IT department through Intune policies. The agent operates inside what is effectively a one-way mirror: it can do the work it has been asked to do, but it cannot see or touch anything outside the boundaries that its policy defines. Pavan Davuluri, Microsoft's Executive Vice President for Windows and Devices, underscored during the pre-briefing that the primitives MXC introduces — security, containment, isolation, and user control — are essential to making AI agents commercially viable. He emphasized that these capabilities are "not unique to OpenClaw" and that "this pattern repeats itself over and over" for any agent running on a Windows device. The primitives that exist in the operating system now "for the file around security, containment, isolating them, having users in control," he said, are what will make agents safe enough for ordinary consumers and corporate deployments alike. Defender, Entra, Intune, and Purview integration arriving in July turns MXC into an enterprise control plane For corporate IT departments, the most significant element of the MXC announcement is not the SDK itself but its integration with Microsoft's existing enterprise security stack through what the company calls Agent 365. Arriving in preview in July, Agent 365 layers Microsoft's Entra identity service and Intune device management platform on top of MXC, so that IT administrators can govern agent containment centrally while developers choose the level of isolation their workload demands. The integration goes further: Microsoft Defender will provide runtime threat protection, Entra will handle identity and access management, Intune will enforce device-level policies, and Microsoft Purview will extend its data governance and compliance capabilities to agent activity. This means that an enterprise could, in theory, allow employees to run AI agents on their corporate machines — even powerful, autonomous agents that execute code and manage files — while maintaining the same kind of centralized visibility and control that IT departments currently have over traditional applications. Microsoft described the identity layer in its official blog: "Windows assigns agents a local ID or a cloud provisioned identity backed by Entra and attributes all activity from the container to that identity, so you can clearly differentiate human from agent." For regulated industries — financial services, healthcare, government — the ability to produce an audit trail that distinguishes between human actions and agent actions on the same machine could prove to be a regulatory requirement, not merely a nice-to-have feature. Every agent action attributable to a specific identity, every containment boundary enforceable through the same policy infrastructure that already governs hundreds of millions of Windows devices — this is the architecture that could finally move AI agents from pilot programs to production. OpenAI, Nvidia, Manus, and Nous Research are already building on MXC — and that changes the calculus Platform announcements at developer conferences are often aspirational. What distinguishes the MXC launch is the breadth and specificity of the partners already building on it. Microsoft named five: OpenAI, Nvidia, Manus, Nous Research (maker of the Hermes agent), and the OpenClaw open-source project. Each is integrating MXC in a distinct way that illuminates a different use case for the technology. OpenAI's involvement is particularly striking. David Wiesen, a member of OpenAI's technical staff, said that "working with Microsoft on the Microsoft Execution Containers (MXC) allows us to explore new patterns for AI agents to safely and efficiently generate and execute code." He added that by combining Codex's capabilities with MXC's execution environment, the goal is "to help developers move from intent to reliable execution faster, while maintaining the security and control enterprises need." The reference to Codex — OpenAI's code-generation agent — suggests that MXC could become the default execution environment for one of the most widely anticipated agent products in the industry. Nvidia is bringing its OpenShell framework to Windows built on MXC, providing what Microsoft described as "an easy-to-deploy package for autonomous, always-on agents safely." Manus, the Chinese-born AI agent startup that gained viral attention earlier this year, is also integrating. Tao Zhang, Manus's Chief Product Officer, said that MXC "gives developers a policy-driven way to define what an agent can access and enforce those boundaries at runtime, so more autonomous agents can operate safely in enterprise environments." And Dillon Rolnick, the CEO of Nous Research, offered what may be the most concise articulation of why MXC matters: "Continuously-running local agents, like Hermes Agent, require intentional isolation. Developers need control over what an agent can access and trust that those controls will hold." How an open-source agent framework became Microsoft's proving ground for AI safety on Windows One of the more revealing stories behind the MXC announcement involves OpenClaw. During the press pre-briefing, a Microsoft developer described how the partnership came together organically — Peter Steinberger, OpenClaw's creator, sent him a direct message in January expressing interest in collaborating. What began as a casual conversation evolved into a full-fledged platform partnership, with Microsoft developers contributing to the OpenClaw Windows companion app, built as a native WinUI application rather than a wrapped web app. The OpenClaw integration serves as what Scott called "the ultimate test app for all the stuff that [the Windows platform team] is making." If OpenClaw — which by its nature gives agents broad autonomy to execute tasks on a user's machine — can run securely within MXC's containment boundaries, then the containment system is robust enough for any agent. Scott explained the philosophy driving the work: "Think of OpenClaw Windows as the ultimate test app... If OpenClaw can succeed on Windows, that means that the Linux support is there, the container support is there, the containment is there." The companion app demonstrates the full spectrum of MXC's enterprise controls — file permissions, network access, screen capture restrictions, location data — all manageable centrally through Intune policies. Microsoft donated the project to OpenClaw and plans to continue contributing to it as open source. As one member of the Windows leadership team put it during the briefing: "All agents, all comers, everyone is welcome on Windows... It's going to run great on Windows, because the primitives are there. The base of the pyramid is solid." Building containment into the OS gives Microsoft a strategic edge over Apple's walled garden and Google's cloud-first model MXC arrives at a moment when the technology industry is grappling with a fundamental tension. AI agents represent what may be the most significant new category of software since mobile applications, and every major technology company is racing to build them. But the security and governance infrastructure required to deploy these agents responsibly in enterprise environments barely exists. Microsoft's approach is distinctive because it locates the trust layer at the operating system level rather than in the agent framework, the model provider, or a third-party security product. This is a deliberate architectural choice. By building containment into Windows itself, Microsoft ensures that the security guarantees hold regardless of which agent, which model, or which framework a developer chooses. It also means that the hundreds of millions of Windows devices already managed through Intune and secured through Defender can, in principle, become agent-ready through a software update rather than a rip-and-replace deployment. Apple's approach to AI agents leans heavily on its walled-garden ecosystem, offering security through restriction — limiting which agents can run and what they can do. Google's approach, centered on its cloud infrastructure, offers security through centralization. Microsoft's approach offers security through declaration and enforcement — allowing any agent to run, but containing its impact through OS-level policy. For enterprises that operate in heterogeneous environments with diverse toolchains and multiple AI providers, the Microsoft model may prove the most practical. The competitive dynamics are already shifting: with OpenAI's Codex, Nvidia’s OpenShell, and independent agent frameworks like Manus and Hermes all building on MXC, Microsoft is positioning Windows not just as the platform where agents run, but as the platform where agents can be trusted to run. The hardest part isn't building the sandbox — it's writing the policies that go inside it MXC is available now in early preview, meaning developers can begin building against the SDK and testing containment policies. The Agent 365 integration with Defender, Entra, Intune, and Purview is scheduled for preview in July — a timeline aggressive enough to suggest that much of the engineering work is already done, but far enough out to allow for refinement based on developer feedback. The real test, however, will come when enterprises begin deploying agents at scale on production networks. Containment is only as good as the policies that govern it, and writing effective agent policies for complex enterprise environments will be an entirely new discipline — one that IT departments have not yet developed and that no vendor has yet figured out how to teach. The technology is promising, but an empty sandbox is just an empty box. Filling it with the right rules, for the right agents, in the right contexts, will require a level of organizational sophistication that most companies are only beginning to contemplate. Still, the significance of what Microsoft announced on Tuesday is difficult to overstate. For the first time, a major operating system vendor has proposed a comprehensive, kernel-level answer to the question of how autonomous AI software should be contained, identified, and governed on the devices where most of the world's work actually gets done. The industry spent two years teaching agents to act. Microsoft is now betting that the bigger business — and the harder engineering problem — is teaching the operating system to watch.
Scientists Find Way to Supercharge Dangerous Computer ‘Worms’ With A.I.
Researchers at the University of Toronto showed how hackers could use artificial intelligence to create a program that could target any known flaw in the world’s computers.
Human Factors in Cybersecurity in Icelandic Small and Medium-sized Enterprises
arXiv:2606.02839v1 Announce Type: cross Abstract: Cybersecurity threats are increasing in all aspects of society due to the integration of digital systems into modern-day life and a volatile geo-political landscape. Technical factors are an ongoing arms race; however, the threat surface from human and social factors is still present, often providing malicious actors the means to bypass complex technical security controls. Understanding human factors in light of technical evolution is essential to ensure security controls remain effective. This study presents the results of a survey on cybersecurity challenges within public and private sector organisations, including critical infrastructure providers, in Iceland (N = 130). From the management perspective, human factors were strongly noted as challenges and barriers to their organisations' security. These challenges include a lack of adequate training or awareness, hiring issues, poor cybersecurity culture, and time and/or financial resource constraints. Based on these findings, recommendations for mitigating threats from human factors are derived. These include: prioritising targeted over generic training to reduce employee fatigue, external government support for financially constrained organisations, and building a strong cybersecurity culture through constructive communication around shared responsibilities.
How AI is reshaping cybersecurity in utility operations
AI is transforming industrial cybersecurity for utilities, enhancing defenses and revealing vulnerabilities.
Cisco sings Mythos' praises - but doesn't say how many bugs the model uncovered
Meanwhile, Anthropic adds 150 partners to Project Glasswing
Instagram AI chatbot tricked by hackers to give access to others' accounts
Some reports have linked the incident to recent cases of high-profile Instagram accounts being hijacked.
Palo Alto VPN bug graduates from advisory to active exploitation
Rapid7 reports that attackers are actively exploiting an authentication bypass flaw in Palo Alto Networks' PAN-OS, necessitating emergency patches.
A Training-Efficient Transformer-Based Anti-Spoofing Network for Logical Access in ASVspoof 5
arXiv:2606.02980v1 Announce Type: cross Abstract: Synthetic and manipulated speech can reduce the reliability of automatic speaker verification systems, so anti-spoofing methods need to be both accurate and efficient in training and inference. This paper focuses on the ASVspoof 5 Track 1 closed condition, where standard cross-entropy training may not give enough attention to hard trials and is not directly aligned with ranking- and threshold-based evaluation metrics. We propose TFPARN, a Transformer-based focal-pairwise attentive ranking network. The system extracts log-Mel features from speech, uses a Transformer encoder to model frame-level information, applies attention pooling to obtain utterance-level representations, and is trained with a combination of focal classification loss and pairwise ranking loss. RawBoost augmentation is used during training, and test-time augmentation is applied during evaluation to improve robustness. Compared with re-implemented AASIST and RawNet2 baselines under the same protocol, TFPARN achieves the best results, with a minDCF of 0.2430 and an EER of 12.52%. Ablation experiments further show that the pairwise loss, focal loss, and attention pooling all improve performance. TFPARN also uses the lowest inference memory among the compared systems, at 1.4 GB, runs at about 0.79 ms per utterance, and reaches its best checkpoint in less training time than AASIST. These results show that TFPARN provides a good balance between detection accuracy and computational cost for logical access anti-spoofing.
Netskope introduces AI Command Center to monitor and secure enterprise AI sprawl | Network World
Netskope launches AI Command Center, a platform designed to help enterprises discover, assess, govern, and secure AI applications and agents.
8 Years of Security Research in 8 Weeks: Transforming Cybersecurity with AI - Cisco Blogs
Cisco is sharing our insights from scanning 1.8 billion lines of code to help tip the scale in favor of cyber defenders through actionable, AI-driven precision.
AI Has Changed the Cybersecurity Threat Landscape for SMBs, Warns Eclipse Networks
Atlanta-Based IT Leader Urges SMBs ... New AI Platforms · If your infrastructure hasn't been audited and your team hasn't been trained, you're adding risk faster than you're adding capability.” ... ATLANTA, GA, UNITED STATES, June 2, 2026 /EINPresswire.com/ -- Eclipse Networks, Atlanta's oldest managed IT and cybersecurity provider serving small and mid-sized businesses, today issued an urgent advisory for business leaders navigating the rapidly evolving threat of artificial ...
Fescaro Warns of Generative AI-Driven Vehicle Cybersecurity Threats < IT·Gaming < 기사본문 - The Elec Inc.
Fescaro Chief Executive Officer ... vehicle cybersecurity landscape shaped by AI advancement, focusing on generative AI-based vehicle hacking attempts and aftermarket security threat cases. In particular, Hong pointed to generative AI-driven vulnerability analysis and attack automation technologies as emerging security risks...
Adoption, Deployment & Impact
The CEO who loves AI autodidacts — and desperately needs his experts
Bob Bradway bet on AI before almost anyone else in biotech. Now Amgen is reaping the rewards — and wrestling with what it means for its scientists.
Enterprise AI agents keep creating data silos. Microsoft's Build answer is Microsoft IQ and Rayfin.
Every new AI agent your team deploys starts from scratch: no memory of how the business works, where data lives, or what rules apply. And as agentic coding tools spin up applications faster than anyone can govern them, each one risks becoming another silo outside your data layer entirely. Microsoft is addressing both problems directly at Build 2026. According to VentureBeat's VB Pulse's Q1 2026 RAG Infrastructure Market Tracker, hybrid retrieval intent among 100-plus employee organizations tripled from 10.3% in January to 33.3% in March, a signal that enterprises have moved past expanding RAG coverage and are now focused on the architecture underneath it. Shared business context is the part retrieval does not solve. On the context side, Microsoft is expanding Fabric IQ, its existing business data context layer, into a broader unified system called Microsoft IQ, adding three additional context sources covering how the organization works, what it knows and real-time global signals from the web, so any agent can tap all four as a single foundation. On the application side, Rayfin, a new open-source SDK and CLI, deploys agent-built applications directly to Fabric as a governed production backend, routing application data into the same platform rather than spinning up new silos. Amir Netz, CTO of Microsoft Fabric, reached for a film analogy to explain where the data platform fits. The green screen of cascading code in "The Matrix" wasn't atmosphere, it was the layer that built the world Agent Smith operated in. "Our job in the world of data is creating reality for agents based on data," Netz told VentureBeat. Microsoft IQ unifies four context sources into a single agent foundation Microsoft IQ brings together four context sources that until now existed separately, designed so a developer can connect a new agent to all four in a single integration step. Work IQ. Captures how the organization operates day to day, drawing on email, documents, meetings and schedules to give agents an understanding of people, teams and workflows. Foundry IQ. Manages institutional knowledge, curating and indexing knowledge bases so agents understand what it means to work within the organization, what rules apply and what procedures to follow. Fabric IQ. Models the live operational state of the business through data, defining entities, relationships and business rules grounded in real-time signals from Fabric Real-Time Intelligence. Ontologies, the layer that captures that operational context, are expected to reach GA in the coming months. Web IQ. Adds real-time global context from the web, giving agents a current picture of the world outside the organization alongside its internal data. "The agents are going to become highly informed virtual employees," Netz said. "That's where the world is heading." Rayfin routes agent-built applications into the same data foundation Building shared context solves one half of the problem. The other is what happens when agents start generating applications. Every new app needs a backend, and without a governed deployment path each one creates a new data silo outside the context layer entirely. Rayfin provides an enterprise-grade back end and deploys agent-built applications directly to Fabric, so application data lands in Microsoft OneLake by default and feeds back into the Microsoft IQ context layer rather than accumulating outside it. Microsoft positions Rayfin against Supabase and Neon, the Postgres-compatible backends that agentic coding tools default to. The differentiator is governance: Rayfin routes the entire application fleet through Fabric's unified data and compliance layer rather than creating isolated silos. Netz described the relationship as bidirectional. The agent building a Rayfin application draws from the organization's ontology. The data that application generates then enriches that ontology for the next agent. Every major data platform is chasing the same answer, but execution is unproven Microsoft is not the only platform building a shared context layer for agents. Snowflake announced its own context capabilities this week with semantic capabilities. Pinecone has its Nexus platform that expands the vector database to become a knowledge engine and Redis has developed its Iris context and memory platform. Microsoft's approach further reinforces the trend that RAG and model availability aren't the issue anymore. "Fabric IQ and Rayfin are important because the enterprise AI challenge is no longer just about the model availability," Robert Kramer, managing partner at KramerERP told VentureBeat. "The real question is whether Microsoft simplifies execution and strengthens trust or adds another layer to an already complex environment."
Blue J and CPA.com Survey Finds AI Adoption Among Tax Firms Has Nearly Doubled in One Year - CPA Practice Advisor
Practitioners are no longer experimenting with AI — they’re increasingly relying on it as a core part of their workflows and broader firm transformation strategies.
Why we Built our own Cloud Agent Infrastructure
Harvey built its own cloud agent infrastructure for legal AI to improve multi-model orchestration, security, zero data retention, and cost control.
Microsoft Build Week 2026: Unified Copilot, Agentic AI, and Partner Deployment | Windows Forum
Sonata Software’s recognition ... rare in enterprise technology. They are not. It is interesting because Microsoft is formalizing the channel it needs for the agentic era before the market has fully agreed what that era looks like. The old partner motion was easier to describe. A systems integrator helped customers migrate workloads, modernize applications, move data to the cloud, deploy productivity ...
Council Post: Building AI Resilience: Energy Considerations For Boards And Executives
The competition unfolding today is about who builds AI capabilities that can endure real-world constraints, including energy, regulation and disruption.
Joint Commission intros new voluntary AI responsibility certification | Healthcare IT News
The program is designed to recognize hospitals and health systems for adopting and implementing artificial intelligence tools with sufficient governance, monitoring processes and education programs in place.
Wolters Kluwer’s Future Ready Healthcare survey: Rapid AI adoption in healthcare highlights worries, opportunities, for both patients and clinicians
Wolters Kluwer’s Future Ready Healthcare survey. Read the press release
Top 6 worries about healthcare AI among clinicians and patients in 2026
AI is now as much a part of U.S. healthcare as any other technology category in wide use across the sector. However, like no other technology, its role is “being actively shaped, not passively adopted” by clinicians and patients alike.
ChatHealthAI: Aligning Electronic Health Record Representations with Large Language Models for Grounded Clinical Reasoning
arXiv:2606.02802v1 Announce Type: new Abstract: Large language models (LLMs) exhibit strong natural-language reasoning abilities for clinical decision support, but struggle to effectively model structured longitudinal electronic health records (EHRs). In contrast, EHR foundation models can learn predictive patient representations, yet lack interpretable language-based reasoning. To bridge this gap, we propose ChatHealthAI, a multimodal reasoning framework that aligns structured EHR representations from a pretrained EHR foundation model with the semantic space of a frozen LLM through a task-aware resampler. By integrating longitudinal patient representations with refined clinical event descriptions, ChatHealthAI enables clinically grounded natural-language reasoning while maintaining accurate patient prediction. We evaluated ChatHealthAI on three clinical predictive tasks from the EHRSHOT benchmark. Results show that ChatHealthAI improves reasoning quality and interpretability while preserving competitive predictive performance. These findings highlight the potential of integrating EHR foundation models with pretrained LLMs for interpretable clinical prediction.
Microsoft targets Anthropic with new model releases
Software giant’s AI chief Mustafa Suleyman says focus is on developing products for business users
OpenAI's Codex update lets agents build interactive enterprise workspaces via Sites and role-specific plugins
Agentic AI is moving rapidly from the developer terminal to the corporate world. On Tuesday, OpenAI announced a major update of its agentic AI platform Codex, introducing domain-specific workflows, a rapid, semi-private web hosting feature within it for enterprises called "Sites," and an in-place editing tool named "Annotations". The release marks a deliberate strategy to transform Codex from a specialized programming assistant into an everyday operating environment for business professionals. Non-developers—including financial analysts, marketers, operators, and researchers—now constitute approximately 20% of the platform’s 5 million weekly users and are adopting the technology three times faster than traditional engineers, according to research shared by OpenAI with VentureBeat and other outlets. OpenAI is capitalizing on this shift to position Codex as the premier application for white-collar task automation. The timing of the announcement is highly strategic, arriving precisely as its own primary investor turned business rival Microsoft this week kicks off its annual BUILD developer conference in San Francisco—where a slate of competing enterprise productivity tools is expected—and hot on the heels of Anthropic’s rapid adoption among knowledge-workers via its Claude Cowork and Claude Code platorms. Annotations enable more precise agentic AI spreadsheet edits and updates For business users, the most critical technical upgrade is the elimination of full-document regeneration. Previously, instructing an AI to update a specific chart or spreadsheet calculation often meant the model had to rewrite the entire file, which frequently broke custom formatting or introduced hallucinations. OpenAI addresses this through Annotations, a localized context-scoping mechanism. As demonstrated in the company's release materials, the platform maps a document's underlying data schema. When a user highlights a specific segment—such as a block of cells in a financial model—Codex isolates those exact data arrays. If an analyst prompts the system to "Add a chart of revenue, EBITDA, and net income over the selected years," the model executes the code strictly within that boundary, generating the visualization while leaving the surrounding cell dependencies, styles, and unselected formulas completely untouched. New role-specific Plugins for enterprise functions that bundle skills and external SaaS app connections To further anchor Codex in daily enterprise operations, OpenAI has introduced modular software bundles and a rapid-prototyping hosting environment. The company is rolling out six role-specific plugins that aggregate 62 popular business applications (including Snowflake, Figma, and Salesforce) and 110 automated skills straight out of the box. Data Analytics: Unifies cloud environments like Snowflake, Databricks Genie, Hex, and Tableau to translate natural language inquiries into data reports and change-analysis dashboards. Creative Production: Connects Figma, Canva, Shutterstock, Picsart, and Fal to generate and iterate on ad variations, campaign boards, and e-commerce assets directly from text briefs. Sales: Integrates pipeline infrastructure across Salesforce, HubSpot, Slack, Outreach, Clay, Rox, and Actively to automate follow-up communications, close plans, and account risk reviews. Product Design: Bridges Figma and Canva environments to audit live user journeys and transform static wireframes into clickable prototypes. Public Equity & Investment Banking: Syncs institutional market feeds—including Moody’s, Daloopa, Datasite, FactSet, LSEG, S&P, PitchBook, and Hebbia—to streamline financial modeling, competitive landscaping, and pitch book preparation. These integrations allow distinct departments—from data analytics and creative production to sales and investment banking—to automate complex, multi-step workflows without requiring IT to build custom API connections. Sites allow users to spin-up dynamic, hosted webpages they can share with their colleagues Concurrently, the new Sites feature introduces an interactive canvas that converts static data inputs or text documents into functional, web-hosted internal applications. Rolling out in preview for Business and Enterprise tiers, Sites allow cross-functional teams to bypass front-end development. Financial leaders, for example, can transform a static spreadsheet into an interactive scenario planner shared via a secure workspace URL, allowing executives to tweak assumptions in a live web app rather than clicking through document tabs. Instead of static decks, Sites promise to keep enterprises updated on their latest metrics and important information in an easily digestible way. Availability & deployment A critical operational distinction in this rollout centers on exactly where these new features can be executed. Codex's existing infrastructure runs natively across multiple surfaces, including IDE extensions and the terminal command line. However, the release documentation notes that Sites are rolling out "through the Codex app" and that plugins are managed via a "Codex plugin directory". An OpenAI spokesperson confirmed that Plugins and Sites are available int he CLI and desktop app, while Sites are hosted by OpenAI. Licensing and pricing These updates operate entirely within OpenAI's closed, proprietary enterprise licensing model. Unlike open-source frameworks, enterprise clients do not maintain code-level ownership over Codex’s integration nodes. Instead, system administrators manage deployment through centralized workspace settings, giving them explicit authority to enable or disable hosted "Sites" and restrict underlying application permissions. These new capabilities deploy seamlessly on top of Codex's existing commercial framework. Users will continue to access the agent via established baseline subscription tiers—such as the individual "Plus" plan ($20/month) or the high-volume "Pro" plan ($100/month)—or through a separate, seat-free pay-as-you-go model that draws down pre-purchased utility credits.
Microsoft teases new era of AI-driven devices at annual developer conference | Reuters
Microsoft announced a sweeping slate of AI initiatives, from autonomous workplace assistants and gadgets to Nvidia-powered PCs and a new in-house reasoning model, in a push to move beyond apps and remake computing around AI .
Microsoft seeks to be AI’s center of gravity again. CEO Satya Nadella is in San Francisco to make the case
Software giant revealed a sweep of product announcements at its Build developer conference on Tuesday.
Large AI Models in Dental Healthcare: From General-Purpose Systems to Domain-Specific Foundation Models
arXiv:2606.02914v1 Announce Type: new Abstract: Background: Oral diseases affect nearly 3.5 billion people worldwide, yet the comparative clinical potential of large-scale AI models in dentistry remains poorly understood. Three distinct model categories have emerged: language-generative models, discriminative vision foundation models, and dental-specific foundation models, with no unified review examining their relationships and collective limitations. Methods: Following PRISMA-ScR guidelines, we systematically searched four databases (PubMed, Google Scholar, Scopus, arXiv), screened independently by two reviewers. After applying inclusion/exclusion criteria, 97 studies (2020-2026) were included. We propose a two-dimensional classification framework organizing models by architectural paradigm and dental specialization degree. Results: Language-generative models excel at text-based tasks (clinical reasoning, licensing exams, patient communication) but show inconsistent performance on image-dependent diagnostics. Adapted SAM and CLIP variants achieve strong tooth segmentation and lesion detection results. Dental-specific models (DentVFM, DentVLM, OralGPT) demonstrate strongest performance on complex multimodal tasks. Integrated pipelines consistently outperform single-model approaches. A data asymmetry is observed: dental-specific pretraining concentrates almost entirely in the vision domain, reflecting scarce large-scale dental text corpora. Conclusions: General-purpose and dental-specific models play complementary roles; the most effective systems combine both within structured pipelines. Safe autonomous deployment requires resolving three persistent barriers: hallucination in generative models, limited annotated dental datasets, and absent standardized clinical evaluation benchmarks.
Microsoft testing wearable AI gadget aimed at office workers
The company said its own workers are testing a "wearable access badge" and a desktop device.
Uber, Autobrains, and Nvidia Launch Robotaxi Pilot in Munich
Uber is partnering with Autobrains and Nvidia to launch a robotaxi service in Munich, utilizing a cost-efficient, camera-based AI system.
The Download: AI can run your admin department now
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. How small businesses can leverage AI From accounting to design to market research and product development, there’s a staggering breadth of skills needed to run a business. Large companies can hire…
NYB.AI Launches Vecura 2.0, Bringing Agentic AI Workflows to Molecular Discovery, with NVIDIA | The Manila Times
SINGAPORE, June 2, 2026 /PRNewswire/ -- Life science research is entering an AI era, but most discovery teams still cannot use frontier models at scale. Advanced AI models, molecular simulation tools, scientific databases, inference optimization and GPU infrastructure remain fragmented across ...
Powering Agentic AI Sales Strategy with Amazon Bedrock AgentCore
This AWS case study details a Field Advisor system that orchestrates over 20 specialized sales agents through a single interface.
23andMe Is Back as Nonprofit Aiming to Reach 100 Million Users
The founder of 23andMe Research Institute wants to reach 100 million users, an ambitious goal after the seller of DNA testing kits emerged from bankruptcy as a nonprofit.
Use.AI and the Shift From AI Tools to AI Workspaces - Slashdot Thought Leadership
How companies can get ahead in the AI space AI adoption has become easier for many organizations. Teams now have access to writing assistants, image generators, research tools, ...
Our Guide to the Summer 2026 Issue
Create Generative AI Value at Scale Kevin Schmitt, Gregory Vial, and Ivo Blohm Key Insight: Organizations are expanding their GenAI use by implementing coordinated cross-functional structures that draw on domain expertise and user innovation. Top Takeaways: Companies that establish a new kind of internal AI organization that researchers have dubbed the “AI spine” are better […]
AI Is Reshaping Jobs Faster Than Companies Are Reshaping Work
BCG's Fourth Annual Global AI at Work Survey Reveals Nearly Half of Respondents Now Spend More Time Managing and Directing AI than Doing the Work Itself...
Nicole Carignan, SVP Security and AI Strategy, Darktrace: "The hardest challenge for leaders is that cyber risk is now moving faster than governance"
I’m Nicole Carignan, SVP Security & AI Strategy at Darktrace, and I work with customers on how AI is changing the threat landscape and how organisations should respond. A lot of my work sits at the intersection of cybersecurity, artificial intelligence and business risk.
How AI Is Quietly Reshaping Traditional Business Models
AI will not replace good leadership, clear thinking, or a sound business model. But it can make traditional businesses faster, sharper, and easier to deal with.
Create Generative AI Value at Scale
Christian Gralingen The Research Over three years (2022-2025), two of the authors (Kevin and Ivo) engaged with 23 Swiss companies that were members of a research consortium focused on generative AI. The study participants represented a diverse array of industries: retail banking, investment banking, health insurance, insurance, medical coding, energy, law, laboratory instrument manufacturing, equipment […]
Why insurers struggle to turn AI investment into better CX
Insurance leaders have spent the past decade investing heavily in modernising pricing engines, underwriting models and digital distribution channels. Yet despite this sustained transformation effort, customer experience (CX) continues to fall short of rising expectations, according to Earnix.
Why AI Is Forcing Leaders to Rethink Business Models, Trust and Leadership
McKinsey’s 2025 global AI survey found that organizations seeing real value from AI are not simply adding tools to existing processes. They are changing strategy, talent, operating models, technology, data and how AI is scaled across the business.Deloitte’s 2025 State of Generative AI in ...
Marlabs 2026 AI Adoption Report Provides Playbook for Companies to Drive Significant AI Value | Markets Insider
80% of Enterprises Capture 25% of AI’s Total Economic ValueNew York, NY, United States, June 2, 2026 -- Marlabs, a leading AI consulting and tr...
AI Doesn't Have ROI
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Geopolitics, Policy & Governance
The China-US tech truce is fragile
A new wave of supply chain conflict is brewing
The geopolitics of Artificial Intelligence | Hindustan Times
This article is authored by Aparajitha Nair, research scholar, Jamia Millia Islamia University, Delhi.
Trump's AI E-(I)-O could let feds pick winners and losers
Government gets a say in 'trusted partner' access, and that worries policy experts
The President Keeps Contradicting Himself on AI
Donald Trump’s new AI order is a lot of nothing.
China tightens grip on tech giants as Xi demands industrial push
China is pressuring platform companies to shift from consumer-focused innovation to supporting national industrial goals, including six priority sectors like quantum technology and embodied intelligence.
The battle for AI sovereignty – GIS Reports
Governments are increasingly treating AI as a critical technology, reshaping the relationship between states and technology firms.
The business case (and necessity) for governing AI | ITWeb
As organisations deploy AI at scale, DVT says leaders need to confront a critical question: who owns the decisions of the machines?
AI & Tech Brief: A new AI executive order - The Washington Post
June 2, 2026 at 2:59 p.m. EDTToday at 2:59 p.m. EDT ... President Donald Trump has signed a pared-down AI executive order that requires less time-consuming government scrutiny of frontier AI models. Sen. Bernie Sanders has a radical idea for how AI could benefit humanity: taxing 50 percent of AI companies’ stock. The AI race is now becoming an IPO race. What to watch ahead of Anthropic, OpenAI and SpaceX’s blockbuster initial public ...
The Fair Lending Model: How the Longest-Running Algorithmic Fairness Programs Work in Practice
arXiv:2606.02957v1 Announce Type: new Abstract: U.S. financial institutions subject to fair lending laws have been running algorithmic fairness programs for decades. Despite this long history, remarkably little is known about how these requirements operate in practice. In this paper, we offer the first empirical account of how financial institutions test for and mitigate algorithmic discrimination on the ground. In doing so, we shed light on how the regulatory design of fair lending law and regulation have shaped the policies, processes, and practices of fair lending programs. Drawing on 35 semi-structured interviews with participants across the fair lending ecosystem, we find that while financial institutions have a floor of fairness practices aimed at preventing discrimination in lending largely absent in other domains, the specifics of how firms test for discrimination and search for less discriminatory algorithms varies widely. We also find that regulatory supervision via fair lending examinations has been the key driver of compliance work, but that the practical impact of fair lending programs often depends on how well they can navigate competing business incentives, perceived legal tensions, and regulatory uncertainty. Ultimately, our findings highlight the unique role that supervisory authority has played in successfully fostering fair lending practices -- a regulatory design feature that is distinct from other areas of civil rights law and almost completely absent from recent policy proposals for dealing with algorithmic discrimination.
UK media websites given power to block Google using their articles in AI search
Watchdog makes ruling on search summaries after publishers complain about drop in click-through traffic and revenue Business live – latest updates Online publishers and news organisations are now able to block their content from appearing in Google’s AI summaries in UK search results, the British competition watchdog has announced. The Competition and Markets Authority (CMA) said the new requirement would “put publishers, like news organisations, in a stronger position to negotiate content deals with Google”. Continue reading...
Reproducibility is the New Copyleft: Defining AGI-oriented Reproducible Builds
arXiv:2606.03019v1 Announce Type: new Abstract: Copyleft, as implemented in licenses such as the GNU General Public License, was a legal hack that used copyright to guarantee user freedom by tying the availability of source code to every act of distribution. Its normative force rested on an implicit technical premise: that source code and object code stand in a well-defined, humanly auditable, and reproducible relationship. Large language models and, prospectively, Artificial General Intelligence (AGI) systems systematically violate this premise. The artifacts jointly required to reconstruct a model -- code, data, weights, hyperparameters, toolchain, and hardware configuration -- are each subject to independent legal, technical, and economic constraints that no current open-source framework fully resolves. Sufficiently capable AI systems can also rewrite licensed source into functionally equivalent derivatives stripped of their original obligations, a form of laundering against which copyleft has no effective defense. This paper argues that a functional analogue of copyleft for AGI must be grounded not in share-alike clauses over code, but in reproducible builds: a practice guaranteeing bit-exact reconstructability from declared inputs. We review the logic of copyleft, critically examine Maffulli's Second Liberation thesis according to which AI fulfills Stallman's dream, and show that the argument collapses unless AGI systems are themselves reproducible. Drawing on the Open Source AI Definition (OSAID), the Model Openness Framework (MOF), OpenMDW, and deterministic-inference research, we define seven requirements for AGI-oriented reproducible builds. We further argue that the Model Context Protocol (MCP) and analogous AI-to-AI coupling mechanisms constitute a new dynamic linking layer for which copyleft-style licensing is ill-suited, and that Masnick's "protocols, not platforms" framework offers a more promising governance template.
Citation, please! UK regulator slaps Google with new publishing rules for search
Action follows Chocolate Factory's changes to AI search results
Pushing the Limits: A Framework to Reform Institutional Ethics Review of Environmentally-Impactful Computing Research
arXiv:2606.03547v1 Announce Type: new Abstract: Computationally-intensive research (CIR) takes place on a wide variety of topics including AI. Its environmental impact is potentially significant yet it does not always fall clearly within the scope of organisational ethics review policy on its own merits. Many academic institutions have ethics oversight bodies (e.g. Research Ethics Committees or Institutional Review Boards) that occupy a potentially powerful position to encourage recognition of these issues and seek reflexive practice in researchers. However, policies are often poorly-defined in respect of environmental issues and thus research is not reviewed, reviewers have little guidance for legitimate critique, and researchers are not challenged to consider planetary limits on computing resources and the interaction of these with their research. This paper aims to address these problems by proposing scoping criteria for institutional ethics policy to bring CIR within the scope of ethics review on its own merits, framing evidential criteria for reviewers to apply in ethics review, and presenting a method by which CIR researchers can reflect on their proposed research in relation to environmental factors, and assess its potential value in the light of planetary limits.
Former VP Mike Pence on Conservatism, AI, Donald Trump
Former US Vice President Mike Pence discusses his new book, "What Conservatives Believe,” what he sees as the future for the Republican Party, regulation of artificial intelligence, and his relationship with President Donald Trump. (Source: Bloomberg)
Kyle included ‘more positive language’ in AI speech after Mandelson advice
Documents raise questions since the advisory firm co-founded by Labour veteran represented big AI companies
UK banks offered access to OpenAI’s GPT-5.5 amid exclusion from Anthropic’s Glasswing expansion
150 new organizations inducted to cyber’s Soho House, including the first outside the US
New trade secret rules: China says its AI data is none of your business | Euronews
Under new Chinese regulations, any algorithm, dataset or program not publicly disclosed now counts as a trade secret.
Executive order sets voluntary cyber reviews for advanced AI – Roll Call
Developers of frontier artificial intelligence models will have the option to voluntarily submit new technologies for review by federal cybersecurity agencies under a new executive order that comes after President Donald Trump backed away from an expected order last month.
AI Firms Threaten Journalism's Future, Warns NYT Publisher
A.G. Sulzberger criticized AI firms for using news content without authorization, calling for a unified media industry response to protect intellectual property.
Creative industry, EU Commission clash over AI copyright review
Representatives of the creative sector have taken the European Commission to task over its prioritization of licensing deals as its preferred answer to the question of how to reimburse copyright holders for AI training.
The White House is at war with itself over who gets to regulate AI
The Trump administration is locked in an internal battle over artificial intelligence regulation that has.. • Internet • One News Page: Tuesday, 2 June 2026
Call for proposals to support the standardization of quantum technologies - EU Funding
Call for proposals to support the ... and interoperable photonic quantum computing platforms in Europe · Call for proposals to support the development of quantum-enabled navigation systems · Call for proposals to support the development of an AI framework for military decision-making and training · Call for proposals to support the development of high-performance energy systems for military applications ... Sign up for our free English newsletter to receive ...
NetChoice warns future presidents could distort Trump cyber AI order
NetChoice expressed concern that while the current executive order is a light-touch approach, future administrations might not honor the same vision.
New bill aims to regulate military uses of AI - Defense One
Sen. Kirsten Gillibrand also wants to establish a clear chain of human accountability for AI on the battlefield.
US SEC releases draft strategic plan for public comment
The SEC's draft strategic plan emphasizes supporting innovation, improving enforcement, and prioritizing technology modernization, including the responsible use of AI.
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