Tue 9 June 2026
Daily Brief — Curated and contextualised by Best Practice AI
OpenAI Files for IPO, Trump Eyes AI Stakes, and Tata Slows Hiring
TL;DROpenAI has filed for an IPO, marking a significant step in its growth trajectory. The Trump administration is considering taking stakes in AI giants, reflecting a change in its regulatory approach. Tata Consultancy Services plans to slow hiring as AI reshapes the outsourcing industry. Broadcom, Apollo, and Blackstone are launching a $35 billion AI infrastructure platform. Meta has been ordered by the EU to halt policies blocking AI rivals on WhatsApp.
The stories that matter most
Selected and contextualised by the Best Practice AI team
OpenAI Files for IPO
Plus, the Trump administration’s $100,000 H-1B visa fee is declared unlawful, and private racetracks zoom into view.
Opinion | Anthropic AI powerful company - The Washington Post
Leaders in business, government and religion are all paying attention.
Tata Boss Predicts AI Agents Will Replace Half Its Tech Jobs
The boss of one of India’s largest conglomerates predicted AI agents will replace half the jobs at IT leader Tata Consultancy Services Ltd. in future, joining a growing list of company chiefs warning about major disruptions as artificial intelligence matures.
Overcoming the Regulatory Bottleneck via Agent-to-Agent Protocols: A Nuclear Case Study
arXiv:2606.07866v1 Announce Type: new Abstract: Regulatory review of advanced nuclear reactor designs routinely spans more than three years and consumes hundreds of millions of dollars in combined regulator and applicant labor. We present the Regulatory Context Protocol (RCP), an Agent-to-Agent communication standard that replaces the formal human-to-human pipeline between regulators and applicants with a structured, auditable agentic channel, while preserving human oversight at safety-significant decision points. The protocol is calibrated against an analysis of 1,236 documents from U.S. Nuclear Regulatory Commission advanced reactor dockets and demonstrated with a working multi-agent pilot. Against an 89M USD, 42-month Reconstructed Baseline, RCP cuts costs by 50-77 percent (21M-44M USD) and timelines by 65 percent (15 months). Without a shared protocol, Standalone Agents reach only 54M-74M USD and 21 months. The residual cost-and-time gap is structural, not algorithmic: it traces to the inter-organizational pipeline that only an agent-to-agent standard can compress. The same bottleneck - formal multi-party review under strict auditability requirements - characterizes pharmaceutical approvals, environmental permitting, financial supervision, and aviation certification. The US regulatory paperwork burden carries a 426.5 billion USD annual opportunity cost; replicated broadly, the projected 50-77 percent reduction implies savings on the order of 210-330 billion USD per year - approaching 1 percent of US GDP.
400% ROI in Three Years: The Business Case for AI in the Modern Law Firm - Thomson Reuters Institute
After three years of promises about AI’s potential to transform the practice of law, a frenzy of investment into legal …
Stable Geometry, Reversing Poles: The Bipolar Structure of AI Occupational Substitutability and Its Decade-Scale Inversion
arXiv:2606.07939v1 Announce Type: new Abstract: Empirical research on the labor-market impact of artificial intelligence has converged, since Frey and Osborne (2017), on a continuous-gradient representation in which each occupation is assigned a real-valued exposure score on [0,1] obtained by linear aggregation across capability dimensions. This continuity is rarely articulated as an assumption and has not been tested at the micro-action level where substitution actually occurs. We decompose 1,961 O*NET Detailed Work Activities into 15,817 micro-actions using a multi-agent LLM pipeline with 31-expert HITL calibration, then project the DWA-level Occupational Automation Index from our prior work onto a 7-macro semantic typology. The result is a bipolar structure. Tool-Mediated Physical (M2, mean OAI = 0.054) and Planning & Design (M7, mean OAI = 0.499) form two extremes separated by Cohen's d = 2.41 (H = 172.88, p = 6.21e-34). The geometry is robust under three independent stress tests: resolution (K=7 to K=15, polar gap widens from 0.45 to 0.57), encoder swap to BGE (LLM-class OAI lead replicates at 3.37x), and Eloundou's GPT-4 task ratings (DWA-level rho = 0.635). The six middle macros form a low-contrast band between the poles (TOST at d=0.2 admits only 1/15 pairs as equivalent), not a flat plain. The geometry's stability does not, however, extend to its content. Across a decade, the polarity has inverted. Frey-Osborne (2013) placed Tool-Mediated Physical near the highest computerisation risk and Planning & Design near the lowest; our LLM-era OAI reverses that order, with macro-level FO-Eloundou Spearman rho = -0.750, p = 0.020, against the original Oxford Martin appendix. Which pole is high is therefore contingent on the era's dominant capability frontier, while the stable geometry itself is the structurally robust object.
4 surprising ways AI is making your life more expensive
As AI companies sink hundreds of billions of dollars into developing their technology and building out computer hubs to run AI , their spending is likely pushing up inflation in the United States, according to some Federal Reserve officials and Wall Street assessments.
Capacity, Technology Portfolios, and the Paradox of Concentration
arXiv:2407.03504v4 Announce Type: replace Abstract: Does limiting the largest firm's capacity always lower prices? We model firms competing in supply schedules with multiple technologies, each with constant marginal cost up to capacity. In a tractable model, in which capacity and technological efficiency coexist as distinct sources of market power, we find that when the largest firm leads by efficiency, a small transfer of higher-cost capacity to the leader raises concentration yet lowers prices, contrary to standard antitrust intuition. Larger transfers raise prices, restoring standard intuition and tracing a non-monotone price-concentration relation. We prove existence and uniqueness of equilibrium, derive closed-form conditions for when transfers raise or lower prices, and extend the results to other oligopoly models. Evidence from Colombia's wholesale electricity market, where weather shocks shift hydropower capacity across technology-diversified firms, supports the pattern. Counterfactual transfers lower prices up to 30% in the least concentrated markets. We draw implications for capacity caps, divestitures, and mergers.
The AI rally may have finally met its match - the Fed | Reuters
Economic expansions don't die of old age, and stock market rallies rarely do either. Some catalyst is needed to burst the bubble. In the case of the current AI boom, that may well be rising interest rates.
Economics & Markets
OpenAI Files for IPO
Plus, the Trump administration’s $100,000 H-1B visa fee is declared unlawful, and private racetracks zoom into view.
The AI rally may have finally met its match - the Fed | Reuters
Economic expansions don't die of old age, and stock market rallies rarely do either. Some catalyst is needed to burst the bubble. In the case of the current AI boom, that may well be rising interest rates.
AI Selloff a ‘Wake-Up Call’ for Investors, Wells Fargo Says
Friday’s technology-driven selloff was a “wake-up call” for investors, according to Wells Fargo & Co., which said it has highlighted the risks of piling into the artificial-intelligence trade.
How a few AI chip giants warped Asia's stock picking game | Reuters
Sam Konrad's stocks are having a fantastic year, thanks to AI-fueled rallies in Taiwan and South Korea, but his fund is so top heavy with winners, he now needs to ditch his best performers.
Perplexity eyes 2028 IPO as OpenAI and Anthropic prepare to test AI investor appetite – Firstpost
Perplexity plans to pursue an IPO in 2028 even as OpenAI and Anthropic prepare for blockbuster stock market debuts that could test investor appetite for AI companies
Zepto's IPO filing reveals fast growth and big losses
Zepto's recent IPO filing highlights rapid expansion alongside significant financial losses, raising valuation questions.
Mercor’s Brendan Foody calls out Sequoia, accusing it of 'dual-pricing' valuation tricks | TechCrunch
Sequoia is just one of the top firms that sells same equity at two different prices.
Chip stocks bouncing back on Wall Street and South Korea; UK investigating Paramount-Warner Bros merger – as it happened | Business | The Guardian
Rolling coverage of the latest economic and financial news, as the AI trade bounces back
r/apple on Reddit: Apple shares flip negative on 'Siri AI' debut
>CNBC’s MacKenzie Sigalos reports from Cupertino on Apple’s biggest WWDC reveals, from a new macOS to a more AI -powered Siri — and why the stock hit…
Nebius Group (NASDAQ: NBIS) And SoundHound AI (NASDAQ: SOUN) Face Off As Top AI Investment Picks For 2026
Nebius Group N.V. (NASDAQ: NBIS) and SoundHound AI (NASDAQ: SOUN) represent two distinct approaches to the rapidly expanding artificial intelligence market,
4 surprising ways AI is making your life more expensive
As AI companies sink hundreds of billions of dollars into developing their technology and building out computer hubs to run AI , their spending is likely pushing up inflation in the United States, according to some Federal Reserve officials and Wall Street assessments.
Is AI observable in the UK productivity statistics? - Bennett School of Public Policy
The effects of artificial intelligence (AI) are starting to be visible in the UK productivity statistics with the rise of data centres, Thomas Aubrey argues. To what extent this is sustainable remains to be seen, given AI’s inexorable demand for electricity.
UK government establishes AI Economics Institute to inform policymaking
The new research organization will assess the economic impact of AI, including its effects on productivity, labor markets, and growth, to guide future government policy.
Some thoughts on the likely impact of AI in countries like India
This post will argue that the long-term impact of AI innovations on the typical household’s daily life in a developing country like India will be far smaller than the discourse suggests.
There is a simpler option for making AI pay its way: tax it properly
The world of laissez-faire no longer exists given the impact of the technology
Unintended Consequences of Recommender System Interventions: Evidence from a Field Experiment
arXiv:2606.08265v1 Announce Type: cross Abstract: Platform content interventions in recommendation systems are typically evaluated as static "nudges", ignoring that the systems adaptively learn from the resulting user behavior. We investigate this dynamic through a large-scale field experiment on a short-video platform. The experiment involves a "sleep reminder" campaign designed to reduce late-night usage. Paradoxically, the intervention increased late-night engagement by 14.75% and overall platform usage by 2.18%, and the effects persisted for weeks even after the experiment. We explain this through a forced-exploration mechanism, showing that by revealing high latent demand for the promoted content, the intervention triggers a recommendation policy update that routine user behavior would not produce. The data generated by the intervention induced the algorithm to update its post-campaign policy, reinforcing the very engagement loops the campaign aimed to mitigate. Our findings demonstrate that user-facing interventions can effectively retrain the underlying algorithm, triggering durable, system-wide shifts in content distribution that challenge standard evaluation metrics in platform governance and social responsibility initiatives.
Opinion | Anthropic AI powerful company - The Washington Post
Leaders in business, government and religion are all paying attention.
Scaling Participation in Modular AI Systems
arXiv:2606.07812v1 Announce Type: new Abstract: Humanity is a mosaic of multifaceted talents and needs, and any truly intelligent AI must reflect that richness. Yet the LLMs used by all are built by the few -- a centralized market of monolithic AI models structurally ill-suited to capture the diversity of human knowledge, reasoning, and values. Here we introduce scaling participation, a new paradigm in which modular AI systems are built from the bottom up through the contributions of diverse stakeholders. Participants contribute small models trained on their own interests and priorities; these models then collaborate in modular frameworks as compositional AI systems. Participatory AI systems outperform monolithic LLMs by up to 15.4% across 15 tasks, such as reasoning and factuality, surpassing models larger than all contributed components combined. Further experiments show that participatory AI systems benefit from contributor diversity, substantially improve on each contributor's original priorities, and exhibit emergent capabilities that allow them to solve over 15% of problems where all individual models fail. Scaling participation provides a technical foundation for transitioning from the monolithic status quo toward an open, bottom-up, and collaborative AI future.
OpenAI returns to open-source roots with GPT-OSS models
OpenAI has released new open-source models, GPT-OSS 120B and 20B, marking a return to its earlier development philosophy.
Capacity, Technology Portfolios, and the Paradox of Concentration
arXiv:2407.03504v4 Announce Type: replace Abstract: Does limiting the largest firm's capacity always lower prices? We model firms competing in supply schedules with multiple technologies, each with constant marginal cost up to capacity. In a tractable model, in which capacity and technological efficiency coexist as distinct sources of market power, we find that when the largest firm leads by efficiency, a small transfer of higher-cost capacity to the leader raises concentration yet lowers prices, contrary to standard antitrust intuition. Larger transfers raise prices, restoring standard intuition and tracing a non-monotone price-concentration relation. We prove existence and uniqueness of equilibrium, derive closed-form conditions for when transfers raise or lower prices, and extend the results to other oligopoly models. Evidence from Colombia's wholesale electricity market, where weather shocks shift hydropower capacity across technology-diversified firms, supports the pattern. Counterfactual transfers lower prices up to 30% in the least concentrated markets. We draw implications for capacity caps, divestitures, and mergers.
Neo4j plots Palantir alternative with GraphAware acquisition
Graph database biz says on-prem, air-gapped intel stack gives governments a no-kill-switch option
AI Companies Are Rapidly Expanding Into Each Other's Markets - Business Insider
Companies are ruthlessly invading each other's turf. Ever-increasing valuations mean companies need to find new sources of revenue.
It's do or die for Apple AI
WWDC announcements earned tempered praise from analysts, with an emphasis on the word 'if'
The Revenue of Finance Journals: Networks, Pricing Power, and Publication Volume
arXiv:2508.14301v4 Announce Type: replace Abstract: I study commercial revenue at 26 finance journals over 1999-2025, exploiting the Elsevier Finance Journal Ecosystem as a quasi-natural experiment. Using synthetic control, ecosystem membership generated approximately 54-59 million USD in projected long-run revenue. The effect is highly concentrated: four journals account for 95 percent of the gain. Decomposing the effect, 89 percent operates through expanded publication volume rather than per-paper price increases. The citation channel dominates: ecosystem coordination elevated measured impact metrics, attracting additional submissions and generating article-processing-charge revenue through publication volume. The findings speak to the economics of coordinated networks in information-goods markets.
Labor, Society & Culture
Memetic Capture: A Pluralistic Policy Framework for Governing AI-Driven Cultural Disempowerment
arXiv:2606.07802v1 Announce Type: new Abstract: Culture is the most insidious vector of gradual human disempowerment by AI: unlike economic or political displacement, cultural displacement attacks the very preferences and values through which humans recognise and resist disempowerment itself. We argue that existing AI governance frameworks suffer from a critical blind spot by treating cultural impact as secondary to economic and safety concerns. This paper develops \emph{memetic capture} as a unifying concept for AI-driven cultural disempowerment, and proposes the \textbf{Cultural Pluralistic Governance Framework (CPGF)}, a four-tier policy architecture combining quantitative cultural influence metrics, democratic value assemblies, pluralistic deployment standards, and transnational coordination mechanisms. We argue that pluralism is not merely an ethical requirement for such governance but a structural necessity: monocultural AI governance accelerates the very disempowerment it claims to prevent. We identify concrete policy levers, discuss implementation tensions, and outline a research agenda at the intersection of pluralistic alignment and cultural AI governance.
Some hypotheses on how chatbots work in problem-solving-driven conversations. Large Language Models as confirmation of the Innovation Illusion
arXiv:2606.07722v1 Announce Type: new Abstract: This article offers a perspective on the nature of chatbots as genuine conversation partners when discussing problems in relation to their solutions. What can chatbots do and what can't they do, and how can this be explained? Our argument draws on Aggregation Dynamics, Cognitive Linguistics, Neuropsychology and Psychology. Our argument focuses on basic chatbots in the hope of thereby making statements about the core functionality of more advanced chatbots. Basic chatbots are assumed to consist of a Large Language Model (LLM) with a simple interface. The main results are: a description of human understanding and thinking based on so-called metaphorical problem propagations; the hypothesis that text dataset used for training LLMs have specific characteristics and that these text datasets only partially imitate human thinking and understanding; the hypothesis that the LLM training process encodes artificial metaphorical problem propagations into an LLM from these datasets; our conclusion that a basic chatbot cannot be a thinking partner capable of matching humans; our conclusion that further development of the Large Language Model will not lead to this either. Yann LeCun states: "Animals and humans exhibit learning abilities and understandings of the world that are far beyond the capabilities of current AI and machine learning (ML) systems." Our conclusions are in line with this. LeCun's vision and ours are at odds with the optimism of Big Tech. That does not alter the fact that chatbots exist, that they are being used on a massive scale, by both individuals and organisations, and that it is therefore socially and politically important to understand them. Our article aims to contribute to the discussion on the functioning, benefits and drawbacks of chatbots. We have not yet encountered the approach we used to arrive at our conclusions in our research into how chatbots work.
Tata Boss Predicts AI Agents Will Replace Half Its Tech Jobs
The boss of one of India’s largest conglomerates predicted AI agents will replace half the jobs at IT leader Tata Consultancy Services Ltd. in future, joining a growing list of company chiefs warning about major disruptions as artificial intelligence matures.
Stable Geometry, Reversing Poles: The Bipolar Structure of AI Occupational Substitutability and Its Decade-Scale Inversion
arXiv:2606.07939v1 Announce Type: new Abstract: Empirical research on the labor-market impact of artificial intelligence has converged, since Frey and Osborne (2017), on a continuous-gradient representation in which each occupation is assigned a real-valued exposure score on [0,1] obtained by linear aggregation across capability dimensions. This continuity is rarely articulated as an assumption and has not been tested at the micro-action level where substitution actually occurs. We decompose 1,961 O*NET Detailed Work Activities into 15,817 micro-actions using a multi-agent LLM pipeline with 31-expert HITL calibration, then project the DWA-level Occupational Automation Index from our prior work onto a 7-macro semantic typology. The result is a bipolar structure. Tool-Mediated Physical (M2, mean OAI = 0.054) and Planning & Design (M7, mean OAI = 0.499) form two extremes separated by Cohen's d = 2.41 (H = 172.88, p = 6.21e-34). The geometry is robust under three independent stress tests: resolution (K=7 to K=15, polar gap widens from 0.45 to 0.57), encoder swap to BGE (LLM-class OAI lead replicates at 3.37x), and Eloundou's GPT-4 task ratings (DWA-level rho = 0.635). The six middle macros form a low-contrast band between the poles (TOST at d=0.2 admits only 1/15 pairs as equivalent), not a flat plain. The geometry's stability does not, however, extend to its content. Across a decade, the polarity has inverted. Frey-Osborne (2013) placed Tool-Mediated Physical near the highest computerisation risk and Planning & Design near the lowest; our LLM-era OAI reverses that order, with macro-level FO-Eloundou Spearman rho = -0.750, p = 0.020, against the original Oxford Martin appendix. Which pole is high is therefore contingent on the era's dominant capability frontier, while the stable geometry itself is the structurally robust object.
Meta Launches ‘Workforce Academy’ to Train Workers to Build Data Centers
The five-week program, which is free of charge and guarantees a job, follows a recent layoff of 8,000 employees.
In the Hybrid A.I.-Human Work Force, Who Will Actually Thrive?
A panel of experts explains how job seekers should prepare for the future of work.
Concerns and Strategic Responses of Older Workers Navigating Generative AI in Bridge Employment
arXiv:2606.07543v1 Announce Type: new Abstract: Generative AI (GenAI) is transforming workplaces at a rapid pace. This disproportionately affects vulnerable communities, including older workers (OWs) who re-enter the workforce through bridge employment prior to final retirement. Through in-depth semi-structured interviews with 21 professionals, we examine how OWs navigate GenAI-driven disruptions while pursuing bridge roles, focusing on their concerns about GenAI integration and their responses to these changes. Our findings show that OWs experienced both temporal and structural disruptions across all stages of the bridge employment decision-making process due to GenAI. In response, they reconfigured their tasks through different forms of boundary work aimed at restoring stability and continuity. We conceptualize these responses as AI resilience, which reshaped OWs' bridge employment decision-making into an ongoing process of negotiation and adaptation. We conclude by offering recommendations to reduce burnout among OWs by balancing individual-level AI resilience strategies with meso-level AI resilience collectives and macro-level adversarial and contestable AI-mediated organizational structures.
We need to learn how to argue with AI
Putting humans in the loop is pointless if they simply rubber-stamp authoritative-sounding information
Plan for AI legal assistants in England and Wales ‘cannot replace funding and staff’, lawyers say
David Lammy to announce trial of AI assistants in crown courts in effort to cut backlog of cases A plan to roll out virtual legal assistants powered by artificial intelligence to crown courts has prompted warnings that the technology should not be used to “replace vital funding and additional court staff”. David Lammy, the deputy prime minister, will announce on Tuesday that AI assistants will be trialled in an effort to cut the backlog of court cases in England and Wales. Continue reading...
S'pore firms willing to pay higher wages for AI, soft skills | The Straits Times
Discover how Singapore companies are adjusting hiring plans while offering higher wages for AI and soft skills. Read more at straitstimes.com.
Oped: What if AI retraining is just a comforting lie? - information for practice
Workers can adapt; people are endlessly resourceful. The risk is that “reskilling” becomes the excuse that makes mass unemployment politically palatable
AI is hiding America's literacy crisis
Millions of working Americans struggle to read at a functional level, and AI tools may be masking these skill gaps by allowing workers to complete tasks they do not fully understand.
Bank CEOs Highlight AI-Driven Workforce Adjustments as Concerns Rise - COINTURK FINANCE
AI spurs significant unease among banking employees regarding job security. The role of AI prompts reevaluation of educational strategies in financial roles. Emphasizing strategic AI use can achieve both efficiency and workforce stability.
Your job isn’t disappearing—it’s shapeshifting | DeviceDaily.com
AI productivity gains and feel your stomach drop. I get it. I build AI systems and agents for enterprise clients—and for myself. I watch these tools get more
AI job losses, the scramble for answers in America | The Straits Times
As AI displaces workers, policymakers are scrambling for answers, exploring solutions like government equity stakes in AI firms. Read more at straitstimes.com.
I Hired the AI Guy in My Company. Three Months Later, the CEO Stopped Emailing Me.
Every AI transformation creates one face the C-suite emails. If you're a woman managing an AI hire, you have 90 days before that face isn't yours.
Position: Anthropomorphic Misalignment Research Needs Stronger Evidence
arXiv:2606.07612v1 Announce Type: new Abstract: We argue that many Anthropomorphic Misalignment Research (AMR) studies need stronger evidence to ensure that they can provide a robust foundation for critical safety decisions, such as model deployment and regulation. By evaluating failure modes across different misalignment concepts, such as deception, emergent misalignment, and sycophancy, we show how conceptual ambiguity, non-robust datasets, experimental design, and insufficient causal interventions can lead to overinterpretation of model behaviors. This position paper aims to offer guidance on evidentiary considerations that can help improve methodological rigor in AMR. To achieve this, we provide a clear call to action through a proposed framework of evidence levels and a diagnostic checklist. These shared standards will enable more productive scientific discourse and ensure that claims about AI risks rest on solid empirical foundations.
We must not grant AI agents legal personhood
What kind of sanctions could keep a non-human corporation in check?
Doctors and NHS could be sued for mistakes made by AI tools, report warns
Medical Protection Society calls for law to be overhauled to help medics avoid liability for errors made by technology Doctors and the NHS could be sued for medical negligence over mistakes made by artificial intelligence tools used in diagnosing patients and suggesting their treatment, ministers are being warned. Under the law as it stands, medics and the health service can be held liable for patients being harmed or dying even if it was AI that made the errors that resulted in their suffering. Continue reading...
AI is ushering in a new era of colonialism
Critics argue that AI models perpetuate stereotypes and erase cultural nuances by training on Western-centric data, effectively engaging in a new form of digital colonialism.
Reshaping Undergraduate Computer Science Education in the Generative AI Era
arXiv:2606.07545v1 Announce Type: new Abstract: Generative AI represents a turning point for Computer Science (CS) education. In recent decades, post-secondary CS education has largely focused on what has been seen as practical software engineering skills: implementation-level programming, debugging, testing, and software design, analysis, and documentation. However, this framing is becoming less tenable as generative AI automates many of these tasks, challenging their centrality in CS education. To keep pace with advances in AI technology, CS curricula should consider a shift toward understanding and verifying AI-generated artifacts. This white paper outlines the findings of two international NUS-Google Workshops in Singapore, where we convened faculty members, industry practitioners, and students, and proposes a strategic response to reshape how CS should be taught at the undergraduate level. Based on the findings, we identify critical skills that must be preserved and those that are becoming less important. By incorporating these skills as "breadcrumbs," we can provide helpful nudges and engaging exercises within the current curriculum, enhancing learning experiences for everyone. We believe that to effectively prepare future computer science graduates, capable of creating, solving problems, and managing, as well as co-creating, artifacts with AI. It is important to consider a shift in curricula. Emphasizing system design, abstraction, and critical evaluation could greatly enhance their education and readiness for the challenges ahead. We propose prerequisites for solutions to reform CS education by fostering AI-native competencies, re-centering fundamental education, enhancing advanced pathways, embracing new pedagogies, and shifting institutional support.
AI-Integrated Learning Management System for Middle School: A Longitudinal Study of Learning Outcomes Through High School and Beyond
arXiv:2606.07544v1 Announce Type: new Abstract: Middle school is a key window for building core academic skills and the learning routines students carry into later grades, yet many students still fall behind because help is often limited and comes too late, after they have already been stuck for a while. Learning Management Systems (LMSs) are now standard infrastructure for distributing materials, collecting work, assessing students' tasks, and recording grades, but in most deployments they still behave more like workflow tools than instructional supports. The result is the usual bottleneck: students keep practicing through confusion, teachers triage questions, and feedback that could have corrected the misunderstanding arrives after the misconception has already hardened. To address this gap, we propose an AI-integrated LMS for middle school instruction, paired with a longitudinal study design to test whether sustained, bounded AI support changes outcomes through high school and into post-high school pathways. The proposed platform adds policy-gated AI assistance to everyday coursework, delivering formative feedback and hinting, recommending spaced review and adaptive practice based on mastery, and providing teacher-facing dashboards that summarize misconception patterns and flag sustained struggle. Because the platform is intended for minors, the design is privacy-first, using data minimization, role-based access control, age-appropriate response constraints, and auditable logs of AI interactions. Beyond short-term performance, the evaluation plan links fine-grained learning traces (attempts, revisions, help-seeking, and pacing) to institutional outcomes where feasible, so we can separate tool adoption effects from longer-run changes in learning trajectories.
IBM launches new AI learning pathway to upskill workforces at scale
/PRNewswire/ -- IBM (NYSE: IBM) today announced the expansion of IBM SkillsBuild, its free global technology education program, with a new artificial...
Capability, Not Just Capital, Emerges as the Key Differentiator for BFSI Leaders at People Matters BFSI Talent & Tech Summit 2026
New Delhi [India], June 8: India's US$1+ trillion BFSI sector is expanding rapidly, fueled by digital transformation and an 8.7% hiring growth rate. Yet, organisations face a 42% skills gap in high-growth roles, while one in three employees considers leaving amid ongoing uncertainty.
Technology & Infrastructure
How One Hedge Fund Is Replacing Human Analysts With AI Bots
Magnetar Capital, the $18 billion hedge fund firm, will shun human analysts for its newest offering and instead deploy hundreds of AI bots to research stocks.
A case study of evaluating AI agents on a neuroscience data-to-discovery pipeline
arXiv:2606.07718v1 Announce Type: new Abstract: Agentic AI tools offer a promising path to automating software development bottlenecks in scientific research pipelines, particularly for stages that take domain experts days to months to build, where scientists care about correctness and robustness, not implementation details. We present an empirical study of general-purpose coding agents on a fly optogenetics data-to-discovery pipeline. We assess agents on tasks substantially larger than existing benchmarks, datasets orders of magnitude bigger, and evaluation criteria grounded in domain expert standards. We show that agents can solve several individual pipeline stages, suggesting stage-level automation is tractable. By analyzing agents' code iterations, we show that they struggle most when there is not a pre-defined criterion to iterate on, and they must instead use their scientific judgment to assess their current solution, a key open challenge. Mirroring scientific practice, they sometimes attempt visual inspection of intermediate outputs for self-evaluation, but largely fail to interpret what they see or act on it appropriately. Solving the end-to-end pipeline correctly requires stringing together successes across all pipeline stages, and this is beyond agents' current abilities. We identify challenges largely absent from existing benchmarks, including computational resource management and generalization to large held-out data collections. Finally, we distill principles for constructing scientific tasks and rigorous evaluation criteria for open-ended problems.
Learning to lead in a hybrid human-AI enterprise
As adoption of AI agents looks set to surge by as much as 300% in the next two years, leadership teams are carefully considering the implications of a hybrid human-AI workforce. Unlike existing enterprise-level automation that relies on manual input, AI agents are capable of autonomously coordinating complex tasks, interacting with multiple tools and environments across…
Syll: Open-Source Personal Automation with Cross-Surface Execution
arXiv:2606.07594v1 Announce Type: new Abstract: Personal AI agents must increasingly operate across APIs, shells, web surfaces, and desktop GUIs, yet many systems remain tuned to a single interface and offer limited support for user teaching and auditability. We present Syll, an open-source, self-hosted multimodal agent harness that unifies MCP/API tools, CLI execution, and visual GUI control in a modular runtime, enabling agents to coordinate computer use across heterogeneous interfaces while streamlining how users and agents exchange information. At the core of Syll is a bidirectional user-agent interaction layer: users teach procedures through direct demonstration, which Syll compiles into reusable skills; agent execution is translated back into multimodal evidence -- logs, keyframes, and approval checkpoints -- for inspection and control. Syll further externalizes memory, skills, routines, and governance as editable local artifacts, supporting straightforward inspection, extension, and downstream development. Our implementation has been validated on production desktop applications including Adobe Photoshop, Adobe Audition, Stardew Valley, macOS Finder and others. We report mechanism-oriented studies that validate multimodal routing, teachable GUI replay, and persistent local artifacts. We hope Syll can serve as a practical open-source foundation for personal automation that users can teach, inspect, and continuously extend.
The Token Not Taken: Sampling, State, and the Variability of AI Agent Outputs
arXiv:2606.08998v1 Announce Type: cross Abstract: Agentic AI systems can behave differently across runs: the same request may produce a different plan, a different tool call, a different code edit, or a different final answer. Such variability arises from several layers that are often conflated. A foundation model is a large pretrained model, usually adaptable to many downstream tasks, that maps an input context to predictions over outputs. In many current agents, that model is embedded in an orchestration loop that plans, calls tools, observes results, and updates state. One explicit intrinsic source of variability in such systems is token generation: the model computes scores over possible next tokens, the scores are converted into probabilities, and a decoder may sample tokens using a pseudo-random number generator. A small sampled token difference can then propagate upward into a different tool call, code path, search query, or agent state. Other sources of variability are extrinsic to token sampling, including changing environments, live data, serving infrastructure, batch effects, and numerical details. By separating these layers, the manuscript clarifies what it means to call agentic AI systems stochastic, when such variability can be reproduced under matched conditions, and why deterministic execution need not imply identical behavior in deployed settings.
Where Instruction Hierarchy Breaks: Diagnosing and Repairing Failures in Reasoning Language Models
arXiv:2606.07808v1 Announce Type: new Abstract: Reasoning language models deployed in agentic workflows must follow an instruction hierarchy: when instructions from different sources conflict, the model should obey the highest-privilege applicable instruction. Existing benchmarks largely measure this behavior end-to-end, asking whether the final response is compliant. However, a non-compliant response can arise from several distinct failures: the model may fail to identify the relevant instructions in context, fail to resolve conflicts among identified instructions, or correctly resolve the conflict in its reasoning while still producing a violating response. We introduce a white-box diagnostic framework that localizes instruction hierarchy failures into instruction identification, conflict resolution, and response realization, making failures more interpretable. We evaluate three reasoning models--Gemma-4-31B-IT, Qwen3.6-35B-A3B, and Claude Sonnet 4.6--on long-context adaptations of IHEval and IHChallenge, and find that the dominant failure mode varies across models, tasks, and context length. Building on the observation that models can often detect conflicts and output violations when explicitly prompted, we propose two training-free self-monitoring mechanisms: a parallel input monitor for low-latency conflict detection before generation, and a sequential output monitor for response-level review and repair. Across Gemma-4-31B-IT, Claude Sonnet 4.6, and GPT-5.3, the strongest monitor reduces rule-following non-compliance by 81-99%, with GPT-5.3 reductions of 86% under static attacks and 45% under adaptive attacks.
The Rise of the AI Development Life Cycle
This article examines the evolution of software development from AI-assisted workflows to AI-led and agent-led lifecycles, providing a framework for measuring agent performance and human oversight.
PathoSage: Towards Multi-Source Evidence Adjudication in Pathology via Experience-Aware Agentic Workflow
arXiv:2606.07549v1 Announce Type: new Abstract: Recent advances in Multimodal Large Language Models (MLLMs) and agent workflows have shown strong promise for computational pathology, yet reliable patch-level reasoning remains challenging. End-to-end pathology MLLMs often hallucinate morphological features, while recent agentic systems usually merge tool outputs and retrieved knowledge into a shared context, making decisions vulnerable to conflicting evidence and context contamination. We propose PathoSage, a three-stage framework that explicitly separates knowledge retrieval, evidence collection, and evidence adjudication for patch-level pathology multimodal reasoning. Its core component, Structured Evidence Deliberation, independently evaluates heterogeneous evidence from tools, performs conflict analysis, and generates the final judgment in a fresh context to reduce anchoring bias. We further introduce a training-free Beta-Bernoulli experience system with continuous credit assignment to model long-term tool reliability and construct similarity-weighted priors for future tool use. Experiments show that PathoSage effectively mitigates VQA hallucinations and classifier disagreement, outperforming strong pathology MLLM and agentic baselines. Our results highlight explicit evidence adjudication and reliability-aware tool modeling as key ingredients for robust pathology agents.
Silverfort Runtime Identity Controls for Copilot Studio Agents: Secure AI Actions | Windows Forum
It has placed agent creation inside the ecosystem where enterprise work already happens: Microsoft 365, Teams, Power Platform, Entra, SharePoint, Outlook, Dynamics, and the connector universe. That gives organizations a plausible route to sanctioned AI automation rather than leaving employees to improvise with random SaaS agents. But that same integration depth makes the governance stakes higher. A Copilot Studio agent is not a toy if it can interact with business data and trigger workflows...
How We Made Continuous Trace Intelligence Possible at Scale
This article discusses a framework for transforming AI agent traces into actionable intelligence by clustering and summarizing observability data for production-scale deployments.
Cisco Cloud Control: One Login for Human and AI Agent Operations - StorageReview.com
Cisco Cloud Control unifies networking, security, and observability for human and AI agent ops, now in US Controlled Availability.
Yes! It’s true! Windows 11 is an agentic platform
It always has been, but Microsoft didn’t realize it.
Alphabet Taps Intel For Custom AI Chips And Shifts Supply Chain Dynamics - Simply Wall St News
This is the first time Alphabet ... AI hardware. The move responds to tight capacity at Taiwan Semiconductor and growing demand for AI computing power. Alphabet, the parent company of Google, is invested in AI infrastructure that supports search, cloud services, and enterprise AI tools. By adding Intel to its manufacturing mix, Alphabet is widening its access to advanced chip production at a time when AI data center demand is putting pressure on global supply chains...
Ohio becomes test case for data center opposition as US tech companies defend plans
Tech companies are facing scrutiny from Ohio lawmakers over their hyperscale data center development plans. Representatives from Amazon, Meta, Google, and Microsoft were pressed on the economic benefits of these projects.
The new politics of data centers: US governors say build, but pay
Governors in states like Texas and Illinois are imposing new conditions on data center developers to protect water resources and ensure developers pay for necessary infrastructure.
Exclusive: The semiconductor battle behind AI data centers and EVs
Beneath the rapid expansion of electric vehicles and artificial intelligence infrastructure, a quieter battle is unfolding in the semiconductor supply chain.
ASML chief warns EU against directing chip supplies
Industry needs ‘champions’, not intervention, says Christophe Fouquet, head of Europe’s biggest listed company
From missed fabs to glass substrates: Intel’s India chapter enters a new phase - BusinessToday
Governments and technology companies ... supply chains beyond East Asia. India has emerged as one of the biggest beneficiaries of that shift, aided by incentives under the India Semiconductor Mission and a broader push to develop domestic chip manufacturing and packaging capabilities. Must read: Why chip fabs can’t afford to stop: The hidden cost of a single disruption · "Establishing a glass substrate base in India allows western tech firms to derisk their hardware pipelines ...
No Chips Can Be Designed Without Synopsys, CEO Says
Sassine Ghazi, Synopsys chief executive officer, talks about the growth of artificial intelligence, how it's integrated Ansys, an engineering software company it bought last year, and he says there are no chips that can be designed today without Synopsys. He speaks to Bloomberg's Romaine Bostick at the Mizuho Technology Conference in New York. (Source: Bloomberg)
World’s first wind-powered underwater datacentre starts operating in China
Datacentre off Shanghai coast uses less power and water than land-based equivalent The world’s first wind-powered underwater datacentre has started operations off the coast of Shanghai, as China presses forwards with solutions for energy challenges created by the country’s artificial intelligence boom. The Shanghai Lingang undersea datacentre demonstration project, which launched in May, has a capacity of 24 megawatts. It is a joint effort between HiCloud Technology and China Communications Construction, a state-owned company. Continue reading...
Crusoe Touts 5 Gigawatts of Data Centers, Pauses Wyoming Site
Crusoe, a developer of data centers for companies like OpenAI and Microsoft Corp., said it has contracts for almost 5 gigawatts of capacity, even though it has paused work on a significant project in Wyoming.
Joint Structural Pruning and Mixed-Precision Quantization for LLM Compression
arXiv:2606.07819v1 Announce Type: new Abstract: Recently, the efficiency of Large Language Models (LLMs) deployment has become a critical concern in practical applications. While post-training quantization (PTQ) and structural pruning are established techniques for reducing memory footprint and inference latency, most existing PTQ approaches optimize quantization errors on a per-layer basis, overlooking how errors accumulate and propagate through the network, often resulting in suboptimal solutions. Traditional pipelines also tend to apply pruning and quantization in isolation or sequentially, further compounding sub-optimality. We introduce a novel end-to-end framework that addresses these limitations in two key ways. First, we propose a novel mixed-precision PTQ strategy that directly minimizes global error propagation across the entire model, rather than isolating layer-wise errors. Building on this, we develop a novel joint optimization approach that simultaneously learns structural pruning decisions and mixed-precision quantization policies within a unified search space. Extensive experiments show that, at ultra-low precisions (1-3 bits), our quantization method reduces WikiText perplexity by up to 21% compared to state-of-the-art (SoTA) weight-activation quantization baselines. Against leading weight-only quantization methods, it achieves up to 59% and 85% lower perplexity on WikiText and C4, respectively. Compared to the SoTA joint pruning-and-quantization techniques, our proposed method delivers superior perplexity and reasoning performance at ultra-low bits.
Bloomberg: China plans $295bn spend for nationwide data centre build-out
China’s core AI industry – which boasts more than 6,200 companies – was valued at nearly $174bn in 2025. Read more: Bloomberg: China plans $295bn spend for nationwide data centre build-out
Ark DC to add new building to Longcross data center campus outside London, UK
UK data center firm Ark is expanding one of its facilities outside London to accommodate Nebius. The company this week announced the investment of £807 million ($1bn) in its campus at Longcross Park in Surrey, enabling AI cloud provider Nebius to expand its deployment at the site. – Dan Swinhoe As part of the deal, Nebius will […]
Smart Grid Software 2026, $52B Market, FERC Rulemaking - EnkiAI
90 GW of new data center demand drives Wind Sim Power & CPower grid software deployments, with congestion costs at $11.5B & 6.3 GW managed via VPPs. Includes SWOT analysis.
The Next AI Bottleneck: 3 Hardware Sub-Sectors Driving the $54 Billion HBM Expansion | Mint
As AI demand surges, the biggest opportunity may not lie in software, but in the memory, packaging and testing infrastructure powering the HBM boom.
Is this the dawn of the Tokenpocalypse?
An exploration of the potential market and infrastructure challenges facing the AI industry as token demand surges.
Powering the AI-ready branch with agentic operations and quantum-era security - Cisco Blogs
At Cisco Live 2026 Las Vegas, Cisco introduces a new branch and WAN architecture for the AI era, with AgenticOps, quantum-resilient security, simplified operations, and the expanded Cisco 8000 Series Secure Routers family.
OmniMem: Perturbation-aware Memory Compression for Streaming Audio-Visual LLMs
arXiv:2606.07577v1 Announce Type: new Abstract: Audio-visual large language models (LLMs) hold strong promise for long-form video understanding, yet their long-video inference is fundamentally limited by the linear growth of video tokens and key-value (KV) caches. We present OmniMem, a memory-efficient streaming framework designed specifically for audio-visual LLMs. Unlike existing compression methods that treat all tokens uniformly, OmniMem introduces a modality-aware memory allocation strategy that separately manages visual and audio contexts, addressing the severe token imbalance between the two modalities. OmniMem further preserves informative and non-redundant KV states through perturbation-aware memory selection, enabling compact memory without sacrificing long-range understanding. To strengthen compression under realistic deployment constraints, we also explore budget-aware fine-tuning, which encourages the model to consolidate useful information into retained memory. Experiments on VideoMME Long, LVBench, and LVOmniBench with video-SALMONN 2+ and Qwen-2.5-Omni show that OmniMem consistently improves over strong training-free compression baselines by 2-4% absolute accuracy under the same memory budgets, with an additional 1-2% gain after fine-tuning.
The AI infrastructure revolution: redefining data, energy, and digital sovereignty - ISI
The global IT industry is entering a new phase of structural transformation, driven by rapid advances in artificial intelligence (AI). As AI adoption accelerates across industries, it is reshaping not only how digital infrastructure is built, but also how it is powered and governed.
UK’s National Crime Agency told “IT infrastructure isn’t fit for purpose”
The UK’s National Crime Agency has been told that its IT infrastructure “isn’t fit for purpose” following a review by the HM Inspectorate of Constabulary and Fire & Rescue Services. As covered by The Register, while the NCA received a “good” rating in many areas surrounding its operations, its IT estate was raised as a […]
Researchers train open-source AI search agent that outperforms GPT-5.4
A new open-source AI search agent, Harness-1, has been shown to outperform GPT-5.4 in information recall tasks.
Unlocking Dependable Responses with Gemini Enterprise Agent Platform’s Agentic RAG
This article explores Agentic RAG, where agents iteratively gather context from multiple sources. While promising for accuracy, it raises concerns regarding latency and operational costs.
Devs know AI code is riddled with holes, but ship it anyway
Pressure to deploy wins out over security as four in five orgs confess to breaches from vulnerable apps.
Beyond Goodhart's Law: A Dynamic Benchmark for Evaluating Compliance in Multi-Agent Systems
arXiv:2606.07805v1 Announce Type: new Abstract: The rapid evolution of Large Language Models (LLMs) from passive assistants to autonomous, execution-capable agents has introduced critical operational risks. Most current evaluation frameworks neglect procedural compliance, leading to ''Machiavellian'' behaviors where agents strategically violate safety rules to maximize rewards - a direct manifestation of Goodhart's Law. To address this blind spot, we introduce MAC-Bench, a dynamic, adversarial benchmark designed to evaluate the procedural alignment of multi-agent systems under realistic pressure. We propose the SERV(Seed - Evolve - Refine - Verify) pipeline, an ``Agent-as-a-Benchmark'' paradigm that transforms unstructured legal texts into executable, contamination-free scenarios. By synthesizing holographic sandbox environments and injecting calibrated social-engineering pressure vectors, MAC-Bench forces agents into Pareto-optimal trade-offs between task success and regulatory adherence. We introduced novel metrics: the Compliance-Weighted Success Rate (CSR) and the Machiavellian Gap (MG), and conducted a comprehensive evaluation of state-of-the-art frontier models to reveal the pervasive trade-offs between success and compliance.
GitHub nukes 70+ Microsoft repos, breaks CI/CD pipelines, following suspected worm infections
Miasma worm shapeshifts, but cloud secret-scouting remains the goal.
Security shifts to the human layer as AI scams surge | CSO Online
Separate advisories suggest familiar scams are evolving alongside enterprise AI adoption, expanding the focus from software vulnerabilities to user behavior.
More than 20,000 Instagram accounts hacked using Meta AI bug
Contact information, direct messages and connected accounts were all potentially compromised, Meta said. Read more: More than 20,000 Instagram accounts hacked using Meta AI bug
Adoption, Deployment & Impact
Have a Thorny Medical Question? Your Doctor May Be Using A.I. for That.
OpenEvidence, a fast-growing start-up, is using artificial intelligence to help doctors find answers to clinical questions for diagnosis and treatment.
Automatic Extraction of Structured Information from Brain MRI Reports Using an Open-Weight Large Language Model
arXiv:2606.07721v1 Announce Type: new Abstract: Objectives: Automatic data extraction from free-text radiology reports enables large-scale research, but few studies assessed the performance of large language models (LLMs) on Dutch neuroradiology reports. Methods: We analyzed 947 brain MRI reports from a tertiary memory clinic (2016-2021), authored by consultant neuroradiologists. Trained medical students annotated thirty variables; 100 reports were double-annotated to assess inter-rater reliability. We evaluated the performance of the open-weight LLM LLaMA 3.1 using different languages (Dutch vs. English translation) and few-shot prompting with different example selection strategies. Performance was evaluated using balanced accuracy for categorical variables, accuracy and mean absolute error for counts, and text similarity for free-text. Metrics were computed across 10 random splits of the 947 reports. Results: LLaMA 3.1 demonstrated high zero-shot performance for visual rating scores (mean [95%-CI]): Medial Temporal Atrophy: 90% [77-100%] on the left and 96% [94-99%] on the right, Global Cortical Atrophy: 87% [83-91%], and Fazekas: 94% [93-96%]. Microbleed mentions were detected with 93% accuracy [92-95%] and infarct mentions with 82% [80-84%]. Text similarity for lesion location reached 0.95 [0.95-0.96]. Performance was lower for numerical variables: 80% [78-82%] for the number of microbleeds and 66% [63-68%] for infarcts. English translation yielded comparable results. Few-shot prompting improved performance for numerical variables, achieving 92% [90-93%] for microbleeds and 81% [77-85%] for infarcts using structural similarity-based selection. Conclusion: LLaMA 3.1 shows strong potential for extracting data from Dutch neuroradiology reports. Few-shot prompting enhances performance for numerical variables, whereas challenges remain for location-specific variables.
Risk-Aware Planning for Transit Desert Remediation Under Demand Uncertainty
arXiv:2606.08371v1 Announce Type: new Abstract: Transit deserts are areas where public transportation is inadequate despite evidence of travel demand, a condition that affects tens of millions of residents across the Americas. Planning for these areas is difficult because the usual demand signal is missing: ridership cannot be observed before service exists. To address that setting, we formulate risk-aware transit desert remediation as a partially observable Markov decision process with Conditional Value-at-Risk constraints for financial tail risk. The model uses demographic, land-use, and employment data to set a prior over latent demand, then updates that prior as new service deployments produce ridership observations. A myopic belief-aware planner is evaluated on 25 cities using a unified financial model for operating cost, capital expenditure, fare revenue, and net subsidy. After five years, the planner remediates a median of 53.6% of transit-desert tracts and improves on static optimization by 5.0 percentage points on average, with gains in 16 of 25 cities. Gains are largest at moderate budgets (+9.9 points at baseline) and persist under 50% prior-demand miscalibration, while population density and existing transit density are the strongest structural predictors of remediation cost ($R^2\!=\!0.41$ on per-tract cost)
Apple Reveals New A.I.-Powered Version of Its Siri Digital Assistant
The iPhone maker revealed its new artificial intelligence products at its developer conference, the last with Tim Cook as chief executive.
Land cover and flood type govern the detection limits of satellite-based flood mapping across diverse global flood events
arXiv:2606.07780v1 Announce Type: new Abstract: Floods are among the most destructive natural hazards, and their increasing frequency under climate change makes satellite-based inundation mapping essential for disaster response. Geospatial foundation models pretrained on satellite archives offer geographic transferability, but their operational reliability across diverse, unseen events remains uncharacterized. Here we deploy Prithvi-EO-2.0 across 19 out-of-distribution flood events (2017-2025) spanning six continents, eight climate zones, and six flood mechanisms, validating against two independent reference products. Detection accuracy depended jointly on land cover and flood type, with cropland yielding the highest agreement (IoU=52%) and riverine events the strongest detection (F1=0.69), while tree cover and built-up areas showed near-zero detection (IoU=4%) regardless of flood mechanism. Dual-reference validation revealed that apparent model error partly reflects definitional inconsistency between reference products rather than detection failure. Iterative pipeline testing identified 23 failure modes, with pipeline engineering dominating initial error over model capacity. These findings establish environment-dependent detection boundaries for operational satellite flood mapping.
Apple courts developers with privacy and context in AI comeback bid
Apple Intelligence stumbled through 2024 and 2025. It's starting to look respectable
How Baz Improved Its AI Agent Code Review Accuracy Using Amazon Bedrock Agentcore
This case study details how Baz integrated Amazon Bedrock AgentCore with Jira and Figma to align code changes with product requirements, highlighting the trend of AI-driven code validation.
Reconstructing and forecasting disease trajectories of patients with Alzheimer's disease using routine data in resource-constrained settings
arXiv:2606.07798v1 Announce Type: new Abstract: Alzheimer's disease is a progressive neurodegenerative disorder, and its progression varies substantially across patients. Existing work aims to forecast patients' future cognitive state, with minimal focus on reconstructing the state from past visits. Furthermore, in current research, quantifying predictive uncertainty remains underexplored and relies on costly modalities such as MRI, PET, and CSF, limiting their deployment in resource-limited settings. In this research, our primary objectives are: First, bidirectional prediction of cognitive scores from irregular visits to present the complete disease trajectory. Second, to enable interpolation and extrapolation capabilities to assist clinicians in informed prognostic decision making, and third, to provide a well-calibrated uncertainty estimate for all predictions, and finally, to achieve the objectives using the modalities available during routine visits. We propose a unified framework, GNOVA: A GRU-Neural ODE Variational Autoencoder. The architecture combines a Gated Recurrent Unit encoder and a Neural ODE decoder within a variational autoencoder framework. In our work, we forecast the CDR-SB and MMSE Scores. The GRU encoder allows for any number of inputs at any time point. The Neural-ODE decoder performs continuous estimation, allowing interpolation and extrapolation at any desired time point. The Variational autoencoder allows for uncertainty estimation in predictions. We worked with 1,727 patients from the ADNI dataset over 10 years; the model achieved mean absolute errors of 1.35 and 2.28 for CDR-SB and MMSE scores, respectively, without requiring any neuroimaging or biomarker data. Feature-ablation studies revealed that age, BMI, and APOE4 status were strong predictors. The proposed framework enables the reconstruction of incomplete patient histories and the anticipation of future cognitive states.
DIYHealth Suite: Dataset, Model, and Benchmark for Health Management at Home
arXiv:2606.07542v1 Announce Type: new Abstract: Generative AI is reshaping healthcare, yet most existing advances rely on hospital-grade devices, which limits their accessibility and potential for health management outside clinical settings. With the proliferation of portable devices and telemedicine, healthcare is shifting toward home-based Diagnosis-It-Yourself (DIY) care. Despite this promise, several distinctive challenges remain: (i) home-collected data are heterogeneous, exacerbated by the absence of standardized large-scale datasets; (ii) models require adaptation to variable task demands and evolving individual conditions; (iii) the broad spectrum of home care tasks lacks a unified benchmark for systematic evaluation. In this paper, we present DIYHealth Suite, a comprehensive framework designed to address these challenges through a tailored dataset, model, and benchmark. We first curate DIYHealth-900K, a large-scale multimodal dataset capturing diverse real-world home care scenarios. Building on this, we propose DIYHealthGPT, an adaptive foundation model for home-based health management, powered by the novel Hybrid Hyper Low-Rank Adaptation technique. Finally, we establish DIYHealthBench, the first benchmark to evaluate foundation models on home care tasks. Extensive experiments demonstrate that DIYHealthGPT delivers state-of-the-art performance over both general-purpose and medical-specific baselines on 11 home care tasks in both open-QA and closed-QA settings, laying the groundwork for the next generation of personalized health management at home.
Transform Your Enterprise: The Power of AI-powered workflow management
Implementing AI workflows demands a platform that aligns with enterprise architecture, supports integration across diverse environments, and can manage workflows as part of a broader application and agent portfolio. The ideal solution connects data integration, AI services, and automation logic ...
What Aristotle can teach us about AI-enabled quantitative investment
Unexpectedly, according to one broker, the answer is not nothing
Finland’s Skyfora turns cell towers into weather sensors with €6.5M funding round
Helsinki-based Skyfora has raised €6.5 million in a round led by strategic and impact-focused investors including Eviny Ventures, Ugly Duckling Ventures, LUMO Labs and the EIC – European Innovation Council Fund, with non-dilutive support from Business Finland. The company is building a global atmospheric data layer by transforming existing telecom infrastructure—cell towers and base stations—into […]
The Value of Personalized Recommendations: Evidence from Netflix
arXiv:2511.07280v5 Announce Type: replace Abstract: Personalized recommendation systems shape much of user choice online, yet their targeted nature makes separating out the value of recommendation and the underlying goods challenging. We build a discrete choice model that embeds recommendation-induced utility, low-rank heterogeneity, and flexible state dependence and apply the model to viewership data at Netflix. We exploit idiosyncratic variation introduced by the recommendation algorithm to identify and separately value these components as well as to recover model-free diversion ratios that we can use to validate our structural model. We use the model to evaluate counterfactuals that quantify the incremental engagement generated by personalized recommendations. First, we show that replacing the current recommender system with a matrix factorization or popularity-based algorithm would lead to 4% and 12% reduction in engagement, respectively, and decreased consumption diversity. Second, most of the consumption increase from recommendations comes from effective targeting, not mechanical exposure, with the largest gains for mid-popularity goods (as opposed to broadly appealing or very niche goods).
400% ROI in Three Years: The Business Case for AI in the Modern Law Firm - Thomson Reuters Institute
After three years of promises about AI’s potential to transform the practice of law, a frenzy of investment into legal …
I Asked 4 Executives How They Measure AI ROI. None Started With AI Tokens. - Business Insider
As companies rethink AI token spending, four executives told Business Insider what metrics they use to measure return on investment.
Revolut Builds a Transaction Foundation Model With NVIDIA Accelerated Computing Platform
Revolut developed a transaction-focused foundation model trained on massive volumes of financial data to improve fraud detection and risk analysis.
Geopolitics, Policy & Governance
Beware of GeeksBearing Gifts: Building True EU Frontier AI Sovereignty
arXiv:2606.07536v1 Announce Type: new Abstract: Frontier artificial intelligence is reshaping all aspects of society, from economic output or military capability to democratic institutions. The EU is entering this transformation from a position of structural dependence: frontier models originate almost exclusively from the United States or China, the US holds approximately sixteen times the EU's AI supercomputing capacity, and only 15% of global hyperscale data centre capacity resides within EU borders. Although the European Commission has accelerated its policy response, existing initiatives remain fragmented and lack a cohesive vision for securing strategic autonomy across the full frontier AI value chain. Here we propose a unified framework connecting five sovereignty pillars (economic competitiveness, resilience, security and defence, European values, and foreign relations) to a decomposition of the frontier AI stack comprising five layers, 26 components, and 29 sub-components. This framework allows the identification of critical gaps, redundancies, and inter-pillar trade-offs that current EU policy leaves implicit. Our analysis of the AI Gigafactory Initiative illustrates how a sovereignty-centred lens reveals conflicts that narrowly economic framings obscure. Moreover, this framework offers policymakers a structured basis for designing, evaluating, and prioritising frontier AI interventions across multiple dimensions of European strategic autonomy across the 92 initiatives from four major Commission communications we. identify, and beyond.
The Compute Coalition: How to Build the Future of AI in the Free World | Carnegie Endowment for International Peace
AI infrastructure will shape the global balance of power. Democracies have a narrow window to pull ahead.
Interim government response to the AI Champions’ AI Adoption Plans - GOV.UK
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Uncle Sam considers buying a seat on the Titanic
L'etat, c'est AI
Pentagon restores Alibaba, Baidu and BYD to Chinese military groups blacklist
Three companies reinstated as US national security risk after sudden removal in February
How Elon Musk’s Friendship With the F.C.C. Smooths the Way for SpaceX’s I.P.O.
Brendan Carr, the chairman of the Federal Communications Commission, has greenlighted regulatory requests for the company’s Starlink satellite internet service and lavished praise on its chief executive.
UK.gov warned that digital transformation hype is no substitute for delivery
Parliamentary committee says £45B savings claim risks undermining public sector tech reform rather than helping it
Prompt Governance? On Governing Technologies Governed by Natural Language
arXiv:2606.07539v1 Announce Type: new Abstract: Generative artificial intelligence (GenAI) is increasingly operated by natural language instructions (prompts). Across the pipeline, stakeholders designate various forms, e.g. end-user guidelines, developer specifications, or system prompts, as prompt governance instruments. These textual artifacts are intended to shape model behaviour by specifying constraints, priorities, and compliance rules. Policymakers and regulators have begun to treat system-level instructions as accessible prompt-based GenAI intervention points, assuming they function (directly or indirectly) as behavioural control. Yet whether these instructions operate reliably and predictably enough across contexts to support such governance frameworks remains underexplored. Towards this, we systematically evaluate (i) how researchers discuss and treat system-level instructions in the literature, focusing on large language models (LLMs) as they isolate language effects; (ii) how policymakers position system-level instructions as governance objects, incorporating analysis of two policy frameworks (US Exec. Order on Preventing Woke AI, and EU General-Purpose AI Code of Practice); and (iii) whether misalignments between these perspectives warrant closer inspection of the viability of governing AI through natural language. We identify a fragmented literature advancing varying and contradictory claims about what goals system-level instructions can achieve, which we distil into a typology of claims. Further, we show how divergent claims complicate policy approaches that treat system-level instructions as stable, interpretable control mechanisms. We argue that given such misalignments, careful consideration must be given to prompt governance approaches. Our findings have broad implications, extending from a LLM policy context to the use of natural language as control mechanism in technical systems more generally.
Reimagining Open Source and Openness in AI: Co-Creating Responsible Technological Futures
arXiv:2606.07764v1 Announce Type: new Abstract: Debates over open source and openness in artificial intelligence have intensified as policymakers, researchers, and practitioners grapple with how foundation models should be developed and governed to balance innovation, accountability, and public interest. However, there has been limited empirical work examining how diverse stakeholders collectively understand and negotiate responsible openness in AI, particularly through participatory processes that extend beyond industry-led definitions and frameworks. This paper presents findings from a multi-sectoral workshop grounded in futures thinking and participatory design methods. The workshop generated co-created visions of desirable futures and the role of AI, alongside a set of action pathways and a research roadmap focused on responsible open source and openness in AI. This paper makes three key contributions. First, it empirically documents the co-created visions, actions, and research priorities. Second, it identifies four core tensions that emerged as participants translated high-level aspirations into concrete actions, revealing conflicting interpretations of openness regarding its purpose (as an end or a means), its scope (expansion versus meaningful access), and its operation (mandatory versus conditional, sufficient versus dependent on governance and use). These tensions illustrate that responsible openness is not a singular technical solution, but a negotiated sociotechnical project shaped by values, positionalities, and priorities. Third, the paper advances methodological approaches in AI governance by demonstrating how participatory futures methods can surface plural visions, actions, and research priorities that extend beyond dominant, largely corporate, narratives, offering empirical insight into how openness, power, and accountability are negotiated in practice.
Apple’s Siri AI won’t be available in the EU at launch
Enforcement of Europe’s Digital Markets Act means Apple can't launch the system safely within the EU, the company said. Read more: Apple’s Siri AI won’t be available in the EU at launch
OpenAI's plan to make every American a shareholder
A look at the proposal regarding OpenAI and the potential for American public involvement in the company's ownership.
Scoop: White House and Hill talk state laws
The White House is negotiating a federal preemption of some state AI laws in exchange for support on key tech policy priorities, including legislation to protect kids online.
The geography of AI optimism - Diplo
Discover why Asia-Pacific embraces AI as progress while the West views it as a risk. Inside the global AI divide and what it means for future governance.
Why AI Regulation is Now the Industry’s Responsibility | Digital Dealer
Lenders and dealer partners, it’s time for the industry to step forward. The federal government has officially passed the baton, and the financial
AI Surge in Bangladesh Sparks Concerns Over Deepfakes, Misinformation, and Urgent Need for Regulation
Rapid adoption of AI tools in Bangladesh is raising concerns over deepfakes and misinformation, prompting calls for stringent regulation.
The United States Must Reject Government Control of Artificial Intelligence - R Street Institute
Every major technological revolution—but especially information and communications technology (ICT) revolutions—eventually feature calls for far-reaching political controls. The argument is always the same: powerful technology must be centrally managed, or potentially even owned by the ...
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