Sensors

Do We Really Want a “Doc in the Loop” as AI Policy? – RACmonitor — <a href="https://news.google.com/rss/articles/CBMiiAFBVV95cUxPYmRKeVBZWFJUbmhXWnk3TkR1T3FfS1FHdmlRMGM1azh3ZHBMOTJtMGdBQnFSbFFrQVprRlZxMkdjQ2VyUmwxVlR2OGJBdVg3R2c1UmtvVTJQdlAtVm1HXy1RTGkyWGxYbXFDMHIzZUtYOWhhbmhTTFZuX0JzNEdERERpdEtVYnIt?oc=5" target="_blank">Do We Really Want a “Doc in the Loop” as AI Policy? – RACmonitor</a>&nbsp;&nbsp;<font color="#6f6f6f">MedLearn Publishing</font>

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From Assistance to Autonomy -- A Researcher Study on the Potential of AI Support for Qualitative Data Analysis — arXiv:2501.19275v4 Announce Type: replace Abstract: The advent of AI technologies, such as Large Language Models, has introduced new possibilities for Qualitative Data Analysis (QDA), offering both opportunities and challenges. To help navigate the responsible integration of AI into QDA, we conducted semi-structured interviews with 15 Human-Computer Interaction (HCI) researchers experienced in QDA. While our participants were open to AI support in their QDA workflows, they expressed concerns about data privacy, autonomy, and the quality of AI outputs. In response, we developed a framework that spans from minimal to high AI involvement, providing tangible scenarios for integrating AI into QDA practices while addressing researchers' needs and concerns. Aligned with real-life QDA workflows, we identify potential for AI tools in areas such as data pre-processing, researcher onboarding, or conflict mediation. Our framework aims to provoke further discussion on the development of AI-supported QDA and to help establish community standards for responsible Human-AI collaboration.

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One Vote, Several Parliaments: An Empirical Analysis of the Algorithmic Ambiguity of the Italian Electoral Law on the 2022 General Election Data — arXiv:2607.11676v2 Announce Type: replace Abstract: Crafa's algorithmic analysis of the Italian electoral law (the "Rosatellum") showed that the statutory text describing the territorial distribution of proportional seats (Art. 83(1)(h)) admits at least three different algorithmic interpretations, which may elect different people from the same votes. We test that conclusion empirically: we implement the full seat-allocation pipeline of the law (Arts. 77, 83, 83-bis, 84, 85) and run the three interpretations on the complete open data of the 2022 Italian general election (Chamber of Deputies). The implementation reproduces the official national apportionment exactly, matches by name 389 of the 391 seats it models (99.5%), and agrees step by step with the official minutes. On these validated data, the sequential interpretation (Algorithm A) is order-dependent: reversing the processing order of the constituencies replaces 6 deputies with 6 others, and 1,000 random orders produce 560 distinct outcomes; a near-exhaustive sweep of 15 million orders closes the set of order-contingent deputies at 140, while 764 of the 1,139 candidates of the admitted lists (67%) are elected under no order at all. In 29% of the sampled orders A also strands seats it cannot assign by any rule stated in the text, and in a further 9% it fills the Chamber with a different party composition. The interpretation applied in practice (Algorithm C) is order-independent, provably so in the absence of ties, but differs from A by 8 deputies. Under the Mattarella-style interpretation (Algorithm B) the documented order leaves two seats unassignable, and 46% of sampled orders strand one to three seats. A Monte Carlo analysis shows the named differences robust to input noise well beyond the residual uncertainty of the data: the ambiguity changes which persons are elected and in which territories, and leaves party strength untouched only where the text's procedure completes.

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A study comparing Canadian and South Korean university students found significant cross-cultural differences in perceptions of generative AI use, with Canadian students more likely to view it as unethical. The survey analyzed 2024 responses, revealing cultural factors influence ethical judgments. Institutional policies were functionally identical.

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Validating the Single Item Kawaii Measure — arXiv:2607.19352v1 Announce Type: cross Abstract: Kawaii is the Japanese instantiation of cuteness. As a multimodal percept theoretically derived from the notion of baby schema, kawaii can be a property of voice and sound, visual appearance and form factor, and movement and expression. However, measuring user perceptions of kawaii remains an open question. In the absence of a validated instrument, a one-item self-report measure has been used extensively, but has not been validated. Here, we report on three types of validity -- convergent, known groups, and cross-context -- and reliability for the single item measure across nine data sets featuring responses to video game character voices and visual appearances and computer-generated voice assistant voices from N=967 unique participants. Our results demonstrate initial evidence of the validity of the one-item measure for voice and visual kawaii perceptions. Further rigour can be pursued with novel stimuli, test-retest validation, and concurrent validity against the upcoming multi-item measure of kawaii.

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Researchers analyzed 14,419 self-published books on Amazon from 2023 to 2026 and found that books with substantial AI-produced content make up a large share of the catalog. These books win a growing share of sales over time, despite being often dismissed as low-quality. Revenue per selling book fell across most genres as the market added selling books faster than revenue.

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Data Annotations as Pedagogical Hints: From Subjective Labels to Critical Thinking — arXiv:2607.20149v1 Announce Type: new Abstract: Machine learning courses often use pre-labeled datasets, hiding the subjectivity of human annotation. This creates students with an overly trusting view of AI data and models, undervaluing interpretive diversity. We investigated whether manual data annotation tasks teach students about subjective labeling. Study Design: An annotation activity was implemented at two universities: Fontys (Netherlands) and IT University Copenhagen (Denmark). Students annotated skin lesion images for hair coverage on a 3-point scale. Surveys were collected from 43 participants measuring their understanding of annotation ambiguity, data quality, bias, fairness, implementation barriers, and pedagogical effectiveness. Key Findings: Self-reported familiarity with course content increased substantially across all concepts. Most students recognised that personal interpretation affects annotations. Students rated the activity as more effective than traditional lectures for understanding bias. Participants were motivated to learn more. Main Drawbacks: Emotional unease from viewing medical images was the primary issue. Many students still requested clearer guidelines to reduce disagreement, suggesting they hadn't internalised that disagreement from different perspectives is a learning feature, not a bug. Recommendations for Future Iterations: Ensure sufficient interpretive ambiguity in materials. Reduce repetitive annotation workload. Mitigate emotional unease from sensitive content. Explicitly frame disagreement as a learning opportunity rather than a problem to solve. Manual data annotations effectively teach students that human judgment shapes model behavior and that disagreement reflects domain complexity, not just noise.

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Examining User Behavior and Cognitive Biases in Personal Password Security — arXiv:2607.19586v1 Announce Type: cross Abstract: Despite increasing awareness of cybersecurity risks, users continue to engage in insecure password practices, such as reusing passwords, choosing weak credentials, and neglecting security recommendations. The study explores the behavioral and cognitive factors that influence password decision-making by integrating insights from behavioral economics, particularly hyperbolic discounting, status quo bias, and present bias. We conducted a survey to analyze how people create, store and manage their passwords, examining whether security habits have improved over time in response to greater awareness. Our findings reveal that immediate convenience often outweighs long-term security considerations, leading users to prioritize memorability over strength. Additionally, we identify key psychological biases that contribute to security procrastination and resistance to adopting more secure authentication practices, such as password managers and multi-factor authentication. The study contributes to human-centered security by bridging the gap between security awareness and action, offering practical insights to design user-friendly authentication policies that align with real-world decision-making tendencies.

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Information Discernment in Large Language Models — arXiv:2607.19355v1 Announce Type: cross Abstract: LLMs are increasingly used with external knowledge sources like the internet. Do they weigh information appropriately -- updating more for reliable sources (source discernment) and more when claims bring priors closer to the truth (truth discernment)? We formalize this as information discernment and introduce Learn2Discern (L2D), an experimental framework and benchmark grounded in three normative axioms with interpretable metrics. To establish external validity, a pre-registered, quota-matched user study (n=299) confirms that real LLM users endorse all three axioms and report that violations reduce their trust and usage intent. Across 13 models and nearly 670K trials, we find consistent failures across both dimensions: models perform near chance on source and truth discernment, rely on source popularity twice as much as source reliability, and update roughly equally whether a claim improves or worsens their position relative to the ground truth. Models integrate external knowledge most effectively on datasets where their priors are already the most accurate. Newer and larger models improve truth discernment but not source discernment, a blind spot that model complexity does not address. We identify simple inference-time interventions that improve both forms of discernment. We release our dataset and survey as a testbed for a core alignment property that scales in importance as LLMs replace traditional search.

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A qualitative study of 25 construction artisans in North-East Italy examined their perspectives on technical innovation. The study identified 15 premises underlying their views, grouped around four issues. It sheds light on an underrepresented sector, providing insight into technological innovation in small enterprises.

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AI integration in talent management helps organizations design strategies for talent retention and engagement. Predictive models and personalized career planning can address retention issues and improve performance. AI solutions promote individualized growth and development, supporting long-term sustainable growth.

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Journalists, media and influencers: An analysis of the conversation in the digital public sphere during the Qatar 2022 World Cup — arXiv:2605.11331v1 Announce Type: cross Abstract: Public digital conversation around major sporting events takes place within a hybrid system in which journalists and the media compete with new intermediaries, including influencers, to gain greater visibility and engage with audiences. This study analyses the Qatar 2022 World Cup as a case of high informational intensity and public opinion monitoring. To that end, social network analysis was applied to X/Twitter using the hashtag #Qatar2022, analysing 1,343 high-engagement accounts, including those of journalists, media and influencers, alongside a random sample of 5,000 users. The findings indicate that journalists are under-represented in the user population as a whole, but significantly over-represented among the highest-engagement accounts, and they maintain stable visibility. The media, by contrast, attract a lower average level of attention and tend to achieve only sporadic peaks of impact. Accordingly, journalistic authority on social media is observed less as dominance in terms of participation volume and more as the capacity to occupy reference positions when public attention is being shaped during the event.

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Researchers present a regression-based approach to Arabic dialect geolocation, predicting speaker origin as continuous latitude-longitude coordinates with a median localization error of 481.2 km. The model uses a hierarchical neural architecture and achieves 64.5% and 45.2% accuracy for country and city predictions, respectively.

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Experiential Versus Instructional Approaches for Eliciting Metacognitive Awareness in AI-Assisted Learning: A Short-Term Longitudinal Study — arXiv:2607.20047v1 Announce Type: cross Abstract: With generative AI (GenAI) entering classrooms the question to which teaching approach best supports metacognitive skill acquisition in AI-assisted learning becomes pressing. In this short-term longitudinal study we investigate two contrasting approaches: experiential learning encompassing hands-on approaches and instructional learning such as classical lectures. We conducted a quasi-experiment with 126 university students from a first-year engineering course which were distributed across the two conditions and completed a two hour session on learning with GenAI in the corresponding learning style. Metacognitive awareness which encompasses both knowledge of cognition (understanding effective AI-use strategies) and regulation of cognition (applying that knowledge in practice) was measured before and after the session. Additionally, students longitudinal metacognitive awareness was tracked over the trimester and assessed again five weeks after the initial intervention. Results reveal that experiential methods outperform instructional approaches in engagement and knowledge of cognition immediately after the intervention. By five weeks, the two groups converged on these measures, while the experiential group showed a delayed, continuous within-group increase in regulation of cognition that was not observed in the instructional group. This suggests that the benefits of experiential approaches extend beyond conventional educational settings to AI-assisted learning, and that knowledge and regulation may develop on different timescales under experiential learning.

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Researchers analyze attributions of consciousness to AI chatbots, finding little scientific evidence to support these claims. A multidimensional taxonomy is developed to categorize attitudes towards AI consciousness, ranging from pretence to delusions. Some attributions are deemed epistemically innocent, while others render the attributor epistemically blameworthy.

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SenWorld: A Digital-Twin Simulation for Generating Context-Rich Evaluation Data — arXiv:2607.19949v1 Announce Type: cross Abstract: Smartphone personal assistants reason over longitudinal personal data, yet evaluating them requires context-rich evaluation data whose correct answers are known, and real device traces are too privacy-sensitive to share. To address this challenge, we present SenWorld, a physically grounded, deterministic, event-sourced digital-twin simulation that generates such data with ground truth fixed by construction. In SenWorld, personas live through a full day in a world built from real map, weather, holiday, and network data; every observable signal is archived in full-system snapshots; and each evaluation case is labeled by a pointer to an existing record rather than by post-hoc annotation or a large language model (LLM) judge. We evaluate this method with 16 personas in Beijing. The generated data closely matches the held-out real-user benchmark in category distribution (Jensen--Shannon divergence (JSD) 0.070) and in the daily rhythm of communication records (JSD below 0.1), though generated records remain shorter than real ones. Without scripted interaction, personas form a fully reciprocated dialogue subgraph and differentiated behavioral repertoires. Projected into 717 evaluation cases, the generated data exposes 78 failures in a production smartphone assistant, concentrating on call and Short Message Service (SMS) records while contacts, schedules, and alarms never fail. The snapshot pointer confirms each failure as an assistant-side retrieval error, with no LLM judge involved. Overall, SenWorld offers a privacy-safe, reproducible, and distribution-checked path to evaluation data whose labels are fixed by construction.

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Economic Evaluations of Language Models — arXiv:2607.19375v1 Announce Type: new Abstract: Language models perform economically valuable work, yet they are not currently assessed for how well they perform every economically valuable task. We introduce EconEvals as an open-source evaluation suite to measure capabilities relevant to tasks, work activities, and occupations in the US labor economy. We ground the evaluation suite in real user queries to language models where possible, and supplement these with synthetic data. Our evaluations improve coverage over OpenAI's GDPval benchmark, which is the existing state-of-the-art that covers 5% of US occupations, at 500x lower cost. Alongside benchmarks, we also introduce a simulation-based exposure measure to estimate how much time current language model capabilities could save across all tasks belonging to all US occupations, with detailed accounting for each estimate. Our estimates indicate that current models could save workers substantial time on at least half of their tasks in 47% of occupations. However, for 79% of tasks where we predict substantial time savings, observed Claude usage is low, suggesting that existing usage lags potential. Beyond inherent constraints of language model chatbots, our data identifies privacy and proprietary systems as the principal bottlenecks limiting further time savings from AI. Overall, we introduce adaptable infrastructure that grounds inferences about language models' labor-market impact in their current capabilities, which can be continually updated as capabilities improve.

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Researchers develop Eutopia, a lending-process simulator, to study long-term fairness in AI-driven decision makers, finding that learning with performative dynamics leads to better efficiency and equity. The simulator tests performative and classical RL algorithms with fairness-aware utilities. Results show improved efficiency, equity, and inclusivity with well-designed fairness-aware utilities.

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Researchers propose using Clinical Pathways as safety specifications for Physical AI in hospital wards, integrating wearable sensors and robotic components. A Runtime Safety Monitor evaluates signals against clinically defined constraints to identify safety violations. This approach combines prediction, reasoning, and verification to ensure safety.

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Recovering Clinical Utility Under Differential Privacy: Empirical Validation of Adaptive Federated Aggregation on Heterogeneous Cardiovascular Datasets — arXiv:2607.19403v1 Announce Type: cross Abstract: Validating federated learning frameworks on real clinical data is an essential step between proof-of-concept demonstrations in controlled synthetic environments and deployment in real multicenter healthcare settings. A prior architectural study by the same authors (Tertulino and Alencar, 2026) demonstrated, on a synthetic six-feature benchmark, that server-side adaptive optimization acts as a temporal denoiser for Differential Privacy noise, answering an open challenge identified in the original pipeline work (Tertulino, 2025). That study used synthetically generated data and explicitly identified real-world validation as a priority future direction. The present work addresses this gap by validating the FedCVR framework on five publicly available real cardiovascular datasets (Framingham, Cleveland, Hungarian, Switzerland, and Long Beach VA), harmonized to the 13-attribute UCI Heart Disease schema and configured as a heterogeneous federated scenario with leave-one-institution-out cross-validation. Results demonstrate that FedCVR preserves its adaptive advantage on real data, achieving an F1-Score of 79.2% and AUC of 0.96 under the operational privacy budget (noise multiplier = 0.8, privacy budget epsilon approximately 4.2), while statistically outperforming standard FedAvg on all evaluated metrics (paired t-tests, all p <= 0.003, significant under the Bonferroni-corrected threshold). The measured privacy cost on real data confirms the graceful degradation pattern observed in the synthetic experiments, providing empirical evidence of the framework's clinical viability in genuine multicenter contexts.

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Researchers used large language models from OpenAI, Anthropic, and Google to simulate freight matching markets with 50 shipper agents. The simulations revealed concentration risks, with a single carrier attracting up to 76% of requests. Disclosing daily capacity reduced concentration by a third and doubled shipper surplus.

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What Does the Credential Still Certify? Cognitive Stewardship for AI-Mediated Education — arXiv:2607.19988v1 Announce Type: new Abstract: Generative AI is changing a basic premise of educational assessment: that submitted work can reliably evidence the human capacities a credential claims to certify. The challenge is not simply whether students use AI, but what remains inferable about learning when some cognitive work has been delegated to a system. This paper develops cognitive stewardship, a framework for AI-mediated assessment that links the learning claim, delegation boundary, evidence standard, and safeguards. We then audit verified public generative AI assessment guidance from 30 universities. Using a pre-specified scoring codebook--a written, source-grounded rubric--four open-weight LLM models applied the rubric as structured coders, with scores averaged to reduce dependence on any single model's bias. The audit shows that public policies are becoming better at classifying AI use than at explaining what evidence and protections preserve credential validity. Boundaries are more visible than evidence standards; safeguards are uneven; and guidance is clearest when AI use resembles final-output substitution rather than feedback, access, verification, or professional workflow. The takeaway is that permission categories are necessary but insufficient. Universities need policies that make the certification logic visible: what learners may delegate, what they must still demonstrate, and how institutions will protect fair evidence rather than merely monitor AI use.

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IBM reported a disappointing quarter with revenue of $17.2 billion, missing Wall Street's expectations. CEO Arvind Krishna attributed the 42% drop in mainframe sales to customers prioritizing other hardware due to price increases, but insisted the decline is temporary.

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Florida could require schools to set rules for AI use — <a href="https://news.google.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?oc=5" target="_blank">Florida could require schools to set rules for AI use</a>&nbsp;&nbsp;<font color="#6f6f6f">Action News Jax</font>

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When the South Writes Its Own AI Rules — <a href="https://news.google.com/rss/articles/CBMidEFVX3lxTE5mVE5ITFdSa3hTOUgxNnVHb0NxdHZ1UmFvZXZPMHo5Ulo5bkNIVlZmR3AwVnJoRmFqSHAzdDBac2xaMUdjSldJclZVcU9aMnhHeF9VcDY0UkhsMVphNjdoTDdXTWFQMG1aYXluY2JGamZkZmVw?oc=5" target="_blank">When the South Writes Its Own AI Rules</a>&nbsp;&nbsp;<font color="#6f6f6f">Orinoco Tribune</font>

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Preparing students for an AI-driven future: CCSD arms teachers, classroom with AI policy — <a href="https://news.google.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?oc=5" target="_blank">Preparing students for an AI-driven future: CCSD arms teachers, classroom with AI policy</a>&nbsp;&nbsp;<font color="#6f6f6f">WCIV</font>

— What's the impact?

Preparing students for an AI-driven future: CCSD arms teachers, classroom with AI policy — <a href="https://news.google.com/rss/articles/CBMi2wJBVV95cUxONkhUenFIRnI1cC15YWJ2QktkekNxN3NtQ0daWERRZEdyTlJNR21pZ3dCWkR0djFJYmNsWmM0XzRZNmd4V1U1RUp3Q0Rwa0VMUFF0R0RpRzI5Y1NOT1E1V19OVlB4Q254VG1GWEFCcHBxdjdnaVc2ZXd0RDVoRF9ZdWI0bzBVb2VrdERkeXJmcXZIeTVKNDU5UVZDNHFVdUtQMTdQRWFBN2JRMC1Xb0NuNTFkbzgyVUxUTnFrWFFuaG0wc2dpVVFIbFJDaW1KVnpEN3lzWW5BOWpsdzAwU3JQbTVsRWVIWFh5N0dSRGo4THo4TWtMSy1FY3h6LXBMZE8tZDhTTWFHTS1hYldGY2FQN0tRMnd2VUZZSkJ6ckZIZVRrQjg3S1BFOEdQVUxqVVFKYy1tdzRPUnIwSEFQVXZ0cmdCZXZJQnlqSnpGWlJWSzB5Y2ZvZC0tWjlHdw?oc=5" target="_blank">Preparing students for an AI-driven future: CCSD arms teachers, classroom with AI policy</a>&nbsp;&nbsp;<font color="#6f6f6f">WCIV</font>

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University of Manchester adopts four-tier AI policy | ETIH EdTech News — <a href="https://news.google.com/rss/articles/CBMitwFBVV95cUxNV0JnZ0ZxcE0wT1pzQ2cyakFScTR4N01tRDVfVTl0MHBJLWduU05vWE54ckxQVzRJd05tV1cyRFg2c3ZXMzk2YWdvV1k5YnN3OFJzM3NFZ0lnQjhFZE03WGhrb3NwNDdLZUU4V1R2S0FuVzJ0NHVoWFMtSnFjcW9QT1pVVDFfaHRuX3V1eG5qOWozWlMtUXB2NGh4d19XZXpROXNqSGJtbkVCdElLaDFaTjY0dk5abk0?oc=5" target="_blank">University of Manchester adopts four-tier AI policy | ETIH EdTech News</a>&nbsp;&nbsp;<font color="#6f6f6f">EdTech Innovation Hub</font>

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State Supreme Court hopefuls clash over AI rules for Washington judges — <a href="https://news.google.com/rss/articles/CBMixwFBVV95cUxNZkRLSUFxNDYzd3hoNjVvbXFPZjlZOHZrSnpDZlQ3cVY2MXZnOHNPM1F2WktPZ04zUU1VSldyZy1CbjRHTnU1MUEyMGRNbU9uWDdPZGRDUEpPRXRaemJwNkhnV3Rmb0FFLWhZc216emFGUjM3cWZ1WnlZeUZnVUkwQ0c5QVJGLU90dEZidWxxX0lJXy1GdThpalZTaDdJdEhlZlk3T1JNWVBtWWtwQUxPWFhSS1ExbmVQZDRHMXdOT1kzRmhpb0c0?oc=5" target="_blank">State Supreme Court hopefuls clash over AI rules for Washington judges</a>&nbsp;&nbsp;<font color="#6f6f6f">The Black Chronicle</font>

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Meta won’t have to face the next planned social media addiction trial — Less than a week before Meta's lawyers were set to return to a Los Angeles courtroom, the plaintiff accusing the platform of inflicting harm dropped the case. Brought by 15-year-old Florida plaintiff going by initials R.K.C., the case was set to be the second in a set of bellwether trials meant to test legal arguments [&#8230;]

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Google justifies its massive AI spending with a booming cloud business — Google's cloud business is thriving, as companies adopting its AI and AI infrastructure services help the tech giant to report record profits.

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White House accuses Chinese AI developer of IP theft — <a href="https://news.google.com/rss/articles/CBMi0gFBVV95cUxORDBGQ1duY05SODRsYVJjOEhvTFpKa3VlanVhQXR3QUNTMFJSVkdwcnoxMHR2WnpqSUNuOGNaSVlpMkloTlpnNUZDY3QxSzZJMTF4Sk1YOEdhM1BWMUh0Y29VbGhQeWlOSEdGdktBbjA2VlBlczZnc3oweG1weTlrc2ZUWkVkcmdNb1RodnI1MVhwQllfZGJWQVZad1ZLa3dIS2M5YUdrSkdpaVZIM2JfMXd5YzI4bFJ5bnNVSmU4anpPdS1sRVNtQmdjXzhrcEFVTXc?oc=5" target="_blank">White House accuses Chinese AI developer of IP theft</a>&nbsp;&nbsp;<font color="#6f6f6f">Nextgov/FCW</font>

— What does this tell you?

White House accuses Chinese AI developer of IP theft — <a href="https://news.google.com/rss/articles/CBMiswFBVV95cUxOVUR2RGdlQU9mcjlEeUhfeERUNWZ5djlFdWNsLUwwNUZBSTVtTnFER3dkSU9RSmFCczNZbzQ5TXZ4YzB2emxBT0Z0MDdjZWY2bWZDaEV2V3pqLWcxdVZWSkFoMDJvSXVfdWhFbjFYc1gtTmZnQUZ0RVR2dVpObDdlM3htdVNoQUxyQ2xrc181X0dCVkZ1Z3h0a0UxUjFRYlRmbnl0WEpnMU1RTDRrVkZTZXVyTQ?oc=5" target="_blank">White House accuses Chinese AI developer of IP theft</a>&nbsp;&nbsp;<font color="#6f6f6f">Nextgov/FCW</font>

— What does this tell you?

Treasury threatens sanctions after White House claims Moonshot distilled Anthropic’s Fable — Treasury Secretary Scott Bessent warned the U.S. government could sanction Chinese AI companies after White House officials accused Moonshot of distilling Anthropic's Fable model to develop Kimi K3.

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Tesla spending skyrockets as Cybercab, Semi, Megapack production timeline slips — Tesla's 26% boost in revenue wasn't enough to offset rising operating expenses and capital expenditures as it pushes to launch a new generation of products.

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Social media addiction lawsuit against Meta is dropped — A closely watched social media addiction lawsuit that had been set to go to trial next week has been dropped after the plaintiff voluntarily dismissed his claims against Meta, leaving none of the major tech companies facing trial in the case.

— What does this tell you?

Buc-ee's faces backlash after allegedly using AI to generate typo-filled art - San Antonio Express-News — <a href="https://news.google.com/rss/articles/CBMipwFBVV95cUxNU2hHbW54UTZKcG5NSVdxY0Q2eGpBZGZyamR3ZEtOMWNQaVM1aUk0WERRQlNDTm01NDlzTTJDX2tJLXp6YTVUSEpGRzY3RUlMSUtZS0p6MlJNVTJUejk0YUktVzAxb1FtOE9sVS1xeXNqV1BVRGhFVWhJSXRRTHdpWWh4NE9qZGw1dElVdlpYRm5TU0pDbDF0TW9LUHhZRk42anJUbVhpQQ?oc=5" target="_blank">Buc-ee's faces backlash after allegedly using AI to generate typo-filled art</a>&nbsp;&nbsp;<font color="#6f6f6f">San Antonio Express-News</font>

— What's the impact?

SoundCloud acquires decentralized music platform Nina Protocol months after its shutdown — SoundCloud has acquired decentralized music platform Nina Protocol, months after the startup announced it would shut down. The deal brings Nina’s artists, editorial archive, and music discovery tools to SoundCloud as the company continues expanding its platform for independent musicians.

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AI needs more rational policy footing in the U.S., says Krebs Group's Chris Krebs — <a href="https://news.google.com/rss/articles/CBMivgFBVV95cUxOajBVeDdpS3MyZGFHZmhQRUQ4TW9EOUdfa0huSlpXWmFGclVEdm5Ra2ZyZ05xdXhoR25DMnlBYTdIYVU1MjVuVVA0cHFSZ29wTDl2OWFROXZZTW9iRnlkdENCN2hJVFEwZVpiQjZkV053RjhOdXg2amhJMFdtNHhQQmsxalFGaUd5ZDhwcEpNOS1fZkoxTUtPWmhoUlE4XzhaT1U2NTlsMlZET0NCNmV3Tkk3cERwVUNsOUo5NFdB?oc=5" target="_blank">AI needs more rational policy footing in the U.S., says Krebs Group's Chris Krebs</a>&nbsp;&nbsp;<font color="#6f6f6f">CNBC</font>

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How an OpenAI’s human mistake led to the AI-powered hack on Hugging Face — OpenAI made a mistake setting up what it called a “highly isolated” testing environment and sandbox. According to cybersecurity experts, that human mistake is what made the AI-powered attack on Hugging Face possible.

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