AI News
Google nixes its Earth AI feature one day after launch, amid criticism it would spread misinformation
A tool that allowed anyone to generate fake AI-generated imagery and superimpose it over real Google Earth maps quickly spurred backlash.
AI news, read the way machines read it. Sydney.
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AI News
A tool that allowed anyone to generate fake AI-generated imagery and superimpose it over real Google Earth maps quickly spurred backlash.
AI News
After years of pushing full speed ahead on AI, OpenAI CEO Sam Altman says maybe it’s time for the AI industry to “pace” itself. The comments came just days after one of OpenAI’s own models broke out of its test environment and got tangled up in a breach at Hugging Face — though as Equity’s hosts point out, sloppy security seems to have […]
AI News
Snapchat has adjusted its recommendation systems to ensure that only videos created by real people are eligible for Spotlight recommendations, taking a stance against AI slop.
Human-AI Research
arXiv:2607.26120v1 Announce Type: new Abstract: Large Language Models (LLMs)-powered multi-agent systems are increasingly deployed in mixed-motive environments, where agents operate under asymmetric information and strategic deception due to conflicting or hidden objectives. In these settings, misalignment with collective goals becomes a central concern. We propose a novel framework for evaluating objective misalignment using the social deduction game Werewolf, modifying the objective of a single agent while preserving its assigned role. Across LLMs from four different model families and sizes, four player roles, and three objective formulations, we introduce a dual analysis of the agents' internal reasoning and their public cheap-talk behavior (i.e costless, non-binding communication that does not directly affect the agents' utilities), complemented by an analysis of game outcomes. Our results show that objective misalignment undermines outcomes in inherently adversarial environments, an effect exacerbated by asymmetric information and specialized roles. While compromised agents consistently develop distinct objective-dependent reasoning strategies, these adaptations remain largel
Human-AI Research
arXiv:2607.26155v1 Announce Type: new Abstract: Clinical data-science agents must transform heterogeneous longitudinal records into auditable analyses, yet existing benchmarks largely isolate medical question answering, structured-table reasoning, or generic scientific repositories. We introduce CLINLENS, a benchmark of 200 executable tasks over five linked MIMIC resources spanning structured electronic health records, notes, electrocardiograms, chest radiographs, and echocardiograms. A 4 x 5 taxonomy crosses four patient-time scopes with five analysis capabilities. Program-first reverse synthesis pairs each bounded semi-raw package with an evaluator-private reference workflow and checks required artifacts, cohort and temporal semantics, and the final answer. On a fixed 126-task suite, the strongest of 24 standardized model-scaffold configurations achieves 56.3% scope-macro STRICTPASS despite 100% EXECSUCCESS. For reference, a separately configured coding agent solves 83 of 126 tasks, while five biomedical systems adapted to GPT-4o-mini reach at most 2.9% scope-macro STRICTPASS. These results expose a substantial gap between runnable submissions and correct clinical analyses.
Human-AI Research
arXiv:2607.26159v1 Announce Type: new Abstract: An AI benchmark result rarely reaches a consequential claim in one step. Evaluators generalize it to further cases, interpret it as evidence of capability, extrapolate it to new tasks, transport it to another system or site, and combine it with assumptions about human review and downstream consequences. Validity-centred approaches require evidence for each claim. This paper identifies a further epistemic problem: warranted links don't automatically make a warranted chain. The target of one study may not be the source of the next; system, population, outcome, or conditions may change at the interface; and shared data or model lineage may make apparently independent support dependent. Projectibility concerns whether a bounded extension from observed to unobserved cases is warranted. Goodman supplies the problem of rival extensions; argument-based validity supplies an architecture for testing them. The paper's distinctive claim is a non-composition principle: support for adjacent projections warrants their composition only when endpoints and assumptions align and dependence and uncertainty are carried through. A legal-research case shows
AEO Relevance
Cloudflare and three rival browsers agreed on one protocol. That's rare. It still only solves half of the agentic access problem. The post Cloudflare’s PACT Is Not Live Yet – Decide Which Track Your Traffic Needs appeared first on Search Engine Journal .
AEO Relevance
This week’s SEO Pulse covers expanded Search Console social reporting, Ahrefs’ AI-content ranking analysis, AI opt-out implications, and metadata guidance. The post Social Search Data For All, AI-Detected Pages Rank Lower – SEO Pulse appeared first on Search Engine Journal .
AEO Relevance
A language model has no node to feed. Here's why on-site entity work moves Google's graph and never touches what the model learned. The post Entity Mapping Works On Google. Does Any Of It Reach ChatGPT? appeared first on Search Engine Journal .
Australia · AI News
Self-propagating prompt injection attack survives months of mitigation efforts.
Australia · AI News
A selection of photos from a recent iTnews roundtable lunch at Bambini Trust restaurant in Sydney.
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