Search engines play a crucial role as digital gatekeepers, shaping the visibility of Web and social media content through algorithmic curation. This study investigates how search engines like Google selectively promotes or suppresses certain hashtags and subreddits, impacting the information users encounter. By comparing search engine results with nonsampled data from Reddit and Twitter/X, we reveal systematic biases in content visibility. Google's algorithms tend to suppress subreddits and hashtags related to sexually explicit material, conspiracy theories, advertisements, and cryptocurrencies, while promoting content associated with higher engagement. These findings suggest that Google's gatekeeping practices influence public discourse by curating the social media narratives available to users.

--Phishing attacks remain a significant threat to modern cybersecurity, as they successfully deceive both humans and the defense mechanisms intended to protect them. Traditional detection systems primarily focus on email metadata that users cannot see in their inboxes. Additionally, these systems struggle with phishing emails, which experienced users can often identify empirically by the text alone. This paper investigates the practical potential of Large Language Models (LLMs) to detect these emails by focusing on their intent. In addition to the binary classification of phishing emails, the paper introduces an intent-type taxonomy, which is operationalized by the LLMs to classify emails into distinct categories and, therefore, generate actionable threat information. T o facilitate our work, we have curated publicly available datasets into a custom dataset containing a mix of legitimate and phishing emails. Our results demonstrate that existing LLMs are capable of detecting and categorizing phishing emails, underscoring their potential in this domain.

As multimodal machine intelligence systems started achieving average animal-level and average human-level fluency in many measurable tasks in processing images, language, and sound, they began to exhibit a new class of cognitive aberrations: machine mirages. These include delusion, illusion, confabulation, hallucination, misattribution error, semantic drift, semantic compression, exaggeration, causal inference failure, uncanny valley of perception, bluffing-patter-bullshitting, cognitive stereotypy, pragmatic misunderstanding, hypersignification, semantic reheating-warming, simulated authority effect, fallacious abductive leap, contextual drift, referential hallucination, semiotic Frankenstein effect, calibration failure, spurious correlation, bias amplification, concept drift sensitivity, misclassification under uncertainty, adversarial vulnerability, overfitting, prosodic misclassification, accent bias, turn boundary failure, semantic boundary confusion, noise overfitting, latency-induced decision drift, ambiguity collapse and other forms of error that mimic but do not replicate human or animal fallibility. This article presents some of the errors and argues that these failures must be explicitly defined and systematically assessed. Understanding machine mirages is essential not only for improving machine intelligence reliability but also for constructing a multiscale ethical, co-evolving intelligence ecosystem that respects the diverse forms of life, cognition, and expression it will inevitably touch.

Alignment is no longer a luxury, it is a necessity. As large language models (LLMs) enter high-stakes domains like education, healthcare, governance, and law, their behavior must reliably reflect human-aligned values and safety constraints. Yet current evaluations rely heavily on behavioral proxies such as refusal rates, G-Eval scores, and toxicity classifiers, all of which have critical blind spots. Aligned models are often vulnerable to jailbreaking, stochasticity of generation, and alignment faking. To address this issue, we introduce the Alignment Quality Index (AQI). This novel geometric and prompt-invariant metric empirically assesses LLM alignment by analyzing the separation of safe and unsafe activations in latent space. By combining measures such as the Davies-Bouldin Score (DBS), Dunn Index (DI), Xie-Beni Index (XBI), and Calinski-Harabasz Index (CHI) across various formulations, AQI captures clustering quality to detect hidden misalignments and jailbreak risks, even when outputs appear compliant. AQI also serves as an early warning signal for alignment faking, offering a robust, decoding invariant tool for behavior agnostic safety auditing. Additionally, we propose the LITMUS dataset to facilitate robust evaluation under these challenging conditions. Empirical tests on LITMUS across different models trained under DPO, GRPO, and RLHF conditions demonstrate AQI's correlation with external judges and ability to reveal vulnerabilities missed by refusal metrics. We make our implementation publicly available to foster future research in this area.

Living environments play a vital role in the prevalence and progression of diseases, and understanding their impact on patient's health status becomes increasingly crucial for developing AI models. However, due to the lack of long-term and fine-grained spatial and temporal data in public and population health studies, most existing studies fail to incorporate environmental data, limiting the models' performance and real-world application. To address this shortage, we developed SatHealth, a novel dataset combining multimodal spatiotemporal data, including environmental data, satellite images, all-disease prevalences estimated from medical claims, and social determinants of health (SDoH) indicators. We conducted experiments under two use cases with SatHealth: regional public health modeling and personal disease risk prediction. Experimental results show that living environmental information can significantly improve AI models' performance and temporal-spatial generalizability on various tasks. Finally, we deploy a web-based application to provide an exploration tool for SatHealth and one-click access to both our data and regional environmental embedding to facilitate plug-and-play utilization. SatHealth is now published with data in Ohio, and we will keep updating SatHealth to cover the other parts of the US. With the web application and published code pipeline, our work provides valuable angles and resources to include environmental data in healthcare research and establishes a foundational framework for future research in environmental health informatics.

Felix Salmon, Emily Peck and Elizabeth Spiers examine the undoing of the three-year-old merger. Then, the hosts discuss runaway AI valuations as Meta spends billions on AI and xAI raises equity off a multi-billion dollar valuation. And finally, what effects are Trump's aggressive immigration policies having on the economy? In the Slate Plus episode: What happened to happy hour? Want to hear that discussion and hear more Slate Money?

A U.S. citizen died during a Russian missile attack on the Ukrainian capital of Kyiv, the State Department confirmed Tuesday afternoon. An American citizen was among the 15 killed in Russian drone and missile strikes on the Ukrainian capital city, Kyiv, on Tuesday, State Department spokesperson Tammy Bruce confirmed in a press conference Wednesday. In response to a reporter's question on U.S. diplomats in Kyiv having to spend the night in a bunker, Bruce said "we can confirm the death of a U.S. citizen in Ukraine." "We are aware of last night's attack on Kyiv that resulted in numerous casualties, including the tragic death of a U.S. citizen," she said, noting, "We condemn those strikes and extend our deepest condolences to the victims and to the families of all those affected." Bruce did not offer any more details on the identity of the citizen killed by the Russian strikes, citing "respect to the family during this obviously horrible time."

While AI could offer transformative benefits, without proper safeguards it could facilitate nuclear and biological threats and cause "potentially irreversible harms," a new report commissioned by California Governor Gavin Newsom has warned. "The opportunity to establish effective AI governance frameworks may not remain open indefinitely," says the report, which was published on June 17. Citing new evidence that AI can help users source nuclear-grade uranium and is on the cusp of letting novices create biological threats, it notes that the cost for inaction at this current moment could be "extremely high." The 53-page document stems from a working group established by Governor Newsom, in a state that has emerged as a central arena for AI legislation. With no comprehensive federal regulation on the horizon, state-level efforts to govern the technology have taken on outsized significance, particularly in California, which is home to many of the world's top AI companies.

The US Department of Defense on Monday awarded OpenAI a 200m contract to put generative artificial intelligence (AI) to work for the US military. The San Francisco-based company will "develop prototype frontier AI capabilities to address critical national security challenges in both warfighting and enterprise domains", according to the defense department's posting of awarded contracts. The program with the defense department is the first partnership under the startup's initiative to put AI to work in governments, according to OpenAI. The company plans to show how cutting-edge AI can vastly improve administrative operations such as how service members get healthcare and also cyber defenses, according to a blog post. The startup claims that all use of AI for the military will be consistent with OpenAI usage guidelines, which are determined by OpenAI itself.

According to Hansons, Turing's PhD dissertation and On Computable Numbers are both hailed as foundational works in the field of theoretical computer science. Lichfield-based Rare Book Auctions, sister company to Hansons, had valued both of the papers at between 40,000 and 60,000. But the dissertation from 1938 or 1939, called Systems of Logic Based on Ordinals, sold for 110,500. Other top selling lots included Computability and λ-Definability and The World Problem in Semi-Groups with Cancellation, which sold for 26,000 and 28,600 respectively. Turing's final major work from 1952, called The Chemical Basis of Morphogenesis, went for 19,500, while his first published paper from 1935, Equivalence of Left and Right Almost Periodicity, sold for 7,800.