Foundation Models (FMs) are large-scale, pre-trained artificial intelligence (AI) systems that have revolutionized natural language processing and computer vision, and are now advancing geospatial analysis and Earth Observation (EO). They promise improved generalization across tasks, scalability, and efficient adaptation with minimal labeled data. However, despite the rapid proliferation of geospatial FMs, their real-world utility and alignment with global sustainability goals remain underexplored. We introduce SustainFM, a comprehensive benchmarking framework grounded in the 17 Sustainable Development Goals with extremely diverse tasks ranging from asset wealth prediction to environmental hazard detection. This study provides a rigorous, interdisciplinary assessment of geospatial FMs and offers critical insights into their role in attaining sustainability goals. Our findings show: (1) While not universally superior, FMs often outperform traditional approaches across diverse tasks and datasets. (2) Evaluating FMs should go beyond accuracy to include transferability, generalization, and energy efficiency as key criteria for their responsible use. (3) FMs enable scalable, SDG-grounded solutions, offering broad utility for tackling complex sustainability challenges. Critically, we advocate for a paradigm shift from model-centric development to impact-driven deployment, and emphasize metrics such as energy efficiency, robustness to domain shifts, and ethical considerations.

Privacy policies are statements that notify users of the services' data practices. However, few users are willing to read through policy texts due to the length and complexity. While automated tools based on machine learning exist for privacy policy analysis, to achieve high classification accuracy, classifiers need to be trained on a large labeled dataset. Most existing policy corpora are labeled by skilled human annotators, requiring significant amount of labor hours and effort. In this paper, we leverage active learning and crowdsourcing techniques to develop an automated classification tool named Calpric (Crowdsourcing Active Learning PRIvacy Policy Classifier), which is able to perform annotation equivalent to those done by skilled human annotators with high accuracy while minimizing the labeling cost. Specifically, active learning allows classifiers to proactively select the most informative segments to be labeled. On average, our model is able to achieve the same F1 score using only 62% of the original labeling effort. Calpric's use of active learning also addresses naturally occurring class imbalance in unlabeled privacy policy datasets as there are many more statements stating the collection of private information than stating the absence of collection. By selecting samples from the minority class for labeling, Calpric automatically creates a more balanced training set.

Trump threatens to walk out on Norah O'Donnell as 60 Minutes EDITS OUT astonishing meltdown White House makes'venomous' split with Israel: Fiery feud engulfs Trump insiders with alliance on the brink I won't ever forget what I saw at Andy Cohen's party. He may admit he's hooking up with guys on every dating app but this is the truth about men like him: KENNEDY Sad secrets of privileged son, 20, accused of murdering his self-made single mother near their $1.9m home, then screaming'Mama' Three Americans among seven killed when avalanche obliterates Himalayan climbers' base camp Thomas Massie remarries 16 months after losing wife of 31 years... as Trump ally launches sick attack Trump stuns 60 Minutes' Norah O'Donnell as he breaks terrifying news about China and Russia nukes Ex-CIA spy shares an easy way to tell if someone is lying... and the tactic he uses to strengthen his love life Justin Baldoni's bombshell $400M case against Blake Lively and Ryan Reynolds is'formally ended by a judge' JD Vance declares himself'UFO' lunatic as he vows to pull back the curtain on government secrets Sex aids and poppers... the sordid discoveries made by royal aides after party Andrew threw for Epstein and Ghislaine Maxwell - and the truth about those massages: ROBERT JOBSON Top Democrat lawmaker becomes international fugitive after she was freed on bail'for stealing thousands from vulnerable man, 83' George Clooney gives rare insight into life with wife Amal and their twins - as he details his relationship with his kids, lauds his'beautiful' family and brands himself'very lucky' Shohei Ohtani's wife makes rare appearance to celebrate Dodgers star's World Series win I learned the horrifying risks of'miracle' ADHD drugs and stopped taking them... but it was too late A girl, 15, bludgeoned to death in a gated enclave, a Kennedy cousin released and the brother who'knows the truth' about the death that haunts Camelot Justin Trudeau's rapper son sounds worse than ever in latest music video despite father's burgeoning romance with Katy Perry Moment'knifeman who hurt 11 people in Huntingdon train rampage storms barber shop moments after stabbing 14-year-old boy' Meghan is mocked for her new Christmas recipe... boiled water! Chefs, your jobs are safe for now! Robots might be poised to replace humans in factories and warehouses, but chefs don't need to worry about losing their jobs anytime soon. In a viral video, which has amassed over 6.3 million views, a humanoid robot attempts to make a stir-fry for its owner - with disastrous results.

Arcadia Publishing built its empire on small-town storytellers. Now it wants to sell their words to an A.I. company no one will name. Enter your email to receive alerts for this author. You can manage your newsletter subscriptions at any time. You're already subscribed to the aa_Nitish_Pahwa newsletter. You can manage your newsletter subscriptions at any time.

U.S. District Court Judge Karin Immergut extends injunction blocking Trump administration from deploying National Guard troops to Portland until Friday.

China waged a campaign of harassment and intimidation directed at a UK university to get it to shut down sensitive research into alleged human rights abuses, documents seen by the BBC show. Sheffield Hallam University staff in China were threatened by individuals described by them as being from China's National Security Service who demanded the research being done in Sheffield be halted. And access to the university's websites from China was blocked, impeding its ability to recruit Chinese students, in a campaign of threats and intimidation lasting more than two years. In an internal email from July 2024, university officials said attempting to retain the business in China and publication of the research are now untenable bedfellows. When the UK government learned of the case, the then Foreign Secretary David Lammy issued a warning to his Chinese counterpart that it would not tolerate attempts to suppress academic freedoms at UK universities, the BBC understands.

I built this'AI aunt' for women after family tragedy in South Africa A gruesome killing in her own family inspired South African Leonora Tima to create a digital platform where people, mostly women, can talk about and track abuse. Leonora's relative was just 19 years old, and nine months pregnant, when she was killed, her body dumped on the side of a highway near Cape Town in 2020. I work in the development sector, so I've seen violence, Leonora says. But what stood out for me was that my family member's violent death was seen as so normal in South African society. Her death wasn't published by any news outlet because the sheer volume of these cases in our country is such that it doesn't qualify as news.

Metrics like FactScore and VeriScore that evaluate long-form factuality operate by decomposing an input response into atomic claims and then individually verifying each claim. While effective and interpretable, these methods incur numerous LLM calls and can take upwards of 100 seconds to evaluate a single response, limiting their practicality in large-scale evaluation and training scenarios. To address this, we propose VeriFastScore, which leverages synthetic data to fine-tune Llama3.1 8B for simultaneously extracting and verifying all verifiable claims within a given text based on evidence from Google Search. We show that this task cannot be solved via few-shot prompting with closed LLMs due to its complexity: the model receives ~4K tokens of evidence on average and needs to concurrently decompose claims, judge their verifiability, and verify them against noisy evidence. However, our fine-tuned VeriFastScore model demonstrates strong correlation with the original VeriScore pipeline at both the example level (r=0.80) and system level (r=0.94) while achieving an overall speedup of 6.6x (9.9x excluding evidence retrieval) over VeriScore. To facilitate future factuality research, we publicly release our VeriFastScore model and synthetic datasets.

Most work interpreting reasoning models studies only a single chain-of-thought (CoT), yet these models define distributions over many possible CoTs. We argue that studying a single sample is inadequate for understanding causal influence and the underlying computation. Though fully specifying this distribution is intractable, it can be understood by sampling. We present case studies using resampling to investigate model decisions. First, when a model states a reason for its action, does that reason actually cause the action? In "agentic misalignment" scenarios, we resample specific sentences to measure their downstream effects. Self-preservation sentences have small causal impact, suggesting they do not meaningfully drive blackmail. Second, are artificial edits to CoT sufficient for steering reasoning? These are common in literature, yet take the model off-policy. Resampling and selecting a completion with the desired property is a principled on-policy alternative. We find off-policy interventions yield small and unstable effects compared to resampling in decision-making tasks. Third, how do we understand the effect of removing a reasoning step when the model may repeat it post-edit? We introduce a resilience metric that repeatedly resamples to prevent similar content from reappearing downstream. Critical planning statements resist removal but have large effects when eliminated. Fourth, since CoT is sometimes "unfaithful", can our methods teach us anything in these settings? Adapting causal mediation analysis, we find that hints that have a causal effect on the output without being explicitly mentioned exert a subtle and cumulative influence on the CoT that persists even if the hint is removed. Overall, studying distributions via resampling enables reliable causal analysis, clearer narratives of model reasoning, and principled CoT interventions.

Current advances in AI and its applicability have highlighted the need to ensure its trustworthiness for legal, ethical, and even commercial reasons. Sub-symbolic machine learning algorithms, such as the LLMs, simulate reasoning but hallucinate and their decisions cannot be explained or audited (crucial aspects for trustworthiness). On the other hand, rule-based reasoners, such as Cyc, are able to provide the chain of reasoning steps but are complex and use a large number of reasoners. We propose a middle ground using s(CASP), a goal-directed constraint-based answer set programming reasoner that employs a small number of mechanisms to emulate reliable and explainable human-style commonsense reasoning. In this paper, we explain how s(CASP) supports the 16 desiderata for trustworthy AI introduced by Doug Lenat and Gary Marcus (2023), and two additional ones: inconsistency detection and the assumption of alternative worlds. To illustrate the feasibility and synergies of s(CASP), we present a range of diverse applications, including a conversational chatbot and a virtually embodied reasoner.