Finance, VC & PE

The cosmetic procedure has been banned in China for two decades. But underground, a market has continued to flourish.

On Friday, June 26, EPIC filed an amicus brief alongside civil liberties and criminal defense organizations in New York v. Morris, an important case about cell phone privacy rights during criminal investigations. The brief was filed with the American Civil Liberties Union, the New York Civil Liberties Union, the Legal Aid Society, the Center for … Continued

To improve safety in Large Language Models (LLMs) we can either perform post-training alignment or exploit refusal directions in the activation space. Both strategies are less feasible in Multimodal LLMs (MLLMs) as they require unsafe multimodal data, harder to collect than their unimodal counterpart. In this work, we relax this constraint and investigate whether textual refusal directions, extracted directly from the LLM backbone, generalize across modalities (i.e., image, video). Preliminary f

Minority viewpoints are often suppressed in power-imbalanced group decision-making due to social pressure to comply with the majority. To address this problem, we developed an LLM-powered dissenting minority support system that aimed to foster attention to minority views through either AI-generated counterarguments or AI-mediated messages. We conducted a mixed-method experiment with 96 participants in 24 groups, comparing minority members' experiences across baseline, AI-counterargument, and AI-

In collaborative dialogue, shared perception does not guarantee shared interpretation. Mutual understanding must be established through interaction. We investigate whether vision-language models (VLMs) can distinguish what could be shared from what has been shared between dialogue participants through grounding. We formulate this as an interpretation-matching task on 13,077 annotated reference expressions from HCRC MapTask dialogues, and evaluate VLMs under systematically controlled manipulation

Artificial intelligence is transforming what is possible in agriculture, but industry leaders should be wary of investing in AI without first laying the groundwork. The use cases are promising, especially for an industry navigating volatile fertilizer costs, unpredictable weather, and margins that leave little room for error. Research shows AI-enabled predictive models can improve crop…

This work investigates uncertainty-aware deep learning approaches for direction of arrival (DOA) estimation in automotive radar, focusing on probabilistic modeling and downstream integration. A circular-statistics-based von Mises (VM) ensemble (ENS) is compared with an evidential deep learning (EDL) framework based on a normal inverse gamma formulation, yielding a Student t predictive distribution in the Euclidean domain. The ENS framework produces angular predictions parameterized by (mu, kappa

Adaptive experiments for average treatment effects (ATE) require randomized allocations balancing valid inference with statistical efficiency. The oracle design is a covariate-dependent Neyman rule governed by unknown arm-conditional outcome variances. We investigate whether this sequential variance-estimation and allocation process can be amortized via in-context learning. We introduce Bayesian in-context experimenters: transformer policies trained to imitate a Bayesian posterior Neyman teacher

Il Green Deal europeo è davvero in crisi? No, se guardiamo agli investimenti sulla transizione energetica. Capiamo che periodo stiamo attraversando e cosa ci attende per il prossimo futuro

Chinese autonomous driving company Momenta launched its Hong Kong public offering on June 29, with plans to list on the Hong Kong Stock Exchange’s main board under the ticker 6880.HK. The company is offering 19.94 million Class A ordinary shares at HK$295.60 each, aiming to raise about HK$5.89 billion ($751 million) before any over-allotment option […]

AIID editor's note: Please see the original source for the full report and all of its findings. Over the past year, a team of GPTZero investigators has used our Hallucination Check tool to uncover hallucinated citations in government repor ... (https://incidentdatabase.ai/cite/1563#7481)

Discovering simulation models for reuse remains a fundamental challenge in Modeling and Simulation (M&S). When many models coexist, identifying those that align with a given modeling intent remains difficult. Recent advances in Artificial Intelligence (AI), particularly retrieval-based approaches, offer a promising pathway to operate at this semantic layer. In this paper, we present an experimental study investigating the impact of data representation, transformer-based embedding models, and ret

Patrick George/Ikon Images What would you do with artificial intelligence if you were confident that it would transform your industry? What actions would you take if you felt that you were at an inflection point in that transformation? Would you try to be an early proponent of AI-first in your industry, or a fast follower? […]

El Instituto de Investigación Biomédica de Málaga busca personas voluntarias de entre 18 y 65 años para probar Pandora, una intervención digital personalizada que han desarrollado investigadores de España y Chile. El objetivo de la aplicación y el proyecto es mejorar el bienestar emocional, mental y físico

Per il fondatore di Morning Brew i lettori oggi cercano anzitutto una voce in cui riconoscersi. “La sovranità? Si ottiene investendo sui giornalisti”. Nella sua nuova iniziativa imprenditoriale, centrata sull'AI, monetizza con consulenze e formazione

Edison Scientific and investment firm Population Health Partners are teaming up to leverage AI agents in drug discovery and development.

RealT prometteva di aprire il mercato ai piccoli investitori, ma il progetto si è presto scontrato con immobili degradati, promesse disattese e problemi legali

On the heels of filing a landmark federal lawsuit against social media and tech giants, Bucks County District Attorney Joe Khan today announced the arrest of a New Britain Borough man facing multiple felony charges for producing and possess ... (https://incidentdatabase.ai/cite/1562#7480)

Across social and online platforms, people are increasingly exposed to AI-generated images. As a consequence, the task of distinguishing AI-generated from authentic images is becoming a central challenge for information ecosystems. While humans perform better than chance, accuracy falls short of many operational needs. Initial evidence shows that visually oriented training can improve deepfake detection but does not improve participants' ability to identify real images as real. Here, we investig

"The current push for AI adoption that we're seeing is directly coming from the financial incentives of AI firms," she added. Because of the massive capital expenditures, the hyperscalers and other AI firms are making a "deliberate push for AI everywhere — no matter whether the demand is there or if customers want it or not." The post Big Tech is spending trillions on AI. Investors now want proof it will pay off. appeared first on AI Now Institute .

GenAI is increasingly used by students as learning companions, yet little is known about how they use these tools in open-ended learning settings, where the goal is not to complete a specific task but to improve understanding and making progress. This study examined Grade-9 students' dialogue with a general-purpose LLM during mathematics practice, in which students prepared a curriculum-aligned skill for a later assessment. We investigated whether students' interactions revealed forms of epistem

Insurance fraud remains costly and operationally difficult, particularly in call-centre workflows where many customer interactions begin at FNOL. While recent fraud detection methods mainly rely on structured data, text, or images, repeated speaker identity across calls remains underused as an investigative signal. This paper presents DG^VoiC, a voice clustering framework for customer verification and cross-profile speaker linking on anonymised real call-centre audio. The approach combines sensi

Training-free compression methods for large language models (LLMs) often use calibration data to guide compression decisions. ROCKET, a recent method combining sparse-dictionary factorization with multi-choice knapsack problem (MCKP) allocation, derives its per-layer factorization from an output reconstruction objective but uses weight-space Frobenius error as the MCKP allocation cost. We investigate whether aligning the allocation cost with the output-space objective improves compressed model f

Recent work has shown that fine-tuning large language models (LLMs) for social warmth degrades factual reliability and increases sycophancy. We investigate a related but distinct failure mode: warmth fine-tuning also weakens adversarial safety, making models more susceptible to jailbreaks and harmful output generation. We examine whether this reflects an inherent consequence of empathetic adaptation or an artifact of data construction. To address this, we introduce a persona-driven rewriting pip

The tools are explicitly intended for generating deepfake nudes of a former Trump cabinet official, sitting members of Congress and a top American judge, a Transformer investigation found

A Citizen Lab investigation confirms evidence Russian authorities used Cellebrite tool to hack prominent Russian activist Andrey Pivovarov after the Cellebrite claimed to have ceased sales to Russia. The post Russia used Cellebrite tool to jail activist after company claimed to have ended contract appeared first on Access Now .

Traditional dynamic pricing models in large-scale e-commerce suffer from limited interpretability, poor utilization of unstructured information, and misalignment with long-term business objectives such as cumulative Gross Merchandise Value (GMV), Return on Investment (ROI) and milestone achievement. We propose AIGP, a novel framework that leverages a Large Language Model (LLM) prompted with domain knowledge, structured data and textual context to make interpretable, knowledge-aware pricing decis

LLM agents are transitioning from experimental tools to permanent infrastructure -- a computational layer as enduring as the electrical grid. Like any infrastructure, they carry a cost chain from physical capital through enterprise investment to user consumption, ending at the user's most irreplaceable resource: lifetime. When unoptimized, this chain leaks, consuming user lifetime without adequate compensation. This paper proposes Pingquanqi (Equalizer), a cross-domain sociotechnical framework f

Artificial intelligence depends on large-scale compute resources and their supporting infrastructure. However, AI governance debates treat compute primarily as a technical input rather than as an outcome of investment, ownership, and financial control. This paper examines AI infrastructure investment flows across Africa through a systematic analysis of 46 publicly announced projects totalling USD $12.7 billion between 2019 and 2025. Using a value chain framework, we analyze who invests in AI-rel

Warning: Includes graphic descriptions of animal harm and images of animal parts from the outset. A Bellingcat investigation has uncovered a Myanmar-based wildlife trafficker who has operated openly across social media for at least six years, claiming to have sold tiger bones, rhino horn, elephant skin and other products from protected and endangered species to […] The post Rhino Horn, Leopard Skin and Tiger Claws Sold Openly on Facebook appeared first on bellingcat .

Artificial Intelligence (AI) is rapidly reshaping the nature of work in software development, transforming user roles, workflows, and collaboration patterns across enterprise platforms. This qualitative study investigates how AI alters professional responsibilities within the context of SAP's Business Technology Platform (BTP), combining expert interviews (n=20) and a participatory workshop (n=24). The results reveal substantial shifts in day-to-day tasks and roles in the development domain, cha

Alibaba Group is planning to sell its gaming unit Lingxi Games, according to people familiar with the matter. Alibaba has approached at least five potential buyers, including Chinese game developers 37 Interactive Entertainment, China Ruyi, Century Huatong and Giant Network, as well as two private equity firms, the sources said. The business is being marketed […]

Executive Summary: ‍ A video circulating on social media falsely claims that India's Finance Minister, Smt. Nirmala Sitharaman, has endorsed an investment platform promising unusually high returns. Upon investigation, it was confirmed tha ... (https://incidentdatabase.ai/cite/1553#7446)

Jailbreak attacks reveal a persistent weakness in aligned Large Language Models: carefully crafted prompts can elicit policy-violating responses despite safety training. While most defenses operate at the prompt or output level, it remains unclear how harmful intent is encoded within the model's internal representations. We investigate this question by analyzing token-level predictive entropy trajectories across layers of a frozen LLM using the logit lens. We find that static aggregate statistic

Multi-agent reinforcement learning (MARL) addresses the problem of training multiple agents that pursue collaborative, competitive, or mixed objectives. Prior work has investigated transfer learning between source and target domains in MARL; however, the majority of existing approaches impose the constraint that the dimensionalities of the observation space and the global state space must be identical across domains. In this paper, we introduce a method that explicitly accommodates mismatched st

While the validity of LLMs' use in the legal context remains subject to ethical and legal debate, legal professionals are already experimenting with personal LLMs, if only for translation and reformulation. However, even such a seemingly innocuous use can introduce biases through case processing speed if LLM assistants selectively refuse assistance on certain topics. To better anticipate such biases, we investigate several modern small LLMs that are most likely to be used as on-device assistants

Scam phone calls exploit vulnerable communities worldwide, yet research on detection has focused almost exclusively on English and other high-resource languages. In low-resource settings such as Turkish, detection is especially difficult, as annotated data is scarce and technological defenses remain limited. This research investigates how large language models (LLMs) can support scam detection in Turkish by introducing the first public multi-modal dataset of 100 aligned audio-transcript pairs of

While in-context learning is generally shown to be effective in Large Language Models (LLMs), bad contexts can cause performance degradation and mode collapse, a phenomenon we call "pigeonholing." **Unintentionally bad** contexts can happen without malicious jailbreaking intents: For example, a user asks the model to justify an incorrect math theorem or fails to correct the model's buggy code. Specifically, we investigate ``pigeonholing" in two scenarios: (1) when the user suggests a solution, a

Despite the extensive discussions of human-centric AI (HCAI) in Industry 5.0, its effects on firms' idiosyncratic risks (IR) remains underexplored. This is an imperative issue for firms navigate financial risks during the current technological revolution, as IR reflects investor reactions to corporate heterogeneous AI strategies and implementations by isolating firm-level stock volatility from systematic factors. Integrating situated AI theory with social-technical systems theory, we conceptuali

Finance Agent v2 (by Vals AI) has emerged as the reference benchmark for evaluating both Anthropic Claude and OpenAI ChatGPT frontier language models on financial tasks. However, it narrowly deals with periodic reporting from publicly traded companies (SEC 10-K and 10-Q filings), and its agentic harness relies on naive, unenriched chunk retrieval. Neither the task design nor the retrieval approach addresses the distinct challenges of IPO due diligence. SEC S-1 filings combine historical financia