Epstein's shadow: Why Bill Gates pulled out of Modi's AI summit Microsoft founder Bill Gates has cancelled his keynote speech at India's flagship AI summit just hours before he was due to take the stage on Thursday. Gates, who has faced renewed scrutiny over his past ties to the late sex offender Jeffrey Epstein, withdrew to "ensure the focus remains on the AI Summit's key priorities", the Gates Foundation said in a statement. India's Prime Minister Narendra Modi had billed the summit as an opportunity for India to shape the future of AI, drawing high-profile attendees, including French President Emmanuel Macron and Brazilian President Luiz Inacio Lula da Silva. Instead, it has been dogged by controversy, from Gates's abrupt exit to an incident in which an Indian university tried to pass off a Chinese-made robotic dog as its own innovation. So, what exactly went wrong at India's flagship AI gathering and why has it drawn such intense scrutiny?

A unique mating call led biologists to this newly discovered pint-sized amphibian. 'Brachycephalus lulai' is a tiny pumpkin toadlet measuring less than 14 millimeters in length. It is sitting on a pencil tip for scale. Breakthroughs, discoveries, and DIY tips sent every weekday. A new pumpkin toadlet species was recently discovered in the mountains of southern Brazil. is just over one centimeter (only 0.39 inches) long and the size of a pencil tip.

Retrieval-Augmented Generation (RAG) systems in the legal domain face a critical challenge: standard, flat-text retrieval is blind to the hierarchical, diachronic, and causal structure of law, leading to anachronistic and unreliable answers. This paper introduces the Structure-Aware Temporal Graph RAG (SAT-Graph RAG), an ontology-driven framework designed to overcome these limitations by explicitly modeling the formal structure and diachronic nature of legal norms. We ground our knowledge graph in a formal, LRMoo-inspired model that distinguishes abstract legal Works from their versioned Expressions. We model temporal states as efficient aggregations that reuse the versioned expressions (CTVs) of unchanged components, and we reify legislative events as first-class Action nodes to make causality explicit and queryable. This structured backbone enables a unified, planner-guided query strategy that applies explicit policies to deterministically resolve complex requests for (i) point-in-time retrieval, (ii) hierarchical impact analysis, and (iii) auditable provenance reconstruction. Through a case study on the Brazilian Constitution, we demonstrate how this approach provides a verifiable, temporally-correct substrate for LLMs, enabling higher-order analytical capabilities while drastically reducing the risk of factual errors. The result is a practical framework for building more trustworthy and explainable legal AI systems.

FIRST ON FOX: A pro-tech advocacy group has released a new report warning of the growing threat posed by China's artificial intelligence technology and its open-source approach that could threaten the national and economic security of the United States. The report, published by American Edge Project, states that "China is rapidly advancing its own open-source ecosystem as an alternative to American technology and using it as a Trojan horse to implant its CCP values into global infrastructure." "Their progress is both significant and concerning: Chinese-developed open-source AI tools are already outperforming Western models on key benchmarks, while operating at dramatically lower costs, accelerating global adoption. Through its Belt and Road Initiative (BRI), which spans more than 155 countries on four continents, and its Digital Silk Road (DSR), China is exporting its technology worldwide, fostering increased global dependence, undermining democratic norms, and threatening U.S. leadership and global security." The report outlines how Chinese AI models censor historical events that could paint China in a bad light, deny or minimize human rights abuses, and filter criticism of Chinese political leaders.

This work addresses the challenge of capturing the complexities of legal knowledge by proposing a multi-layered embedding-based retrieval method for legal and legislative texts. Creating embeddings not only for individual articles but also for their components (paragraphs, clauses) and structural groupings (books, titles, chapters, etc), we seek to capture the subtleties of legal information through the use of dense vectors of embeddings, representing it at varying levels of granularity. Our method meets various information needs by allowing the Retrieval Augmented Generation system to provide accurate responses, whether for specific segments or entire sections, tailored to the user's query. We explore the concepts of aboutness, semantic chunking, and inherent hierarchy within legal texts, arguing that this method enhances the legal information retrieval. Despite the focus being on Brazil's legislative methods and the Brazilian Constitution, which follow a civil law tradition, our findings should in principle be applicable across different legal systems, including those adhering to common law traditions. Furthermore, the principles of the proposed method extend beyond the legal domain, offering valuable insights for organizing and retrieving information in any field characterized by information encoded in hierarchical text.

In the absence of abundant reliable annotations for challenging tasks and contexts, how can we expand the frontier of LLM capabilities with potentially wrong answers? We focus on two research questions: (1) Can LLMs generate reliable preferences among wrong options? And if so, (2) Would alignment with such wrong-over-wrong preferences be helpful? We employ methods based on self-consistency, token probabilities, and LLM-as-a-judge to elicit wrong-over-wrong preferences, and fine-tune language models with preference optimization approaches using these synthesized preferences. Extensive experiments with seven LLMs and eight datasets demonstrate that (1) LLMs do have preliminary capability in distinguishing various shades of wrong, achieving up to 20.9% higher performance than random guess; (2) Alignment with wrong-over-wrong preferences helps LLMs to produce less wrong and sometimes even outright correct answers, while overall improving model calibration.

As the planet's largest economies struggle to forge consensus on the future of artificial intelligence, Brazil's Luiz Inacio Lula da Silva wants to ensure the developing world isn't left out of the debate. The Brazilian leader has added AI to his list of priorities for his country's presidency of the Group of 20 nations this year, seizing on the position to try to shape regulatory discussions that are raging from Europe to Asia to the United Nations, where the technology is expected to be a major theme of this week's General Assembly. Already seeking reforms to global institutions like the U.N. Security Council, Lula wants to use November's G20 leaders summit to craft a governance framework that includes the interests of Global South nations and forces AI superpowers China and the U.S. to the table, according to two people familiar with his views.

Brazil's national data protection authority has ordered Meta to halt the use of data originating from the country to train its AI models. Meta's current privacy policy enables the company to use data from its platforms, including Facebook, Instagram, and WhatsApp to train its artificial intelligence models. However, that practice will no longer be permitted in Brazil after its national data protection authority gave the company five days to change its policy on Tuesday. Brazil said the company will need to confirm it has stopped using the data or face a daily non-compliance fine of 50,000 Brazilian Reals (almost 9000), citing "the imminent risk of serious and irreparable or difficult-to-repair damage to the fundamental rights of the affected data subjects." Meta said it was "disappointed" with the Brazilian authority's decision, saying it was a "step backward for innovation."

Prime Minister Fumio Kishida will visit France, Brazil and Paraguay from Wednesday through May 6, the government said Friday. In Paris on Thursday, Kishida plans to give a keynote speech at a ministerial council meeting of the OECD and meet with French President Emmanuel Macron. The speech will reflect Kishida's intention to lead discussions to resolve socio-economic challenges for the international community, Chief Cabinet Secretary Yoshimasa Hayashi said at a news conference. Kishida is also set to deliver speeches at OECD events themed on generative artificial intelligence and on cooperation with Southeast Asia. In Brasilia on May 3, Kishida will meet with President Luiz Inacio Lula da Silva, this year's chair of the Group of 20 major economies, and hold a joint news conference.

Femicide refers to the killing of a female victim, often perpetrated by an intimate partner or family member, and is also associated with gender-based violence. Studies have shown that there is a pattern of escalating violence leading up to these killings, highlighting the potential for prevention if the level of danger to the victim can be assessed. Machine learning offers a promising approach to address this challenge by predicting risk levels based on textual descriptions of the violence. In this study, we employed the Long Short Term Memory (LSTM) technique to identify patterns of behavior in Brazilian police reports preceding femicides. Our first objective was to classify the content of these reports as indicating either a lower or higher risk of the victim being murdered, achieving an accuracy of 66%. In the second approach, we developed a model to predict the next action a victim might experience within a sequence of patterned events. Both approaches contribute to the understanding and assessment of the risks associated with domestic violence, providing authorities with valuable insights to protect women and prevent situations from escalating.