Bootstrap Bio and Manhattan Genomics, which were pursuing gene editing in human embryos to prevent serious disease, have shut down. Two companies that launched last year with plans to create gene-edited babies have already shut down, citing money issues and internal conflict. One of them, Manhattan Genomics of New York, closed abruptly shortly after announcing a team of scientific advisers in October that included a prominent fertility doctor, a data scientist who worked for de-extinction company Colossal Biosciences, and a scientist who pioneered a "three-parent" IVF technique. The other, California-based Bootstrap Bio, said it ceased operations in late 2025, as first reported by Mother Jones. Manhattan Genomics and Bootstrap Bio had ambitions to edit DNA in human embryos with the goal of preventing serious disease in babies.

Senator accused of steamy affair with her bodyguard in bombshell lawsuit from his WIFE: 'Bring MDMA so I can guide you' Socialite who accused playboy twins of sex attack at Hamptons'castle' is found dead in unexplained circumstances Amy Schumer's friends reveal true meaning of thin bikini pictures and why they're'monitoring her'... as depth of ex Chris Fischer's heartbreak is laid bare Hunter Biden's stripper baby mama asks for him to be ARRESTED over claims he is still failing to pay her child support Ellen Greenberg's fiancé Sam Goldberg breaks cover as feds reopen probe into her'suicide'... and late teacher's mother shares incredible sign sent from beyond the grave Nicole Richie addresses her daughter's new identity after unveiling transformation on her 18th birthday '90s Vogue model Niki Taylor looks amazing as she sizzles at age 50 for new campaign Karoline Leavitt reveals the thinking behind Trump's call to cancel elections Family of Tyler Robinson's transgender lover speaks ...

Gunfire and anti-aircraft blasts heard near Venezuela's presidential palace as chaos unfolds after Maduro's ouster Stephen Miller declares Greenland should be part of US and'nobody will fight' over country's future Timothee Chalamet's declaration of love to his pneumatic girlfriend Kylie Jenner at the Critics Choice Awards has left me with a terribly rude thought: CAROLINE BULLOCK Trump vowed to deport one million migrants. But insiders say explosive data that Kristi Noem is desperate to hide tells the REAL story... The View audience left stunned as woke anti-MAGA co-host defends Trump's arrest of Maduro Trump says US will have to pay oil companies to rebuild Venezuela's aging infrastructure as he declares HIMSELF in charge of ambitious 18-month plan Furious fallen dictator Nicolás Maduro's hearing descends into chaos as he gets into shouting match with man claiming he was prisoner of his regime JFK's grandson Jack Schlossberg, 32, looks heartbroken as he attends sister Tatiana's funeral after she died of cancer aged just 35, with Joe Biden seen crying Kylie Jenner's curves spark surgery rumors as Timothee Chalamet grabs her behind at Critics Choice Awards SNL star Chloe Fineman reveals'botched' cosmetic treatments in candid photos as fans beg her to stop Secrets of JD Vance's'home attacker': Suspect is transgender daughter of wealthy surgeon Democrat donor as ultra-privileged life is revealed Ancient Bible reveals timeline for humanity's final'day' before divine judgment Gmail users advised to'turn off' two features NOW amid email privacy concerns'Super flu' still spreading uncontrollably... as cities see record number of cases and hospitalizations My shock discovery made me rethink everything I know about death. This is exactly what happens after your heart stops beating... and what you'll see, reveals neurosurgeon Caroline Kennedy cradles granddaughter at her daughter Tatiana Schlossberg's funeral, as doctor widower holds onto their son Gmail users advised to'turn off' two features NOW amid email privacy concerns Google users have been warned that they've been secretly opted in to a feature that allows the tech giant to access all their private emails. According to electronics design engineer Dave Jones of Australia, all Gmail users have had their accounts automatically selected to allow Google to scan their messages and attachments to help train its AI models like Gemini.

The boss of the AI tool ChatGPT has revealed that his eyeball-scanning orbs are coming to the US, as questions still swirl around this dystopian step into the future. Sam Altman announced Wednesday that the identity verification technology will now be available in six cities - Atlanta, Austin, Los Angeles, Miami, Nashville, and San Francisco. The expansion into the US is all part of Altman's plan to create a new global identity and financial network. Currently, Altman's cryptocurrency company World has rolled out the orb devices in more than 35 cities across over 20 countries worldwide. The main purpose of these eyeball scanners is to verify that each user is a'unique human,' not a bot or duplicate account.

Haiti police raid gang leader's stronghold in capital 3 hours agoShareSaveLeonardo RochaBBC World Service Americas regional editor Jaroslav LukivBBC NewsShareSaveReutersGang control in Port-au-Prince has led to an almost complete breakdown of law and order The government of Haiti says police have launched a large-scale operation in a shantytown controlled by powerful gang leader Jimmy Chérizier, who is widely known as Barbecue. The authorities say several gang members have been killed in the Lower Delmas area of the capital Port-au-Prince. Local reports say military drones carrying explosives are being used in the operation. He said it was the work of a special task force created two days ago to tackle insecurity.Reuters Jimmy'Barbecue' Chérizier has become one of the most powerful gang leaders in Haiti Chérizier, aged 47, is the feared leader of Viv Ansam (Live Together), a coalition of gangs that control much of the city. It is not clear whether Kenyan police officers deployed in Haiti last year to help fight the gangs are involved in the security operation.

Understanding the effects of economic shocks on firms is critical for analyzing economic growth and resilience. We introduce a Web-Based Affectedness Indicator (W AI), a general-purpose tool for real-time monitoring of economic disruptions across diverse contexts. By leveraging Large Language Model (LLM) assisted classification and information extraction on texts from over five million company websites, W AI quantifies the degree and nature of firms' responses to external shocks. Using the COVID-19 pandemic as a specific application, we show that W AI is highly correlated with pandemic containment measures and reliably predicts firm performance. Unlike traditional data sources, W AI provides timely firm-level information across industries and geographies worldwide that would otherwise be unavailable due to institutional and data availability constraints. This methodology offers significant potential for monitoring and mitigating the impact of technological, political, financial, health or environmental crises, and represents a transformative tool for adaptive policy-making and economic resilience. Economic shocks, whether driven by public health crises, technological disruptions, geopolitical conflicts, or climate events, pose significant challenges to businesses and policymakers alike. Timely and accurate monitoring of these shocks is critical for crafting effective responses and enhancing economic resilience. However, traditional methods for measuring the impacts of such disruptions - such as surveys and administrative data - are often limited by costs, time lags, and coverage. In this study, we introduce the Web-Based Affectedness Indicator (W AI), a scalable and cost-effective tool for real-time monitoring of economic disruptions at the firm level. By analyzing textual data from millions of company websites, W AI provides granular insights into how firms experience and respond to external shocks. This 1 methodology overcomes traditional limitations by leveraging ubiquitous online content and state-of-the-art natural language processing (NLP) models to generate a dynamic and comprehensive view of economic affectedness. W AI can provide information on a wide range of challenges, including supply chain disruptions, financial crises, and climate-related shocks.

Measuring the accuracy of face recognition (FR) systems is essential for improving performance and ensuring responsible use. Accuracy is typically estimated using large annotated datasets, which are costly and difficult to obtain. We propose a novel method for 1:1 face verification that benchmarks FR systems quickly and without manual annotation, starting from approximate labels (e.g., from web search results). Unlike previous methods for training set label cleaning, ours leverages the embedding representation of the models being evaluated, achieving high accuracy in smaller-sized test datasets. Our approach reliably estimates FR accuracy and ranking, significantly reducing the time and cost of manual labeling. We also introduce the first public benchmark of five FR cloud services, revealing demographic biases, particularly lower accuracy for Asian women. Our rapid test method can democratize FR testing, promoting scrutiny and responsible use of the technology.

We present a novel approach for enhancing diversity and control in data annotation tasks by personalizing large language models (LLMs). We investigate the impact of injecting diverse persona descriptions into LLM prompts across two studies, exploring whether personas increase annotation diversity and whether the impacts of individual personas on the resulting annotations are consistent and controllable. Our results show that persona-prompted LLMs produce more diverse annotations than LLMs prompted without personas and that these effects are both controllable and repeatable, making our approach a suitable tool for improving data annotation in subjective NLP tasks like toxicity detection.

Direct Preference Optimization (DPO) has been widely adopted for preference alignment of Large Language Models (LLMs) due to its simplicity and effectiveness. However, DPO is derived as a bandit problem in which the whole response is treated as a single arm, ignoring the importance differences between tokens, which may affect optimization efficiency and make it difficult to achieve optimal results. In this work, we propose that the optimal data for DPO has equal expected rewards for each token in winning and losing responses, as there is no difference in token importance. However, since the optimal dataset is unavailable in practice, we propose using the original dataset for importance sampling to achieve unbiased optimization. Accordingly, we propose a token-level importance sampling DPO objective named TIS-DPO that assigns importance weights to each token based on its reward. Inspired by previous works, we estimate the token importance weights using the difference in prediction probabilities from a pair of contrastive LLMs. We explore three methods to construct these contrastive LLMs: (1) guiding the original LLM with contrastive prompts, (2) training two separate LLMs using winning and losing responses, and (3) performing forward and reverse DPO training with winning and losing responses. Experiments show that TIS-DPO significantly outperforms various baseline methods on harmlessness and helpfulness alignment and summarization tasks. We also visualize the estimated weights, demonstrating their ability to identify key token positions.

The world is currently experiencing an outbreak of mpox, which has been declared a Public Health Emergency of International Concern by WHO. No prior work related to social media mining has focused on the development of a dataset of Instagram posts about the mpox outbreak. The work presented in this paper aims to address this research gap and makes two scientific contributions to this field. First, it presents a multilingual dataset of 60,127 Instagram posts about mpox, published between July 23, 2022, and September 5, 2024. The dataset, available at https://dx.doi.org/10.21227/7fvc-y093, contains Instagram posts about mpox in 52 languages. For each of these posts, the Post ID, Post Description, Date of publication, language, and translated version of the post (translation to English was performed using the Google Translate API) are presented as separate attributes in the dataset. After developing this dataset, sentiment analysis, hate speech detection, and anxiety or stress detection were performed. This process included classifying each post into (i) one of the sentiment classes, i.e., fear, surprise, joy, sadness, anger, disgust, or neutral, (ii) hate or not hate, and (iii) anxiety/stress detected or no anxiety/stress detected. These results are presented as separate attributes in the dataset. Second, this paper presents the results of performing sentiment analysis, hate speech analysis, and anxiety or stress analysis. The variation of the sentiment classes - fear, surprise, joy, sadness, anger, disgust, and neutral were observed to be 27.95%, 2.57%, 8.69%, 5.94%, 2.69%, 1.53%, and 50.64%, respectively. In terms of hate speech detection, 95.75% of the posts did not contain hate and the remaining 4.25% of the posts contained hate. Finally, 72.05% of the posts did not indicate any anxiety/stress, and the remaining 27.95% of the posts represented some form of anxiety/stress.