The AI job cuts are here - or are they? Amazon's move this week to slash thousands of corporate jobs fed into a longstanding anxiety: that Artificial Intelligence is starting to replace workers. The tech giant joined a growing list of companies in the US that have pointed to AI technology as a reason behind layoffs. But some question whether AI is fully to blame - and have voiced scepticism that recent high-profile layoffs are a telling sign of the technology's effect on employment. Chegg, the online education firm, cited the new realities of AI as it announced a 45% reduction in workforce on Monday.

Random Forest ensembles are a strong baseline for tabular prediction tasks, but their reliance on hundreds of deep trees often results in high inference latency and memory demands, limiting deployment in latency-sensitive or resource-constrained environments. We introduce DiNo (Distance with Nodes) and RanBu (Random Bushes), two shallow-forest methods that convert a small set of depth-limited trees into efficient, distance-weighted predictors. DiNo measures cophenetic distances via the most recent common ancestor of observation pairs, while RanBu applies kernel smoothing to Breiman's classical proximity measure. Both approaches operate entirely after forest training: no additional trees are grown, and tuning of the single bandwidth parameter $h$ requires only lightweight matrix-vector operations. Across three synthetic benchmarks and 25 public datasets, RanBu matches or exceeds the accuracy of full-depth random forests-particularly in high-noise settings-while reducing training plus inference time by up to 95\%. DiNo achieves the best bias-variance trade-off in low-noise regimes at a modest computational cost. Both methods extend directly to quantile regression, maintaining accuracy with substantial speed gains. The implementation is available as an open-source R/C++ package at https://github.com/tiagomendonca/dirf. We focus on structured tabular random samples (i.i.d.), leaving extensions to other modalities for future work.

Machine Learning (ML) models are widely used in high-stakes domains such as healthcare, where the reliability of predictions is critical. However, these models often fail to account for uncertainty, providing predictions even with low confidence. This work proposes a novel two-step data-centric approach to enhance the performance of ML models by improving data quality and filtering low-confidence predictions. The first step involves leveraging Instance Hardness (IH) to filter problematic instances during training, thereby refining the dataset. The second step introduces a confidence-based rejection mechanism during inference, ensuring that only reliable predictions are retained. We evaluate our approach using three real-world healthcare datasets, demonstrating its effectiveness at improving model reliability while balancing predictive performance and rejection rate. Additionally, we use alternative criteria - influence values for filtering and uncertainty for rejection - as baselines to evaluate the efficiency of the proposed method. The results demonstrate that integrating IH filtering with confidence-based rejection effectively enhances model performance while preserving a large proportion of instances. This approach provides a practical method for deploying ML systems in safety-critical applications.

The cloze test, widely used due to its low cost and flexibility, makes it possible to assess reading comprehension by filling in gaps in texts, requiring the mobilization of diverse linguistic repertoires. However, traditional correction methods, based only on exact answers, limit the identification of nuances in student performance. This study proposes an automated evaluation model for the cloze test in Brazilian Portuguese, integrating orthographic (edit distance), grammatical (POS tagging) and semantic (similarity between embeddings) analyses. The integrated method demonstrated its effectiveness, achieving a high correlation with human evaluation (0.832). The results indicate that the automated approach is robust, sensitive to variations in linguistic repertoire and suitable for educational contexts that require scalability.

Understanding people's preferences is crucial for urban planning, yet current approaches often combine responses from multi-cultural populations, obscuring demographic differences and risking amplifying biases. We conducted a largescale urban visual perception survey of streetscapes worldwide using street view imagery, examining how demographics -- including gender, age, income, education, race and ethnicity, and personality traits -- shape perceptions among 1,000 participants with balanced demographics from five countries and 45 nationalities. This dataset, Street Perception Evaluation Considering Socioeconomics (SPECS), reveals demographic- and personality-based differences across six traditional indicators -- safe, lively, wealthy, beautiful, boring, depressing -- and four new ones -- live nearby, walk, cycle, green. Location-based sentiments further shape these preferences. Machine learning models trained on existing global datasets tend to overestimate positive indicators and underestimate negative ones compared to human responses, underscoring the need for local context. Our study aspires to rectify the myopic treatment of street perception, which rarely considers demographics or personality traits.

Large language models (LLMs) hold great promise for medical applications and are evolving rapidly, with new models being released at an accelerated pace. However, benchmarking on large-scale real-world data such as electronic health records (EHRs) is critical, as clinical decisions are directly informed by these sources, yet current evaluations remain limited. Most existing benchmarks rely on medical exam-style questions or PubMed-derived text, failing to capture the complexity of real-world clinical data. Others focus narrowly on specific application scenarios, limiting their generalizability across broader clinical use. To address this gap, we present BRIDGE, a comprehensive multilingual benchmark comprising 87 tasks sourced from real-world clinical data sources across nine languages. It covers eight major task types spanning the entire continuum of patient care across six clinical stages and 20 representative applications, including triage and referral, consultation, information extraction, diagnosis, prognosis, and billing coding, and involves 14 clinical specialties. We systematically evaluated 95 LLMs (including DeepSeek-R1, GPT-4o, Gemini series, and Qwen3 series) under various inference strategies. Our results reveal substantial performance variation across model sizes, languages, natural language processing tasks, and clinical specialties. Notably, we demonstrate that open-source LLMs can achieve performance comparable to proprietary models, while medically fine-tuned LLMs based on older architectures often underperform versus updated general-purpose models. The BRIDGE and its corresponding leaderboard serve as a foundational resource and a unique reference for the development and evaluation of new LLMs in real-world clinical text understanding. The BRIDGE leaderboard: https://huggingface.co/spaces/YLab-Open/BRIDGE-Medical-Leaderboard

Nvidia, the artificial intelligence (AI) chip leader, will build seven new supercomputers for the United States Department of Energy (DOE), CEO Jensen Huang has said. The company has $500bn in bookings for its AI chips, Huang said on Tuesday in a keynote address at the company's GTC event in Washington, DC, the US capital. It is striking deals around the world while also navigating a US-China trade war that could determine which country's technology is most used across the globe. Investors are looking for clarity on what chips the tech company will be able to sell to the vast Chinese market, but Huang in his keynote speech praised policies by US President Donald Trump while announcing new products and deals. These included network technology that will let Nvidia AI chips work with quantum computers.

'Monster' hurricane Melissa makes landfall in Jamaica as multiple people are left dead: Live updates Here are the REAL danger signs you're drinking too much. Forget the crippling headache and brain fog, now doctors reveal the five little-known alarm bells... if you suffer these this is what it's time to do Three US Air Force members are found dead overnight after husband'murdered wife and her colleague before killing himself' Alec Baldwin's daughter Ireland, 30, makes rare sighting with mom Kim Basinger, 71... after calling her family'poisonous' Warning gold rally is turning into a'mini-bust' as prices keep falling I know the pathetic truth about Kristen Bell's'cry for help' that will settle this domestic violence scandal once and for all: KENNEDY'Humiliating' truth about influencer TooTurntTony and his extreme stunts: He's ripped, makes $3m a year and has all the hottest girls... but a dark reality lies beneath LIZ JONES: Why I believe ruthless Kate's the driving force behind Andrew's eviction - and why no one now dares cross her Netanyahu orders'powerful strikes in Gaza' after accusing Hamas of violating ceasefire terms following'faked' return of hostage remains Doctors thought I was on drugs... but they were left horrified when they looked inside my ear A simple, non-surgical medical procedure is giving men the penis shape that ALL women secretly love. The real reasons you wake up at 3am. No it's not just regular insomnia - there's hidden causes that are so easy to fix. Chris Evans, 44, welcomes first child with wife Alba Baptista, 28, as baby's gender and name is revealed Ex-SNL stars break silence on show's'challenging' workplace amid firing bloodbath and mass cast exodus Man's simple diet and exercise regime allows him to run marathons at 91.

Russian troops are making a concerted push in eastern Ukraine and have gained a foothold in the strategic hub of Pokrovsk, Ukrainian President Volodymyr Zelensky says. Moscow's soldiers outnumber Kyiv's 8-1 in the area and Ukraine cannot match that, Zelensky added while insisting Russia had not yet achieved the planned result. Russia has been trying to capture Pokrovsk for two years. The key supply and transport hub provides supplies and reinforcements to the eastern front - and it would get Moscow closer to occupying the entirety of the Donetsk region. It would also put towns of the heavily fortified fortress belt - Kramatorsk, Slovyansk, Kostyantynivka and Druzhkivka - within easier reach of Moscow.

'Monster' hurricane Melissa makes landfall in Jamaica as multiple people are left dead: Live updates Alec Baldwin's daughter Ireland, 30, makes rare sighting with mom Kim Basinger, 71... after calling her family'poisonous' Netanyahu orders'powerful strikes in Gaza' after accusing Hamas of violating ceasefire terms following'faked' return of hostage remains Warning gold rally is turning into a'mini-bust' as prices keep falling Poignant moment Trump is gifted priceless Abe golfing relic ahead of signing landmark deal... and issuing gushing praise of Japan LIZ JONES: Why I believe ruthless Kate's the driving force behind Andrew's eviction - and why no one now dares cross her Boss of Google's self-driving car company makes dystopian statement about the vehicles killing people Bill Gates now says climate change won't be as serious as he fears - and calls for more spending on vaccines instead Chris Evans, 44, welcomes first child with wife Alba Baptista, 28, as baby's gender and name is revealed I traveled to Latin America for a discount tummy tuck... Apple Martin releases music video after nepo baby's singing was slammed as'off-key drunken karaoke performance' Jennifer Lawrence admits she's planning on a boob job as she reveals all the plastic surgery she's had The mysterious interstellar visitor traveling through our solar system may finally reveal its true nature in just hours, as scientists wait for it to emerge from behind the sun. While many astronomers are convinced the object known as 3I/ATLAS will be confirmed as a comet, some scientists have said the three-mile-long visitor could be an artificially constructed craft that's maneuvering around the solar system. Scientists expect to determine which scenario is correct once they observe exactly where the object exits perihelion, saying that a noticeable shift in its trajectory tomorrow could indicate that 3I/ATLAS is artificially powered. In space travel, the most effective moment to accelerate or decelerate a spacecraft is when it is closest to a massive body. Firing the engine at this point, an effect known as the Oberth effect, produces the greatest change in speed.