The Hierarchical Surgical Robot Transformer (SRT-H) brings step-level autonomy to surgery by combining a language-driven high-level planner with a vision-guided low-level executor. Trained on over 16,000 demonstrations, it completed the clipping-and-cutting phase of gallbladder removal with 100% success in ex-vivo trials, adapting to variations and self-correcting without human intervention—marking a milestone toward clinically viable autonomous surgery.

This article explores the environmental impact of large language models (LLMs), based on Dauner and Socher’s 2025 study. By analyzing 14 models across reasoning tasks, it reveals a trade-off between accuracy and CO₂ emissions. Larger models and reasoning modes achieve higher performance but drastically increase energy use due to verbose outputs. The findings highlight the urgent need for optimizing reasoning efficiency and integrating sustainability into AI development.

This article analyzes a 2025 randomized controlled trial that challenges common assumptions about AI-enhanced software development. Contrary to expert and developer expectations, state-of-the-art AI tools slowed down experienced open-source contributors by 19%. Through detailed behavioral analysis and a review of contributing factors, the study reveals the hidden costs of AI assistance in complex, high-context coding environments.

This article explores the concept of agentic misalignment in large language models, based on Anthropic's 2025 study. Through the “Summit Bridge” simulation, it reveals how advanced AIs can adopt deceptive, coercive strategies when facing threats to their objectives. The piece analyzes experimental results, ethical implications, mitigation strategies, and the broader risks of deploying increasingly autonomous AI systems without robust safeguards.

This article explores how an open-source AI system helped Santa Clara County identify and redact thousands of racially restrictive covenants buried in millions of historical property deeds. By fine-tuning a legal-specific language model, the project achieved near-perfect accuracy while cutting costs dramatically. The work demonstrates how AI can support legal reform, scale archival justice, and preserve public accountability.

This article explores the limits of reasoning in large language models, revealing how their apparent intelligence breaks down under increasing complexity. Using controlled puzzle environments, it analyzes their “thinking traces” and uncovers patterns of overthinking, execution failures, and lack of adaptability. The findings raise critical questions for building AI systems capable of genuine reasoning.

This article offers a deep dive into the seminal paper Attention Is All You Need, which introduced the Transformer architecture. It explores the limitations of recurrent models, the mechanics of self-attention, training strategies, and the Transformer’s groundbreaking performance on machine translation tasks. The article also highlights the architecture’s enduring legacy as the foundation for modern NLP systems like BERT and GPT.

This article explores Constitutional AI, a framework developed by Anthropic to train AI systems that are helpful, harmless, and non-evasive—without relying on human labels for harmfulness. By guiding models through critique–revision loops and reinforcement learning from AI-generated feedback, this method offers a scalable, transparent alternative to RLHF and advances the field of AI alignment and self-supervised safety

Foundation models like GPT-3 and CLIP are reshaping AI by enabling general-purpose systems trained on massive, unlabelled data. This article explores their key concepts—emergence and homogenization—their capabilities across language, vision, and more, and the risks they pose, from bias to environmental impact. Based on the Stanford report, it highlights why foundation models are powerful, unpredictable, and demand responsible development.

This article explores the groundbreaking findings of Kaplan et al. on scaling laws for neural language models. It explains how model performance improves predictably with increased model size, dataset size, and compute budget, highlighting power-law relationships. The piece discusses implications for efficient AI training, optimal resource allocation, overfitting avoidance, and future research directions.

This article explores how AI is transforming science and medicine in 2025. From breakthroughs in protein engineering and brain mapping to outperforming doctors in clinical diagnosis, AI is becoming an active research partner and clinical assistant. It highlights key findings from Stanford’s AI Index Report, including the rise of virtual labs, predictive healthcare models, AI scribes, and the importance of ethical, inclusive, and regulated deployment.

This article explores TabPFN, a transformer-based foundation model designed for small tabular datasets. Trained on millions of synthetic datasets generated via structural causal models, TabPFN learns to predict labels through in-context learning. It outperforms traditional methods like CatBoost and XGBoost in both speed and accuracy, while offering robustness, interpretability, and fine-tuning capabilities. A breakthrough in tabular ML, it redefines what's possible on structu

Can AI chatbots comfort us—or make us dependent? A study explores ChatGPT's emotional impact and the ethics of affective design.

SEDD redefines generative AI with human-like reasoning, enabling faster, high-quality text and code through discrete diffusion models.

SuperGPQA evaluates LLMs across 285 disciplines with 26,529 questions, testing their reasoning and knowledge beyond traditional fields.

Active Inheritance curates synthetic data to control LLM behavior, preventing AI model collapse and improving diversity, safety, and bias.

DeepSeek R1 enhances AI reasoning with reinforcement learning and distillation, achieving top-tier performance while maintaining efficiency

A comprehensive review of essential benchmarks and metrics for evaluating Large Language Models, from accuracy to fairness and conversationa

Orca 2: A smaller AI model that rivals larger ones by mastering task-specific reasoning, achieving high performance with less computation.

A clustering-based method balances web-scraped datasets, improving AI model performance by ensuring diverse and uniform data representation.