An Apple study shows that large language models (LLMs) can improve performance by using a checklist-based reinforcement learning scheme, similar to a simple productivity trick of checking one's work.
DeepMind introduces Ithaca, a deep neural network that can restore damaged ancient Greek inscriptions, identify their original location, and help establish their creation date, collaborating with historians to advance understanding of ancient history.
A detailed comparison of the architectures of recent large language models (LLMs) including DeepSeek-V3, OLMo 2, Gemma 3, Mistral Small 3.1, Llama 4, Qwen3, SmolLM3, and Kimi 2, focusing on key design choices and their impact on performance and efficiency.
This book covers foundational topics within computer vision, with an image processing and machine learning perspective. It aims to build the reader’s intuition through visualizations and is intended for undergraduate and graduate students, as well as experienced practitioners.
DeepMind researchers propose a new 'streams' approach to AI development, focusing on experiential learning and autonomous interaction with the world, moving beyond the limitations of current large language models and potentially surpassing human intelligence.
Newsweek interview with Yann LeCun, Meta's chief AI scientist, detailing his skepticism of current LLMs and his focus on Joint Embedding Predictive Architecture (JEPA) as the future of AI, emphasizing world modeling and planning capabilities.
This article examines the dual nature of Generative AI in cybersecurity, detailing how it can be exploited by cybercriminals and simultaneously used to enhance defenses. It covers the history of AI, the emergence of GenAI, potential threats, and mitigation strategies.
AlexNet, a groundbreaking neural network developed in 2012 by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton, has been released in source code form by the Computer History Museum in collaboration with Google. This model significantly advanced the field of AI by demonstrating a massive leap in image recognition capabilities.
ByteDance Research has released DAPO (Dynamic Sampling Policy Optimization), an open-source reinforcement learning system for LLMs, aiming to improve reasoning abilities and address reproducibility issues. DAPO includes innovations like Clip-Higher, Dynamic Sampling, Token-level Policy Gradient Loss, and Overlong Reward Shaping, achieving a score of 50 on the AIME 2024 benchmark with the Qwen2.5-32B model.
AAAI survey finds that most respondents are sceptical that the technology underpinning large-language models is sufficient for artificial general intelligence.
>"More than three-quarters of respondents said that enlarging current AI systems ― an approach that has been hugely successful in enhancing their performance over the past few years ― is unlikely to lead to what is known as artificial general intelligence (AGI). An even higher proportion said that neural networks, the fundamental technology behind generative AI, alone probably cannot match or surpass human intelligence. And the very pursuit of these capabilities also provokes scepticism: less than one-quarter of respondents said that achieving AGI should be the core mission of the AI research community.
>"More than three-quarters of respondents said that enlarging current AI systems ― an approach that has been hugely successful in enhancing their performance over the past few years ― is unlikely to lead to what is known as artificial general intelligence (AGI). An even higher proportion said that neural networks, the fundamental technology behind generative AI, alone probably cannot match or surpass human intelligence. And the very pursuit of these capabilities also provokes scepticism: less than one-quarter of respondents said that achieving AGI should be the core mission of the AI research community.
