According to DeepLearning.AI, Andrew Ng reflects on the social energy and technical depth of AI Dev x NYC and how in-person events spark collaboration, community, and new opportunities in the AI developer ecosystem. Source: DeepLearning.AI on X, 2025-11-21, https://hubs.la/Q03Vk0V70 According to DeepLearning.AI, this edition of The Batch covers self-driving cars operating on U.S. freeways. Source: DeepLearning.AI on X, 2025-11-21, https://hubs.la/Q03Vk0V70 According to DeepLearning.AI, Moonshot releases Kimi K2 Thinking. Source: DeepLearning.AI on X, 2025-11-21, https://hubs.la/Q03Vk0V70 According to DeepLearning.AI, an Anthropic cyberattack report sparks controversy. Source: DeepLearning.AI on X, 2025-11-21, https://hubs.la/Q03Vk0V70 According to DeepLearning.AI, models learning to search their own parameters are discussed. Source: DeepLearning.AI on X, 2025-11-21, https://hubs.la/Q03Vk0V70 According to DeepLearning.AI, the post focuses on AI developments and does not include commentary on cryptocurrency market impacts. Source: DeepLearning.AI on X, 2025-11-21, https://hubs.la/Q03Vk0V70

According to @DeepLearningAI, the Halloween edition of The Batch highlights four AI risk themes for readers to examine: chatbots that warp reality, bubbles swelling to burst, crawlers trapped in digital webs, and drones that decide who lives or dies. Source: DeepLearning.AI tweet, Oct 30, 2025. The post frames these topics as the real scares coming from silicon and provides the issue link at hubs.la/Q03R1YDH0, offering a concise list of AI risk narratives. Source: DeepLearning.AI tweet, Oct 30, 2025.

According to @DeepLearningAI, researchers introduced GAIN-RL, a method that fine-tunes language models by training on the most useful examples first using a simple internal signal from the model, source: DeepLearning.AI on X dated Oct 5, 2025 and The Batch summary at hubs.la/Q03M9ZjV0. According to @DeepLearningAI, on Qwen 2.5 and Llama 3.2, GAIN-RL matched baseline accuracy in 70 to 80 epochs instead of 200, roughly 2.5 times faster, source: DeepLearning.AI on X dated Oct 5, 2025 and The Batch summary at hubs.la/Q03M9ZjV0. According to @DeepLearningAI, this acceleration can cut compute costs and shorten iteration cycles for teams building math- and code-focused assistants, which is directly relevant for trading assessments of AI training efficiency and cost structures, source: DeepLearning.AI on X dated Oct 5, 2025 and The Batch summary at hubs.la/Q03M9ZjV0.