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.