# Deep Learning İlgili Makaleler

HTX Haber Merkezi, kripto endüstrisindeki piyasa trendleri, proje güncellemeleri, teknoloji gelişmeleri ve düzenleyici politikaları kapsayan "Deep Learning" hakkında en son makaleleri ve derinlemesine analizleri sunmaktadır.

Turing Award Laureate Sutton's New Work: Using a Formula from 1967 to Solve a Major Flaw in Streaming Reinforcement Learning

New research titled "Intentional Updates for Streaming Reinforcement Learning" (arXiv:2604.19033v1), involving Turing Award laureate Richard Sutton, addresses a core challenge in deep reinforcement learning (RL): the "stream barrier." Current deep RL methods typically rely on replay buffers and batch training for stability, failing catastrophically when learning online from single data points (streaming). The authors propose a fundamental shift: instead of prescribing how far to move parameters (a fixed step size), their "Intentional Updates" method specifies the desired change in the function's output (e.g., a 5% reduction in value prediction error). It then calculates the step size needed to achieve that intent. This idea is inspired by the Normalized Least Mean Squares (NLMS) algorithm from 1967. Applied to value and policy learning, this yields algorithms like Intentional TD(λ) and Intentional AC. The method inherently stabilizes learning by adapting the step size based on the local gradient landscape, preventing overshooting/undershooting. In experiments on MuJoCo continuous control and Atari discrete tasks, Intentional AC achieved performance rivaling batch-based algorithms like SAC in a streaming setting (batch size=1, no replay buffer), while being ~140x more computationally efficient per update. The work demonstrates significant robustness, reducing reliance on numerous stabilization tricks. A remaining challenge is bias in policy updates due to action-dependent step sizes. Overall, this approach advances efficient, online, "learn-as-you-go" RL, enabling adaptive systems without massive data buffers or compute clusters.

marsbit05/10 06:28

Turing Award Laureate Sutton's New Work: Using a Formula from 1967 to Solve a Major Flaw in Streaming Reinforcement Learning

marsbit05/10 06:28

Farewell to Brute Force Computing: Reconstructing the Valuation Logic of AI for Science through HKUST's "GrainBot"

In 2026, Hong Kong's AI sector is rapidly transitioning from infrastructure development to deep application deployment. A key example is GrainBot, an AI tool developed by a team led by Prof. Guo Yike at HKUST, which represents a significant shift from general-purpose AI to specialized scientific discovery. GrainBot addresses critical challenges in materials science, particularly in analyzing microstructures like grain boundaries in materials used in semiconductors, batteries, and solar panels. Traditionally, this required manual, time-consuming, and error-prone analysis of microscopy images. GrainBot automates this process using computer vision and deep learning to accurately identify, segment grains, and quantify geometric features. It also correlates microstructural data with macro-material properties, as demonstrated in its application to perovskite solar cell research. This breakthrough highlights a broader trend in AI for Science (AI4S), where value is measured not by user metrics but by accelerated R&D cycles and novel discoveries. GrainBot’s potential to drastically shorten development timelines or uncover new materials with superior properties underscores a new valuation logic centered on industrial intellectual property. Hong Kong’s strength in combining domain expertise (e.g., materials science, chemistry) with AI capabilities creates a competitive advantage, positioning it as a hub for "autonomous labs" that integrate AI analysis with robotic experimentation. This model enables high-value patent output through fully automated, data-driven R&D, supporting a "Hong Kong R&D + Bay Area manufacturing" framework. However, challenges remain, particularly regarding data scarcity and silos in scientific research. High-quality, annotated datasets are limited, and data sharing barriers must be overcome through secure mechanisms like privacy computing for broader commercialization. GrainBot symbolizes a convergence of algorithmic innovation and scientific rigor, redirecting investment focus from sheer compute power to AI’s ability to solve real-world physical challenges. Hong Kong’s progress in AI4S signals emerging opportunities in a trillion-dollar AI-driven discovery market.

marsbit03/05 09:42

Farewell to Brute Force Computing: Reconstructing the Valuation Logic of AI for Science through HKUST's "GrainBot"

marsbit03/05 09:42

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