I propose a novel framework that integrates stochastic differential equations (SDEs) with deep generative models to improve uncertainty quantification in machine learning applications involving structured and temporal data. This approach, termed Stochastic Latent Differential Inference (SLDI), embeds an Itô SDE in the latent space of a variational autoencoder, allowing for flexible, continuous-time modeling of uncertainty while preserving a principled mathematical foundation. The drift and diffusion terms of the SDE are parameterized by neural networks, enabling data-driven inference and […]

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arXiv:2601.04217v1 Announce Type: new Abstract: The use of large language model (LLM)-based AI chatbots among college students has increased rapidly, yet little is known about how individual psychological attributes shape students’ interaction patterns with these technologies. This qualitative study explored how college students with different attachment styles describe their interactions with ChatGPT. Using semi-structured interviews with seven undergraduate students and grounded theory analysis, we identified three main themes: (1) AI as a low-risk emotional space, where participants across […]

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We present a novel approach for hand-object action recognition that leverages 2D point tracks as an additional motion cue. While most existing methods rely on RGB appearance, human pose estimation, or their combination, our work demonstrates that tracking randomly sampled image points across video frames can substantially improve recognition accuracy. Unlike prior approaches, we do not detect hands, objects, or interaction regions. Instead, we employ CoTracker to follow a set of randomly initialized points through each video and […]

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Introduction Disclaimer: This article is a cross post from Pytorch Medium Blog Post. One of the most exciting advancements, that has pushed the frontier of the Artificial Intelligence (AI) in recent years, is Deep Reinforcement Learning (DRL). DRL belongs to the family of machine learning algorithms. It assumes that intelligent machines can learn from their actions similar to the way humans learn from experience. Over the recent years we could witness some impressive real-world applications of DRL. The […]

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Dubai, UAE – December 2026: The global crypto community is about to witness a groundbreaking celebration of innovation, transparency, and true community spirit with the launch of iCrypto Awards: People’s Choice 2026, the world’s first blockchain and crypto awards determined entirely by the public.  Organized by iEvents PRO, the iCrypto Awards recognize the most outstanding projects, exchanges, companies, media outlets, and individuals shaping the future of blockchain, Web3, DeFi, NFTs, metaverse, AI innovation, tokenization, Layer 2 technologies, and […]

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Anthropic has released Bloom, an open source agentic framework that automates behavioral evaluations for frontier AI models. The system takes a researcher specified behavior and builds targeted evaluations that measure how often and how strongly that behavior appears in realistic scenarios. Why Bloom? Behavioral evaluations for safety and alignment are expensive to design and maintain. Teams must hand creative scenarios, run many interactions, read long transcripts and aggregate scores. As models evolve, old benchmarks can become obsolete or […]

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Rust vs. Haskell Rust and Haskell don’t shy away from powerful features. As a result, both languages have steep learning curves when compared with other languages. Trying to learn Rust or Haskell can be frustrating, especially in the first couple of months. But if you already know Rust, you have a head start with Haskell; and vice versa. In this article, we want to show how knowledge of one of these languages can help you get up to […]

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