# Feb 4, 2025

- **AI systems with 'unacceptable risk' are now banned in the EU**  
  The **EU's AI Act** now prohibits AI systems deemed to pose **"unacceptable risk,"** including those that manipulate decisions or exploit vulnerabilities, marking a significant regulatory shift in AI governance.

- **US Bill Proposes Jail Time for People Who Download DeepSeek**  
  **Senator Hawley's proposed bill** could impose **up to 20 years in prison** and a **$1 million fine** for downloading AI models like DeepSeek from China, reflecting a significant shift in U.S. policy towards AI technology and international collaboration.

- **DoppelBot: Replace Your CEO with an LLM**  
  **DoppelBot** allows users to fine-tune a large language model (LLM) on their own Slack messages, effectively creating a personalized assistant that can handle various tasks, from **API design** to **legal advice**.

- **OmniHuman-1: Human Animation Models**  
  **OmniHuman** introduces an **end-to-end multimodality-conditioned human video generation framework** that creates realistic human videos from a single image and various motion signals, significantly enhancing output quality, especially with audio inputs.

- **Efficient Reasoning with Hidden Thinking**  
  The **Heima framework** introduces a novel approach to **Chain-of-Thought reasoning** by utilizing a **single thinking token** to create compact hidden representations, significantly enhancing efficiency in Multimodal Large Language Models (MLLMs).

- **Decorator JITs: Python as a DSL**  
  **Decorator JITs** in Python leverage a **Domain Specific Language (DSL)** approach, allowing libraries like JAX and Triton to compile Python functions into optimized machine code without traditional execution, enhancing performance in machine learning tasks.

- **Open-source DeepResearch – Freeing our search agents**  
  **Open-source DeepResearch** aims to enhance LLM capabilities by integrating an **agentic framework**, which allows for complex task execution like web browsing and document reading, significantly improving performance metrics on benchmarks such as GAIA.

- **Radiant Foam: Real-Time Differentiable Ray Tracing**  
  **Radiant Foam** introduces a **novel scene representation** that combines the efficiency of splatting with the accuracy of volumetric mesh ray tracing, achieving real-time rendering without the need for specialized hardware.

- **I Use Cline for AI Engineering**  
  **Cline** stands out in the crowded AI coding assistant market by functioning as a **systems-level tool** that enhances complex engineering workflows through flexible context management and model switching capabilities.

- **DeepRAG: Thinking to Retrieval Step by Step for Large Language Models**  
  **DeepRAG** enhances **retrieval-augmented reasoning** by modeling it as a **Markov Decision Process (MDP)**, allowing for strategic and adaptive retrieval that reduces factual hallucinations in Large Language Models (LLMs).

- **How to Scale Your Model: A Systems View of LLMs on TPUs**  
  **Scaling LLMs on TPUs** requires understanding hardware interactions and optimizing parallelism to achieve efficient training and inference, addressing common questions about costs and memory needs.

- **Constitutional Classifiers: Defending against universal jailbreaks**  
  **Constitutional Classifiers** are a novel defense mechanism against **universal jailbreaks**, achieving a **jailbreak success rate reduction** from 86% to **4.4%** while maintaining a minimal increase in refusal rates and compute costs.

- **Auto-AVSR: Audio-Visual Speech Recognition with Automatic Labels**  
  **Auto-AVSR** is an open-source framework for **audio-visual speech recognition** that achieves a **20.3% word error rate (WER)** for visual speech recognition and **1.0% for audio speech recognition** on the LRS3 dataset, enhancing model reproducibility and performance.

- **On the Reasoning Capacity of AI Models and How to Quantify It**  
  **Recent advances in Large Language Models (LLMs)** reveal that while they excel in benchmarks like GPQA and MMLU, they struggle with complex reasoning tasks, necessitating **new evaluation methodologies** to assess their true capabilities.

- **Discussion on Federated Learning**  
  **Federated Learning** enables **decentralized learning**, allowing models to be trained across multiple devices without sharing raw data, enhancing privacy and security in machine learning applications.