Mixture of Experts (MoE) models are a type of neural network architecture designed to improve efficiency and scalability by activating only a small subset of the entire model for each input. Instead of using all available parameters at once, MoE models route each input through a few specialized "expert" subnetworks chosen by a gating mechanism. This allows the model to be much larger and more powerful without significantly increasing the computation needed for each prediction, making it ideal for tasks that benefit from both specialization and scale.

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Sources:

1. https://arxiv.org/pdf/2407.06204
2. https://arxiv.org/pdf/2406.18219
3. https://tinyurl.com/5eyzspwp
4. https://huggingface.co/blog/moe

Meta AI has announced the Llama 4 family of large language models, highlighting two initial releases: Llama 4 Scout and Llama 4 Maverick. These new models feature native multimodality and an innovative mixture-of-experts architecture for enhanced efficiency and performance. Llama 4 Scout excels with a 10 million token context window, while Llama 4 Maverick demonstrates top-tier capabilities in understanding both text and images. These models were trained using distillation from a larger, more powerful model called Llama 4 Behemoth, which is currently still in training. Meta is making Llama 4 Scout and Llama 4 Maverick available for download to encourage open innovation and integration into various applications, including Meta AI features across their platforms. The release signifies a new phase for the Llama ecosystem, emphasizing advanced intelligence and practical usability.

This research article offers a comprehensive overview of deep learning (DL), positioning it as a vital technology within the Fourth Industrial Revolution. It meticulously examines various DL techniques, categorising them into supervised, unsupervised, and hybrid approaches, while also highlighting their diverse applications across sectors like healthcare, cybersecurity, and natural language processing. The paper further discusses the properties and dependencies of DL, differentiating it from traditional machine learning. Finally, it identifies key research directions and future aspects for advancing DL, aiming to serve as a valuable guide for both academic and industry professionals.

Source: https://www.researchgate.net/publication/353986944_Deep_Learning_A_Comprehensive_Overview_on_Techniques_Taxonomy_Applications_and_Research_Directions

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Researchers introduced AlphaDev, a deep reinforcement learning agent, that discovered faster sorting algorithms by framing the problem as a game played with CPU instructions. This AI agent outperformed existing human-developed benchmarks for small sorting routines, leading to their integration into the LLVM standard C++ sort library, a widely used component. AlphaDev achieved these improvements by optimizing for actual measured latency at the CPU instruction level, even finding novel instruction sequences called "swap move" and "copy move." The study also demonstrated AlphaDev's potential to generalize to other algorithm optimization challenges beyond sorting, such as protocol buffer deserialization, suggesting a new approach to fundamental algorithm discovery.Source: https://www.nature.com/articles/s41586-023-06004-9

This collection of sources centres on Microsoft's development of the Majorana 1 chip and its implications for quantum computing. The document explores the potential of topological qubits based on Majorana fermions to overcome limitations of existing superconducting qubit technologies from companies like IBM and Google. It highlights the necessity of achieving a million qubits for fault-tolerant quantum computing and discusses potential applications in cryptography, drug discovery, AI, and optimisation. The document also outlines the challenges in scaling quantum computers and Microsoft's roadmap for achieving a functional quantum supercomputer. Furthermore, it analyses Microsoft's competitive position and the potential impact of Majorana 1 on various industries.

Source: https://www.researchgate.net/profile/Douglas-Youvan/publication/389169814_Microsoft's_Majorana_1_A_Paradigm_Shift_Toward_Scalable_and_Fault-Tolerant_Quantum_Computing/links/67b757c2207c0c20fa8f5d36/Microsofts-Majorana-1-A-Paradigm-Shift-Toward-Scalable-and-Fault-Tolerant-Quantum-Computing.pdf

This research introduces ReAct, a novel prompting method that enhances language models by synergizing reasoning and acting. ReAct prompts language models to generate interleaved reasoning traces and actions, allowing dynamic reasoning and interaction with external environments. Experiments across diverse tasks like question answering, fact verification, text-based games, and web navigation demonstrate ReAct's superiority over isolated reasoning or action approaches. The approach not only improves task performance but also enhances model interpretability and trustworthiness. Further analysis shows the importance of both reasoning to guide actions and acting to inform reasoning. Moreover, initial experiments involving the application of ReAct in closed loop systems for tasks like robotic action planning reveals that ReAct produces more robust results. The work shows the potential for human intervention and correction, making this method a promising step towards better human-machine collaborations.

Source: https://arxiv.org/pdf/2210.03629

This research paper evaluates the performance of DeepSeek, a new large language model (LLM), against other popular models like Claude, Gemini, GPT, and Llama. The comparison focuses on two classification tasks: determining the authorship of text (human or AI-generated) and classifying academic citations based on their function. The study introduces new datasets, MadStatAI and CitaStat, for benchmarking LLMs in these tasks. Results indicate that DeepSeek is competitive, outperforming some models but generally falling short of Claude in accuracy. However, DeepSeek offers a balance of performance and cost-effectiveness, while Claude is more expensive and DeepSeek is comparatively slower. This work highlights the potential of DeepSeek and contributes valuable resources for future LLM research.Source: https://arxiv.org/html/2502.03688v1

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DeepSeek-AI introduces DeepSeek-R1, a reasoning model developed through reinforcement learning (RL) and distillation techniques. The research explores two models: DeepSeek-R1-Zero, trained purely via RL, and DeepSeek-R1, which incorporates multi-stage training and "cold-start" data before RL to improve reasoning capabilities and readability. The paper highlights DeepSeek-R1-Zero's emergent reasoning behaviors and DeepSeek-R1's performance comparable to OpenAI's o1-1217 on reasoning tasks. Distillation from DeepSeek-R1 is used to create smaller, more efficient models, demonstrating that reasoning patterns can be effectively transferred. The research also details the challenges and unsuccessful attempts during development, such as using Process Reward Models and Monte Carlo Tree Search. The models and distilled versions are open-sourced to support further research in the community.

This research paper explores the use of sparse autoencoders to extract interpretable features from Anthropic's Claude 3 Sonnet language model. The authors successfully scale this method to a large model, uncovering a diverse range of abstract features, including those related to safety concerns like bias, deception, and dangerous content. They investigate feature interpretability through examples and experiments, demonstrating that these features not only reflect but also causally influence model behavior. The study also examines the relationship between feature frequency and dictionary size, and compares the interpretability of features to that of individual neurons. Finally, the paper discusses the implications of these findings for AI safety and outlines future research directions.

Source: https://transformer-circuits.pub/2024/scaling-monosemanticity/