Gene Chuang’s Post

You don't have to understand the type of math Terence Tao studies (I don't) to glean takeaways from his comments about using AI to explore the boundaries of math. Having strong subject matter expertise is crucial for getting the most out of AI tools. AI use cases are often complex and context-dependent, so identifying which ones are valuable also requires at minimum a basic understanding of how the tools work, including their capabilities and limitations. Determining which problems can best be addressed with AI, how to define them properly, and then integrating the tool into the workflow demands a high level of expertise - both topic and tool. Here's a great quote: "I think at the frontier, we will always need humans and AI. They have complementary strengths. AI is very good at converting billions of pieces of data into one good answer. Humans are good at taking 10 observations and making really inspired guesses." Lane Dilg, thanks for sharing the article :)

Introducing **Qwen QVQ** by Alibaba - A Leap in Multimodal Reasoning! 🌟 Imagine an AI that can look at a complex physics problem or analyze a math puzzle and solve it step by step like a master physicist or mathematician. QVQ, a cutting-edge model built on Qwen2-VL-72B, designed for multimodal reasoning—combining language and vision to tackle tough problems! 🧠 What Makes QVQ Special? - Supercharged Visual Reasoning: QVQ excels in understanding visuals and reasoning through challenges, making it a powerhouse for solving complex math and science problems. - Higher Benchmarks: It scored a whopping 70.3 on the MMMU dataset, outperforming previous models in visual understanding and problem-solving. - Step-by-Step Thinking: QVQ shines in breaking down problems logically, ensuring clear and accurate reasoning. - 🔬 Where Does It Excel? Tested QVQ on 4 tough benchmarks: - MMMU: Multidisciplinary challenges to test visual reasoning. - MathVista: Logical puzzles and math problems with graphs. - MathVision: Real-world math competition problems. - OlympiadBench: Physics and math challenges from international competitions. Showing incredible problem-solving skills in diverse scenarios, performance comparable to openai o1. 🚧 Limitations to Keep in Mind: While QVQ is groundbreaking, it’s still evolving: - It sometimes mixes languages or goes off-track in reasoning. - Complex multi-step problems can cause it to lose focus. - Safety and ethical deployment need more work. Muhammad Rizwan Munawar Reasoning AI is the future now. #AI #ML #NLP #LLM #REASONINGAI #GENAI

 New Machine Learning Research: Simple and Efficient Parameter Upcycling for Mixture of Experts A team from Cohere and the University of Oxford, including Nikolas Gritsch, and Qizhen (Irene) Zhang, has developed BAM (Branch-Attend-Mix), a new method for optimizing large language models using a Mixture of Experts (MoE) approach. - Research goal: To enhance the efficiency and performance of large language models by upcycling both feed-forward network (FFN) and attention parameters into MoE models. - Research methodology: BAM initializes an MoE by branching from a pre-trained dense model into specialized domains like law, mathematics, and coding. It leverages both FFN and attention parameters, employing a parallel attention transformer architecture to compute attention and FFN experts concurrently. - Key findings: BAM outperforms the baseline BTX model in perplexity and downstream task performance. For instance, BAM achieved a 10% reduction in perplexity in code generation and an 8% improvement in legal text analysis tasks. - Practical implications: These improvements reduce computational costs and time in deploying large-scale language models, making them more efficient in specialized applications such as legal document review, code synthesis, and complex mathematical problem-solving. Stay tuned for more insights on the latest ML advancements! #ai #dataannotation #ml

Very interesting article about a topic I believe is quite relevant and topical. Recently I have been involved in a project along these lines.

𝐄𝐱𝐩𝐥𝐨𝐫𝐢𝐧𝐠 𝐋𝐢𝐯𝐞𝐁𝐞𝐧𝐜𝐡: 𝐀 𝐍𝐞𝐰 𝐅𝐫𝐨𝐧𝐭𝐢𝐞𝐫 𝐢𝐧 𝐋𝐋𝐌 𝐁𝐞𝐧𝐜𝐡𝐦𝐚𝐫𝐤𝐢𝐧𝐠 🌟 Great news for the AI and machine learning community! Let's dive into 𝐋𝐢𝐯𝐞𝐁𝐞𝐧𝐜𝐡, a pioneering benchmark developed by researchers aiming to robustly evaluate Large Language Models (LLMs) without the common pitfalls of dataset contamination or biases from human or LLM judging. 🔍 𝐊𝐞𝐲 𝐅𝐞𝐚𝐭𝐮𝐫𝐞𝐬 𝐨𝐟 𝐋𝐢𝐯𝐞𝐁𝐞𝐧𝐜𝐡: 𝐑𝐞𝐠𝐮𝐥𝐚𝐫𝐥𝐲 𝐔𝐩𝐝𝐚𝐭𝐞𝐝: LiveBench is dynamic, with questions sourced from the latest math competitions and academic papers, updated monthly. 𝐎𝐛𝐣𝐞𝐜𝐭𝐢𝐯𝐞 𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧: Utilizes automatic scoring based on objective truth, avoiding biases that can occur with human or automated judges. 𝐂𝐨𝐦𝐩𝐫𝐞𝐡𝐞𝐧𝐬𝐢𝐯𝐞 𝐓𝐞𝐬𝐭𝐢𝐧𝐠: Includes a variety of tasks such as math, coding, reasoning, language, instruction following, and data analysis, designed to thoroughly test LLM capabilities. 📈 The rigorous nature of LiveBench is evident as even the top performing models achieve below 60% accuracy, illustrating the challenge it poses and its role in driving model improvements. 🌍 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲 𝐈𝐧𝐯𝐨𝐥𝐯𝐞𝐦𝐞𝐧𝐭: The benchmark invites contributions and collaboration from the global AI community. To get involved or to learn more about how you can use LiveBench for your projects, check out their resources on GitHub or visit the LiveBench.ai leaderboard. 🛠️ 𝐖𝐡𝐲 𝐢𝐬 𝐋𝐢𝐯𝐞𝐁𝐞𝐧𝐜𝐡 𝐢𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭? Traditional benchmarks quickly become outdated as they are absorbed into the training datasets of new models. LiveBench addresses this by offering a continually updated and contamination-free testing environment. 👀 Stay tuned for updates and developments from this exciting initiative, which is setting new standards in the evaluation of AI models! #AI #GenAI #LLM #MachineLearning #DataScience #Benchmarking #TechnologyUpdates #ArtificialIntelligence

#8 Zuuup Through Papers Does test time compute branching CoT(GPT strawberry's berry 😄 ) have gains in reasoning tasks? An exhaustive evaluation answering the question. The Problem: The paper explores when and why Chain-of-Thought (CoT) prompting improves the performance of Large Language Models (LLMs). It provides insights into the strengths and limitations of CoT across a range of tasks, highlighting that CoT is not a one-size-fits-all solution. Methodology: A meta-analysis of over 100 papers and experiments on 20 datasets across 14 LLMs was conducted. The authors evaluated CoT's impact across symbolic, mathematical, logical, and commonsense reasoning tasks. They also analyzed the specific components of CoT, such as planning and execution. Key Findings: CoT offers significant performance gains, particularly in tasks involving math, logic, and symbolic reasoning (up to 66% improvement). For non-symbolic tasks (e.g., commonsense reasoning), the benefits are minimal or even detrimental. CoT primarily boosts the execution phase in symbolic tasks but struggles when compared to external symbolic solvers, which can handle computations better. Opinion & Future Directions: CoT has demonstrated usefulness in specific domains like mathematics and logic. However, research must evolve beyond prompt-based CoT to expand its utility across a broader range of tasks. Integrating CoT with tools like symbolic solvers or creating new paradigms like interacting agents could be the key to its broader application. Paper Link : https://lnkd.in/gZVAWBgu #AI #GenerativeAI #ChainOfThought #Reasoning #SymbolicAI #MachineLearning #LLMs #NaturalLanguageProcessing

reasoning models as a part of AI offerings - great perspective on math and AI. #AI #math #reasoningmodels #LLMs https://lnkd.in/gMivKhay

Ever wondered how we fine-tune models to make them smarter and more accurate? The answer lies in the concept of a "cost function," a cornerstone in machine learning, statistics, and optimization. The history of cost functions dates back to optimization problems in mathematics, where the goal was to find the best possible solution, such as minimizing errors or costs. As statistical methods and machine learning evolved in the mid-20th century, cost functions became more formalized, helping researchers determine the best parameters for their models. In simple terms, a cost function measures how well we're performing and guides us in making improvements. #DataScience #MachineLearning #DataAnalysis #Statistics #AI ♻️ Find this helpful? [Repost] ✍️ Anything to add about this subject? [Comment] 👍 Nice post, Yashica! [Like]