Sheen S. Levine’s Post

Scientists Use Game Theory to Improve Language Models 😲 Researchers at MIT's CSAIL have developed a new technique using game theory, called the "consensus game," to improve the reliability of large language models 🤖📚. This approach enhances AI's text comprehension and generation by integrating multiple perspectives during training, leading to more nuanced and accurate communication 🗣️. By simulating a cooperative game 🎮 between different AI components, the method achieves better performance in tasks like reading comprehension, math problem-solving, and dialogues, making AI responses more consistent and trustworthy 🌟. Read more at [MIT News](https://lnkd.in/e7Wik2sP).

Hallucinations? Maybe not ... What if your chat bot was being completely accurate but its output was based on a conceptual understanding of the world that lies beyond what your brain is able to grasp … If you've been curious about how LLMs think and reason, and what may lie ahead with super large models such as the Amazon Web Services (AWS) Olympus model, check out my latest Medium article in which I explore recent research studies from Mor Geva Pipek and her colleagues that look into the "Dark Matter" of transformer models - their massive neural networks. #ai #generativeai #genai #llm #foundationalmodel #neuralnetwork

Brilliant article! Best part: Humans rely on a type of biological neural network (called "the brain") to process information. Each brain has been trained since birth on a wide variety of both text and audiovisual media, including large copyrighted datasets. (Predictably, some humans are prone to reproducing copyrighted content or other people's output occasionally, which can get them in trouble.)

🚀 "Give Your LLMs a Left Brain" - Join Stephen Chin at #GIDS on April 24th and discover how to enhance generative AI with the analytical prowess of knowledge graphs. 🧠 Explore: 💡How the human brain's hemispheres inspire better AI functionality. 💡Integrating Large Language Models (LLMs) with updated, factual knowledge graphs. 💡Using Retrieval Augmentation Generation (RAG) for more reliable AI outputs. Ideal for developers, AI researchers, and tech innovators, this session will guide you through the techniques to ground AI's creative capabilities with solid, logical data processing. Learn More & Register: https://lnkd.in/dUckUz3m #GenerativeAI #KnowledgeGraphs #TechLeadership #GIDS2024 #Neo4j Neo4j

Understanding powerful AI systems is like deciphering an alien language. But @Anthropic cracked the code. Their groundbreaking research unveils the “dictionary learning” method, allowing us to peek inside the “mind” of language models like Claude. Imagine having an fMRI scan of the AI’s brain, revealing key concepts and their relative positioning. See the comments to dive deeper into this game-changing research. 📖

This episode summarizes four innovative methods for assessing and improving Large Language Models (LLMs). SUPER evaluates research experiment execution, MathGAP assesses mathematical reasoning abilities, Rarebench measures performance in the context of rare diseases, and FP6-LLM focuses on enhancing computational efficiency. These benchmarks address crucial limitations in current LLMs, offering valuable tools for advancing AI development across diverse applications. https://lnkd.in/d557RV3k

Of the many questions surrounding the future of artificial intelligence, hallucination detection has received a substantial amount of attention. Solving this problem is critical to improving the trustworthiness of modern language models (LMs).

Welcome Levin Hornischer, Assistant Professor at the Munich Center for Mathematical Philosophy (MCMP), and Thomas Icard, Professor of Philosophy and (by courtesy) of Computer Science at Stanford University!  These two new IAS fellows have started over the summer, and work on the intersection of computer science, cognitive science, and philosophy. Hornischer and Icard's research is focused on better understanding AI and machine learning, aiming to develop a reliable theory for underlying neural networks.  While at IAS, Hornischer and Icard plan to interact with international experts to identify promising interactions between logic and AI. To kick this off, they organized a brilliant workshop on July 16 and 17 to address and discuss the issues of explainability, interpretability, and verifiability in modern AI systems, while also exploring if and how logic can inspire new developments in AI.  Interested how logic can contribute to AI? View their kick-off video to learn more >  https://lnkd.in/eefEJFVs More about Levin Hornischer > https://lnkd.in/eqWPsRy7 More about Thomas Icard >https://lnkd.in/exuXAw-b   #AI #Logic #machinelearning #cognitivescience #computerscience #philosophy  #interdisciplinary

Hello Everyone! 👋 I'm thrilled to share my latest blog post on "Language Modeling and Its Applications" based on my internship project at Innomatics Research Labs. 🤖 In this blog, I explore: What is Language Modeling? Applications of Language Modeling in NLP Steps to Model a Language 🔗 Dive deeper into the world of language modeling and its significance in NLP by reading my blog post. #genai #languagemodelling #llms #ai #nlp

#ai #research 🤖 ⁉ The article discusses how AI can develop either selfish or cooperative personalities through game theory and large-scale language models, based on research from Nagoya University. It uses the prisoner's dilemma to evolve AI personalities, showing AIs can switch between cooperation and selfishness, mirroring human behavior. This evolution of AI personalities suggests potential future dynamics in societies with mixed AI and human populations, emphasizing the transformative potential of language models in AI research and their role in shaping AI characteristics for societal contribution. Reference: Reiji Suzuki et al, An evolutionary model of personality traits related to cooperative behavior using a large language model, Scientific Reports (2024). DOI: 10.1038/s41598-024-55903-y