🧩 AI solving reCAPTCHA & LLM problem solving
5 papers on chatbots, neural assets, and solving LLM problems with advanced prompting
Welcome to your weekly AI Fridays, where we bring you the latest advancements in AI and machine learning research! Each week, we curate the top studies to keep you at the cutting edge of tech.
Here’s what’s new:
🤖 ELIZA Reinterpreted: Learn the true origin of ELIZA, originally designed to study human-machine interaction rather than being the chatbot it’s known as today.
🔓 Breaking reCAPTCHAv2: Discover how researchers developed an AI that solves Google’s reCAPTCHAv2 with 100% accuracy, exposing vulnerabilities in the system.
🖼️ RNA: Relightable Neural Assets: A neural representation for 3D assets that enables high-quality, relightable rendering without the need for complex computations.
🧠 Parameter Efficient Reinforcement Learning from Human Feedback: Explore a new RLHF method that reduces training time and memory requirements while maintaining performance.
💡 Enhancing LLM Problem Solving with REAP: A new approach that significantly improves LLMs' problem-solving abilities using reflection and advanced prompting techniques.
ELIZA Reinterpreted: The world's first chatbot was not intended as a chatbot at all (🔗 Read the Paper)
ELIZA, commonly regarded as the first chatbot, was actually created as a research platform for studying human-machine conversation and cognitive processes, with its unintended rise to fame and mischaracterization as a chatbot resulting from fortuitous timing and accidental dissemination.
Breaking reCAPTCHAv2 (🔗 Read the Paper)
This study demonstrates the vulnerability of Google's reCAPTCHAv2 system by developing an AI-based method that solves 100% of captchas, surpassing previous attempts and matching human performance, while also revealing the system's heavy reliance on cookie and browser history data for user authentication.
RNA: Relightable Neural Assets (🔗 Read the Paper)
This paper introduces a neural representation for complex 3D assets that precomputes shading and scattering, enabling high-fidelity, relightable rendering without the need for expensive computations or complex shader implementations in downstream applications.
Parameter Efficient Reinforcement Learning from Human Feedback (🔗 Read the Paper)
This study demonstrates that Parameter Efficient Reinforcement Learning from Human Feedback (PE-RLHF) achieves comparable performance to traditional RLHF while significantly reducing training time and memory requirements, potentially enabling broader adoption of RLHF for aligning large language and vision-language models with human preferences.
Enhancing LLM Problem Solving with REAP: Reflection, Explicit Problem Deconstruction, and Advanced Prompting (🔗 Read the Paper)
REAP (Reflection, Explicit Problem Deconstruction, and Advanced Prompting) significantly enhances LLMs' problem-solving capabilities for complex tasks, demonstrating substantial performance gains across multiple models while improving output clarity and cost-efficiency.
🎬 And that's a wrap! See you Tuesday 👋