Our AI research team at Martian is advancing one of the most important areas of AI: Mechanistic Interpretability. This involves understanding how models make decisions by examining their internal mechanisms, similar to looking inside a clock to see how it works. Our research has long-term implications for AI, with safety being a primary consideration. By understanding the inner workings of AI models, we can better predict and mitigate potential risks, ensuring more reliable and secure AI systems. We are putting the outputs of this research to work today in our LLM Router, which predicts the best large language model (LLM) for a given prompt based on a customer’s cost, latency, and output quality preferences. #AI #MachineLearning #ArtificialIntelligence #TechInnovation #MechanisticInterpretability #Research #MartianAI #AITransparency
🎉 𝐄𝐱𝐜𝐢𝐭𝐢𝐧𝐠 𝐁𝐫𝐞𝐚𝐤𝐭𝐡𝐫𝐨𝐮𝐠𝐡 𝐢𝐧 𝐀𝐈 𝐈𝐧𝐭𝐞𝐫𝐩𝐫𝐞𝐭𝐚𝐛𝐢𝐥𝐢𝐭𝐲! Last few weeks have seen a surge in AI Interpretability work: Anthropic researchers work on understanding Claude 3 models [Scaling Monosemanticity in AI](https://lnkd.in/eaYkgSNM) OpenAI released work on understanding GPT-4 [Extracting concepts from GPT-4] (https://lnkd.in/gFzijQwM) What's even more fascinating is their use of 𝘚𝘱𝘢𝘳𝘴𝘦 𝘈𝘶𝘵𝘰𝘌𝘯𝘤𝘰𝘥𝘦𝘳𝘴 (𝘚𝘈𝘌𝘴). Curious why SAEs are so effective and scalable? Our research at Martian provides some compelling insights: https://lnkd.in/gkvN2cXG At Martian, we leverage category theory—a mathematical approach focusing on relationships rather than object internals—to understand why SAEs perform so well. This theory underpins our broader efforts in "model mapping," a series of methods that promise enhanced interpretability without intensive manual analysis. 🔬 𝐌𝐨𝐝𝐞𝐥 𝐌𝐚𝐩𝐩𝐢𝐧𝐠: 𝐀 𝐂𝐨𝐦𝐩𝐮𝐭𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐌𝐢𝐜𝐫𝐨𝐬𝐜𝐨𝐩𝐞 𝐟𝐨𝐫 𝐀𝐈 Our model mapping initiative acts like a microscope, revealing how AI models operate without dissecting their inner workings. This scalable approach benefits from increased computational power, paving the way for more efficient AI development. 🚀 𝐉𝐨𝐢𝐧 𝐌𝐚𝐫𝐭𝐢𝐚𝐧 𝐨𝐧 𝐎𝐮𝐫 𝐉𝐨𝐮𝐫𝐧𝐞𝐲! We're pushing the boundaries of AI interpretability and alignment. Learn more and get involved at [withmartian.com](https://withmartian.com).
Fantastic strides in AI research! 🌟 I'm thrilled by how Mechanistic Interpretability can enhance AI safety and reliability. What has been the most surprising discovery from your research so far, and how do you see it revolutionizing the industry? 🚀
Aervivo COO | Quality Internet for ALL!
1yWow, that's some interesting stuff! Your doing great stuff Aaron!