{"href":"https://api.simplecast.com/oembed?url=https%3A%2F%2Fa16z.simplecast.com%2Fepisodes%2Fsafety-in-numbers-keeping-ai-open-a6JhgHZb","width":444,"version":"1.0","type":"rich","title":"Safety in Numbers: Keeping AI Open","thumbnail_width":300,"thumbnail_url":"https://image.simplecastcdn.com/images/cb3cb63d-d719-41f5-ba00-eb0980ab6359/ba22896f-39ce-4f34-b0d4-0e6b5e376fb2/a16z-pod-episode-title-1080x1080-2.jpg","thumbnail_height":300,"provider_url":"https://simplecast.com","provider_name":"Simplecast","html":"<iframe src=\"https://player.simplecast.com/1ec669ce-e24c-42b5-8058-114e323a3836\" height=\"200\" width=\"100%\" title=\"Safety in Numbers: Keeping AI Open\" frameborder=\"0\" scrolling=\"no\"></iframe>","height":200,"description":"Arthur Mensch is the co-founder of Mistral and the co-author of Deepmind’s pivotal 2022 \"Chinchilla\" paper.\n\nIn September 2023, Mistral released Mistral-7B, an advanced open-source language model that has rapidly become the top choice for developers. Just this week, they introduced a new mixture of experts model – Mixtral — that’s already generating significant buzz among AI developers.\n\nAs the battleground around large language models heats up, join us for a conversation with Arthur as he sits down with a16z General Partner Anjney Midha. Together, they delve into the misconceptions and opportunities around open source; the current performance reality of open and closed models; and the compute, data, and algorithmic innovations required to efficiently scale LLMs."}