{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/7818eb6984954b5bb407edbf6ce9bcaf\" frameborder=\"0\" width=\"1440\" height=\"1080\" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>","height":1080,"width":1440,"provider_name":"Loom","provider_url":"https://www.loom.com","thumbnail_height":1080,"thumbnail_width":1440,"thumbnail_url":"https://cdn.loom.com/sessions/thumbnails/7818eb6984954b5bb407edbf6ce9bcaf-1699647099126.jpg","duration":3995.24,"title":"Understanding AI Bias in Language Models","description":"S/o to my new friend @srohanahmed! Give Rohan a follow! \n\nLarge Language Models: https://datasci101.com/what-are-llms-part-1\nMaven GenAI Course: https://maven.com/britney-muller/generative-ai-fundamentals/\nNote: I give Google a hard time in this video but want to acknowledge that they've done a lot for the field of ML/AI. Heck, they gave us Transformers in 2017! My intention was to draw attention to the fact that if these AI mistakes can happen at Google, they can happen anywhere.\n\nIn this video, I discuss the importance of understanding AI bias in language models, specifically focusing on Bard and ChatGPT. I explain how these models are trained on uncurated and unconsented content from the internet, which can lead to biased outputs. I also highlight the need for transparency in training data and the potential risks associated with relying on these models."}