Generative AI technology is advancing rapidly, enabling computers to create in innovative ways like never before.
Challenges in generative AI include the need for more data for training large language models and optimization of compute costs for training and serving models.
Specialized tools and infrastructure are required to address issues in data, compute, and prompt engineering within the generative AI stack.
Conversational AI has seen a journey of ups and downs, but is now experiencing a new era of advancement and popularity.
The introduction of Transformers revolutionized conversational AI, leading to impressive advancements in natural language processing tasks.
The future of conversational AI looks promising with applications expanding into search, brand interaction, creativity, and social experiences, offering exciting opportunities and challenges.
Using human feedback to refine large language models is key for aligning them with user values and preferences.
Reinforcement Learning from Human Feedback (RLHF) is a crucial technique for enhancing the quality of LLM outputs.
Incorporating human touch into LLMs raises questions about scalability, cost, decision-making regarding whose feedback matters, and potential policy implications.