Large Language Models at Scale - Challenges and Opportunities

Dive into a comprehensive podcast episode featuring Nils Reimers, Director of Machine Learning at Cohere, as he discusses large language models at scale. Explore the challenges of running models with billions of parameters on massive datasets, and learn about finding the right model size for specific tasks, model distillation, and innovative methods for transferring knowledge from large to smaller models. Gain insights into Nils' background, including his creation of the Sentence Transformers library, and his journey from IT security to machine learning. Discover the evolution of NLP, the potential of LLMs, and the challenges of catastrophic forgetting. Delve into discussions on neural search, adaptive models, latency issues, and the future of API-based solutions. Learn about Cohere's approach to open-source, competitive edge, and upcoming projects. Explore topics such as data silos, model updating, and the balance between making and saving money with AI technologies. Gain valuable insights into the world of large language models and their practical applications in various industries.

[] Nils' preferred coffee
[] Nils' background
[] Takeaways
[] Subscribe to our Newsletters and IRL Meetups, and leave your reviews!
[] Nils' history
[] From IT Security to Machine Learning
[] Tangibility of IT and Security
[] NLP transition
[] Bad augmentation to new capabilities of LLMs
[] Nils' concern during his PH.D.
[] Making Money from Machine Learning
[] Catastrophic forgetting
[] Updating solutions
[] Neural search space and building adaptive models
[] Filtering models
[] Latency issues
[] Models running in parallel
[] Generative models problems
[] Nils' role at Cohere
[] To build or not to build API
[] Search models
[] Large use cases
[] Open source discussion within Cohere
[] Competitive Edge
[] Future world of API
[] LLMs in Production Conference Part 2 announcement!
[] Hopeful direction of Cohere's future
[] Data silos
[] Where to update the database and code
[] Nils' focus
[] Make money or save money
[] Cohere's upcoming project
[] Time spent red teaming the models
[] Wrap up