The Future of Large Language Models (LLMs)
At the recent Pathway SF Meetup, Łukasz Kaiser and Jan Chorowski discussed the exciting developments in the field of Large Language Models (LLMs) and their potential impact on the future of natural language processing.
Who are Łukasz Kaiser and Jan Chorowski?
Łukasz Kaiser and Jan Chorowski are renowned experts in the fields of machine learning and language processing. They have been at the forefront of research in developing large language models that are capable of generating human-like text and understanding natural language with remarkable accuracy.
The Rise of Large Language Models
In recent years, large language models such as GPT-3 and BERT have revolutionized the way we interact with language. These models are trained on vast amounts of text data and are able to generate coherent and contextually relevant text responses to a wide range of prompts. This technology has been used in various applications such as chatbots, language translation, and content generation.
The Potential of LLMs
Łukasz Kaiser and Jan Chorowski highlighted the immense potential of large language models in transforming industries such as healthcare, finance, and education. These models can be used to automate tasks, enhance communication, and improve decision-making processes. They also emphasized the importance of ethical considerations when deploying LLMs to ensure that they are used responsibly and in a way that benefits society as a whole.
Conclusion
The future of Large Language Models is undeniably promising, with Łukasz Kaiser and Jan Chorowski leading the way in advancing this groundbreaking technology. As LLMs continue to evolve and become more sophisticated, they have the potential to revolutionize the way we interact with language and significantly impact various industries and fields.
3 key things covered here in a nutshell:
9:44 As data gets scarce, put a lot of computation with a little, right retrieved data, the RL needs to good and have more layers/compute, the LLMs will become more intelligent!
21:50 Retriever is engineering, LLM is magic. Quite a good way to look at it because many simplistic description of RAG use cases are around external data but this goes beyond that!
27:05 Use LLM for pre processing for better data storage + better retrieval at runtime. Rather than doing a dense async multiplication and burdent the GPU just call it to a little procedure to fetch data as needed and still save compute and increase LLM context window. Pathway essentially serves this purpose itself apparently.
Happy to save your 15 minutes. 🙂
basically, nothing new said here. just things you can find already on youtube for some time already. the title overstates its content.