Agenda

Enterprises today face increasing pressure to incorporate GPT-scale large language models, or LLMs, throughout their product suites. However, these LLMs are complex, cumbersome, and costly to deploy. Unlike the human brain, which consumes a mere 30 watts of power, LLMs require massive compute and power footprints that are harming the planet. To address these issues, significant research is being conducted into LLM optimization but skyrocketing demand is outpacing these improvements. In this talk, we show how a new approach, grounded in neuroscience research, can significantly improve the performance of LLMs and provide a sustainable path forward. 

In this presentation, Subutai will:

• Provide an overview of the Generative AI landscape and the challenges companies face in deploying LLMs.
• Review recent neuroscience discoveries that show how our brain achieves its incredible computational efficiency.
• Demonstrate how Numenta’s AI platform can apply these discoveries to AI and enable enterprises to deploy powerful LLMs at scale in an extremely cost-effective manner.

Sree Ganesan, who leads Software Products at Intel’s Habana Labs, will present approaches to bringing efficiency to Generative AI—from the software perspective—and will share things you may not know about what you can do with Gaudi Acceleration. We’ll look at opportunities developers have to optimize performance and build value that can yield positive results. 

In this panel, we explore the challenges and opportunities that large, medium, and small companies are facing when they try to pre-train and/or fine-tune Large Language Models (LLMs). More specifically, given the fact that not all companies have the time and resources to pre-train LLMs from scratch, we present options for executing pre-training, or at least fine-tuning, activities in easier and, most importantly, cheaper ways than what is done today. We will therefore discuss how you too can start to efficiently and efficaciously leverage the amazing capabilities provided by LLMs, as well as some important factors that we discovered and that make a significant difference in the creation of high-performance, robust, and reliable LLMs.

ChatGPT is the ‘sputnik’ moment for AI in US and Europe – it is now obvious how AI will be fundamental. There is an imperative to future-proof industry by investing in the exploration of the impact of next-generation AI technologies. But, it is also fuelling an urgency for enterprises to capitalize on and deploy core AI technologies today. Models continue to evolve but deployment is becoming more and more important. Smart users are comparing the compute costs and data risks of using massive, proprietary LLMs with smaller, open-source models, fine-tuned to their specific needs. They are searching for much more efficient solutions for deployment today and in the future. In this talk we will discuss:
·         Why LLMs like GPT-4 may not be the best solution for many enterprise AI tasks
·         Identifying the most exciting, enterprise-ready AI solutions for deployment today
·         Why your compute platform matters when using generative AI