At EDBI’s inaugural masterclass in collaboration with NVIDIA, we delved into the world of Large Language Models (LLM) and glimpsed into the opportunities and challenges in its landscape led by Dr. Ettikan K Karuppiah, director and technologist and Dr. Deb Goswani, head of developer programs – big data and analytics at NVIDIA.
Here’s our key takeaways:
LLMs: A Speeding AI Revolution
Over the last five years, generative AI became talk-of-town with LLMs at the forefront. NVIDIA, known for its hardware innovation, has been instrumental in this evolution with their cutting-edge GPU architecture that slashed training time from a month to just a week for 1 trillion parameter models. It boosted prompt learning by 5x while delivering a staggering 30x increase in real-time inference throughput.
What does that mean for Enterprises?
It became apparent that enterprises that embrace LLMs potentially stand to gain substantial revenue growth. The question here is who will get there first?
The adoption of LLMs is evident in practical applications like GitHub Copilot, where users accept 30% of code suggestions, users of content generation, companies with large customer support functions. Healthcare is another arena where clinical predictive tools, fueled by LLMs, are changing the game by analysing unstructured clinical notes from electronic health records. The key advantage of LLMs lies in their ability to function effectively without the need for extensive labeled data. The exponential increase in the number of parameters, from millions to trillions, has enabled multi-task capabilities. This also levels the playing field for AI application start-ups and challenges established Natural Language Processing (NLP) providers.
Trend Towards Enterprise-Specific LLMs and Associated Challenges
Enterprises are recognising the value of developing their own LLMs tailored to their specific domains and use-cases. These models are poised to become the virtual identity of companies, enhancing interactions by incorporating personalized data. However, the transition towards enterprise-specific LLM adoption presents a set of challenges that require careful consideration.
Challenges include ensuring data privacy and confidentiality, content quality, real-time integration, and accessing AI experts skilled in building LLMs.
Despite these obstacles, the benefits of LLM adoption for enterprises outweighs its challenges – ranging from faster time-to-market and reduced operational costs to improved productivity, risk reduction, and enhanced customer engagement.
The promise of LLMs in transforming businesses is evident – everything becomes faster, and eventually better.
Article edited with assistance from AI.
