Digital HUB is an open online community of financial and data science professionals pursuing practical applications of AI in their everyday functions. Digital HUB community provides expert, curated insights into financial applications of Generative AI, Large Language Models, Machine Learning, Data Science, Crypto Assets and Blockchain.
A key focus for The Digital HUB publication is to provide best practices for the safe deployment of AI at scale such as: assessing the ability to execute, determining an organization’s digital DNA, fostering skill development, and encouraging responsible AI.
Digital HUB is an open online community of financial and data science professionals pursuing practical applications of AI in their everyday functions. Digital HUB community provides expert, curated insights into financial applications of Generative AI, Large Language Models, Machine Learning, Data Science, Crypto Assets and Blockchain.
A key focus for The Digital HUB publication is to provide best practices for the safe deployment of AI at scale such as: assessing the ability to execute, determining an organization’s digital DNA, fostering skill development, and encouraging responsible AI.
Large Language Models (like ChatGPT): Demonstrating the Transformative Capabilities of AI
Large Language Models (like ChatGPT) have taken the world by storm, demonstrating the transformative capabilities of AI. To understand where we are headed, we look under the covers and examine the evolution of “Generative AI” universe in the figure below. Key findings are: 1) open-source vs closed determine the pace of AI innovation. Open source allows for a wider pool of startups and researchers; 2) As you can see in the figure, out of the three main models, one architecture is winning the race with significant number of projects. Now, the trends: 1) for the near term, I expect AutoGPT – a multi-tasking and self-feedback version of ChatGPT – to gain traction. More on this in the next edition; 2) for enterprise applications, pre-trained generative AI models need to be fine-tuned with internal proprietary data. This trend will give rise to many smaller models applied to specific use cases; 3) The success of generative AI will shed light on the “machine learning” methods that can be applied to many data science applications without the need for extensive resources compared to generative AI models.
