ChatGPT was one of the best things to ever happen to data science – not so much because of what it can do, but because, virtually overnight, it made AI and data science mainstream. 

However, while most data scientists now have experience with ChatGPT and other large language model (LLM)-based technologies as end users, few have had experience in building their own LLM-based tools.

In this episode, Dr Mudasser Iqbal joins Dr Genevieve Hayes to discuss the data science behind LLMs and how to go about doing just that. 

Guest Bio

Dr Mudasser Iqbal is the Founder and CEO of TeamSolve, a company dedicated to leveraging AI for digital transformation with a sustainable focus. He has extensive experience in Industrial AI, including multiple patents, and was recognised as an MIT Young Innovator. He also played a key role in the growth of his previous start-up, Visenti, and its subsequent acquisition by Xylem Inc.

Talking Points

• The data science behind LLMs.
• How TeamSolve’s Lily, compares to ChatGPT and the advantages of a domain-specific, private chatbot, such as Lily, over a more general, public chatbot, such as ChatGPT.
• How knowledge graphs can be combined with LLMs to overcome many of the shortcomings of LLMs.
• The changing attitudes of organisations around the use of generative AI tools.
• What the emergence of cutting-edge AI tools, such as LLMs, mean for more traditional data science tools, such as analytics dashboards.
• The future of generative AI, and the potential benefits and risks to society.

Links

• TeamSolve
• Connect with Mudasser on LinkedIn
• Connect with Genevieve on LinkedIn
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