Lecture 11

Making the leap with GenAI in procurement

Many CPOs question the transformative impact that generative AI (gen AI) can deliver. The arguments range from overly niche functionality to concerns about the accuracy and security of using the large language models (LLMs) that underpin it. More often, though, we hear skepticism about the start-up cost and initial duplication of existing work. Sixty-six percent of CPOs surveyed in McKinsey’s CPO 100 survey in H2, 2023, believe gen AI is still years from generating substantive business results.

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Preventing Front Page Headlines: LLM Use Case AssessmentsAll companies want to do something with LLMs and use GenAI in their pitch. Sometimes it’s top-down when C-level executives want their company to catch up with the trends, and sometimes bottom to top as an initiative of data science teams.

We have seen some successful examples from big companies, such as Zalando Fashion assistant, implemented to help customers discover the most relevant products based on their query, and Booking AI Trip Planner, which guides travellers in finding an ideal destination or accommodation for their preferences.
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Data Interpreter: An LLM Agent For Data Science

Large Language Model (LLM)-based agents have demonstrated remarkable effectiveness. However, their performance can be compromised in data science scenarios that require real-time data adjustment, expertise in optimization due to complex dependencies among various tasks, and the ability to identify logical errors for precise reasoning. In this study, we introduce the Data Interpreter, a solution designed to solve with code that emphasizes three pivotal techniques to augment problem-solving in data science: 1) dynamic planning with hierarchical graph structures for real-time data adaptability;2) tool integration dynamically to enhance code proficiency during execution, enriching the requisite expertise;3) logical inconsistency identification in feedback, and efficiency enhancement through experience recording. We evaluate the Data Interpreter on various data science and real-world tasks. Compared to open-source baselines, it demonstrated superior performance, exhibiting significant improvements in machine learning tasks, increasing from 0.86 to 0.95. Additionally, it showed a 26% increase in the MATH dataset and a remarkable 112% improvement in open-ended tasks.
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What should Data Science Education Do With LLMs:

High-tech solutions aren’t yet mainstream in the world of fashion, but we believe they should be. We first became convinced of Watson’s power after a visit to the IBM Labs in Bangalore. Bearing data about our brands, we presented a problem to solve: of two similar products, why did one sell and the other didn’t? IBM developed a proof-of-concept solution that gave us the answer. Clearly, AI could help our company succeed.
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ChatGPT Statistics And Facts You Need to Know in 2024Over the last months, ChatGPT has broken several records. For instance, five days after its release, the chatbot crossed the million-user mark, and within two months, it skyrocketed to 100 million active users, securing its position as the second fastest-growing consumer app in history.And there is more: in less than a year, the platform hit 100 million weekly users, with this figure continuing to climb. The latest ChatGPT statistics reveal impressive growth, user familiarity, and session statistics, providing insights into its performance and impact. ChatGPT's social media traffic has also been significant, with substantial engagement from platforms like YouTube, X (formerly Twitter), LinkedIn, and Facebook.

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