Lecture 3

The tech stack underlying Generative AI, however, is in some ways similar to others that came before. It consists of three layers: infrastructure, platform, and applications. Infrastructure is generally accepted as the most established, stable, and commercialized layer. Incumbents offer compute, networking, and storage, including access to specialized silicon (microprocessors) like NVIDIA’s GPUs and Google’s TPUs optimized for AI workloads. Meanwhile, the application layer is evolving rapidly and consists of leveraging and extending foundation models, which is Generative AI’s equivalent of a platform.
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Generative AI stands poised to help companies reinvent themselves—by streamlining operations, delivering better customer experiences and otherwise driving growth in many ways. That’s why, in a global survey by Accenture of 2,300 business leaders nearly all (97%) said they believe that generative AI is a “game-changing” technology worthy of long-term investment.1 However, despite these great expectations, many companies struggle to turn their generative AI pilots into scalable solutions. A big reason for this, our research also shows, is that most organizations are still not “data ready” for generative AI. For example, when Accenture surveyed 2,000 CXOs in 2024, 48% said their organizations lacked enough high-quality data to operationalize their generative AI initiatives.
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Unsupervised ML identifies homogeneous and heterogeneous patterns in information provided. Applications of these algorithms are briefly described below: 

• Clustering/segmentation: A distance-based technique is used to segment or cluster data points based on inherent homogeneity or similarity. This is typically used to identify cohorts of customers, consumers or groups that display similarities in behavioural patterns
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