These lectures discuss artificial intelligence (AI), particularly focusing on the distinctions between analytical AI, which understands and explains data, and generative AI, which creates new content. The speaker raises concerns about the current hype around generative AI, arguing that many users lack a fundamental understanding of machine learning models and data structures. A significant issue highlighted is hallucination in generative models, where they produce incorrect or nonsensical information due to limitations in their training data, prompting a discussion on creativity versus error. The lectures also explore the complexities of applying AI to subjective or biased topics, the debate around general artificial intelligence (AGI) and superintelligence, and the importance of understanding the data and biases that influence AI outputs. Ultimately, the speaker emphasizes the need for critical thinking when engaging with AI and views it as a powerful tool for augmenting existing processes and increasing efficiency.https://namjulee.github.io/njs-lab-public/work?id=2025-introductionToDesignComputation

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