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What is the difference between weak and strong artificial intelligence and what is the turing test for strong artificial intelligence?
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What are pretrained models, and how do they work?
How does AI intersect with human bias and societal inequities?
Why is data considered crucial in AI projects?
What techniques can be used to make AI models more fair?
How does the bias in training data affect the performance of AI models?
How can you optimize AI models for edge deployment?
Discuss how AI is used to identify vulnerabilities.
How can AI be used to predict patient outcomes?
Why is it important to address bias in AI models?
Explain the difference between supervised, unsupervised, and reinforcement learning.
Provide examples of industries where fairness in AI is particularly critical.
How does explainable AI (XAI) improve trust in AI systems?
How does a cloud data platform help in managing Gen AI projects?
What are some open problems you find interesting?
What are the benefits and risks of using AI in financial risk analysis?
