Practical AI From ML to AI to Generative AI
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Jun 21, 2023 The hosts explore how generative AI is reshaping the landscape of machine learning. They discuss the evolution of AI terminology and the key differences between supervised learning and generative models. Insights are shared on the time-saving capabilities of generative AI, supported by a real-life example of creating a presentation. The conversation also navigates the ethical risks of generative technologies, reflecting on humanity's changing identity and the delicate balance between advancement and intention.
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AI as Data Transformation
- AI and machine learning models, at their core, transform data.
- These models differ from regular software functions because their logic isn't explicitly programmed but learned from data.
Training AI Models
- AI models have a 'missing piece' architecture and are trained using algorithms with numerous parameters.
- Training involves optimizing parameters to minimize error through an iterative trial-and-error process.
Supervised Learning Still Dominant
- Supervised learning, using labeled examples for training, remains dominant in AI.
- Despite newer approaches, supervised learning models likely constitute 95% of real-world applications.