Part 7/10:
As technology progresses, Huang posited that we are likely to witness a paradigm shift where smaller, more efficient AI models dominate the landscape, reducing the amount of data and energy required for training and deployment. By compressing the intelligence of these models, future implementations may rely on lesser resources without sacrificing performance – a crucial step in mitigating AI's environmental impact.