The Future of Generative AI - Possibilities and Challenges

Explore the future of generative AI in this thought-provoking lecture by Professor Michael Wooldridge, delivered at the Royal Institution in partnership with The Alan Turing Institute. Delve into the fundamentals of machine learning and neural networks before tracing the evolution of AI from Silicon Valley investments to the groundbreaking Transformer Architecture. Examine the creation and capabilities of GPT-3, its impact on AI reasoning, and the unexpected knowledge acquisition of large language models. Analyze the practical applications and limitations of ChatGPT, addressing concerns about errors, bias, toxicity, and copyright issues in AI-generated content. Investigate the concepts of interpolation versus extrapolation in AI learning, and contemplate the possibility of achieving General AI. Reflect on the nature of human general intelligence and ponder the potential for machine consciousness. Gain a comprehensive understanding of the current state and future prospects of generative AI, its societal implications, and the ethical considerations surrounding this rapidly advancing technology.

What is machine learning?
How do neural networks work?
How Silicon Valley money created Big AI
The birth of Transformer Architecture
How was GPT-3 trained and created?
A massive step change in AI
How GPT-3 passed the 90s AI reasoning test
How has AI learned things it wasn't taught?
Chat GPT and how NOT to use it
Why do LLMs get things wrong so often?
The problems of bias and toxicity
Copyright issues with LLMs
Interpolation vs Extrapolation
Is this the dawn of General AI?
The different varieties of General AI
What actually is human general intelligence?
Is machine consciousness possible?
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