The Turing Lectures: The future of generative AI



AI Summary

Summary in Outline Form

  • Introduction
    • Host: Hurry Su, Research Application Manager at the Turing Institute
    • Event: Last Turing lecture of 2023, first hybrid Turing lecture
    • Audience: Mix of returning attendees and newcomers
    • Turing Lectures: Flagship series since 2016, featuring data science and AI experts
    • Turing Institute: National Institute for data science and AI, named after Alan Turing
    • Discourse Format: Includes a Q&A session and a silent period before the lecture begins
  • Background on AI
    • AI’s slow progress until the 21st century
    • Machine learning breakthroughs around 2005
    • Misconceptions about machine learning
    • Importance of training data
    • Classification tasks and their applications (e.g., Tesla’s self-driving mode)
    • Neural networks inspired by brain’s neurons
    • Factors for AI’s advancement: scientific advances, big data, and cheap computing power
  • Large Language Models (LLMs)
    • GPT-3’s impact and its 175 billion parameters
    • Training data: 500 billion words from the web
    • LLMs as powerful autocomplete tools
    • Emergent capabilities beyond training
    • Issues with LLMs: getting things wrong, bias, toxicity, copyright, GDPR, and limitations outside training data
  • General AI
    • Definitions and types of General AI:
      1. Full general intelligence: Machines capable of any human task
      2. Cognitive tasks: Understanding and reasoning
      3. Language-based tasks: Any task communicable in language
      4. Augmented LLMs: LLMs with specialized subroutines
    • Current state of AI: Strong in natural language processing, but lacking in other dimensions of intelligence
  • Machine Consciousness
    • Debate over AI sentience
    • Consciousness as the “hard problem”
    • AI’s lack of subjective experience and mental life
  • Q&A Highlights
    • AI’s energy consumption and climate change
    • Responsibility for AI’s errors
    • AI-generated content and potential feedback loops
    • Combining symbolic AI with big AI
    • Future directions: multimodal AI, virtual reality, and entertainment
    • Human beings compared to LLMs
  • Conclusion
    • Acknowledgment of the Turing lecture series
    • Upcoming Christmas lecture at the Royal Institution