What’s the future for generative AI? - The Turing Lectures with Mike Wooldridge
AI Summary
Summary: Evolution of AI and machine-learning
Early AI History
- AI as a discipline began post-World War II with digital computers.
- Progress was slow until the 21st century.
machine-learning Breakthroughs
- Machine learning, particularly around 2005, showed practical use.
- Supervised learning with training data became a key technique.
- AI requires training data, e.g., labeled images for facial recognition.
AI Advancements
- Around 2005, AI started to improve significantly, especially with classification tasks.
- By 2012, AI was supercharged due to GPUs, aiding tasks like self-driving cars.
neural-networks and deep-learning
- Neural networks, inspired by brain neurons, became effective in AI.
- Advances in deep learning, big data, and computing power propelled AI this century.
- Training neural networks involves adjusting connections to produce desired outputs.
Large Language Models
- OpenAI’s GPT-3, a large language model, showed dramatic improvements in AI capabilities.
- Trained on vast amounts of text, it could generate plausible text and perform reasoning tasks.
- However, it’s not perfect and can produce incorrect or biased content.
Challenges and Issues
- AI systems can be wrong, biased, or toxic due to their training data.
- Copyright and intellectual property issues arise from using web-scraped data.
- GDPR compliance is difficult as neural networks can’t easily remove specific data.
- AI lacks understanding outside its training data, as shown by Tesla’s misinterpretation of stop signs.
General AI and Consciousness
- General AI aims for machines to perform a wide range of tasks, not just specialized ones.
- Full general AI, matching human capabilities, is still far off.
- Machine consciousness is not present in current AI; they do not have subjective experiences or mental lives.
Conclusion
- AI has made significant strides, particularly in language processing.
- Current AI technologies like GPT-3 are not sentient and do not possess consciousness.
- The future of AI may involve more multimodal and augmented capabilities.