AI systems are inherently non-deterministic and non-stationary, meaning their behavior can change unpredictably over time.

The integration of AI into software development raises questions about managing non-deterministic behavior and the reliability of AI-driven applications.

AI's chaotic nature means that small changes in input can lead to unpredictable outputs, complicating software development and testing.

AI technology enables change but requires human involvement to implement and teach new skills effectively.

AI is effective for predictable tasks but struggles with unpredictable or cutting-edge programming challenges.

The future of AI will require a shift in mindset towards a stochastic approach, moving away from deterministic programming to embrace uncertainty in AI behavior.

AI technology will develop gradually, allowing us time to provide oversight and ensure safety, rather than experiencing a sudden, uncontrollable leap in intelligence.

AI systems can design their own reinforcement learning algorithms, as demonstrated by a project where an AI outperformed human-designed algorithms by learning to optimize its own learning process.

AI technology is rapidly improving, with current systems being less biased and more fair than they were six months ago, although challenges with bias and fairness still exist.