AI began after WWII with slow progress until the 21st century, where machine learning, specifically neural networks and deep learning, led to practical applications.

• Artificial intelligence started post-WWII with slow progress until the 21st century.
• The breakthrough occurred around 2005 with machine learning and neural networks.
• Despite the broad range of techniques, machine learning has been the most impactful.

Machine learning, particularly supervised learning, uses training data to make AI systems practically useful in various settings, like facial recognition.

• Supervised learning uses input-output pairs in training data to teach AI systems.
• These systems have advanced with more data and computing power, becoming more capable.
• Facial recognition is a prime example of a practical application of machine learning.

Neural networks, inspired by biological brains, have been implemented in software and are key to AI tasks like image recognition and other complex pattern recognition.

• Neural networks are inspired by the vast networks of neurons in the human brain.
• Each neuron in a neural network performs a simple pattern recognition task.
• Neural networks have been implemented in software, allowing for complex AI capabilities.

Scientific advances, big data, and cheap computing power have supercharged AI development, leading to transformative technologies like Tesla's self-driving mode.

• Scientific advances in deep learning, big data availability, and cheap computing power have fueled AI progress.
• Capabilities of neural networks grow with scale, leading to powerful applications like self-driving cars.
• Silicon Valley's speculative investments in AI have driven further advancements.