Why isn't AI SMARTER if Transistors are SO FAST?

About this Video

• Video Title: Why isn't AI SMARTER if Transistors are SO FAST?
• Channel: AI Neuro Insight
• Speakers: Charles Simon
• Duration: 00:12:34

Introduction

This video begins a series exploring why Artificial Intelligence (AI) isn't as smart as it could be, despite the speed of transistors. The introduction compares and contrasts the functionality of transistors and neurons to establish a foundation for understanding the limitations of current AI.

Key Takeaways

• Transistors: Work by using a small control signal to manage a larger signal. They operate digitally (0 or 1), offering speed, low power, and reliability. Modern CPUs utilize billions of transistors, significantly increasing processing speed.
• Neurons: Receive inputs through dendrites, using neurotransmitters to open ion channels, changing internal voltage. Firing occurs when a threshold is reached, sending an electrical spike (action potential) down the axon. This process is digital; minor voltage changes don't affect neurotransmitter release.
• Energy Efficiency: Neurons use far more energy (a million times more) and are significantly slower (500 million times slower) than NAND gates. However, neurons operate at a low duty cycle (0.2%), conserving energy.
• Synapses: The connections between neurons are crucial. Synapse weight (number of ion channels) influences signal transmission; synapses can increase/decrease in weight, act as non-volatile memory, and even grow/form new connections, allowing the brain to reconfigure itself.
• Brain Structure: Individual neurons don't do much thinking; the connections between them and their coordinated activity are key to intelligence. The brain's structure is vastly different from a CPU, utilizing massively parallel processing.