"The Fascinating Connection Between Self-Taught AI and Human Brain Function"

"The Fascinating Connection Between Self-Taught AI and Human Brain Function"

Introduction:
Artificial intelligence (AI) has made significant advancements in recent years, particularly in the field of self-supervised learning algorithms. These algorithms have shown remarkable similarities to how the human brain works, demonstrating impressive linguistic and image recognition abilities without the need for external labels or supervision. In this article, we will explore the fascinating parallels between self-taught AI and the functioning of the human brain. Additionally, we will delve into the concept of the Golden Circle, which aligns with the principles of human decision-making and connects seamlessly with the workings of the brain.

Self-Taught AI and the Brain:
Large language models, a type of self-taught AI, undergo training algorithms where a neural network predicts the next word in a sentence based on a few initial words. This mimics how humans, including animals, learn without labeled data sets. The brain's ability to explore and gain an understanding of the environment parallels the self-supervised learning algorithms in AI. By creating gaps in the data and asking the neural network to fill in the blanks, these algorithms replicate the brain's continuous predicting nature, whether it's an object's future location or the next word in a sentence.

The Importance of Feedback Connections:
While self-supervised learning has shown promise in modeling human language and image recognition, it falls short in understanding the intricacies of brain function. Feedback connections play a crucial role in the brain, yet current AI models lack these connections. In our visual system, for example, the presence of two specialized pathways aids in predicting the visual future. This realization highlights the need for AI models to evolve beyond self-supervised learning and incorporate feedback connections to truly understand brain function.

The Golden Circle and Human Decision-Making:
The Golden Circle, a concept popularized by Simon Sinek, goes beyond a mere communications hierarchy. It aligns with the principles of human decision-making, which makes it an intriguing connection to the workings of the brain. The Golden Circle emphasizes starting with the "why" before moving to the "how" and "what." This approach resonates with how our brains process information. The limbic system, responsible for decision-making and emotions, plays a crucial role in shaping our choices. By appealing to the "why" or the emotional aspect, we engage the limbic system and influence decision-making processes.

Connecting Self-Taught AI, the Brain, and the Golden Circle: The parallels between self-taught AI, the brain, and the Golden Circle are striking. Both self-supervised learning algorithms and the brain rely on predicting the future to gain a comprehensive understanding of the world. The Golden Circle's emphasis on starting with the "why" corresponds to the emotional and decision-making aspects of the brain. By recognizing these connections, we can leverage AI advancements to better understand brain function and enhance our decision-making processes.