The Interplay of Inquiry: From Ancient Teachings to Modern AI

The Interplay of Inquiry: From Ancient Teachings to Modern AI

Ali Rıza SARAL

 

In both ancient teachings and modern artificial intelligence (AI), the importance of asking questions stands as a cornerstone of knowledge acquisition and understanding. Delving into the significance of questioning in religious scriptures and customs reveals parallels with the mechanisms driving AI models like transformers, shedding light on the timeless nature of inquiry in human cognition and technological advancement.

 

Ancient teachings, whether found in religious texts or cultural customs, have long emphasized the value of asking questions. In these traditions, questioning serves as a pathway to deeper understanding, fostering critical thinking, exploration, and personal growth. The act of asking questions is not merely about seeking answers but also about engaging with the material, leading to profound insights and self-discovery.

 

Matthew 7:7-8 (NIV): "Ask and it will be given to you; seek and you will find; knock and the door will be opened to you. For everyone who asks receives; the one who seeks finds; and to the one who knocks, the door will be opened."

 

This emphasis on questioning finds resonance in the realm of modern AI, particularly in attention mechanisms and transformer models. Attention mechanisms enable AI models to focus on specific parts of input data, effectively asking questions about what information is relevant or important in a given context. By continually refining and improving these questions, AI models enhance their understanding and performance over time, mirroring the iterative process of learning and inquiry seen in human cognition.

 

Moreover, as AI models learn from data, they refine their representations of knowledge, akin to individuals refining their understanding of ancient teachings through study and reflection. Just as religious scholars deepen their comprehension of scriptures through ongoing inquiry, AI models iteratively improve their understanding of the world through exposure to more data and feedback, enhancing their ability to generate insights and make predictions.

 

The interconnectedness between ancient wisdom and modern technological advancements underscores the universal principles underlying the quest for understanding and knowledge. By recognizing the parallels between human cognition and AI processes, we gain a deeper appreciation for the enduring significance of inquiry in shaping our understanding of the world, both past and present.

 

In essence, the interplay of inquiry, from ancient teachings to modern AI, highlights the timeless nature of questioning as a fundamental aspect of human cognition and technological progress. As we continue to explore the depths of knowledge, may we embrace the transformative power of asking questions, both in our spiritual and intellectual pursuits, and in the ever-evolving landscape of artificial intelligence.

 

 

 

 

 

  

A Comparison of Human Attention Mechanism with ANN Transformer’s

Human Focus and Attention

When you're trying to solve a problem or understand a concept, the process can be broken down into the following steps, which align closely with the query-key-value attention mechanism:

1.      Clarify Your Mind on What You Seek (Query):

·         Query: This is your specific goal or the question you want to answer. For instance, if you're looking for a way to solve a specific type of math problem, your query might be "methods to solve quadratic equations."

·         In human terms, this means you have a clear idea of what you're searching for. You might even phrase it as a specific question or a goal in your mind.

2.      Limit the Relevant Texts (Keys):

·         Keys: These are the potential sources of information where you might find your answer. In a book, keys could be different chapters, sections, or paragraphs.

·         As you read or search through the book, your attention is drawn to parts of the text that are likely to contain information relevant to your query. You mentally filter out sections that are clearly not related to your query.

3.      Clarify What You Really Need to Find (Values):

·         Values: These are the pieces of information within the keys that are relevant to your query. In our example, values could be specific formulas, examples, or explanations of quadratic equations.

·         Your brain evaluates the keys and extracts the most relevant values. For instance, it might highlight a specific formula or a step-by-step solution that directly addresses your query.

Applying the Attention Mechanism

Here’s how this process looks in a neural network with an attention mechanism:

1.      Query: The neural network receives a specific query vector representing the information it needs to find.

2.      Keys: It then looks at all the potential information (keys) in the input data. Each piece of data is associated with a key.

3.      Values: For each key, there is a corresponding value which contains the actual information.

The attention mechanism works by:

·         Calculating the relevance: The network computes how relevant each key is to the query by calculating attention scores (often using a dot product of the query and key vectors).

·         Weighting the values: The network uses these scores to weigh the values. Higher scores mean more relevance and thus more weight.

·         Aggregating the values: The weighted values are then combined to form an output that is focused on the most relevant information.

Human Attention and Learning

In human learning, this process is less mechanical but conceptually similar:

·         You clarify your goal (query), making sure you know what you’re looking for.

·         You scan through potential sources (keys), identifying where relevant information might be located.

·         You focus on extracting relevant information (values), filtering out what is not needed and concentrating on what will help you solve your problem.

Example in Practice

Suppose you're reading a textbook to solve a physics problem about projectile motion. Here’s how you might apply this:

1.      Query: You decide you need to find the formula for the range of a projectile.

2.      Keys: You flip through the chapters and sections that cover projectile motion.

3.      Values: You find a section with the heading "Range of a Projectile" and start reading. You focus on the equations and examples that directly address your query.

In summary, the human process of focusing involves clarifying your objective, filtering through relevant sources, and pinpointing the precise information needed, closely mirroring the query-key-value attention mechanism in neural networks