The next mind computer

First we recreated the body—then we recreated the mind.

Your mind operates on patterns. Deep patterns of thought and action, shaped through experience and refined through practice. These patterns form the foundation of human learning.

The Apprentice Way

In the workshops of Renaissance Florence, Leonardo da Vinci discovered the path to mastery through direct engagement with his master Andrea del Verrocchio. This apprenticeship represented more than training—it showed how human excellence evolves.

The apprentice pattern leverage our neural networks. We learn through observation and action, ultimately deepening our understanding. Through practice and wide connections we develop creative intuition. Each error builds new neural pathways, and each success reinforces the effective patterns.

The Master's Touch

The power of apprenticeships is the master's tight guidance and wider point of view. They see a readiness for activity in their apprentice. Since the masters wider point of view, distinguished from the apprentices narrower view, they guide the productive struggles and avoid the dangerous mistakes. They guide without constraining talent, and challenge without overwhelming at once.

This represents how expert human capability evolves. You've probably had a mentor of this sort guide you in your infancy. The excellence you now possess grew out through the careful cultivation of pattern recognition systems, and a wider and more cohesive point of view on the disciplines.

The mentor shapes the very patterns of thought and action that define a apprentice's foundational expertise.

The Natural Evolution

This biological systems are effective. Through your action, practice and guidance, you develop complex capabilities naturally. This pattern of human learning reveals essential truths about how capabilities are installed—these truths point toward a further evolution of computational learning systems.

The Computational Evolution

The human mind requires sleep to process learning. This is our biological limit to build new neural pathways, which ultimately become our able knowledge. To strengthen these connections, we practice and struggle during waking hours. Only through this cycle of push and pull can you create and carry new understanding of the world.

Computers operate beyond these organic constraints.

Breaking Organic Bounds

A computer reads and writes at the speed of electricity.

Computer processors think multiple streams of information simultaneously. Computers can create: notes while engaging in conversation, analyze patterns while forming a response, record (listen) and transcribe audio at the same time. They can write memory and collect old "files" simultaneously.

AI acceleration of learning and information gathering breaks your human biology clock speed.

Through movies like "The Creator," we see glimpses of future artificial minds interfacing with the world naturally, processing information at exponential speeds. Like downloading a new language in seconds. This capacity for rapid download, creates a new step in our knowledge evolution ladder.

The New Architecture

Current LLM interactions remain static. Each conversation starts blank, with disconnected context about who you are, and previous learning. But imagine a interface where each time you press "new chat", the LLM feel like a friend or co-founder. Like a human with working memory, not just generic facts.

Like a friend who learn and grow together with you, one engaged in iterative interaction and intellectual discussion.

On the technical side, a system that builds its own evolving knowledge base. That creates metadata from each exchange, each problem solved, each trial and error. Like a human.

Mirroring organic neural growth, but without biological constraints. This system perpetually connects words and understanding. A system, like humans, building structured knowledge through engaging discussions and goal oriented error correction.

The Acceleration Point

Human knowledge gathering is a perpetual loop. Practice leads to errors and successes. Improvement leads to expertise. Expertise create active creators, finally forming a master.

Computational systems challenges this quo.

An LLM captures and organizes information and generates the "correct" response in accordance to its worldy wisdom gathered by words and images. Then it builds structured vectorized information points plugged in to the LLM brain network. This instantly create new knowledge of any topic.

While human learning requires periods of rest and integration, a well-designed LLM system perpetually gather and connect information in real-time.

Adding knowledge is an instant feature.

The Learning Phases

LLM evolution follow human-like phases.

The Student Phase

Most LLM chats are student level. The model processes information but doesn't retain learning. It participates in trail and error but doesn't build knowledge of it. Like a student taking notes without internalizing concepts.

But LLMs maintain awareness of its thinking tracks. It can write monitoring logs, and observe it's thinking. And run infinite loops to find the best: interactions, successes, strategies and common errors.

Unlike humans, LLMs inhabit the foundation for instant iterative learning.

The Apprentice Phase

Great learning can occur in apprentices. LLMs can actively participate in problem-solving, engaging its human counterpart. Building it's essence with: trial and error, direct feedback, and structured understanding of a problem.

This alters the relationship between humans and machines. An LLM is not limited to mere receiving of information—it participates in knowledge synthesizing and generation. This builds: context understanding, refined strategies, and strong workflow thinking.

The Mastery Phase

At this stage, the LLM's knowledge begins to overlap with human expertise. Its problem-solving capacity expands into true augmentation of a human. The model can apply structured thinking with principles, and use human creativity imitation to new challenges.

It's now an evolved computer with a deep knowledge base layer, and a "thinking" layer. Refined through iterative learning and close feedback loop with it's human user.

The Self-Organizing System

LLMs breathe off of structured information. This means creating an environment that contributes to calm and effective knowledge utilization. Creating a simple system mimicking human neurons and neuron chemicals that decide what information is worth encoding as new knowledge.

Knowledge Management

Current knowledge systems rely on human-structured data. But an evolution-capable LLM tool manages its own knowledge base: by identifying the important metadata, creating accurate connections, and building useful nuanced context from each interaction.

Via prompt engineering, LLM interactions generate nuanced reflections of code and language.