While it may be conceivable to create an AI capable of solving any task using a singular method, the history of scientific advancement consistently demonstrates that diverse challenges are best addressed with a variety of technologies. It is imperative to select tools based on the task at hand, rather than tailoring tasks to fit a specific tool.

We must not disregard the wealth of knowledge accumulated by humanity. Furthermore, we believe that a true breakthrough in AGI will emerge from the intersection of two paradigms: the Ascending and Descending approaches to AI development.

The Ascending paradigm is grounded in data-driven learning. Systems developed under this paradigm often emulate biological processes in solving cognitive tasks based on experiential learning. This approach encompasses technologies such as neural networks, genetic algorithms, evolutionary computing, and neuromorphic computing.

Conversely, the Descending paradigm focuses on simulating high-level cognitive processes through symbolic, logical, and agent-based methodologies. It includes technologies such as knowledge bases, expert systems, and logical systems.

In a breakthrough development that could reshape our understanding of artificial intelligence, our researchers have unveiled a groundbreaking "Hybrid Model of the Knowledge Representation" that promises to bridge the gap between machine learning and human-like cognition.

At the heart of this revolutionary system lies an intricate engineering approach that experts are calling one of the most sophisticated attempts yet to replicate human-like knowledge processing. Unlike traditional AI systems, this new architecture implements a complex hierarchical structure that mirrors the way human minds organize and process information.

In a significant departure from conventional Artificial Intelligence design, we have designed a architectural framework that could reshape the landscape of machine intelligence. Embryo, introduces a revolutionary dual-pillar structure that explicitly separates knowledge storage from processing mechanisms — a stark contrast to current industry standards:

As Artificial Intelligence, particularly Large Language Models (LLMs), continues to dominate headlines and attract billions in investment, this innovative approach signals a potential paradigm shift in the field. While companies like OpenAI and Google have pursued increasingly large, and even more large models, our architecture deliberately breaks with this trend.

It is important to understand that this isn't merely an incremental improvement — it's a complete reimagining of how Artificial Intelligence systems should be structured.

The implications of this architectural choice extend far beyond technical elegance. By maintaining distinct systems for knowledge storage and processing, Embryo addresses several critical challenges that have plagued contemporary AI systems, particularly the persistent issues of hallucination and opacity that have troubled even the most sophisticated LLMs.

The Embryo's combination of efficiency, transparency, and adaptability positions it as a game-changer in the AI landscape, particularly for organizations requiring robust, resource-efficient AI solutions.

So, let's dive deeper into the technology architecture

At the system's core lies the Abstract Layer, a revolutionary framework housing over 40,000 meticulously organized entity classes. This layer serves as the AI's fundamental understanding of the world, operating on pure semantic relationships rather than traditional language constraints.

The Abstract Layer's sophistication becomes apparent in its handling of conceptual hierarchies. Unlike conventional systems that rely on rigid categorization, this layer implements a flexible, context-aware classification system. For example, it can understand that a "chair" isn't just a piece of furniture, but exists within multiple conceptual frameworks – as a tool for sitting, a design object, or even a status symbol in certain contexts.

Building upon the Abstract Layer, the Factographic Layer introduces real-world context and relationships. This layer maintains an extensive database of factual information, but more importantly, understands the interconnections between these facts.

The layer's sophisticated architecture allows it to:

• Process and store billions distinct factual relationships
• Maintain temporal consistency across data points
• Understand causality and correlation
• Adapt to new information without disrupting existing knowledge structures

The Event Layer represents perhaps the most sophisticated attempt yet to give machines an understanding of narrative and temporal relationships. Using advanced tree structures, this layer can:

• Track and understand complex sequences of events
• Recognize patterns across different timeframes
• Understand cause-and-effect relationships
• Maintain multiple parallel narrative threads

The layer's tree structures allow for unprecedented complexity in understanding how events relate to each other. It's not just about storing sequences of events, the Event Layer makes able Embryo to understand the rich tapestry of how events influence and relate to each other, much like human memory does.

The logical layer of the system operates with knowledge about reasoning and logical inference processes. It is important to note that reasoning is not a pre-installed component of the system, but rather represents a specific type of knowledge that can be modified and adapted.

The example of tools which can be suitable for the logical layer are:

• Production systems
• Boolean algebra functions

The process of selecting and applying productions is regulated by special meta-knowledge that determines their activation conditions. The basic elements of production rules and logical formulas use facts stored in the factographic layer of the system, which ensures tight integration between architecture levels.

The Task Layer represents a sophisticated architectural component that implements a universal algorithmic system operating on artificial intelligence knowledge bases.

Key Capabilities:

• Enables creation, storage, and management of algorithmic solutions
• Facilitates dynamic problem-solving processes
• Supports comprehensive algorithm mapping and preservation
• Maintains solution persistence across diverse problem domains

The fundamental principle based on concept that any problem with a definable algorithmic solution can be systematically described, encoded, and preserved within the system's memory architecture. This principle ensures scalability and adaptability in handling complex computational challenges.

Embryo stores information in different ways, kind of like how our brains do. It needs quick access to what's happening right now - like our current conversation - but also Embryo keeps longer-term knowledge stored away.

Intelligent agents represent a breakthrough in artificial intelligence architecture, functioning as sophisticated software entities capable of performing complex cognitive operations on knowledge bases. These remarkable digital constructs serve as the fundamental operational units in advanced AI systems.

Key Features:

• Hierarchical Integration: Agents can seamlessly incorporate lower-tier agents, creating a dynamic, multi-layered intelligence structure
• Autonomous Generation: High-level agents can be spontaneously created by the Embryo to address specific challenges and objectives
• Scalable Complexity: Their versatility ranges from executing elementary Embryo operations to functioning as complete, independent AI models

This approach to AI architecture enables unprecedented flexibility and efficiency in problem-solving capabilities. Whether operating as minimal computational units or full-fledged AI models, intelligent agents represent the cornerstone of next-generation artificial intelligence systems.

The modular nature of these agents not only enhances system adaptability but also paves the way for more sophisticated and autonomous AI solutions, marking a significant milestone in the evolution of artificial intelligence technology.

An equally important part of the technology is the Ascending paradigm based on the application of machine learning methods. These are the technologies we use to create digital sense organs for Embryo.

We use many models to solve specific intellectual problems, have achieved outstanding results and now have SOTA level technologies which already used on commercial ways

Technical Excellence of Speech Synthesis

• Premium voice quality rivaling human speech
• Compact, efficient model architecture
• Seamless integration capabilities
• Device-independent functionality

One of the most challenging obstacles in implementing top-down AI technologies has been the complexity of translating informal human speech into formal, machine-readable language. This fundamental challenge has long been a bottleneck in advancing AI applications.

We've developed a revolutionary approach that combines neural networks with logical processing systems (we have a plans to reveal it in future). This hybrid architecture has enabled us to achieve:

• Transformation accuracy exceeding 90% (Other 10% are solved by Embryo)
• Robust natural language understanding
• Seamless integration of human input with AI processing

Through the integration of the technologies discussed above (though this list is not exhaustive), we have successfully developed what can be considered an embryonic form of Artificial Intelligence.

Our work demonstrates that a systematic, engineering-based methodology is not only viable but essential for advancing AI development. This foundation provides a robust platform for future innovations and improvements in artificial intelligence systems.