The First Dragon Ball: Neural Networks – Emulating the Human Brain
The human brain, the very source of intelligence, is an incredibly complex network comprising billions of neurons. The first ‘technical Dragon Ball’ in our quest for AGI is the meticulous emulation of this biological marvel: artificial neural networks (ANNs). In essence, ANNs aim to construct a virtual network of ‘neurons’ using computer code and mathematical models, with the hope of replicating the human brain’s capacity to process information and learn. Data flows from the input layer, undergoes intricate processing through multiple hidden layers, and finally yields results in the output layer. The greater the number of layers – a concept known as ‘deep learning’ – the more complex the information that can be processed.
While the concept of ANNs has existed for quite some time, its practical realization hinges on the exponential growth of computing power and algorithm optimization. Today, ANNs have become a cornerstone of modern AI. Consider the automatic classification of albums in your mobile phone or the ability of a voice assistant to understand your commands – all of this is powered by the brilliance of neural networks operating behind the scenes. Neural networks are inspired by the structure and function of the human brain and are designed to learn from data. They consist of interconnected nodes, or neurons, arranged in layers. Each connection between neurons has a weight associated with it, which represents the strength of the connection. During the learning process, the weights are adjusted to minimize the difference between the predicted output and the actual output. This process is called training.
Deep learning, a subfield of machine learning, utilizes neural networks with many layers (deep neural networks) to extract intricate features and patterns from data. The more layers a network has, the more complex the patterns it can learn. Deep learning has achieved remarkable success in various applications, including image recognition, natural language processing, and speech recognition. For example, deep learning models can accurately identify objects in images, translate languages, and transcribe speech.
The development of neural networks has been driven by several factors, including the availability of large datasets, advancements in computing power, and the development of sophisticated algorithms. Large datasets are essential for training neural networks effectively. The more data a network is trained on, the better it will perform. Advancements in computing power have made it possible to train very large and complex neural networks in a reasonable amount of time. Sophisticated algorithms, such as backpropagation, are used to train neural networks by adjusting the weights of the connections between neurons.
The Second Dragon Ball: Vector Databases – The Cyber Library
However, simply having a ‘brain structure’ is not sufficient. We also require an efficient ‘memory bank’ to store and retrieve vast amounts of knowledge. Traditional databases rely on precise keyword searches, making it challenging to understand information characterized by ‘similar meaning’ or ‘conceptual relatedness.’ Therefore, the second Dragon Ball – the Vector Database – has emerged. This database functions like a ‘cyber library,’ managing knowledge in a novel way by converting information such as text, images, and sounds into digital vectors. This allows information with similar meanings to be located close to each other in a mathematical space, enabling content searches based on ‘meaning.’ If you need to find a book on ‘space travel,’ the vector database can quickly recommend all relevant books to you. Many AI applications, such as intelligent customer service systems and document question-answering systems, are increasingly reliant on vector databases, which enhances the accuracy and efficiency of information retrieval.
Vector databases are designed to store and retrieve vector embeddings, which are numerical representations of data points in a high-dimensional space. Vector embeddings capture the semantic meaning and relationships between data points, allowing for similarity searches based on meaning rather than exact keyword matches. For example, a vector database could be used to store vector embeddings of text documents. A user could then search for documents that are semantically similar to a given query, even if the query does not contain any of the same keywords as the documents.
Vector databases are particularly useful for applications such as recommendation systems, search engines, and fraud detection. In recommendation systems, vector databases can be used to store vector embeddings of user profiles and product descriptions. A user can then be recommended products that are semantically similar to their past purchases or interests. In search engines, vector databases can be used to store vector embeddings of web pages. A user can then search for web pages that are semantically similar to their query. In fraud detection, vector databases can be used to store vector embeddings of transactions. Fraudulent transactions can then be identified as those that are semantically similar to known fraudulent transactions.
The Third Dragon Ball: Transformer – Machine Attention
To enable machines to truly comprehend the nuances of human language, including context, subtext, and puns, machines must possess extraordinary ‘reading comprehension’ abilities. The third Dragon Ball – the Transformer architecture, especially its core ‘attention mechanism’ – provides machines with this almost ‘mind-reading’ ability. When processing a word, the Transformer can simultaneously pay attention to all other words in the sentence and determine which words are most crucial for understanding the meaning of the current word. This not only transforms the way machines read but also elevates natural language processing to a new level. Since the publication of the groundbreaking paper ‘Attention Is All You Need’ in 2017, the Transformer has become the dominant architecture in this field, giving rise to powerful pre-training models such as GPT and BERT.
The attention mechanism is a key component of the Transformer architecture. It allows the model to focus on the most relevant parts of the input sequence when processing each word. This is done by assigning weights to each word in the input sequence, based on its relevance to the current word. The weights are then used to compute a weighted sum of the input sequence, which is used as the input to the next layer of the network.
The Transformer architecture has revolutionized the field of natural language processing. It has achieved state-of-the-art results on a wide range of tasks, including machine translation, text summarization, and question answering. The Transformer architecture is also used in many other applications, such as image recognition and speech recognition.
The Fourth Dragon Ball: Chain of Thought – A Methodology for Thinking
Being able to ‘speak’ is far from enough; AGI also needs robust logical reasoning skills. The fourth Dragon Ball, Chain of Thought (CoT) technology, teaches AI how to analyze problems in depth rather than simply guessing answers. Like solving an application problem, CoT guides the model to analyze the problem step by step, forming a ‘thinking trajectory,’ and then provides a well-reasoned final answer. Research from Google and other institutions indicates that large models using CoT prompts perform significantly better in multi-step reasoning tasks, providing strong support for AI’s logical capabilities.
Chain of Thought (CoT) prompting is a technique that encourages large language models to generate a sequence of intermediate reasoning steps before providing the final answer to a question. This technique has been shown to improve the performance of large language models on a variety of reasoning tasks, including arithmetic reasoning, logical reasoning, and common-sense reasoning.
The basic idea behind CoT prompting is that by breaking down a complex problem into smaller, more manageable steps, the model is better able to understand the problem and arrive at the correct answer. The intermediate reasoning steps provide a ‘chain of thought’ that the model can follow to solve the problem.
The Fifth Dragon Ball: Mixture of Experts – An Ensemble of Specialists
As the number of model parameters increases dramatically, training and operating costs also become a significant burden. It is at this point that the fifth Dragon Ball – the Mixture of Experts (MoE) architecture – emerges. This architecture adopts a ‘divide and conquer’ strategy, training multiple small ‘expert networks’ that are proficient at handling specific tasks. When a new task arises, the intelligent ‘gating network’ only activates the necessary experts to maintain efficient operation. This approach enables AI models to achieve immense scale and powerful performance at an acceptable cost.
The Mixture of Experts (MoE) architecture is a neural network architecture that combines multiple ‘expert’ sub-networks to improve performance and scalability. In an MoE model, the input is first processed by a ‘gating network,’ which determines which experts are most relevant to the input. The input is then passed to the selected experts, which process the input and generate outputs. The outputs of the experts are then combined to produce the final output of the model.
The MoE architecture allows models to scale to extremely large sizes without sacrificing performance. This is because the model only needs to activate a small subset of the experts for each input. This makes the MoE architecture well-suited for tasks that require a large amount of knowledge or expertise.
The Sixth Dragon Ball: MCP – A Universal Toolkit
To transform AI into a true ‘actor,’ it needs to be able to call tools and connect to the outside world. The sixth Dragon Ball – Model Context Protocol (MCP) – proposes the concept of adding a ‘toolkit’ to AI. In essence, this enables AI to call external tools through standardized interfaces to achieve richer functionality. This is akin to equipping smart people with all the tools they need, enabling them to find information and perform tasks at any time. Today’s intelligent agents (AIAgents) embody this concept, as AI can assist with tasks such as booking restaurants, planning trips, and analyzing data. This undoubtedly represents a crucial step in AI progress.
Model Context Protocol (MCP) is a framework that allows AI models to interact with external tools and APIs in a standardized way. MCP defines a set of protocols and interfaces that enable AI models to discover, access, and utilize external tools. This allows AI models to perform a wider range of tasks and to interact with the real world.
MCP is based on the idea that AI models should not be limited to the knowledge and capabilities that are built into them. Instead, they should be able to access and utilize external tools and APIs to augment their capabilities. This allows AI models to solve more complex problems and to adapt to new situations.
The Seventh Dragon Ball: VSI – Physical Intuition Brain
To integrate into human society seamlessly, AI must also possess the ability to understand the real world. The seventh Dragon Ball – Visual Spatial Intelligence (VSI)-related technologies – aims to provide AI with an ‘intuitive brain’ that understands physical laws. In simple terms, VSI allows AI to comprehend visual information obtained through cameras or sensors, improving its cognition of the relationships between objects. This is the foundation for realizing technologies such as autonomous driving, intelligent robots, and virtual reality. It undoubtedly constitutes an important bridge connecting digital intelligence and physical reality.
Visual Spatial Intelligence (VSI) is the ability of an AI system to understand and reason about the visual and spatial aspects of the world. VSI involves techniques such as object recognition, scene understanding, and spatial reasoning. Object recognition allows AI models to identify and classify objects in images and videos. Scene understanding enables them to interpret the relationships between objects and the overall context of a scene. Spatial reasoning allows them to reason about the spatial properties of objects and their relationships, such as their size, shape, and position.
VSI is essential for applications such as autonomous driving, robotics, and augmented reality. In autonomous driving, it enables vehicles to perceive and navigate their surroundings. In robotics, it allows robots to manipulate objects and interact with their environment. In augmented reality, it enables virtual objects to be seamlessly integrated into the real world.
The Summoning Ritual
When these seven ‘technical Dragon Balls’ converge, the outline of AGI begins to emerge more clearly. Imagine the biomimetic structure of neural networks, the vast knowledge derived from vector databases, the Transformer’s understanding of information, in-depth thinking facilitated by the chain of thought, efficient operation through the hybrid expert architecture, combined with MCP to interact with external tools, and finally, the use of visual spatial intelligence to understand the material world. The fusion of all these technologies will help us progress towards a new era – the era of the AGI Dragon. The journey to achieve AGI is complex and multi-faceted, requiring advancements in various fields of artificial intelligence. The seven ‘Dragon Balls’ described above represent some of the most promising areas of research and development that are contributing to this quest. As these technologies continue to evolve and converge, we move closer to a future where AI can truly understand, reason, and interact with the world in a human-like way.