AI Systems: Technical Architecture and Functional Analysis: From Large Language Models to Personal Implementation

Introduction: Current State and Technical Challenges of AI Technology

Artificial Intelligence (AI) technology, particularly Large Language Models (LLMs), has established a new paradigm in computational science following the emergence of ChatGPT in late 2022. While these systems superficially provide natural language dialogue interfaces, their essence lies in highly optimised probabilistic numerical computation systems.

Fundamentally different from traditional rule-based systems, modern LLMs achieve language understanding and generation through statistical pattern recognition. Systems do not “think” or “understand”; instead, they mathematically predict “the word with the highest probability of appearing next” based on patterns learned from massive datasets. This probabilistic approach forms the technical foundation enabling natural dialogue with humans.

However, the internal structure of these systems remains a black box for many users. A comprehensive technical understanding is required, spanning from processing mechanisms based on Transformer architecture to large-scale distributed system implementation and individual-level development possibilities.

This paper systematically analyses the complete picture of LLMs from the following technical perspectives:

Primary Analysis Areas:

• Computational mechanisms and algorithmic structures in language processing
• Technical specifications and infrastructure requirements of large-scale distributed systems
• Technical comparison of cloud platform strategies
• Development of miniaturisation technologies and edge computing adaptation
• Technical feasibility of personal development environments

Through these analyses, we clarify the current position of AI technology from an engineering perspective and provide technical considerations regarding future development directions.

Chapter 1: Processing Mechanisms of Language Models

Token Processing System Implementation

The process by which modern LLMs handle human language is implemented as a series of mathematical transformations. The starting point of this processing chain is tokenisation. Input text is divided into the smallest processable units. In Japanese, “人工知能技術の発展” (development of artificial intelligence technology) is decomposed into units like “人工” (artificial), “知能” (intelligence), “技術” (technology), “の” (of), “発展” (development).

Each token is then converted to a numerical ID. GPT-4 uses a token set of approximately 100,000 vocabulary items, establishing correspondences such as “人工” → “1234”, “知能” → “5678”. This numericalization converts text into a format that is computer-processable.

More importantly, vectorisation processing converts each token into high-dimensional vectors. In GPT-4, each token is represented as a 12,288-dimensional vector. This vector space has the property that semantically similar words are positioned close together. The vector relationship between “king” and “queen” exhibits a mathematically similar structure to the vector relationship between “man” and “woman”.

Revolutionary Design of Transformer Architecture

The Transformer architecture, which forms the core of modern LLMs, was a revolutionary technology published by Google’s research team in 2017. This technology, introduced in the paper “Attention Is All You Need”, is a groundbreaking design that overcomes the sequential processing constraints of traditional Recurrent Neural Networks (RNNs) and enables parallel computation.

Traditional AI systems processed sentences sequentially. For the sentence “Today is good weather”, processing occurred one word at a time: “Today” → “is” → “good” → “weather”. However, Transformers can process all words simultaneously and analyse their relationships through parallel computation.

This realisation of parallel processing has dramatically reduced processing time. While conventional methods saw processing time increase proportionally with word count, Transformers can efficiently process long sentences and maximise GPU computational capability. This technical innovation formed the foundation enabling current large-scale language models.

Attention Mechanism: AI’s Attention System

The attention mechanism is literally a system that decides “where to direct attention”. Just as humans direct more attention to important parts when reading text, AI also directs more “attention” to important relationships within sentences.

As a concrete example, consider the sentence: “Mr Tanaka is reading a book in the library. He is a student.” To understand who “he” refers to in “He is”, AI distributes attention as follows:

• “He” → “Mr. Tanaka”: Relevance 90%
• “He” → “library”: Relevance 5%
• “He” → “book”: Relevance 3%
• “He” → “student”: Relevance 2%

Thus, relevance is calculated for all word pairs in the sentence, quantifying numerically which words are strongly related to which words.

Technical Implementation of Self-Attention

Crucial is the “self-attention” mechanism. This technology calculates the relationship between each word in a sentence and all other words in the sentence.

In technical processing, three types of numerical sets are generated from each word: Query, Key, and Value. Query represents “what is being searched for”, Key represents “what information about”, and Value represents “actual information content”. Through these mathematical operations, relationships between words are calculated with the following formula:

Attention(Q,K,V) = softmax(QK^T/√d_k)V

Here, d_k is a scaling factor for dimensionality. This calculation implements a mechanism where the higher the similarity between Query and Key, the more weight is allocated to the corresponding Value.

Multi-Head Attention: Parallel Analysis from Multiple Perspectives

In actual Transformers, an advanced technology called “multi-head attention” is implemented. This is a mechanism that analyses a single sentence simultaneously from multiple different perspectives.

Examples of different perspectives:

• Grammatical perspective: Emphasises subject-predicate relationships
• Semantic perspective: Emphasises semantically related words
• Positional perspective: Emphasises relationships between adjacent words
• Temporal perspective: Emphasises chronological relationships

In GPT-4, 128 attention heads operate in parallel in each layer, each capturing different types of relationships. This is equivalent to 128 experts analysing the same sentence from various perspectives and integrating the results. The outputs of each head are integrated to generate richer, more multifaceted contextual representations.

Deepening Understanding Through Hierarchical Processing

In Transformers, understanding is deepened progressively by stacking attention processing in multiple layers. In GPT-4’s 96-layer structure, each layer plays the following roles:

• Layers 1-20: Understanding basic relationships at the word level
• Layers 21-50: Structural analysis at phrase and clause level
• Layers 51-80: Semantic integration at the sentence level
• Layers 81-96: Context and logical relationships across the entire document

Each layer receives output from the previous layer and constructs a more abstract, higher-order understanding of the data. This hierarchical processing realises progressive understanding from characters → words → sentences → paragraphs → entire document.

Autoregressive Generation and Probabilistic Sampling

In the language generation stage, Transformers execute autoregressive prediction. This is based on a conditional probability distribution, P(w_t|w_1,…,w_{t-1}), which calculates probability distributions over the entire vocabulary at each step to select the next token.

The following methods are implemented as sampling strategies during generation:

Greedy Decoding: Always select the highest probability token Top-k Sampling: Random selection from the top k candidates Top-p (Nucleus) Sampling: Selection from the candidate set with cumulative probability ≤ p Temperature Scaling: Adjusts the sharpness of the probability distribution

Temperature parameters control the balance between creativity and consistency. High temperatures generate diverse expressions, while low temperatures produce deterministic output.

Technical Advantages of Transformers:

• Efficient learning of long-range dependencies
• Acceleration through parallel processing
• Comprehensive analysis from multiple perspectives
• Higher-order abstraction through hierarchical understanding
• Scalable architecture design

Through this Transformer architecture and attention mechanism, AI achieves advanced language understanding and generation previously impossible. The technical foundation enabling human-like natural language processing lies in the combination of statistical pattern recognition and probabilistic prediction.

Chapter 2: Technical Implementation of Large-Scale Systems

System Specifications and Computational Scale of GPT-4

GPT-4’s technical specifications reach scales that challenge the limits of computational capability in modern computer science. Its 1.8 trillion parameters correspond to 18 times the number of human brain neurons (approximately 100 billion), organised into a 96-layer Transformer architecture.

The system’s overall memory requirement is approximately 7.2 terabytes. This is equivalent to 200-400 times the typical personal computer memory (16-32GB). This memory is used not only for parameter storage but also for intermediate computation data during inference, attention matrices, and temporary storage of gradient information.

A single inference process requires approximately 1 quadrillion floating-point operations. This scale matches the number of operations a typical personal computer can execute per second. To complete such computation-intensive processing within practical response times (2-5 seconds), advanced parallel processing by dedicated hardware is necessary.

Distributed Computing Infrastructure

The computational infrastructure supporting GPT-4 is built on Microsoft Azure’s worldwide data centre network. Major facilities are located in North America (Virginia, Texas, California, and Washington) and Japan (Saitama and Osaka).

Each data centre houses tens of thousands of GPU servers equipped with dedicated power supply systems and liquid cooling systems. A single data centre’s power consumption matches that of a medium-sized city, with industrial-level cooling technology introduced to cool its systems.

Distributed processing is implemented in three hierarchies. Model parallelisation distributes the 96 layers across multiple GPU groups, data parallelisation processes multiple user requests simultaneously, and pipeline parallelisation executes each stage of inference processing in an assembly-line fashion.

GPU inter-communication uses dedicated high-speed interconnects such as InfiniBand and NVLink, providing bandwidth (several terabits/second) over 100 times that of regular Ethernet. This high-speed communication eliminates communication bottlenecks in distributed computing.

Storage Systems and Performance Optimisation

To enable high-speed reading of 7.2 terabytes of parameter data, NVMe SSD arrays and dedicated file systems are used. This storage system provides I/O performance over 1000 times that of typical database systems, enabling full parameter distribution within seconds.

In performance optimisation, KV (Key-Value) cache technology plays an important role. This technology memorises Key and Value tensors used in attention calculations and reuses them in subsequent calculations. This significantly reduces computational load for interactive processing, allowing additional questions in the same context to be processed at approximately 10% of the original computational cost.

Technical Characteristics of System Specifications:

Item	Specification	Technical Significance
Parameter count	1.8 trillion	18 times the human brain neurons
Layer count	96 layers	Hierarchical feature extraction
Memory usage	7.2TB	400 times standard PC memory
Computational load	1 quadrillion operations/inference	Advanced parallel processing is required

While this large-scale system achieves previously impossible natural language understanding and generation, it also highlights technical challenges that require enormous computational resources and power consumption.

Chapter 3: Technical Analysis of Cloud Platform Strategies

Implementation Strategies of Major LLM Systems

Modern major LLM systems employ various cloud infrastructure strategies, and these choices significantly impact each system’s technical characteristics and performance. Behind these implementation strategies, technical optimisation, strategic partnerships, and risk management considerations interact in complex and interdependent ways.

ChatGPT/GPT-4 operates under an exclusive implementation contract with Microsoft Azure. This contract is part of a comprehensive technical partnership established in 2019, which has enabled technical integration beyond the use of simple cloud services. Azure’s computational resources are optimised for GPT-4’s 96-layer Transformer architecture, with dedicated AI clusters constructed.

This exclusive implementation enables fine-tuned adjustment of hardware configuration, network design, and software stack tailored to OpenAI’s specifications. In particular, memory placement and communication patterns optimised for large-scale parallel processing are implemented, achieving processing efficiency difficult to realise on other cloud platforms.

Technical Advantages of Multi-Cloud Strategy

Meanwhile, Anthropic’s Claude system employs a multi-cloud strategy, utilising Amazon Web Services (AWS) as its primary platform, alongside Google Cloud. On AWS, primarily Amazon EC2 P4d instance groups are used, with each instance equipped with 8 NVIDIA A100 GPUs.

The technical merit of multi-cloud implementation lies in its ability to leverage the technical strengths of each platform. Google Cloud’s TPU (Tensor Processing Unit) demonstrates performance exceeding GPUs for specific computational patterns, with this technical advantage utilised in some of Claude’s processing.

The multi-cloud architecture adopted by Genspark represents an even more advanced approach. Seamless processing and distribution between Microsoft Azure, AWS, and Google Cloud platforms is implemented, with dynamic load balancing automatically executing optimal resource allocation.

Technical Considerations of Availability and Performance

From a system availability perspective, a multi-cloud strategy demonstrates significant advantages. Compared to the typical single-cloud availability of 99.9%, properly designed multi-cloud systems can achieve availability of 99.99% or higher. This means reducing annual downtime from 8.76 hours to 52.6 minutes.

In performance optimisation, minimising latency through geographic distribution is a crucial element. User requests are processed at the nearest data centre, minimising network latency. For access from Japan, data centres in Saitama and Osaka are used preferentially, resulting in a reduction in response time.

Technical Comparison of Cloud Strategies:

System	Primary Cloud	Strategy	Technical Characteristics
ChatGPT	Microsoft Azure	Exclusive contract	Deep optimisation, dedicated infrastructure
Claude	AWS-based	Multi-cloud	Redundancy, flexibility
Genspark	Three-provider combination	Dynamic allocation	Optimisation, high availability

From a cost-optimisation perspective, a multi-cloud strategy provides opportunities for dynamic price adjustments. Through price fluctuations on each platform, the utilisation of spot instances, and the efficient allocation of reserved cases, a cost reduction of 15-25% is realised compared to a single cloud. This optimisation becomes an important factor for economic sustainability in large-scale AI operations.

Chapter 4: Development of Small Language Model Technology

Model Efficiency, Technology, and Algorithm Optimisation

The development of Small Language Model (SLM) technology is one of the most important technical innovations in the AI field. This technology fundamentally reconsiders the enormous computational resources required by traditional LLMs, aiming to realise practical language processing capabilities even in limited computational environments.

The core technology of model efficiency is Knowledge Distillation. This method transfers knowledge from large teacher models (GPT-4 level) to significantly miniaturised student models. Technically, output probability distributions generated by teacher models serve as training data for training small models. Through this process, the essential parts of large model inference capabilities are reproduced in a form with dramatically reduced parameter counts.

Pruning technology systematically removes low-importance parameters from trained models. Importance evaluation uses gradient-based methods that mathematically analyse each parameter’s impact on the final output. Properly executed pruning can suppress performance degradation to below 5% while removing over 90% of the original model’s parameters.

Quantisation technology reduces parameter numerical precision. By converting conventional 32-bit floating-point numbers to 8-bit integers or 4-bit representations, memory usage can be reduced by 25-75%. NVIDIA research’s 4-bit quantisation reduces memory usage by 87% while maintaining 85-95% of performance.

Edge Computing Adaptation Technology

At the forefront of standalone implementation technology, the Qualcomm Snapdragon X Elite processor features an NPU (Neural Processing Unit) with 45 TOPS (Tera Operations Per Second) processing capability. This processing power achieves performance matching that of data centre-class GPUs from several years ago in mobile devices.

NPU architecture achieves 10-100 times the power efficiency of conventional CPU/GPU through dedicated circuit design specialised for AI processing. The Snapdragon X Elite’s NPU achieves high power efficiency of 3 TOPS/Watt, enabling 8-10 hours of continuous AI processing on smartphone batteries.

In offline operation technology, hierarchical memory management plays an important role. Frequently accessed model parameters are stored in high-speed SRAM (32MB), intermediate computation results are stored in shared memory (LPDDR5X), and long-term storage data is stored in storage (UFS 4.0). This hierarchical structure realises efficient processing with limited memory resources.

SLM Technology Performance Indicators:

Efficiency Method	Parameter Reduction Rate	Memory Reduction Rate	Performance Retention Rate
Knowledge Distillation	80-95%	80-95%	85-95%
Pruning	70-90%	70-90%	90-98%
Quantisation (8bit)	0%	50-75%	95-99%
Quantisation (4bit)	0%	75-87%	85-95%

From a privacy protection perspective, complete offline processing on edge devices provides decisive advantages. Medical information, financial data, confidential documents, and more can be safely processed without user data being transmitted to external networks. Additionally, network latency is eliminated, enabling immediate responses within tens of milliseconds.

Through these technological advances, the AI processing paradigm is shifting from “cloud-centric” to “edge-first”, with advanced AI functionality on personal devices becoming standard.

Chapter 5: Technical Feasibility of Personal-Level AI Development

Stepwise Approach to Development Tools

The background enabling realistic personal-level AI development lies in the dramatic simplification of development tools. The machine learning world, previously accessible only to university research labs and large corporations, now offers environments for stepwise learning tailored to technical levels.

At the easiest level, “no-code development” tools like Hugging Face AutoTrain are available. This system enables AI creation solely through mouse click operations, eliminating the need for programming knowledge. These tools incorporate “meta-learning” technology, utilising knowledge learned from tens of thousands of past projects to select and construct optimal AI models for new data automatically.

At the intermediate level, cloud-based development environments, such as Microsoft Azure AutoML and Google Cloud AutoML, are available. These make enterprise-level authentic AI development accessible to individuals. Azure AutoML manages all AI development processes, from data preparation to model training, evaluation, deployment, and operational monitoring, in an integrated environment.

Google Cloud AutoML’s characteristic is the ability to utilise AI research results that Google has accumulated over many years. Purpose-specific tools are provided, including AutoML Vision for image recognition, AutoML Natural Language for natural language processing, and AutoML Tables for tabular data, enabling completion in hours that would typically take experts weeks to accomplish manually.

Technical Differences Between Development and Execution Stages

An important understanding in AI development is that “the stage of creating AI” and “the stage of using created AI” have completely different technical requirements. Starting development without understanding this difference can lead to unexpected difficulties.

The development/training stage requires enormous computational resources to train AI models from large amounts of data. Training a medium-scale language model (model with 700 million parameters) requires high-performance GPU memory of 32GB or more and hundreds of hours of continuous computation. This stage has an experimental character, involving trial and error as researchers search for optimal settings.

Conversely, the inference/execution stage uses completed AI models for actual predictions and responses. At this stage, response speed, memory efficiency, and system stability are prioritised. Inference-dedicated engines like Ollama reduce the original model size by 75% through 4-bit quantisation technology, maintaining stable memory usage even during extended conversations.

Requirements Comparison by Development Stage:

Processing Stage	Computational Load	Memory Requirement	Primary Goal	Processing Time
Development/Training	Very high	32-128GB	Accuracy maximization	Hours to days
Inference/Execution	Lightweight	4-16GB	Speed and efficiency	Milliseconds to seconds

Understanding this difference enables the selection of appropriate tools and the construction of an efficient development process. A two-stage approach utilising cloud high-performance resources for training and lightweight engines for practical use represents the current optimal solution.

Realistic Costs and Skill Requirements for Personal Development

The economic feasibility of personal AI development has improved dramatically through cloud service pay-per-use systems. Currently, models at GPT-2 scale (1.5 billion parameters) can be trained with cloud usage fees of around tens of thousands of yen.

Particularly important is the existence of the Hugging Face Transformers library. This library provides free access to over 20,000 pre-trained models, enabling the construction of specialised AI systems in short periods through “fine-tuning” based on these. Fine-tuning is a technology that provides additional training to models already trained on large datasets, adapting them to specific purposes.

Transfer Learning technology enables the development of high-performance models even with small datasets by adapting knowledge from large pre-trained models to specific fields. For example, AI assistants specialised for the medical field can be created by providing additional training on medical documents to general language models.

Personal Development Feasibility:

Development Level	Initial Cost	Development Period	Required Skills	Product Quality
No-code	1,000-10,000 yen	Hours	Basic PC operations	Commercial use possible
Low-code	10,000-100,000 yen	Days-weeks	Basic IT knowledge	Enterprise level
Full custom	50,000-500,000 yen	Weeks-months	Programming	Highest performance

Currently, even personal development using specialised frameworks like PyTorch or TensorFlow provides flexibility applicable from the research level to commercial systems. Platforms like Papers with Code immediately publish implementation code for the latest academic research, creating an environment where individual developers can access cutting-edge technology.

This technical change shifts AI development from the stage of “using services provided by large companies” to the stage of “individuals creating their own AI”, dramatically expanding possibilities for innovative AI applications at the individual level.

Chapter 6: Functional Evolution of Next-Generation AI Systems

Emergence of Cooperative Multi-AI Systems

The most significant advance in next-generation AI systems is the shift from processing everything with a single, giant AI to a method of multiple specialised AIs cooperating to solve problems. This resembles human organisational management—teams of experts with specialised skills collaborating to enable more efficient and precise results.

In Genspark’s implemented “multi-agent functionality”, four AI agents specialised in requirement analysis, dialogue management, task decomposition, and execution coordination collaborate hierarchically. The requirement analysis agent possesses advanced natural language understanding capabilities that identify not only explicit content from users’ ambiguous requests but also implicit requirements inferred from context.

The dialogue management agent implements technology called “adaptive question generation”. This applies information theory concepts, mathematically calculating which questions can most efficiently collect necessary information. This optimisation enables the collection of the required information with 40-60% fewer questions compared to conventional simple questioning methods.

Communication between agents employs “distributed consensus algorithms”. Each agent makes independent judgments, but final decisions are determined by consensus among all agents. This mechanism is similar to mechanisms used in blockchain technology, with voting weights adjusted based on each agent’s expertise and past success rate.

Strategy of Combining Multiple AI Systems

In practical AI utilisation, “hybrid strategies” combining the technical strengths of different AI systems are effective. For example, the combination of Genspark for initial requirement organisation, Claude for detailed analysis, and GPT-4 for final document creation, achieves comprehensive quality improvement that is difficult to achieve with single systems.

Genspark’s requirement clarification functionality implements “missing information detection algorithms”. These algorithms systematically identify information elements necessary for task completion and automatically detect deficient parts. For example, for the request “I want to create presentation materials”, essential parameters like target audience, presentation time, material format, and expertise level are automatically extracted.

Claude’s detailed analysis functionality enables the processing of long documents (approximately 100,000 tokens, or 75,000 words) through “hierarchical attention mechanisms”. Documents are structured in three layers—paragraph level, section level, and overall level—with different importance weights applied at each layer. Additionally, “Constitutional AI technology” executes self-verification of generated results and automatically detects logical contradictions.

GPT-4’s document creation functionality generates optimal expressions according to reader attributes through “style transfer learning”. Vocabulary selection, sentence structure, and logical development are automatically adjusted to target readers, with optimal expressions selected from a vocabulary of 50,000 items.

Hybrid Strategy Effects:

Processing Stage	Responsible System	Technical Strength	Quality Achievement Rate
Requirement Clarification	Genspark	Multi-AI coordination	99%
Detailed Analysis	Claude	Long text processing, logical verification	95%
Document Creation	GPT-4	Style adaptation, vocabulary optimisation	98%

Technical Judgment Criteria for AI System Selection

To select the most appropriate AI systems, a quantitative evaluation of throughput (processing capability), latency (response time), cost efficiency, and specialised performance is necessary. ChatGPT achieves a processing capability of 10,000 tokens/second with a 2-5 second response time, while Claude achieves a processing capability of 8,000 tokens/second with a 1-3 second response time.

From a privacy protection perspective, data retention period, processing location, encryption level, and access control become important evaluation indicators. Cloud-based systems typically retain data for 30 days, but locally executing systems like Ollama realise complete privacy protection.

A cost efficiency analysis requires a comprehensive evaluation of the token unit price, computational complexity, and resource utilisation efficiency. GPT-4’s pricing is 3 cents per 1,000 input tokens and 6 cents per 1,000 output tokens, but actual cost efficiency varies significantly depending on the nature of the task. For tasks requiring complex reasoning, high-performance models often result in lower costs ultimately.

Technical Selection Judgment Framework:

• Clarification of performance requirements (response time, accuracy, processing capability)
• Organisation of cost constraints (initial costs, operational costs, expansion costs)
• Identification of security/privacy requirements
• Evaluation of system integration complexity and maintainability
• Consideration of future scalability and technical sustainability

This technical evaluation framework enables the optimal selection of AI systems and effective combinations according to purpose, realising advanced problem-solving that is difficult to achieve with single systems. A major characteristic of modern AI technology is the ability to construct optimal AI utilisation strategies according to respective needs and constraints, from individual users to enterprises.

Conclusion: Development Direction and Technical Prospects of AI Technology

Important Turning Points in Technical Development

Through this paper’s analysis, it has become clear that AI technology is at multiple important turning points. Large-scale and efficiency advances are progressing simultaneously, with shifts from cloud-centric to edge device emphasis, evolution from single giant models to collaborative systems of multiple specialised AIs, and expansion of technical feasibility at individual levels through democratisation of development.

The Transformer architecture and attention mechanism, which form the technical foundation of LLMs, have achieved revolutionary progress in natural language processing. However, fundamental constraints exist for further large-scale growth, as the computational load increases proportionally to the square of the input length. As a solution to this constraint, the development of Small Language Model (SLM) technology and efficiency methods has become an important direction.

Model efficiency technologies, such as knowledge distillation, pruning, and quantisation, enable the reproduction of large-scale system performance in miniaturised implementations. 45 TOPS processing capability on NPU-equipped devices realises performance at data centre class from several years ago in mobile environments, pioneering new application fields through complete offline processing.

Optimal Combination of Cloud and Edge

From a cloud infrastructure perspective, the shift from single platform dependence to a multi-cloud strategy demonstrates technical advantages. System availability improvement (from 99.9% to 99.99% or higher), latency minimisation through geographic distribution, and 15-25% cost reduction through dynamic resource allocation are realised.

In distributed computing technology, a three-layer structure of model parallelisation, data parallelisation, and pipeline parallelisation has become a standard implementation, with high-speed communication (several terabits/second) via InfiniBand and NVLink, eliminating communication bottlenecks. Computational load reduction through KV cache technology and efficient memory management ensures practical response performance.

Maturation of Personal Development Environments

AI development democratisation is realised through the dramatic improvement of technical accessibility. From no-code environments to full custom development, stepwise approaches tailored to technical levels are established, enabling practical AI system development by individuals with budgets ranging from 1,000 to 500,000 yen.

Technical barriers have been significantly lowered through the Hugging Face Transformers library’s over 20,000 models, access to the latest research via Papers with Code, and cloud computational resource pay-per-use systems. Transfer learning and fine-tuning technologies enable the development of high-performance models even with small datasets.

Direction of Next-Generation Systems

Multi-agent architecture is positioned as a technical solution that resolves the balance problem between single-model generality and specialisation. Cooperative processing by specialised agents in requirement analysis, dialogue management, task decomposition, and execution coordination simultaneously realises 30-50% computational resource reduction and quality improvement.

Inter-agent coordination through distributed consensus algorithms, efficient information collection via adaptive question generation, and self-verification functionality enabled by Constitutional AI technology form the technical foundation of next-generation AI systems.

Future Technical Challenges and Solution Directions

Future technical challenges include further improving computational efficiency, strengthening privacy protection technologies, reducing energy consumption, and enhancing system reliability. As solutions to these challenges, the utilisation of quantum computing, the development of federated learning, the practical application of neuromorphic chips, and the introduction of formal verification methods are expected.

Important Indicators of Technical Development:

• Computational efficiency: Continuous improvement of TOPS/Watt ratio
• Privacy: Realisation of complete local processing
• Responsiveness: Immediate response in millisecond units
• Availability: System uptime of 99.99% or higher
• Economics: Reduction of development/operation costs at the individual level

Path to Social Implementation of AI Technology

AI technology is in the transition stage from the technical maturity period to the practical popularisation period. The performance improvement of large-scale systems, the development of miniaturisation technology, the optimal combination of cloud and edge, and the creative utilisation at the individual level become important factors determining future technical development.

Based on the technical foundation analysed in this paper, AI technology is predicted to progress toward broader and deeper social implementation as a tool expanding human intellectual capabilities. What’s essential is not fearing AI technology, but rather appropriately utilising it after understanding its mechanisms and limitations.

AI is not an existence that steals human work, but rather plays a role as a partner supporting humans to expand their capabilities and concentrate on more creative, high-value activities. Through appropriate utilisation based on technical understanding, it becomes possible to maximise the potential of AI technology.

The important challenge required of us living in the present is to realise balanced AI utilisation—neither falling behind the wave of this technological revolution nor being overwhelmed by technology.