If the first article discussed "how to train" an AI assistant, then this article will discuss "how to select" a brain.
Many people's impression of AI is still limited to "chatbots," but in fact, the evolution of AI has quietly crossed three key stages: from simple language sense and perception of the world, to true logical reasoning. For businesses, understanding the differences between these three is crucial to avoiding the waste of resources such as "using a cannon to shoot a small bird" or "using a small vehicle to haul a large load."
First level: LLM – Statistician in the world of words
The most basic LLM (Large Language Model) is essentially an extremely intelligent "text predictor". It learns all human language patterns through the Transformer architecture and excels at summarizing, translating, and conversing.
However, LLM has an inherent limitation: it doesn't truly "understand" the world. It knows "cats have four legs" because the statement appears statistically frequently, not because it has actually seen cats. Due to this lack of observation of the physical world, traditional LLM often struggles with tasks that require spatial awareness, physical logic, or contextual understanding.
Second stage: MLLM – Enabling AI to open its eyes and see the world.
To break down the barriers of text, MLLM (Multimodal Large Language Model) was developed. It adds multimodal capabilities such as vision to the language model, enabling AI to not only understand text, but also process information such as images and charts.
This represents a qualitative leap. MLLM is no longer just about listening to you; it can "see" the product images you upload, understand trend charts in reports, and perform cross-modal fusion analysis. These models (such as Qwen-VL or Gemini) can upgrade enterprise applications from simple text-based customer service to intelligent assistants capable of processing visual information. However, even when they can see the world, they may still experience logical gaps when dealing with complex causal relationships and in-depth decision-making.
Third Stage: Reasoning Model – Thinkers with the ability to "self-reflect"
The latest breakthrough in AI capabilities is in Reasoning Models, which focus on logic and decision-making. The core of these models (such as DeepSeek-R1 and Phi-4) is no longer just providing answers, but demonstrating their "thinking process".
Through CoT (Concept Chain) technology, reasoning models can break down complex problems into multiple steps and then deduce conclusions step by step. This has significant implications for enterprise applications.
- Medical diagnosis: It not only provides diagnostic results, but also deduces the pathological logic.
- Law and Compliance: It can provide compliance advice step by step based on laws and precedents.
- Mathematics and Engineering: Possesses precise computational and logical reasoning abilities.
In summary: How should businesses choose?
Connecting these three elements, we see the complete path of AI from "understanding language" to "understanding the world," and then to "being able to think":
- LLM: Suitable for handling purely text-based tasks (such as document summarization and email composition).
- MLLM: Suitable for scenarios that require understanding images and multimodal data (such as e-commerce search and security monitoring).
- Reasoning Model: Suitable for professional fields with high barriers to entry and requiring rigorous logic and decision-making paths (such as financial risk control and scientific research analysis).
In Taiwan AI Cloud's practical experience, companies should not blindly pursue the latest technologies, but rather choose appropriate model architectures based on the "task nature" of their business. Only by allocating the right resources to the right positions can AI transformation truly take root and flourish.