Since the emergence of human civilization, with the development of history, human beings have continued to create and enrich their own culture. In the 21st century, computer clusters and high-speed algorithms are interpreting the civilization created by human beings and pushing it to an epoch-making peak. Artificial intelligence will be the main driver. In addition to building the database infrastructure with Table as the basic element of the database like the traditional database (Oracle, MS SQL, IBM DB), the other is based on the graph processing of advanced hardware in the 21st century, and uses mathematical reasoning as the method, and Forms as the basic element of the database. Distributed relational Forms databases and application/development platforms are providing the market with an alternative option, the Form-World platform. The relationship refers to a relational transformation, which satisfies the definition of transformation in abstract mathematics, solves the automatic processing of data with transformation, and is the driving mechanism of artificial intelligence reasoning.

The process of artificial intelligence is a process of deduction, inference, and summary through the repeated cycle of a large number of cultures produced by human civilization, constantly enriching its own knowledge base, and constantly evolving. Artificial intelligence will become one of the most important milestones in the rapid development of human civilization. Artificial intelligence is the first time since the beginning of human civilization that human thinking, consciousness, perception, and knowledge base are systematically placed outside the human body to implement. Forms are the most common and effective method used to record and document human social activities since the dawn of human civilization. Through the statistical comparison of previous form data, human beings continue to summarize and summarize valuable data and conclusions, enrich their wisdom and talents, and promote innovation and development. Here, it is entirely dependent on the wisdom of the human brain itself. The Form-World platform provides the market with an easy-to-use engineering method, which can quickly mirror a large number of forms generated by human beings in response to the changing needs of society and the market on the Form-World platform, and automatically generate the system through the built-in AI engine of Form-World (no need to write a program in the process), so as to continuously update the applicable service system for human beings in response to the needs of society and the market. The Form may be an image of a paper form, a PDF, Excel and any application software that produces a picture on the screen. The Form-World platform can effectively integrate those discrete and decentralized forms into a centralized data processing system and mirror on screen. For humans, those conclusions that require brain intelligence to remember, inductive reasoning and to be summarized are automatically processed by the Form-World platform AI engine to produce, and then get mirroring Forms on screen. The Form-World Engineering method not only continues the Form used by humans to record and record social activities, but also helps humans to centralize those discrete Forms in a centralized system for automated processing while still retaining the Forms created by human wisdom. and the habits used.

The final results of human thinking, consciousness, perception, etc., such as various scientific theories, natural laws, concepts, production processes, financial and economic operations and their knowledge bases, are being embedded in artificial intelligence systems and are constantly evolving. The automatic reasoning module and neural network created by some scientific theories constitute the underlying foundation of artificial intelligence and are the core of artificial intelligence, especially those algorithms that are derived and constructed by mathematical methods ensure the rigor and convergence of the automatic reasoning process. Rigor stipulates that the reasoning process of artificial intelligence must follow the scientific theories, axioms, inferences, formulas, concepts, etc. that have been created, and there must be no errors; Convergence will constrain AI from transcending the laws of nature, being uncontrollable, or creating paradoxes in the reasoning process. The core design of the Form-World platform is based on the collection of Forms, starting from axioms, reasoning and induction, and the mathematical method ensures the rigor and convergence of data transformation. At the core layer of AI, the number of inference modules and the speed of neural networks determine the effectiveness of AI. Among the various types of automated reasoning modules, natural language automated reasoning modules (chatbots) are currently the fastest growing and most popular, and market acceptance is driving their development.

The outer layer of an intelligent system is the bridge between humans and it. The inner layer is to inference, educate, explore, search, request solutions, etc., and the outer layer is information, knowledge, and solutions. Language is the way to communicate, but it's not the only way. Language is a string of symbols that record and present human thoughts, consciousness, and perception. Different strings of symbols record the various forms of human thought, consciousness, and perception. These symbols include: words or words in natural language, such as the natural language used by popular chatbots; Special symbols or strings of symbols for various disciplines, such as mathematics, chemistry, physics, computer languages, etc.; Portraits (icons), illustrations, etc. In addition, humans also communicate with AI systems through gestures, movements, expressions, and voices. or indirectly communicate with the AI system through some simple tools and module operations. The built-in AI engine of the Form-World platform is designed and built based on this concept, and reserves the necessary interface for connecting with natural language. By communicating with people (once, or many times), AI systems perceive and infer people's intentions (even if someone makes a mistake) to provide the most accurate service, and at the same time reason and infer the results in the process of communication, the conclusions enrich their knowledge base and make themselves smarter. These results and conclusions should be more advanced than before, for example: they should be higher in terms of productivity, lower costs, more efficient in the financial and economic spheres, etc.; Results and conclusions may become new scientific theories, axioms, inferences, formulas, concepts, etc.

Artificial intelligence is a human civilization that is thinking, conscious, perceptual, and constantly evolving on the basis of the existing human culture, and can only be an extension of human thinking outside the body. Any claim that the evolutionary process is supernatural or out of control can only be caused by a lack of rigor and convergence at the core of the AI system. The construction, refinement, and maintenance of the AI core layer will continue to exist with the AI lifecycle. The Form-World platform is built in N dimensions and evolves with the development of perceptual artificial intelligence, and the scientific nature of its construction (mathematical method: rigor and convergence) will lay the foundation for its application in the past, present, and future, and will always exist with its lifetime.

The scope of AI is very broad. Today, the LLM (Large Language Model) that is being talked about all over the world is only one of them, not all of them. The boundaries of language are fuzzy. Craftsmen use this fuzzy boundary to automatically generate multiple words with fuzzy boundaries for the context of a certain topic, and then capture words by giving appropriate or necessary weight in ratios (parameters). These words are enough to meet or even exceed the scope of human imagination and consciousness, which amazes most people and makes the current large language model more competent for humans themselves. Competence is a success. Allowing LLM to develop without constraints and convergence can guide the direction of human thinking. The author of the movie "Future World" did not mention the reason why robots control the world? Perhaps the reason is the current LLM.

The concept of fuzzy boundaries that can be traced began four or five decades ago. At that time, all computer screens in the world were character terminals, not like today's computers are all image terminals, but with the development of hardware technology, image terminals began to appear. In order to solve the jagged defects of the surface/line of the continuous function displayed on the digital image terminal, mathematicians have cited a variety of function transformations to make up for the jagged defects, and the quadratic surface interpolation principle is one of them. There is improvement, but it still cannot deal with the jaggedness caused by image enlargement and reduction. Finally, the craftsmen used fuzzy boundaries (eye-catching tricks) to make up for the appreciation of human vision. For example, the empirical parameters of the second-order matrix (the result of convolution processing) is one of them, but it has nothing to do with quadratic surfaces. The boundaries of the 2D and 3D images we see now are the digital processing of the pixel gradients of the blurred boundaries (also called convolution processing). These pixels and gradients (pixels, gradients) can form approximate various surfaces/lines to improve the jaggedness of the original boundaries. The algorithm can determine that the approximate pattern of its own jaggedness should be connected with them by a certain surface/line according to the position of each jagged shape and several adjacent jagged shapes, so as to achieve an ideal smooth transition. These parameters are the earliest prototypes of the current large language model's empirical parameters. Two adjacent (several) jagged edges are equivalent to the context of the current large language model. The processing of fuzzy boundaries began to successfully serve humans and was accepted.

Later, NVIDIA made game cards and used the above-mentioned fuzzy boundary (empirical parameter) technology on the Thread/Process for concurrent processing of multiple dynamic images, and developed GPUs, including the basic layer and its interface provided to the game development platform. The fuzzy processing has been encapsulated in the bottom layer and GPU. The 2D/3D boundaries of the images in the game are blurred to create a good visual effect. Blurred, the slightest bit can not hinder people's pleasure in the game process. The fuzzy boundary and its processing have been successful again. So far, the fuzzy boundary has only processed two- and three-dimensional space. With the support of the game market, the GPU market has flourished, and product upgrades have continued. It has laid a powerful mechanism for multi-dimensional concurrent processing, which is more suitable for the execution of neural networks.

However, the greater success is the OpenAI team, who understood the principle of fuzzy processing technology in the digitization process of continuous functions, and generalized and extended it to other fields with fuzzy boundaries. They adopted NVIDIA's GPU and related platforms and applied fuzzy processing to language and video. The boundaryless characteristics of language and video can be decomposed and create empirical parameters with fuzzy boundaries. A word, phrase, sentence, or action can have multiple interpretations if it is associated with a context. The weight ratio of the parameter can influence the result of the context. The fuzzy boundary of language is not like two- or three-dimensional space, it has n disordered dimensions. A simple example: a sentence with n contexts of a topic can be decomposed into m directions of meaning, and each direction can be further decomposed... It reveals that the fuzzy boundary of n dimensions is already something that does not exist in human consciousness. Craftsmen give different weight ratios to fuzzy boundaries, which can produce different results. Therefore, as long as appropriate distributed processing (neural network) can be used to satisfy human beings in addition to visual effects, another more interesting cultural and consciousness effect. The biggest cause of interest is something beyond what people can imagine. LLM was born and began to develop. Before this, empirical parameters were only obtained through some simple methods, but the combination of language and vision can be infinite. To attract people's curiosity, more empirical parameters and more advanced methods are needed. The topology of convolution in n dimensions has surpassed the previous simple algorithms (two- and three-dimensional space) and developed towards n dimensions. GPU neural networks provide n𠆤-dimensional algorithm hard environments, and the rest are only soft methods, so the LLM movement began. So far, fuzzy boundaries have been generalized, and craftsmen have tried to use them in various fields.

The biggest difference between deep learning and ordinary learning is that it is creative. The essence of deep learning is to build on the basis of words that have already had a contextual association, and then make a new association between the new context and the previous one. If the latter is regarded as the context of the former, it can still be summarized as the association of context. Since the existence of human civilization, language has been a string of symbols that records human consciousness (including observation of things, natural laws, etc.). These recorded strings of symbols should be summarized and decomposed into contexts of certain meanings, disciplines, or other purposes, and under the action of appropriate weight ratios, the following text will always have a deeper content generation (creativity) for the previous text. The manifestation of depth is not only to trace back past events, content, phenomena, things, etc., but also to reveal more things with the development of the times. In the process, the results generated by the context will be inherited. Inheritance can continue to be associated for subsequent needs. The creativity of deep learning, if not rigorous and convergent, is allowed to develop, or may lead the direction of human consciousness. Learning is endless, and learning should be endless. This is the deep learning in the broad sense of Form-World. Form-World Relational Transformation is an abstract concept (2007, US patent US8051107), and the transformation meets the mathematical definition of transformation. The concept is used in an implementation carrier, named Form-World platform, and it is used in various industries, mainly dealing with data association, including data automatic processing. The platform can associate the content generated before and after, and the associated relationship can still be inherited.

Undoubtedly, LLM provides great convenience for mankind, and people began to follow it. When most people recognize a thing at the same time, it can become the truth. Science and inference are sometimes established in this way. For example: the apple fell that year, and the general theory of relativity later, etc., all had a group of people standing for their own teams, although the two conclusions were different.

I am afraid that only mathematics can conduct rigorous reasoning and inference from axioms and definitions. Compared with mathematics, it is obviously nonsense to use LLM for reasoning and inference. It is just out of fuzziness for fuzziness. Mathematics is a rigorous system of reasoning built upon axioms, whereas large language models (LLMs) generate outputs probabilistically based on fuzzy contexts. The "reasoning" of LLMs resembles the extraction of possibilities from vast, ambiguous experiences, rather than deriving logical necessities. This also reveals that much of human cognition in everyday life is inherently fuzzy, rather than mathematically precise. This is not a denial of the value of LLMs, but a reminder: their "truths" are products of collective consensus, not outcomes of logical proof. The boundaries of human consciousness are always fuzzy. Relying on the group may improve the fuzzy consciousness, but it is still fuzzy. LLM can list a lot of answers for you (also applicable to the plural), and its content has surpassed the scope of group consciousness. People are of course amazed, but those answers are also fuzzy when they think about it carefully. In engineering, humans have exhausted all kinds of disciplines to prove, calculate, design... to verify the most rigorous scientific methods. But the actual engineering implementation still adds a lot of safety factors. The safety factor is a component added by people due to the uncertainty of environmental factors, and uncertainty is the specific manifestation of cognitive fuzziness. Humans always desire rigorous reasoning and inference, and constantly explore and evolve themselves, but people spend more of their lives in fuzziness. Fortunately, LLM can always provide group consciousness with clearer content.

LLM is more like a dynamic unstructured retrieval system, which is very different from the traditional static structured retrieval system (to be continued).