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——Focus on digital intelligence and change business
In the past few years, we have witnessed the rapid development of AI technology. From computer vision to speech recognition, these perceptual intelligence technologies have brought us a series of unprecedented conveniences. However, in contrast, the large model has brought about a substantial improvement in cognitive intelligence and has a far-reaching impact. As with any nascent technology, large models face some challenges while rapidly evolving. Especially in the implementation of applications in various industries, large models face problems such as insufficient accuracy, insufficient linkage with business systems, concerns about data security, and high computing costs. These problems directly affect the effective application of large models in actual business and limit their development in the industry.
On the other hand, now the large-scale model industry has begun to "roll up", and there is even a grand occasion of "Hundred Models War". In this case, for enterprises that want to enter the large-scale model, how to find a more suitable entry point, combine the technical capabilities of the large model with their original technical product system and resource advantages, and build differentiated competition barriers are questions that need to be answered.
How to solve these problems? Aisuo, a leading company in cognitive intelligence, provides a valuable solution: combining large models with technologies such as data governance and data assetization to build domain-specific knowledge networks + large models. This combination allows the large model to better understand and adapt to the business needs of specific domains, improving its accuracy in practical applications. At the same time, AISHU improves data governance capabilities, reduces computing power costs, and effectively solves data security issues through standardized data governance and capitalization, large-scale model all-in-one machines, etc., and finally realizes the large-scale application of large-scale models in various industries. For AISHU, this method can make good use of its accumulation in data asset management and content management, and clearly distinguish it from other large-scale model manufacturers, blazing its own path of differentiation.
Next, we will take the innovative practice of Aishu as an example to discuss the method and application prospect of domain cognitive intelligence construction based on large models.
Upgrade the data asset management and operation system with a large model, and build a domain cognition middle platform
From the experience of Aishu, to promote the application of domain cognitive intelligence, a key step is to build a domain cognitive platform and use large-scale model technology to improve the ability to understand business and data, and better realize data capitalization and business intelligence.
Data is known as the new type of oil in the 21st century, and it is a key factor driving the development of modern business, especially for the application of large models, data is an irreplaceable basis. The quality, completeness, consistency, and availability of data have a direct impact on the effectiveness of large models. However, the current data governance and assetization process faces many challenges, especially when dealing with and managing large-scale, diverse, and dynamically changing business data. Traditional data processing methods, such as manual cleaning, quality management, and governance of preset rules, are often inefficient and cannot meet the fast, accurate, and secure needs of modern enterprises for data.
With the development of large models and cognitive intelligence, we have new possibilities. Specifically, with the help of large models, the efficiency of data governance and management can be effectively improved in the following aspects: large models have the ability to learn from massive data, can effectively correct spelling errors in text, and structure unstructured data, thereby greatly improving the efficiency of data cleaning and quality management; traditional data governance requires manual preset rules, while large models can automatically perform some data governance tasks, such as data inspection and data repair, to further optimize the data governance process; Efficient data search; large models can identify and process texts containing sensitive information, such as ID numbers, bank account numbers, etc., to effectively protect data privacy; with the help of large models, we can automatically generate descriptive analysis reports on data, conduct topic model analysis on massive text data, extract keywords, and generate summaries, making data analysis more efficient and intuitive.
Based on the practical experience of AISHU, by building a data-driven business domain cognition middle platform, the efficiency of data governance and management can be greatly improved, thereby laying a solid data foundation for domain cognition intelligent applications.
The integrated architecture of the cognitive center in the field of love data
When building Aisu's domain cognition platform, the integration of large models and knowledge networks is the core link, especially in the aspects of structured and unstructured data understanding, data governance, correlation reasoning analysis and data services, etc., showing strong value and application potential. Of course, large models play an indispensable role in the process of data knowledge. Through the training and application of large models, originally scattered and difficult-to-understand business data can be transformed into valuable business knowledge. This transformation process is called data knowledge. The results of data knowledge can provide enterprises with in-depth business insights and help them better understand and grasp business dynamics. In terms of decision support, large models can conduct in-depth analysis and understanding of large amounts of data, and extract valuable information for decision-making, thereby improving the accuracy and efficiency of decision-making.
So, how does Aisu's domain cognition platform play a role in practical applications? We can gain a more practical understanding from a smart city case.
A case study of a smart city customer in Aishu
In this case, AISHU helps clients build a cognitive functional area covering the AnyFabric business cognitive platform and the AnyDATA knowledge network platform to support upper-level applications in areas such as economic regulation, market supervision, public safety, urban management, people's livelihood security, and transportation. Among them, the AnyFabric business cognition platform consists of a data functional area and an AI functional area. The data functional area helps customers build a big data resource pool for government affairs, and realizes data standardization, aggregation, processing, quality control, and other life cycle governance. The AI functional area builds an AI capability resource pool to realize human event analysis, vehicle event analysis, and urban transportation event analysis. The AnyDATA knowledge network platform covers a rich resource pool of application capabilities, such as video surveillance and electronic fences in the Internet of Things field, trend analysis in decision-making, abnormal early warning, data mining, statistical analysis in supervision, monitoring and early warning, command and dispatch, etc.
From the above examples, it can be seen that Aisu's domain cognition platform has demonstrated its powerful capabilities in terms of data understanding, business process intelligence, data knowledge, decision support, and data security protection. This enables the domain-aware middle platform to better serve the digitalization of government and enterprises and realize business intelligence.
Realize the most natural human-computer interaction with domain cognitive assistant
Although the domain cognition center can significantly improve data processing capabilities and lay the foundation for various cognitive intelligence services. However, these capabilities are "invisible and intangible" to users and cannot be truly perceived. In order to allow more users, especially business personnel, to directly use the capabilities of large domain cognitive intelligence models, it is necessary to build a more natural way of human-computer interaction and lower the learning and use threshold. The cognitive intelligence model provides us with a new perspective to understand, explore and deal with complex industry issues. Especially in human-computer interaction, large model-based cognitive assistants have shown strong potential. Aisu's AnyShare Cognitive Assistant is a good attempt.
AnyShare Cognitive Assistant Can Realize Human-Machine Self-language Conversation
Cognitive assistants, as the name suggests, are designed to help people better understand, acquire and use knowledge. Cognitive assistants based on large models can process various natural language inputs, making the human-computer interaction process more natural and smooth. During this interaction, people can ask the cognitive assistant various complex questions, or ask the assistant to help generate, organize or parse the content. Whether it is searching for esoteric professional questions, generating professional reports, or even assisting in reading an incomprehensible paper, cognitive assistants can understand in-depth in the way of human thinking, and give practical answers or suggestions.
Next, we will take Aishu's AnyShare cognitive assistant as an example to explore in what ways a cognitive assistant based on a large model can bring beneficial help.
Some capabilities of Aishu AnyShare Cognitive Assistant
In the production and consumption process of content and knowledge, the AnyShare cognitive assistant based on the large model can provide all-round support.
1. On the content production side, cognitive assistants can assist in creation and knowledge operation.
On the production side, AnyShare Cognitive Assistant assists users in creation. Through intelligent citation, the assistant can understand and obtain relevant citation information, and then provide it to the user; the writing assistance function can provide users with appropriate creative suggestions by understanding the user's creative goals; the intelligent completion and grammatical error correction functions can predict and generate complete sentences based on the content entered by the user, and at the same time correct grammatical errors.
In addition, AnyShare Cognitive Assistant also assists in knowledge operations. It can automate knowledge topics and generate hashtags to help users find the required knowledge faster; the active knowledge recommendation function can push corresponding knowledge content according to users' needs and behaviors; automated knowledge aggregation can integrate relevant knowledge to provide users with a complete knowledge system.
2. The content consumption terminal realizes intelligent search and assisted reading.
On the consumer side, AnyShare Cognitive Assistant promotes the innovation of intelligent search and assisted reading with its deep learning and natural language processing capabilities. Intelligent search is not simply to search for information through keyword matching, but to provide more accurate search results by understanding the user's query intent and context. It covers multiple dimensions such as image search, expert search, knowledge quiz and knowledge search, and can provide users with richer information sources. This enables users to quickly find the information they need in a mass of data.
After the user obtains information through intelligent search, the auxiliary reading function can further improve the user's understanding and absorption of information. For example, the content summary function can help users quickly understand the main points of an article or report without reading the full text; the knowledge question and answer function allows users to ask questions to obtain answers to specific questions they have during the reading process; the associated knowledge function can recommend other related knowledge or information based on the user's current reading content to enhance the user's knowledge breadth; functions such as outline generation and table comprehension help users understand complex texts and data in a structured way, and further improve reading efficiency.
3. Optimize the process with task management, and realize the seamless connection of different devices and platforms with multi-terminal expansion.
In terms of task management and multi-terminal expansion, AnyShare Cognitive Assistant provides a complete workflow solution. The task planning function uses intelligent reasoning to help users reasonably arrange the execution order of tasks and foresee possible problems and conflicts, thereby improving work efficiency. The content automation function uses the natural language processing capabilities of the large model to automatically complete some text-related tasks, such as writing reports and generating summaries, which greatly reduces the workload of users.
Finally, the multi-terminal expansion function realizes the seamless connection of AnyShare Cognitive Assistant on different devices and platforms. Whether it is Office software on a computer, an application on a mobile device, or even a browser, users can obtain the services they need through the AnyShare cognitive assistant to achieve efficient workflow.
It should be pointed out that the large-scale model technology brings not only a more natural way of human-computer interaction, but more importantly, an improvement in the level of intelligence. Cognitive intelligence has been widely used in the industry before the big model brings "intelligence emergence". However, compared with previous cognitive intelligence, in many of the above tasks, large model technology can bring significant improvement in capabilities. Taking intelligent search as an example, compared with the traditional keyword-based search method, intelligent search based on large models can understand the context, and even integrate cross-document and cross-field information to provide users with more complete and in-depth answers. In terms of intelligent question answering, the cognitive assistant based on the large model can generate intuitive and clear answers according to the user's questions, combined with global knowledge and contextual information. This can not only help users quickly understand complex knowledge, but also provide in-depth insights and suggestions, providing strong support for users' decision-making process.
Next, we use a chemical customer case of Aisu to analyze how to use a cognitive assistant based on a large model for more natural human-computer interaction and how to improve efficiency in practical applications.
Application case of a customer of a chemical group in Aisu
The chemical industry has a huge amount of expertise, terminology and complex processes. AnyShare, combined with the AnyDATA cognitive intelligence framework, can deeply understand and learn these professional knowledge, and then provide support to professionals in a more natural way. For example, during a complex process development phase, a researcher may need a deep understanding of a specific chemical reaction. In this case, AnyShare Cognitive Assistant can provide detailed reaction mechanism, possible side effects, safety control points and other professional knowledge through AnyDATA's knowledge network, effectively assisting researchers in their work. In the chemical industry, people often need to look up various complex chemical reactions, and the information of these reactions is often scattered in different databases and literatures. AnyShare can understand users' search needs, automatically summarize information from various data sources, and quickly return accurate and comprehensive search results.
Cognitive assistants based on large models can also provide powerful support in the development of new products, production process optimization, and environmental safety assessment in the chemical industry. It can understand complex technical requirements and provide corresponding solutions or suggestions. For example, in the process of new product development, AnyShare can provide the latest research trends, new materials, new processes and other information to help the R&D team understand the most cutting-edge scientific research progress and effectively promote the development of new products.
Cognitive assistants based on large models not only provide powerful professional knowledge support, but also greatly improve the experience of human-computer interaction. Its natural language understanding and generation capabilities allow users to intuitively ask questions and needs, and get accurate and detailed answers. This is of great significance for obtaining information quickly and accurately and improving work efficiency.
Solve the computing power cost and security problems of large models with a large model all-in-one machine
In the era of digital intelligence, the commercial application of large models is particularly important. However, the large-scale commercial use of large models faces two obvious problems, namely computing power cost and data security. In this regard, the AnyShare large-scale all-in-one machine proposed by Aisu may be a solution.
First of all, the cost of AI computing power is undoubtedly a major obstacle to the large-scale commercialization of large models. The operation and training of large models require powerful computing power and storage capacity, which require high hardware investment and energy consumption. For example, the training of some advanced deep learning models, such as OpenAI's GPT-4 model, requires a lot of computing resources and time, which requires a huge amount of capital investment. In this context, how to effectively reduce the computing power cost of large models has become an urgent problem to be solved in the industry.
In this regard, Aisu's AnyShare large-scale all-in-one machine AS19000 gives a powerful answer. With optimized hardware configuration and algorithm optimization technology, it greatly reduces the computing resources required to run large models. The all-in-one machine can more accurately match the needs of large models by providing computing and storage capabilities suitable for large model applications, thereby improving resource utilization and reducing hardware investment and energy consumption. All of this makes the large-scale commercial use of large models more possible.
Then, the issue of data security is another major challenge for large-scale commercialization of large models. The training and application of large models involve a large amount of data, which includes various sensitive information such as users' personal information and business secrets. If data security is not effectively guaranteed, the risk of data leakage will be greatly increased. For example, the incident of Samsung employees leaking chip data is a typical data security incident.
For this problem, Aisu large-model all-in-one machine provides an effective solution. It can realize privatized deployment, which means that all data processing and model operation are completed in the user's own hardware environment, and there is no need to upload data to the public cloud platform. This design greatly reduces the risk of data being intercepted or stolen during transmission, thereby better ensuring the security of business data.
In general, Aisu's large-scale model all-in-one machine paves the way for large-scale commercial use of large models by solving the two major problems of computing power cost and data security. This is undoubtedly a powerful exploration of the commercialization of large models, and it also opens up broader possibilities for its future development.
Domain cognitive intelligence empowers thousands of industries
In the future era of digital economy, domain cognitive intelligence is like an expedition ship bound for the unknown. It will lead us into a new world of digital intelligence and help various industries such as finance, manufacturing, medical care, and education achieve intelligent leaps.
In the financial industry, domain cognitive intelligence will not only be used as a tool, but more like an all-knowing consultant. Through advanced algorithms such as big data analysis and forecasting models, it will provide financial institutions with real-time market dynamics, provide investors with accurate investment advice, and even predict future risks, making the operation of the financial market more stable and orderly.
In the field of manufacturing, every process and every product in the manufacturing industry will become more precise, efficient, and even automated under the guidance of domain cognitive intelligence. The factory's production line is no longer just a cold machine running, but interacts and learns with humans through domain cognitive intelligence to achieve intelligent optimization and create unprecedented productivity.
In the medical field, domain cognitive intelligence may become a doctor's right-hand man. Through in-depth mining and learning of massive medical data, it can help doctors diagnose diseases more accurately, provide patients with more personalized treatment plans, and even solve some medical problems, bringing people a healthier life.
The field of education will also benefit from domain cognitive intelligence. Through the technology of deep learning and adaptive learning, we can build a personalized teaching platform, so that each child can learn in the most suitable way and rhythm, and realize the true sense of teaching students according to their aptitude.
These are just the tip of the iceberg, and the potential and value of domain cognitive intelligence far exceeds our imagination. In the tide of the digital economy, it will be a powerful engine to promote industrial digitization, creating countless new possibilities and depicting a brand new future. We look forward to walking towards this new world full of surprises and hopes under the guidance of domain cognitive intelligence.
Text: Yuemanxilou / Data Ape
