1. Introduction
In today's era of rapid technological development, generativeAIAnd large-scale models have become important forces in promoting industrial transformation. With the maturity and popularity of artificial intelligence technology, its applications have expanded from the personal field to the enterprise level, covering a wide range of industries. It not only has a huge impact on the lives of ordinary people, but has greatly facilitated our study and life, but also has an impact on all walks of life that is like a stone causing a thousand waves. This article will mainly explore the application of these technologies at the enterprise level and analyze their potential impacts and challenges.
2. The core advantages and challenges of the big model
Big model definition
AI big model refers to a large number of parameters and complex computing structures in the field of artificial intelligence.Machine LearningModel. They passDeep LearningAlgorithms and neural network architectures, trained in massive data, can learn and understand various patterns and features, thereby showing excellent performance on a variety of tasks. At the same time, AI large-scale models require a large amount of computing power and high-quality data resources during industrial-scale training.
The big model is classified according to the input content:
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Language Big Model (NLP): refers to a type of big models that are widely used in the field of Natural Language Processing (NLP). By analyzing and learning a large amount of text data, they can understand and generate natural language texts, and can complete tasks including but not limited to text classification, sentiment analysis, machine translation, automatic summary, intelligent question-and-answer, text generation and other tasks.
For example: GPT series (OpenAI),BERT(Google), Wen Xinyiyan (Baidu). -
Visual Mockup (CV):
Pointing toComputer VisionLarge models used in the field of Computer Vision, CV, are important applications in image processing and video analysis. This type of model is based on advancedNeural NetworkArchitectures, such as convolutional neural networks (CNNs), can handle a variety of visual tasks including image recognition, object detection, semantic segmentation, video analysis, etc.
For example: VIT series (Google), Wenxin UFO, Huawei Pangu CV, INTERN (Shangtang). -
Multimodal big model:
Refers to large models that can process a variety of different types of data, such as multimodal data such as text, images, audio, etc. This type of model combines the capabilities of NLP and CV to enable a comprehensive understanding and analysis of multimodal information, allowing for a more comprehensive understanding and processing of complex data.
For example: DingoDB multimode vector database (Jiuzhang Yunji DataCanvas), DALL-E (OpenAI), Wukong Painting (Huawei).
2. Core abilities
Combined with the above-mentioned large-scale model classification, generative artificial intelligence demonstrates the following three core capabilities:
- Language generation ability: First, strong language generation capabilities, which means generating diverse, semantically consistent, and humanized texts in an open field. This is the soul and advantage of the large language model differentiates it from other computer-generated languages.
- Natural language dialogue ability: Secondly, the powerful natural language dialogue ability makes it possible for human-machine to conduct natural language dialogue in the open field.
- Generalization ability: Finally, the powerful transitionality, with only a small amount of data, the model can be trained on the agent task, and fine-tuning can be used to adapt to different downstream tasks, showing its powerful adaptability, thus highlighting the ability of the big model to learn from one example and apply it to other aspects.
3. Potential flaws
In addition to the above advantages, large models also have the main flaw of the big model illusion. In other words, large models also make mistakes in the process of diversity output, which appears simultaneously with its creativity.
In fact, this illusion is driven by external information. Because large models and human-generated languages only behave similarly, the internal mechanisms are completely different. In addition, the boundaries of the base model have become apparent. On the one hand, the output results may be inaccurate and the quality may not be controlled. On the other hand, large models are unreliable and large models are also highly dependent on prompt words.
3. Landing direction
At present, among the many application scenarios of generative artificial intelligence, advanced applications such as design and planning, as well as low-level applications such as service and marketing are relatively easy to implement. For example, securities firms can create a new generation of artificial intelligence middle platform brain based on large models to help companies realize intelligent service scenarios such as account opening, customer service, and artificial intelligence stock selection.
On the contrary, it is difficult to achieve large-scale model application scenarios involving key business areas of the enterprise, such as autonomous driving, customized manufacturing production and quality control. These core businesses have low fault tolerance for technology and high requirements for reliability and accuracy.
1. Industry vertical model
You can pre-train or fine-tune the task-related data for a large model for a specific task or scenario to improve performance and effectiveness on that task. For example, in advanced applications such as design and planning, as well as low-level applications such as service and marketing, generative artificial intelligence is easier to implement.
2. Build industrial applications based on large-scale models
Big model for specific industries or fields. They are usually pre-trained or fine-tuned using industry-related data to improve performance and accuracy in the field. For exampleAI quality inspection, In 3C products, automotive parts manufacturing and other industries, AI quality inspection can detect defects on product surfaces through computer vision technology, improving detection efficiency and accuracy. For example, a mobile phone product uses AI technology to detect the frame surface and evaluates the model detection effect through JPEG images of different compression qualities to ensure the quality of the product.
3. Integrate large models with other technologies and tools to create industrial applications.
Integrate large models with other technologies and tools to create industrial applications and enable a wider range of application scenarios. By integrating large-scale models and machine learning technologies, enterprises can monitor industrial equipment in real time, predict potential failures and maintain them in advance. For example, GE (GE) uses this approach to achieve predictive maintenance of equipment, significantly reducing unplanned downtime.
4. Architecture mode of large models
In order to ensure the security, reliability and controllability of the big model, six architectural patterns proposed by Academician Zhang Bian are quoted here:
- Prompt project: Improve the effectiveness of generative artificial intelligence applications by optimizing the prompt content. For example, if you ask the big model which is bigger, 9.11 or 9.9, you will get the wrong answer. However, when the user prompts for a decimal point, the large model provides the correct results. Therefore, timely construction is an important factor affecting the generation of results. The proposed quality directly determines the accuracy and quality of the output results. In practical applications, optimization of prompt content has become an important means to improve the effectiveness of generative artificial intelligence applications.
- Search function: In response to factual issues, in order to improve the certainty of generated content, generating artificial intelligence requires combining search functions to launch an external knowledge base search mechanism to help large models generate more accurate, detailed and targeted answers.
- Field fine adjustment: Fine-tuning can significantly improve the output quality of generative artificial intelligence by incorporating domain knowledge and private data into specific domains and adapting them to specific domain needs. For example, after training in medical expertise, large models can complete the practicing physician qualification examination with an accuracy rate of more than 90%. In addition, during the diagnostic inference process, the big model also provides a reasonable explanation for the results.
- Knowledge graph and vector database: The combination of knowledge graphs and vector databases can help generative artificial intelligence better understand and process semantic information in text and solve problems such as factual knowledge, illusions, and explanatory in the model. When an enterprise deploys large models, it can improve the accuracy of the generated results by building vector databases and coordinating them with the document database.
- Internal monitoring and control: Internal monitoring and control enable large models to handle exceptions by artificially controlling detection of data deviations and drifts. At the same time, by introducing agent reinforcement learning, big models can react themselves, integrate perception, behavior and learning, and reduce the occurrence of errors.
- Multi-level security guarantee:. With the development of large-scale models, security, misuse and abuse have become common problems, involving political standards, morality, and ethics. Only by establishing multi-level security guarantees and promoting the implementation of the governance system can we ensure the healthy and sustainable development of large-scale models. At the moment, this is an urgent question.
5. Technological breakthroughs and future prospects
1. Technological innovation
With the rapid development of generative artificial intelligence, the industry has also raised doubts about the prospects. Regarding issues that are generally concerned by this industry, generative AI is a major technological breakthrough in the history of human development. Therefore, humans have spent decades to solve three key technical problems in the field of artificial intelligence, text semantic vector representation, generative pre-trained converter, and self-supervised learning.
The most important technological innovation lies in the semantic vector representation of text, which has achieved a leap from information form processing to information content processing.
The real meaning of this technology is that it can transform language problems into mathematical problems. Initially, text only represents symbols that exist in discrete spaces, which are difficult to analyze with mathematical tools. At present, language has been translated into vectors. Computers can interpret semantics and process information content based on vectors, thereby helping humans truly enter the era of artificial intelligence. The semantic vector representation of text is a major technological breakthrough in the field of artificial intelligence. It transforms language problems into mathematical problems, paving the way for the arrival of the era of artificial intelligence.
2. The third generation of artificial intelligence
The key direction of the development of the third generation of artificial intelligence is to build explainable and robust artificial intelligence theories and methods to eliminate the panic that people feel. Secondly, develop safe, controllable, reliable, reliable and scalable technologies to promote the vigorous development of the artificial intelligence industry. The third is to promote the innovative application and industrialization of artificial intelligence. This shows that the research and development of artificial intelligence technology not only requires breakthroughs from the academic community, but also needs to be closely integrated with industry needs to transform technological innovation into practical applications, thereby bringing economic benefits and social progress.
3. Knowledge-driven + data-driven
"Knowledge-driven + data-driven" - integrates four elements of knowledge, data, algorithms and computing capabilities to ensure that artificial intelligence technology not only has strong intelligent capabilities, but also plays a stable and long-term role in a variety of application scenarios.
6. Conclusion
In the current rapidly developing artificial intelligence technology, large models show great potential in all walks of life. At the same time, in this challenging journey, only by continuously improving the safety, reliability and controllability of large models can they truly achieve their wide application.
In the future, we must not only pay attention to breakthroughs in technology itself, but also think about how to integrate deeply with the actual industry. Only in this way can enterprises explore and explore important variables for their future development, and large models can create more value and opportunities for human society and welcome the comprehensive arrival of the intelligent era.