Technology cloud report original.
In the era of large models, computing power, algorithms, and data feeding are inseparable. If you look at the entire industry chain, there is another key element behind the algorithm that deserves attention, and that is AI basic software.
Algorithms are the key to realizing AI functions, while basic software provides platforms and tools for algorithms to run. As the backbone of the model ecosystem, AI basic software will become the most important efficiency support for the application of large models, and form a new paradigm of model training through the method of large model + small model.
Nowadays, the popularity of AI large-scale models is soaring in China, and it is blooming everywhere, which also pushes back the development of basic software. In this context, what is the AI basic software market and what are the new opportunities in the future? This is worthy of attention and discussion.
Basic software is the foundation of artificial intelligence
Wei Kai, deputy director of the Yunda Institute of the China Academy of Information and Communications Technology, said in a public speech that basic software is the foundation of artificial intelligence, and the development of basic software for artificial intelligence determines the depth, height, and breadth of artificial intelligence development. important.
In this regard, Wei Kai explained that the importance of basic software has three specific manifestations. First, the development of artificial intelligence is inseparable from high-quality basic software, especially the ease of use and completeness of the engineering of basic software. Implementation needs to be realized by combining basic software with business and data; second, the basic software of artificial intelligence must play a role in the enterprise, it must be integrated with the scene, and it must be well operated and maintained; third, it needs to have security and credibility guarantee conditions.
AI basic software is a necessary part of building and running AI applications, and usually includes the following aspects:
Machine Learning Frameworks and Libraries : These are the basic tools for building and training AI models. For example, TensorFlow, PyTorch, and Scikit-learn are all widely used machine learning frameworks and libraries.
Model training and deployment platforms : These platforms provide a series of tools and services to support the whole process from data processing, model training to model deployment and services. For example, Google's Cloud ML Engine and Amazon's SageMaker are such platforms.
**Data processing and analysis tools:** In AI applications, data is crucial. Data processing and analysis tools can help users efficiently process and analyze data to meet the training needs of AI models. For example, Pandas, NumPy, and Spark are all commonly used data processing and analysis tools.
Optimization and automation tools : These tools can help users optimize the performance of the model, or automate some repetitive tasks. For example, TensorBoard can help users visualize the training process of models, and AutoML can automate the process of model selection and tuning.
In general, AI basic software provides a series of necessary tools and services to enable users to build and run AI applications more conveniently and efficiently.
At present, with the continuous popularization and in-depth application of AI technology, China's AI basic software market is developing rapidly. Gartner predicts that the revenue of this market will grow from US$4.767 billion to US$13.858 billion in the next five years, and the compound annual growth rate (CAGR) will reach 28%.
It is reported that there are more than 3,000 manufacturers in China's AI software market, most of which are AI generalists, which can independently provide customers with natural language processing (NLP), computer vision (CV) and machine learning (ML) technologies.
These vendors provide end-to-end personalized enhancement services, consulting services and operational services to solve customers' specific business problems.
As the market continues to expand, so will the number of Chinese AI software companies. At present, players in the market mainly include these two categories, one is Baidu, Alibaba, Tencent and other large Internet companies, and the other is Megvii Technology, Jiuzhang Yunji DataCanvas and other professional AI companies.
These companies not only provide internationally competitive machine learning frameworks and platforms, but also develop characteristic products and services tailored to the characteristics of the Chinese market.
Take Jiuzhang Yunji DataCanvas as an example. At present, the company has a data science product system centered on "openness, automation, and cloud native", including providing efficient tools for building intelligent applications for data scientists, application developers, and business experts. Package——DataCanvas APS machine learning platform;——Provide scalable, highly available and fault-tolerant real-time big data processing capabilities, flexibly develop, deploy and run various real-time analysis applications, and help enterprises complete the efficient construction of real-time business data models , DataCanvas RT real-time decision-making center platform for creating real-time AI scenarios and a series of platform software products required for enterprise-level AI applications.
And in the field of global artificial intelligence open source, a number of the world's first open source projects independently developed to fill the technological gap in the field of AI. In addition, under the technological upsurge of AIGC, the open source team of Jizhang Yunji D-lab is conducting cross-research to accelerate the integration and innovation of AI cutting-edge technologies.
In May of this year, Jiuzhang Yunji DataCanvas announced its cooperation with the China Institute of Information and Communications Technology on "industrialization of high-quality AI infrastructure".
On the basis of previous rich cooperation in standard formulation, evaluation and evaluation, technological innovation, and industrial research, the two parties will give full play to their respective resource advantages in theoretical research, technological innovation, and application practice in the direction of AI infrastructure, and open up the upstream and downstream ecological chains of AI infrastructure. Jointly build an open, powerful and flexible AI infrastructure ecosystem.
Opportunities and challenges go hand in hand: high-quality products and services are the key to breakthrough
At the same time, the market competition is becoming increasingly fierce, and the speed of technological upgrading is also getting faster and faster.
Entering the era of large models, the challenges facing AI basic software are obvious: how to support larger-scale model training? How to optimize the performance and efficiency of the model? How to simplify the deployment and use of the model? For these problems, AI-based software needs to find new solutions.
First, in order to support larger-scale model training, AI basic software needs to provide more powerful computing capabilities.
This may involve more efficient distributed computing technology, more optimized hardware acceleration technology, etc. This is a technical challenge, but also an opportunity. For companies with technological advantages, they can meet the needs of users and gain market share by providing a more powerful and efficient AI training platform.
Second, as the size of the model increases, the performance and efficiency optimization of the model becomes more important.
This requires AI-based software to provide more advanced optimization tools and services.
For example, model compression technology can reduce the size of the model and improve the running speed of the model; automatic parameter adjustment tools can automatically find the optimal model parameters to improve the accuracy of the model.
These technologies can not only help users better use large models, but also provide new business opportunities for AI-based software companies.
Thirdly, with the increasing complexity of AI applications, how to simplify the deployment and use of models has become particularly important .
This requires AI-based software to provide more concise and easy-to-use APIs, more powerful deployment tools, and smarter service platforms. For AI basic software companies, this is an opportunity to improve user experience and increase user stickiness.
"The massive multi-modal data management at the bottom layer and the more precise analysis and decision-making needs at the upper layer will promote the integration of digital intelligence into the deep water area, bringing new opportunities for building AI basic software," Li Haoran, senior analyst of artificial intelligence and big data at IDC China Talked about in a speech at the Hangzhou General Artificial Intelligence Forum.
For the basic software of the development service platform that customers pay more attention to, technology companies should build from six aspects: full lifecycle components, low-code/no-code, automatic machine learning, algorithm model library, visualization, deployment and operation and maintenance, and pay attention to integration with the cloud. Integration of services and big data components.
In this regard, Zhou Xiaoling, vice president of Jiuzhang Yunji DataCanvas, said that the company has deployed these important technical capabilities for a long time, and has applied a complete set of systematic AI basic software products in industries such as finance, communications, transportation, manufacturing, and energy.
He went on to talk about the development of AI technology from decentralized models to integrated intelligence, and then to general artificial intelligence, which has greatly promoted the wave of digital intelligence in governments and enterprises; each industry has its own development characteristics and transformation stages. There is considerable demand for the iterative upgrade of AI platforms and AI application capabilities in cloud, automation, multi-modal, distributed and other technical fields, and there is still huge room for AI application development from operation to operation.
In general, the era of large models has brought new requirements and challenges to AI basic software, and also brought new opportunities. For Chinese AI-based software companies, how to seize these opportunities will largely determine their competitive position in the future market.
On the one hand, they need to continue to invest in research and development, improve the technical level, and meet the needs of users for large models; on the other hand, they also need to continue to innovate, provide differentiated products and services, and win market share.
What are the opportunities for AI-based software in the age of big models?
The answer lies in how to meet the needs of users and how to provide high-quality products and services. Only those companies that can keep up with the pace of the times, actively innovate and keep making progress can gain a foothold and thrive in this era full of challenges and opportunities.
about the future
Looking back at the development of AI, we can see that hardware has always been the largest field of investment, but with the advancement of technology and the maturity of the market, software is gradually improving its position in the AI industry chain.
According to IDC's prediction, after 2023, major manufacturers will invest more in the construction of underlying basic software, which is also a trend that has already begun to emerge.
In addition, IDC predicts that the future growth of the AI market will mainly come from three aspects:
First of all, it is based on large model applications to replace the AI applications built in the past few years. As mentioned earlier, large models can learn more complex patterns and thus achieve better results on various tasks.
With the development of technology and market, we foresee that many existing AI applications will be replaced by new applications based on large models, which will generate huge market increments.
Second, it is the incremental market brought about by generative AI. Generative AI, such as Generative Adversarial Network (GAN) and Variational Autoencoder (VAE), can generate new and realistic data, and has a wide range of application prospects, such as art creation, game design, virtual reality, etc. With the development of technology, we foresee that generative AI will open up new market areas and bring new increments.
Finally, there are new AI-powered enterprise-level applications. AI technology can help enterprises improve efficiency, reduce costs, and innovate business models. With the in-depth application of AI technology, we foresee that more enterprise-level applications will appear, which will be a market point with huge explosive potential.
In general, the development trend of the AI industry chain is diversification and deepening. On the basis of hardware investment, the construction of underlying basic software will become more and more important.
At the same time, large-scale model applications, generative AI and enterprise-level applications will be the three major sources of growth in the future market. This has brought new opportunities and challenges to the upstream and downstream links of the AI industry chain. Only by keeping up with the trend and seizing opportunities can we keep ahead in this fast-growing market.
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