Artificial Intelligence | Data Analysis | Chat GPT
Deep Learning | Data Mining | High Performance Computing
Today's society is a technological society and an era of rapid development of computing power. With the rapid development of data centers, east-to-west computing, high-performance computing, data analysis, and data mining, large models have developed rapidly. The large model is the product of the combination of "big computing power + strong algorithm", which is the development trend and future of artificial intelligence. At present, a large-scale ecology has begun to take shape. It can realize the AI transformation from "manual workshop" to "factory mode". Large models are usually trained on large-scale unlabeled data to learn certain features and rules. When developing an application based on a large model, the large model can be fine-tuned, or the tasks of multiple application scenarios can be completed without fine-tuning; more importantly, the large model has self-supervised learning capabilities and does not require or rarely require manual labeling Data for training, reducing training costs, thereby speeding up the process of AI industrialization and lowering the threshold for AI applications.
Compared with traditional machine learning, deep learning learns from data, while large models use a large number of models to train data. Deep learning can process any type of data, such as pictures, text, etc.; but these data are difficult to complete with machines. Larger models can train more categories, multiple levels of models, and thus handle a wider variety of types. Also: When working with large models, a more comprehensive or complex mathematical and numerical support may be required. Deep learning algorithms do not need to train a large number of models to learn the connection between features like large models. Deep learning algorithms are based on neurons, and large models use a large number of parameters to train neural networks. This article starts with large models and deep learning to solve the problem of whether large models are the future of deep learning.
As an expert in the field of deep learning and artificial intelligence, Blue Ocean Brain Liquid Cooling Workstation supports a variety of computing platforms. Through the hyper-convergence and virtualization management platform, it can realize the pooling of multiple heterogeneous computing resources of x86, ARM and other chip architectures, and can Realize on-demand scheduling and unified management of computing resources according to business characteristics, and realize heterogeneous integration. At the same time, it provides computing-intensive, computing-storage balanced, storage-intensive, edge, and AI models to meet the needs of different artificial intelligence computing scenarios, making it more flexible and efficient.
The status quo of large model development
Large models (pre-training models, basic models, etc.) are the product of the combination of "large computing power + strong algorithms". Large models are usually trained on large-scale unlabeled data to learn certain features. When the large model is developed and applied, the large model can be fine-tuned, such as secondary training of small-scale labeled data for certain downstream tasks or can be completed without fine-tuning. Migration learning is the main idea of pre-training technology. When the data of the target scene is insufficient, the AI model based on the deep neural network is first trained on the public data set with a large amount of data, and then migrated to the target scene. Small datasets are fine-tuned to bring the model to the required performance. In this process, the deep network model trained on the public data set is called "pre-trained model". Using a pre-trained model greatly reduces the need for the model to work downstream on labeled data volumes, thus addressing some new scenarios where large amounts of labeled data are difficult to obtain.
From the perspective of parameter scale, the AI large model has successively gone through three stages: pre-training model, large-scale pre-training model, and ultra-large-scale pre-training model, and the number of parameters has achieved a breakthrough from billions to trillions. From the perspective of modal support, the AI large model has gradually developed from supporting a single task in a single mode of pictures, images, text, and voice to supporting multiple tasks in multiple modalities.
The foreign ultra-large-scale pre-training model started in 2018 and will enter the "arms race" stage in 2021. In 2017, Vaswani et al. proposed the Transformer architecture, which laid the foundation for the mainstream algorithm architecture in the field of large models; the structure proposed by Transformer enables deep learning model parameters to reach hundreds of millions of scale. In 2018, Google proposed the BERT large-scale pre-training language model, which is a two-way deep pre-training model based on Transformer. This has greatly stimulated the development of the field of natural language processing. Since then, a large number of new pre-training language models based on BERT, ELNet, RoberTa, and T5 enhanced models have emerged one after another, and pre-training technology has developed rapidly in the field of natural language processing.
In 2019, OpenAI will continue to launch GPT-2 with 1.5 billion parameters, which can generate coherent text paragraphs, realize early reading comprehension and machine translation, etc. Immediately afterwards, Nvidia launched Megatron-LM with 8.3 billion parameters, Google launched T5 with 11 billion parameters, and Microsoft launched Turing-NLG with 17 billion parameters. In 2020, OpenAI launched the GPT-3 super-large-scale language training model, with parameters reaching 175 billion. It took about two years to achieve a breakthrough in the scale of the model from 100 million to hundreds of billions, and it can realize poetry, chat, Generate code and other functions. Since then, Microsoft and NVIDIA jointly released the 530 billion parameter Megatron Turing natural language generation model (MT-NLG) in October 2020. In January 2021, the Switch Transformer model launched by Google became the first trillion-level language model in history with up to 1.6 trillion parameters; in December of the same year, Google also proposed the GLaM general sparse language model with 1.2 trillion parameters. Outperforms GPT-3 in 7 few-shot learning domains. It can be seen that the number of large language model parameters maintains an exponential growth momentum. This kind of rapid development is not over yet. In 2022, some large-scale models of conventional formats will emerge, such as text-to-image Diffusion released by Stability AI, and ChatGPT launched by OpenAI.
In China, the research and development of super-large models is developing extremely rapidly, and 2021 will be the year when China's AI large-scale models will explode. In 2021, SenseTime released a large-scale model (INTERN), with 10 billion parameters, which is a huge training effort. During the training process, there are about 10 or more monitoring signals to help the model adapt to various vision or NLP tasks. As of mid-2021, SenseTime has built the world's largest computer vision model, which has more than 30 billion parameters; in April of the same year, Huawei Cloud Combined Cycle Intelligence released Pangu NLP ultra-large-scale pre-training with a scale of 100 billion parameters Language model; jointly with Peking University to release the Pangu α ultra-large-scale pre-training model, with a parameter scale of 200 billion. Alibaba Dharma Institute released the PLUG Chinese pre-training model with 27 billion parameters, and jointly released the M6 Chinese multi-modal pre-training model with a scale of 100 billion parameters with Tsinghua University; in July, Baidu launched the ERNIE 3.0 Titan model; In December, Baidu launched the ERNIE 3.0 Titan model with 260 billion scale parameters. The parameters of the M6 model of Bodhidharma Academy reached 10 trillion, which directly increased the parameters of the large model by an order of magnitude. In 2022, based on the research results of Tsinghua University, Alibaba Dharma Institute and other research results and supercomputing foundation, the "brain-level artificial intelligence model" Bagua Furnace will be established, and its model parameters will exceed 174 trillion.
Although some Chinese companies have not officially launched their own large-scale model products, they are also actively conducting research and development, such as Yuncong Technology. Starting in 2020, the practice of pre-training large models in various fields such as NLP, OCR, machine vision, and voice has not only further improved the performance of the enterprise's core algorithms, but also greatly improved the production efficiency of the algorithms. It has already been used in urban governance, It has shown value in industrial applications such as finance and intelligent manufacturing.
What does the big model bring to the artificial intelligence industry
1. Large models accelerate the process of AI industrialization and lower the threshold for AI applications
Artificial intelligence is in the application landing stage from "usable" to "easy to use", but it is still in the early stage of commercial implementation, mainly facing the fragmentation of scene requirements, high cost of manpower research and development and application calculation, and lack of long-tail scene data leading to model training There are industry problems such as insufficient precision and large gaps between model algorithms from laboratory scenarios to real scenarios. The emergence of large models has lowered the threshold for AI landing applications in terms of increasing model versatility and reducing training and development costs.
1. The large model can realize the AI transformation from "manual workshop" to "factory model"
In the past ten years, obtaining training models through "deep learning + large computing power" has become the mainstream technical way to realize artificial intelligence. Since the three elements of deep learning, data and computing power are available, there has been an upsurge of "big refinement model" in the world, and a large number of artificial intelligence companies have also been born. However, in the past 10 years since the emergence of deep learning technology, AI models have basically been trained for specific application scenarios, that is, small models belong to the traditional customized and workshop-style model development methods. Traditional AI models need to complete a comprehensive process from R&D to application, including a complete set of processes consisting of requirements definition, data collection, model algorithm design, training scheduling, application deployment, and operation and maintenance. This means that in addition to the need for excellent product managers to accurately define requirements, it also requires the solid professional knowledge and collaboration capabilities of AI R&D personnel to complete a large amount of complex work.
In the traditional model, in order to meet the needs of various scenarios during the R&D phase , AI R&D personnel need to design a customized and dedicated neural network model. The model design process requires researchers to have sufficient professional knowledge of the network structure and scenario tasks, and to bear the cost of trial and error and time for designing the network structure. One way to lower the design threshold for professionals is to automatically search for technical routes through the network structure, but this solution requires high computing power. Different scenarios require a large number of machines to automatically search for the optimal model, and the time cost is still high. A project often requires a team of specialists to be on site for several months to complete. Among them, data collection and model training evaluation to meet the target requirements usually require multiple iterations, resulting in high labor costs.
In the landing stage , the models developed through the "one model, one scene" workshop mode are not suitable for many tasks in vertical industry scenarios. For example, in the field of panoramic perception of driverless cars, multiple models such as multi-pedestrian tracking, scene semantic segmentation, and field of view object detection are often required to work together; the same application as object detection and segmentation, skin cancer detection trained in the field of medical imaging And AI model segmentation cannot be directly applied to pedestrian vehicle detection and scene segmentation in surveillance scenic spots. Models cannot be reused and accumulated, which also leads to high thresholds, high costs and low efficiency for AI implementation.
The large model learns from huge and multi-type scene data, summarizes the general capabilities of different scenarios and different businesses, learns a kind of characteristics and rules, and becomes a model library with generalization capabilities. When developing applications based on large models or responding to new business scenarios, large models can be adapted, such as secondary training on small-scale labeled data for certain downstream tasks, or multiple application scenarios can be completed without custom tasks, to achieve General intelligence capabilities. Therefore, using the general capabilities of large models can effectively respond to diverse and fragmented artificial intelligence application requirements, and provide the possibility to realize large-scale artificial intelligence applications.
2. The large model has self-supervised learning ability, which can reduce the cost of AI development and training
The traditional small model training process involves a lot of manual work of parameter adjustment and tuning, which requires a large number of AI professional R&D personnel to complete; at the same time, model training has high data requirements and requires large-scale labeled data. However, it is difficult to obtain data in many industries, and the cost of labeling is high. At the same time, project developers need to spend a lot of time collecting raw data. For example, the influence of artificial intelligence in medical image-intensive fields such as pathology, dermatology, and radiology in the medical industry continues to expand and develop. However, medical images usually involve user data privacy, and it is difficult to obtain them on a large scale for training AI models. . In the field of industrial visual defect detection, taking cloth defects as an example, the types of fabrics that need to be detected on the market include white gray cloth, colored gray cloth, finished cloth, colored cloth, pure cotton, blended fabrics, etc. There are many kinds of defects, and the color and thickness are difficult to identify. Only by collecting data in the factory for a long time and continuously optimizing the algorithm can we do a good job of defect detection.
The large model uses the self-supervised learning function to automatically learn and distinguish the input raw data, and reasonably constructs tasks suitable for model learning. It does not need or seldom use manually labeled data for training, which largely solves the problem of manually labeled data labels. The problems of high cost, long period, and accuracy reduce the amount of data required for training. This greatly reduces the cost of collecting and labeling large-scale model training data, is more suitable for small-sample learning, and helps to extend traditional limited artificial intelligence to more application scenarios.
We believe that compared with the traditional AI model development model, the large-scale model has a more standardized process in the research and development process, and has greater versatility in the implementation process, which can be generalized to a variety of application scenarios; Compared with traditional model training that requires manual labeling, supervised learning ability can significantly reduce R&D costs, which together make large models of great significance to the AI industry, and provide directions for solving the difficulty of implementing AI and promoting the process of AI industrialization.
2. Large models bring more powerful intelligence capabilities
In addition to strong general capabilities and a high degree of standardization in the R&D process, the biggest advantage of large models lies in their "good effect." It enhances the self-learning ability by "feeding" big data to the model, so that it has a stronger degree of intelligence. For example, in the field of natural language processing, Baidu, Google and other exploration giants have shown that the effect of NLP technology based on pre-trained large models has surpassed the best machine learning capabilities in the past. OpenAI research shows that in the six years from 2012 to 2018, the amount of computation used in the training of the largest artificial intelligence models has grown exponentially, doubling in 3.5 months, compared to Moore's Law every 18 The rate of doubling in a month is much faster. The magnitude of the parameters of the next-generation AI large model will be comparable to the synaptic level of the human brain. It may not only be able to process language models, but will also be a multi-modal AI model that can handle multiple tasks, such as language, vision and sound.
Deep Learning Platform Architecture
At the same time, the training of large models is inseparable from the deep learning platform architecture. Deep learning (DL, Deep Learning) is a new research direction in the field of machine learning (ML, Machine Learning). It is introduced into machine learning to make it closer to the original goal-artificial intelligence (AI, Artificial Intelligence). Deep learning is to learn the internal laws and representation levels of sample data. The information obtained during the learning process is of great help to the interpretation of data such as text, images and sounds. Its ultimate goal is to enable machines to have the ability to analyze and learn like humans, and to be able to recognize data such as text, images, and sounds. Deep learning is a complex machine learning algorithm that has achieved results in speech and image recognition that far exceed previous related techniques. The training of large models is inseparable from the deep learning platform architecture.
1. Three-element system of deep learning platform
In response to the diverse needs of industry applications, the deep learning platform with an open source development framework as the core has built a service system from model development to deployment, including three core levels: development framework, algorithm model, development tools and capability platform. In the era of large-scale industrial production of artificial intelligence, the versatility of deep learning technology is becoming stronger and stronger, and the standardization, automation and modularization features of deep learning platforms are becoming more and more prominent. Base. The platform provides a variety of innovative applications for the industry in the form of direct call of mature algorithm technology and customized development of personalized scenarios, and finally forms an artificial intelligence enabling ecology with rich resources, multi-party participation, and collaborative evolution. During the development and evolution of the deep learning platform, three core levels of "framework-algorithm-tool" have gradually formed.
The bottom layer is an open source development framework. As the core hub of the deep learning platform, the open source development framework connects intelligent computing chips such as GPUs and ASICs, and supports various applications such as computer vision, natural language processing, and voice. Deploying full-process capabilities makes it possible to efficiently develop and iterate various algorithms and deploy large-scale applications. One is to build programming models and development capabilities for developers by providing programming interface APIs, coding languages, etc.; the other is to realize model compilation and training optimization by relying on functions such as parallel training, dynamic and static conversion, and memory optimization; the third is to provide hardware access Capability, by simplifying the technical details of the underlying hardware, establishing a connection channel between the model and computing power, and solving the problem of difficult model adaptation and deployment.
The middle layer represents the algorithm model, and the deep learning platform gives developers industry-level modeling capabilities. The pre-training method is used to reduce data collection, labeling time and labor costs, shorten the model training process, realize rapid model deployment, and accelerate the development of AI technical skills. According to the technical route and application value, it can be divided into three types of algorithm models: one is the basic algorithm that has been practiced in the industry, such as VGGNet, ResNet and other mainstream SOTA models; the other is to provide natural algorithm language processing, computer vision, multi-modality, etc. The pre-training model of small sample subdivision scenarios in the field can quickly realize the transfer of algorithm skills; the third is the application model for specific industry scenarios (such as industrial quality inspection, security inspection, etc.), and recommends appropriate applications according to the user's real industry landing needs. Combining landing models and hardware, and providing relevant examples.
The upper layer is a suite of tools and a capability platform that supports the development and deployment of models at all levels to meet the needs of developers at various stages. The main functions are reflected in the following aspects: First, it lowers the threshold of technical application. By providing integrated and standardized basic training technology tool components, it supports visual analysis and pre-trained model application, lowers the threshold of training and model development, cloud job delivery and Other functions; provide cutting-edge technology research and development tools, support technical capabilities such as federated learning, automatic machine learning, biological computing, and graph neural networks, and provide support for model innovation; thirdly, provide specific information such as image classification, target detection, and image segmentation to meet industry requirements. The actual demand is an end-to-end development kit for business scenarios, covering data enhancement, modular design, distributed training, model tuning and other processes, as well as a cross-deployment platform to realize the rapid application of AI capabilities; the fourth is to provide full life cycle management, Build an integrated deep learning model development platform, provide full-cycle services from data processing, model training, model management to model reasoning, accelerate the whole process of artificial intelligence technology development and application implementation, and realize management, control and collaboration.
2. The core role of the deep learning platform
One is to drive the iterative improvement of core technologies. With the gradual maturity and popularization of deep learning technology, standardized and modular process tools have become the common appeal of developers, and deep learning platforms have emerged as the times require. By providing algorithm libraries including convolution, pooling, full connection, binary classification, multi-classification, backpropagation, etc., the platform avoids the waste of resources caused by "reinventing the wheel". Achieve innovative breakthroughs at a higher level, realize "standing on the shoulders of giants" innovation, and accelerate the iterative improvement of artificial intelligence technology.
The second is to promote collaborative innovation in the upstream and downstream of the industrial chain. As the control center that connects the underlying hardware architecture, top-level software system, and user interface in the PC and mobile Internet era, the operating system is the core tool that drives the industrial ecology of companies such as Microsoft, Nokia, Apple, and Google. In the era of artificial intelligence, the deep learning platform also plays the role of connecting the top layer (top-level application) and the bottom layer (lower-layer chip), analogous to the "operating system in the era of artificial intelligence". The emergence of deep learning platforms enables various algorithms to be efficiently developed and iterated based on existing hardware systems and to deploy large-scale applications, laying the foundation for the continuous development of deep learning.
The third is to shorten the intelligent upgrade path of thousands of industries. At present, the application of artificial intelligence engineering has ushered in a window of rapid development. How to shorten the cycle of artificial intelligence algorithms from modeling to actual production and improve application efficiency has become the core issue of concern in various industries. The deep learning platform provides practical engineering solutions covering the entire process of artificial intelligence capability generation, application, and management from manufacturing to tools, technologies, and mechanisms, and solves problems such as shortage of professional talents, high data costs, and modeling faced by artificial intelligence. Problems such as difficult enterprise development and low resource efficiency in intelligent upgrading meet the urgent needs of enterprise AI capacity building and lay the foundation for intelligent upgrading.
The fourth is to carry the momentum of industrial ecological prosperity. Deep learning is a typical co-creation technology field. Prosperity and sustainable development can only be achieved by building a healthy and sound industrial ecology. Driven by the deep learning platform, build a communication bridge connecting the industry and academia, and gather talents, technologies, markets and other industry ecological resource elements through developer communities, event summits, training courses, etc. While exporting technical capabilities and empowering industries to upgrade, we will continue to develop the inertial way of thinking using artificial intelligence technology to overcome pain points and difficulties in various industries, further drive downstream demand, and form a virtuous circle of industrial ecology.
Technical innovation focus of deep learning platform
1. Open source development framework, the core of the deep learning platform
As the basic core of the deep learning platform, the open source development framework combines key technologies such as programming paradigms and large-scale distribution to create an easy-to-use, efficient, and scalable framework engine that solves a wide range of problems in industrial applications. Training, software adaptation and hardware, focusing on improving the development efficiency and ease of use of artificial intelligence products and software and hardware solutions.
1. The unified programming paradigm of dynamic and static greatly improves the efficiency of algorithm development
The unified programming paradigm of dynamic and static greatly improves the efficiency of algorithm development. The framework programming paradigm is a different way for developers to abstract complex problems into program codes when writing programs. It is mainly divided into two programming paradigms: imperative programming (dynamic graph) and declarative programming (static graph). Among them, dynamic graph programming has Due to the convenience of development, developers can obtain execution results in real time when adjusting local codes, which is easy to debug and reduce time costs. However, due to the lack of global calculation graph Pass and optimization of video memory, such as fusion between operators and video memory inplace, etc., There are deficiencies in performance, memory usage, etc. The static graph, on the other hand, compiles and optimizes all program codes that users can define in advance, which has significant advantages in terms of power consumption and performance. At present, mainstream frameworks in the industry such as Google TensorFlow and Flying Paddle have laid out a unified programming paradigm of dynamic and static, and are compatible with supporting two programming paradigms of dynamic graph and static graph. The acceleration and deployment of static image training greatly improves the accuracy of developer algorithm development and the effect of production deployment.
2. Large-scale distributed training technology effectively improves the carrying capacity of giant model research and development
Large-scale distributed training technology effectively improves the carrying capacity of ultra-large-scale model development. At present, the scale of the algorithm model is increasing exponentially. Taking the ERNIE3.0 large model as an example, the model parameters are 260 billion, the storage space is 3TB, and the calculation amount is 6.2E11 Tera FLOPs. A single server, taking Nvidia V100 as an example, has a single card with 32GB of memory and 125Tera FLOPS of computing power, which is difficult to meet the training needs of hundreds of billions of parameter models, and the data pressure is high/reading and writing models, storage, training, etc. Large-scale distributed training architecture layout, incorporating the transmission and calculation of kilocalorie computing power (equivalent to the computing power of a national supercomputing center) into the general practice framework of mainstream enterprises, combining platform features and end-to-end characteristics of the computing power model self-adaptation Distributed training technology has become an important direction of innovation. For example, flexible resource scheduling management technology combined with computing power platform, automatic selection of optimal parallel strategy technology, efficient computing and communication technology, etc.
3. The unified high-speed inference engine satisfies the large-scale deployment and application of end-edge cloud multi-scenario
In the face of diverse deployment environments, cloud reasoning capabilities have become an important symbol of open source development frameworks and inclusive tools in the industry. In the intelligent era of the Internet of Things, the development framework must have an inference engine architecture fully supported by the end, edge, and cloud, as well as an internal expression and operator library integrated with the training framework to achieve instant training and the most complete model support. Inference implementation capabilities should span server, mobile, and web front ends, and model compression tools can help developers achieve smaller, higher-performance models. During the deployment process, the development framework should also provide a full-process reasoning and scene deployment toolchain to achieve rapid deployment in hardware-constrained environments. Distillation of tools or technologies to further optimize and support the implementation of inference engines in various hardware scenarios such as servers, mobile terminals/edge terminals, and web pages.
From an ecological point of view, Paddle also supports the adoption of other framework models on the Paddle platform, and also supports the conversion of Paddle models into ONNX format for deployment, providing developers with diverse and personalized choices.
4. Standardized software and hardware collaborative adaptation technology is the key to creating localized application empowerment
Industry-leading framework platform companies try to provide a unified adaptation solution that can meet multiple hardware access, including unified hardware interface, operator development mapping, graph engine access, and neural network compiler.
The first is to build a unified hardware access interface and complete the standardized access management of different hardware abstraction layer interfaces. For example, the Flying Paddle framework supports plug-in hardware access functions to realize the decoupling of the framework and hardware. Developers only need to implement standard interfaces to register new hardware backends in the framework.
The second is to provide operator development and mapping methods, and use the programming language provided by the chip to write operator Kernel or operator mapping methods to access hardware. Specifically, the operator multiplexing technology can be used to reduce the number of operators; by providing hardware primitive development interfaces, operators can be reused on different hardware; for problems that existing operators cannot meet the operation logic and performance requirements, developers can use their own Define the operator without recompiling and installing the Paddle framework.
The third is to provide a graph engine access method, and realize hardware access through the adaptation between the framework calculation graph and the hardware graph engine. In order to adapt to the deep learning framework more efficiently, hardware manufacturers usually provide graph engines, such as NVIDIA's TensorRT, Intel's OpenVINO, etc. The framework only needs to realize the conversion of the intermediate representation of the model to the intermediate representation of the manufacturer's model to adapt.
The fourth is to build a neural network compiler to realize automatic optimization compilation technology, and use basic operators to automatically integrate and optimize to realize complex operator functions, reduce adaptation costs and optimize performance. For example, Baidu's neural network compiler CINN has the characteristics of convenient access to hardware and improved computing speed. Compared with TVM in the industry, CINN additionally supports the training function; compared with Google's XLA, CINN provides automatic tuning technology, which can better realize software and hardware collaboration and maximize hardware performance.
2. Model library construction, algorithm innovation, precipitation and integrated management are key capabilities for rapid empowerment
The model library is the key capability of the deep learning platform to promote AI inclusiveness and realize rapid industrial empowerment. In order to solve the problems of high research and development threshold and long cycle in the process of artificial intelligence algorithm engineering implementation, the deep learning platform builds the model library as the core capability of the platform. Developers rely on the model library to realize algorithm capabilities without writing code from scratch. , realize the continuous reuse of application models, thereby promoting the diversification and large-scale development of artificial intelligence applications. At present, deep learning platforms build algorithm model libraries based on their own development framework, providing the ability to quickly build artificial intelligence applications, such as the launch of Meta, which provides algorithm model libraries, simple APIs and workflows; Blue Ocean Brain builds industrial-level model libraries and provides scene-oriented applications The model development kit realizes the ability of direct model calling and secondary development, and improves the efficiency of algorithm development and application.
The deep learning platform continues to innovate in the field of cutting-edge technology, accumulates advanced algorithm capabilities, and promotes the application of SOTA models. On the one hand, the deep learning platform has become an important carrier of advanced algorithm models. Globally, more than 60% of the innovative algorithms proposed in the AI field are verified using the international mainstream development open source framework; The deep learning platform strengthens the capacity building of the SOTA model library and promotes the continuous generation of original algorithms. At present, the model library of the international mainstream deep learning platform continues to strengthen the accumulation of cutting-edge algorithm models, depositing algorithm capabilities into the model library of the deep learning platform, and providing developers with cutting-edge technical capability support.
The improvement of the model library is accelerated through the practice of application scenarios, and the industrial empowerment capability is continuously strengthened. In order to meet the needs of diversified industrial scenarios and effectively promote the application of AI algorithms, the model library mainly improves the platform's industry empowerment capabilities through two aspects. One is to expand the capability boundary of the model library by refining the application scenarios and enriching the algorithm coverage direction. The model library is based on basic algorithms such as computer vision and natural language processing, and refines the application scenarios of capabilities according to actual industry needs, and provides industry-practiced models for subdivided tasks such as image segmentation, vehicle detection, and personalized recommendations. In addition, by introducing pre-training models, developers are provided with flexible and scalable algorithm capabilities, which can realize rapid application in small-sample tasks. For example, Blue Ocean Brain currently supports more than 500 industrial-level open source algorithm models, which have been used in finance, energy , transportation and other industries are widely used. The second is to start from the actual industrial application scenarios, focus on the implementation of AI engineering, and solve the problem of model accuracy and performance balance in actual application scenarios by providing lightweight and low-energy industrial deployment models.
3. Perfect tools and platforms, covering the whole cycle of data processing, model training and reasoning deployment
The deep learning platform lays out relevant tool components and platforms around cutting-edge technology development and deployment new paradigms, data model full-process visual analysis management, enterprise-level high-precision application construction, and full-platform deployment.
The first is to create systematic tools for new learning paradigms. The deep learning platform provides the required compilation and operation mechanisms and solutions for cutting-edge learning paradigms such as reinforcement learning, federated learning, graph learning, quantum computing, and biological computing, and realizes a wide range of models. Application scenarios.
The second is to develop a full-process R&D tool set covering data management, model development, and reasoning deployment. Practical application implementation is the starting point and foothold of the deep learning platform. The platform provides end-to-end data preparation and model training by providing development kits and tool components. And optimization, multi-terminal deployment capabilities to help the industry practice engineering and efficient deployment.
The third is to provide enterprise-level high-precision application construction and full-platform deployment capabilities. As an important outlet of the deep learning platform, the enterprise development service platform integrates the underlying core open source framework and upper-level data processing, model development and construction, model training management and end-side deployment capabilities. Assist enterprises to realize one-stop model customization capabilities. For example, the Blue Ocean Brain deep learning platform creates a zero-threshold deep learning platform for enterprises with different development capabilities. It can combine network structure search and transfer learning to complete tasks such as language understanding, language generation, image classification, object detection, and graphic text generation, supporting enterprises. Realize multi-side flexible and secure deployment on public cloud, local server, and mobile devices.
4. Extension of professional field, continuous exploration around scientific discovery and quantum intelligence
Leading deep learning platform and framework companies are accelerating their deployment around more forward-looking vertical professional fields such as biomedicine and quantum intelligence, lowering the threshold for cutting-edge scientific research and development, and improving application development efficiency. At present, cutting-edge academic research has entered a new stage of multidisciplinary integration and the development of technical tools. Artificial intelligence technology has become one of the important routes to promote the development of cutting-edge science, and has achieved many breakthroughs and breakthroughs. While innovating, it also poses new challenges to the tool capabilities of deep learning platforms. Leading enterprises focus on the following directions to enhance the R&D capabilities of the platform in professional fields.
The first is to focus on quantum intelligence, apply quantum computing, and tap the application potential of artificial intelligence algorithms. Quantum computing has information carrying capacity and parallel computing processing ability that cannot be compared with traditional computing, and is expected to solve the computing bottleneck problem caused by the increase in the number of artificial intelligence model parameters. Leading companies provide quantum computing toolkits based on deep learning platforms to promote the integration of quantum technology and artificial intelligence machine learning models, and support quantum circuit simulators, training discrimination, and generation of quantum models; modules such as circuit simulation provide developers with artificial intelligence, R&D tools for quantum applications in combinatorial optimization, quantum chemistry and other fields can improve operational efficiency and lower the threshold for quantum application R&D.
The second is to focus on key directions in the biomedical field such as protein structure prediction and compound property prediction, and build a set of biological computing and model development tools. The combination of artificial intelligence and biomedical technology can greatly improve the accuracy and efficiency of tasks and become an important direction of industrial layout.
Summary and Outlook
With the development of deep learning technology, large models have become the future of deep learning. A large model is a deep learning model that can process large amounts of data to obtain accurate predictions.
First, large models can efficiently handle large amounts of data. Traditional machine learning models can only handle a small amount of data, while large models can handle a large amount of data, resulting in more accurate predictions. Furthermore, large models can efficiently handle unstructured data such as images and videos.
Second, a large model can improve the accuracy of the model. Large models can capture complex relationships among data, thereby improving model accuracy. Additionally, large models can be trained faster, resulting in accurate predictions faster.
Finally, large models can better support deep learning. Deep learning requires a lot of data, and large models can support deep learning, so as to better leverage the advantages of deep learning.
In conclusion, large models are the future of deep learning. It can effectively process a large amount of data, improve the accuracy of the model, train faster, and better support deep learning, thereby improving the efficiency of deep learning.