The following is the Top ten AI technology trends in 2021 From Zhiyuan Research Institute recommended by recordtrend.com. And this article belongs to the classification: artificial intelligence, research report.
Trend 1: data and mechanism Fusion Modeling in scientific computing
The combination of machine learning and scientific computing, that is, the fusion of data and mechanism computing, provides a new means and paradigm for scientific research, and becomes a typical representative of Frontier computing. The modeling based on mechanism takes the basic physical laws as the starting point for deduction and pursues the expression of simplicity and beauty; the modeling based on data summarizes the laws from data and pursues the application effect in practice. These two modeling methods have played an important role in the history of science.
In recent years, an important trend in the development of scientific computing is from mechanism based paradigm to mechanism based paradigm. Many important problems in many frontier scientific fields often involve multiple coupled physical processes occurring on different spatial and temporal scales, which are characterized by high anisotropy, singularity, non-uniformity and uncertainty. Human beings can only know part of the principles and data. At this time, the combination of mechanism and data will become a powerful means to study these problems.
Trend 2: integration and breakthrough of deep learning theory
Deep learning has achieved remarkable success in the field of application, but its theoretical basis is still very weak. The mechanism behind why deep learning performs better than traditional machine learning methods is still unclear. The theoretical analysis of deep learning needs to explore and innovate from different perspectives of mathematics, statistics and calculation, as well as representation ability, generalization ability, algorithm convergence and stability. The current fragmented understanding of deep learning theory will usher in further integration and breakthrough, from the understanding of shallow network and local nature to the deepening of deep network and global nature, and finally be able to fully answer the major theoretical questions about deep learning ability and limit.
Trend 3: evolution of machine learning towards distributed privacy protection
At present, many countries and regions around the world have issued data regulatory regulations, such as HIPAA (US health insurance convenience and Liability Act), gdpr (European Union general data protection regulations), etc., which restrict the interaction of private data among multiple institutions through strict regulations. Distributed privacy preserving machine learning protects the input data of machine learning model training by means of encryption and distributed storage, which is a feasible scheme to break the data island and complete the multi agency joint training modeling.
Trend 4: further development of large-scale self-monitoring pre training methods
The emergence of gpt-3 has inspired researchers to continue to explore and study large-scale self supervised pre training methods in a broader range of vision. In the future, the self supervised pre training model based on large-scale image, voice, video and other multimodal data, as well as cross language data, will be further developed, and researchers will continue to explore ways to solve the problems of the current large-scale self supervised pre training model Have cognitive ability and other methods.
Trend 5: information retrieval model and system based on causal learning has become an important development direction
Artificial intelligence algorithm is the core technology of intelligent information retrieval system such as recommendation system and search engine, which has a profound impact on the work and life of hundreds of millions of Internet product users. The current information retrieval models based on artificial intelligence algorithm mostly focus on the establishment of correlation between variables in a given data, and the correlation is not equivalent to the more original causal relationship, which leads to serious deviation of current information retrieval results, poor ability to resist attacks, and lack of interpretability of models.
In order to realize the real intelligent information retrieval system, the retrieval model based on causal learning is an inevitable hurdle. Causal learning can identify the causal relationship between variables in information retrieval, clarify the cause and effect of the development and change of things, comprehensively understand the nature of user needs and retrieval methods, correct the deviation in the retrieval model, and improve the interpretability, operability and traceability of the retrieval system.
Trend 6: brain like computing system evolves from “special” to “general”
Various brain like computing systems based on brain like computing chips are gradually showing their advantages in dealing with some intelligent problems and low-power intelligent computing. However, from the perspective of design method, brain like chips often determine their hardware functions and interfaces by induction according to the requirements of target applications, and customize the software of chemical tool chain, which leads to the problems of tight coupling of software and hardware, and the limitation of target applications.
Brain like computing chip design will draw inspiration from the existing processor design methodology and its development history, and achieve complete hardware functions based on the theory of computational completeness and application requirements. At the same time, the basic software of brain like computing will integrate the existing brain like computing programming languages and frameworks, propose a high-level programming abstraction and unified development framework independent of specific chips, and develop brain like computing compilation optimization and mapping optimization technology for target chips, so as to realize the gradual evolution of brain like computing system from “special” to “general”.
Trend 7: brain like computing develops from scattered independent research to multi-point iterative research
Brain like computing has achieved a lot of basic research results in many aspects, but the current research still presents a relatively independent narrow vertical distribution characteristics, and has not yet formed a mutually promoting horizontal situation. In the future, brain like computing will pay more attention to the combination of single point independent research and other levels of research, promote the mutual cooperation and promotion of basic theoretical algorithms, chip hardware platform, evaluation and test benchmark, programming and compilation tools and system applications of brain like computing, build a more full stack of brain like computing iterative development ecology, and enter a healthy track of progress.
Trend 8: neuromorphological hardware features are further explored and used to achieve more advanced intelligent systems
New neuromorphological devices, such as RRAM (variable resistance memory) and PCM (phase change memory), have played an important role in the field of artificial intelligence. The intelligent hardware system based on these devices can effectively improve the speed and energy efficiency of intelligent algorithm execution, and maintain the performance of the algorithm.
However, most of the current hardware intelligent systems only make use of some of the characteristics of neural morphological devices, such as non-volatile, linear, etc., and lack the application of more abundant characteristics of devices, such as volatile, nonlinear, random and so on. Through the comprehensive exploration of devices, the next generation of intelligent system will closely combine the various requirements of algorithms with the rich characteristics of devices, so as to further expand the function and application scope of intelligent system, and improve the performance and efficiency of the system.
Trend 9: AI from brain structure elicitation to structure and function elicitation
Brain inspired artificial intelligence not only emphasizes the imitation of brain structure and neural morphology, but also needs to understand the function and mechanism of human neurons and neural circuits. This is because there is no simple one-to-one correspondence between brain structure and brain function, that is, similar structures may have different functions.
For example, as an ancient structure, the hippocampus has similar structures in human and animal brains, but they use different memory coding methods. In order to avoid the confusion of memory, the hippocampal body of animals adopts the method of “pattern separation”, that is, neurons form different groups of neurons to store memory. However, the human hippocampus adopts the coding method of “concept and association”, that is, the same group of neurons can store multiple different memories. This unique way of memory coding may be a key factor for the emergence of human intelligence, which helps to explain the unique cognitive abilities of human beings compared with other species, such as the ability of abstract thinking and creative thinking.
Trend 10: AI computing center becomes the key infrastructure in the intelligent Era
In recent years, the demand of artificial intelligence for computing power is growing rapidly, and it has become one of the most important computing power resources. AI computing is the core driving force of the development of the intelligent era, and the Artificial Intelligence Computing Center Based on artificial intelligence computing power emerges as the times require.
The AI computing center is based on the latest AI theory and adopts the leading AI computing architecture. It is a “four in one” integrated platform integrating public computing services, data open sharing, intelligent ecological construction and industrial innovation. It can provide computing power, data and algorithm and other AI full stack capabilities. It is a new type of computing power that the rapid development and application of AI rely on infrastructure. In the future, with the continuous development of intelligent society, Artificial Intelligence Computing Center will become the key information infrastructure, promote the deep integration of digital economy and traditional industries, accelerate industrial transformation and upgrading, and promote high-quality economic development.
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