10 Key Technology Trends for 2022 According to Baidu Research
In times of uncertainty, we rely on technologies that provide us with solid support. At the beginning of 2022, as the global pandemic continues to cause upheaval and the world economy faces numerous challenges, artificial intelligence has become a key driver of innovation and development thanks to its rapidly evolving core technologies, stronger cross-domain connections, and expanding collection of industry applications.
Core AI technologies are making continuous breakthroughs, with more proven examples of integrated innovation. For example, in the pre-training of large models, research directions such as cross-modality, cross-language and knowledge expansion have pushed the boundaries of large-scale models and inspired new potential for general artificial intelligence.
“In addition, in interdisciplinary research, AI is a common variable for scientific work and technological development. In fields such as quantum computing, biology and chemistry, AI has become one of the most interesting areas of research. AI is also accelerating the development of industries such as autonomous driving, robotics, aerospace and healthcare. It has also played a key role in achieving goals such as reducing carbon emissions and social inclusion”
Baidu Research shared the 10 key technology trends of 2022. The company hopes that in this time of uncertainty, AI will light the way for innovation and set the ship of development in motion.
Large-scale pre-trained model
The large-scale pre-trained model reflects the trend toward expanding knowledge, unified cross-modal modeling and multi-task learning, reducing dependence on labeled data, and accelerating real-world applications.
Self-supervised large-scale models trained on massive data can solve a range of AI tasks using a single unified model and paradigm. This technology has overcome the bottlenecks of traditional technologies by reducing dependence on large labeled datasets and significantly improving the efficiency, versatility and generalization of AI models.
In 2022, research and development is expected to shift from increasing model size to practical deployment. Large-scale models will continue to improve in performance, versatility, generalization, and operational and economic efficiency.
At the same time, the threshold for implementing real-world AI scenarios, such as smart offices and smart finance, will decrease. For example, artificial intelligence built on cross-modal large models will drive the adoption of AIGC (AI-generated content), which promises to stimulate creative cognition, improve content diversity and reduce production costs.
AI for science
AI for science has become a new research field capable of changing the paradigm of scientific research. In 2021, machine learning helped mathematicians discover two major conjectures. The combination of machine learning, multiscale modeling and high-performance computing solved real-time simulation problems for extremely large random quantum circuits.
AI has also shown great potential in scientific research, especially in data processing, conducting new experiments and creating more efficient computational models.
The new AI for science is expected to further unite two paradigms of scientific research: data-driven and theoretical deduction. In the next few years, AI will be integrated into many different fields, such as mathematics, physics, chemistry, materials and engineering, and will play a more important role in the development of fundamental science.
AI-based computational biology
AI-based computational biology continues to develop rapidly, achieving breakthroughs in many fundamental studies.
Covid-19 has caused a constantly growing demand for AI in the biomedical industry. For example, AI can be used to improve the accuracy and speed of targeted genome editing or to predict protein folding structures. The success of the mRNA vaccine technology has further advanced the development of RNA- or protein-based macromolecules and vaccines.
AI-based computational biology is poised for new breakthroughs in fundamental research and applications, ranging from protein-based drug design, formulation and drug screening to mRNA-based monoclonal antibodies, cancer therapy and other forms of immunotherapy. The fusion of AI and computational biology will significantly accelerate drug development, as well as reduce costs and facilitate precision medicine and personalized therapy.
Privacy computing
In recent years, privacy computing has attracted growing attention. It will enable fuller use of data and create infrastructure that strengthens user trust. Security and compliance have become essential for unlocking the value of data amid the emergence of global rules on personal information and data security.
Trusted confidential computing and federated computing have gained recognition because they enable data protection, sharing and distribution from a technical standpoint.
Thanks to improved performance of privacy computing technologies, mutual advancement of technologies and compliance standards, and multi-party cooperation to strengthen technical credibility, best practices can be developed for relevant applications in scenarios such as computational biology, financial analysis and data transactions.
In the long run, privacy computing technology may drive the default sharing of encrypted data and computation, gradually building infrastructure for user trust.
Integration of quantum hardware and software
The integration of hardware and software in quantum computing is being implemented increasingly widely, as real demand accelerates the convergence and innovation between quantum computing and other industries. In 2022, design, preparation, measurement and control technologies for quantum chips are expected to keep improving.
The number of qubits (the smallest unit of information in a quantum computer) will grow in scale. More breakthroughs will be made by reducing noise or adapting to noise. Quantum computing software and services will evolve in a cross-platform direction, and users will gain broader quantum back-end capabilities on cloud quantum computing platforms. Quantum computing platforms carrying solutions for quantum hardware-software integration will gradually demonstrate their commercial potential.
Through deep integration and innovation of quantum computing with intelligent manufacturing, artificial intelligence, chemical medicine, financial technology and other fields, numerous practical applications with significant quantum advantages will emerge. Government agencies, academia and industry will cooperate more closely to create high-quality quantum devices and develop quantum technology talent, taking the first steps toward building a quantum computing industry chain.
Autonomous driving
Autonomous driving technology is entering a new driverless stage, as several models of robotic cars link the technology with application scenarios.
Technological advances and policy regulation will bring driverless driving closer to reality in 2022. Robotic cars represent next-generation vehicles that will revolutionize transportation.
Autonomous driving is penetrating a wide range of use cases, such as passenger vehicles, public transport, freight, warehouse transport, retail, sanitation and special operations in mines and ports, creating significant value and contributing to further social progress.
Deep space exploration
AI and aerospace technologies are opening new possibilities for intelligent deep space exploration.
Deep space exploration is the highest embodiment of human curiosity about the universe. Demand is growing for autonomous spacecraft to conduct scientific research and mineral extraction on the Moon or other planets, as well as for further exploration of outer space.
In the field of construction machinery automation, 24-hour continuous unmanned earthmoving has been achieved. Autonomous environmental perception and motion planning algorithms used in all-terrain vehicles can also include sensors with functions such as autonomous obstacle avoidance and decision-making, as well as flexible autonomous manipulator operation.
In addition, AI technology is expected to play a key supporting role in detecting and repairing spacecraft damage, building digital twin simulation laboratories, and detecting and analyzing deep space big data.
Human-machine symbiosis
While the global pandemic continues to force humanity to live under social distancing, the development of digital and intelligent technologies has given us the opportunity to close this distance by accelerating symbiosis between people, digital avatars and robots.
This shift is supported by continuous development of AI technologies such as vision, speech and natural language processing, as well as continuous learning supported by the integration of hardware, networks, computing and ecosystem platform content. Together, these have formed a cross-system technical support framework.
With the acceleration of integration and innovation across various advanced information technologies, more platforms will emerge that combine virtual, real and intelligent interaction for industrial and consumer scenarios, strengthening the deep integration of the digital and real economies and enriching people's work and daily life.
Green AI
When developing AI, environmental and low-emission factors are increasingly taken into account, helping to achieve carbon emission reduction and carbon neutrality goals.
As AI technology accelerates and integrates innovation across industries, data centers and large-scale AI computing create important social value, but at the same time create challenges in terms of energy costs and environmental impact. Today the world is vigorously pursuing energy saving and carbon emission reduction, which calls for urgent development of more eco-friendly "green AI" technology to reduce energy consumption in model training and deployment.
In the next few years, green AI technologies will continue to flourish, creating systems based on energy-efficient architecture design, training and inference strategies, and data use to form evaluation benchmarks that consider both performance and energy consumption.
More AI processors with higher computing power and lower energy consumption are also expected to continue to be invented. Leading AI companies will build large-scale, intensive models to improve the performance of downstream processes and reduce overall energy consumption costs, policy will encourage the construction of green and low-carbon data centers, and AI technology will be applied to raise the energy efficiency ratios of infrastructure and other measures.
Inclusive AI
AI will become more inclusive, with a focus on creating value, allowing more attention to be paid to the needs of SMEs and minority groups. Inclusive AI concerns not only AI specialists but also the broader population that will benefit from the technology.
Open-source platforms based on deep learning frameworks have significantly lowered the threshold for developing AI technology. Public datasets, large model bases and regional intelligent computing centers will continue to develop to help small and medium-sized enterprises reduce costs, improve efficiency and stimulate innovation.
A national AI training system will also gradually be built to facilitate the retraining of traditional industry personnel through scientific AI education.
The development of AI should also benefit all levels of society. Under political guidance and the promotion of the concept of sustainable development, enterprises will focus on creating value. As AI service providers pay more attention to the needs of minority groups such as the elderly and children, they will develop inclusive AI services and products that allow everyone to enjoy the digital world.
