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Alibaba Forecasts Top 10 Tech Trends

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Cloud-network-device convergence leads the tech trends that would shape the tech industry this year, according to a recent study from Alibaba DAMO Academy, the global research initiative by Alibaba Group.

By analyzing millions of public papers and patent filings over the past three years and conducting interviews with nearly 100 scientists, DAMO provides the top 10 technology trends for the next two to five years that are expected to make an impact on the different sectors of the economy and the society in general.

“The evolution of digital technologies has further accelerated technological progress and industrial development. The boundary of technologies is extended from the physical world to mixed reality while more and more cutting-edge technologies find their way into industrial applications,” said Jeff Zhang, head of Alibaba DAMO Academy.

“Digital technology plays an important role in powering a green and sustainable future, whether it is applied in industries such as green data centers and energy-efficient manufacturing or in day-to-day activities like paperless offices. With technology, we will create a better future,” he added.

In the next two to three years, a surge of applications are expected to run on top of a new computing system. AI would be broadly applied in the research process of applied science and silicon photonic chips in large-scale data centers. AI would pave the way for the integration of renewable energy sources in power grids. People-centric precision medicine would become a major trend and there would be groundbreaking improvements in the performance and interpretability of privacy-preserving computation as well as in a new generation of XR glasses.

Here are the tech trends Alibaba expects to gain even more traction and impact the different industries in the months ahead:

1. Cloud-network-device convergence

The rapid development of new network technologies will fuel the evolution of cloud computing toward a new computing system: cloud-network-device convergence. In this new system, clouds, networks and devices have a more clearly defined division of labor.

Cloud-network-device convergence is the catalyst that will drive the emergence of new applications to fulfill more demanding tasks such as high-precision industrial simulation, real-time industrial quality inspection and mixed reality.

2. AI for science

In the past hundreds of years, the scientific community had two basic paradigms: experimental science and theoretical science. Today, the advancement of AI is making new scientific paradigms possible.

Machine learning can process massive amounts of multidimensional and multimodal data and solve complex scientific problems, allowing scientific exploration to flourish in areas previously thought impossible. AI will not only accelerate the speed of scientific research but also help discover new scientific laws.

3. Silicon photonic chips

Silicon photonic chips can be expected to be widespread in in high-speed data transmission in large-scale data centers. As the size of transistors approaches physical limits, the speed of electronic chip development can no longer meet the increasing data throughput demand brought by the rise of high-performance computing.

Unlike electronic chips, silicon photonic chips use photons instead of electrons to transmit data. Photons do not directly interact with each other and can travel longer distances. Silicon photonic chips can, therefore, provide higher computing density and energy efficiency. The rise of cloud computing and AI drives the rapid development of silicon photonics technology.

4. AI for renewable energy

In the next three years, AI is expected not only to pave the way for the integration of renewable energy sources in power grids but to contribute to the safe, efficient and reliable operation of the power grid as well. The rapid development of technology in renewable energy such as wind and solar power in recent years has made renewables a tempting energy source to add to the power grid.

However, difficulty in grid integration, low energy utilization rate and storage of excess energy are major roadblocks along the way. Due to the unpredictable natures of renewable energy power generation, integrating renewable energy sources into the power grid presents challenges that affect the safety and reliability of the grid. The application of AI in the industry is pivotal in improving the efficiency and automation of electric power systems, maximizing resource usage and stability. This will be conducive to achieving carbon neutrality.

5. High-precision medicine

Medicine is a field that is highly dependent on individual expertise, often involving a lot of trial and error and may ultimately have varying efficacies from patient to patient. The convergence of AI and precision medicine is expected to boost the integration of expertise and new auxiliary diagnostic technologies and serve as a high-precision compass for clinical medicine. With this compass, doctors can diagnose diseases and make medical decisions as quickly and accurately as possible. These advances will allow us to quantify, compute, predict and prevent severe diseases.

In the next three years, people-centric precision medicine would become a major trend that will span multiple fields of healthcare, including disease prevention, diagnosis and treatment. AI will become synonymous with a highly precise compass that will enable the pinpointing of diseases and their treatments.

6. Privacy-preserving computation

For a long time, the application of privacy-preserving computation has been limited to a narrow scope of small-scale computation due to performance bottlenecks, lack of confidence in the technology and standardization issues. However, as more integrated technologies such as dedicated chips, cryptographic algorithms, whitebox implementation and data trusts are emerging, privacy-preserving computation will be adopted in scenarios such as processing massive amounts of data and integrating data from all domains. This is the headway made from processing small amounts of data and data from private domains. The adoption will boost new productivity that is powered by data from all domains.

In the next three years, groundbreaking improvements in the performance and interpretability of privacy-preserving computation can be expected. The emergence of data trust entities would enable data sharing services based on the technology.

7. Extended reality (XR)

The development of technologies such as cloud-edge computing, network communications and digital twins brings XR into full bloom. XR glasses promise to make immersive mixed reality Internet a reality. This technology plants the seed that will sprout into a new industrial ecosystem that encompasses electronic components, devices, operating systems and applications. XR will reshape digital applications and revolutionize the way people interact with technology in entertainment, social networking, office, shopping, education and healthcare.

In the next three years, a new generation of XR glasses would emerge, and these would have an indistinguishable look and feel from ordinary glasses entering the market. They would serve as a key entry point to the next generation of Internet.

8. Perceptive soft robotics

Unlike conventional robots, perceptive soft robots are robots with physically flexible bodies and enhanced perceptibility to pressure, vision and sound. These robots take advantage of state-of-the-art technologies such as flexible electronics, pressure adaptive materials and AI, which allow them to perform highly specialized and complex tasks and deform to adapt to different physical environments.

The emergence of perceptive soft robotics will change the course of the manufacturing industry from the mass production of standardized products to specialized, small-batch products. In the next five years, perceptive soft robotics will replace conventional robots in the manufacturing industry and pave the way for the wider adoption of service robots in daily life.

9. Satellite-terrestrial integrated computing

Terrestrial networks and computing systems provide digital services for densely populated areas while no service is available in sparsely inhabited areas such as deserts, seas and space. STC connects high-Earth orbit (HEO) and low-Earth orbit (LEO) satellites and terrestrial mobile communications networks, achieving seamless and multidimensional coverage. STC also creates a computing system that integrates satellites, satellite networks, terrestrial communications systems, and cloud computing technologies. This way, digital services can be more accessible and inclusive across the globe.

In the next five years, satellites and terrestrial systems will work as computing nodes to constitute an integrated network system providing ubiquitous connectivity.

10. Co-evolution of large-and small-scale AI models

Large-scale pretraining models, also known as the foundation models, are the grounding breakthrough technique from weak AI to general AI, which relatively boosts the performance of various applications using conventional deep learning. However, the merit in the higher performance and the drawback in the power consumption are not well balanced, limiting the exploration of large-scale models.

The future AI is shifting from the race on the scalability of foundation models to the co-evolution of large- and small-scale models via clouds, edges and devices, which is more useful in practice.

For more detailed information, please visit the full report here:

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