Alibaba DAMO Academy, the global research initiative by Alibaba Group, listed the top 10 trends that it believed would shape the tech industry in the next two to five years.
DAMO said it analyzed millions of public papers and patent filings over the past three years and conducted interviews with nearly 100 scientists to come up with the top 10 trends that would have accelerated breakthroughs and make impacts across the different sectors in the economy and the society at large.
“In the next three years, we expect to see AI broadly applied in the research process of applied science, the widespread use of silicon photonic chips in large-scale data centers, AI paving the way for integration of renewable energy sources into the power grid, people-centric precision medicine becoming a major trend, groundbreaking improvements in the performance and interpretability of privacy-preserving computation, as well as a new generation of XR glasses,” said Jeff Zhang, head of Alibaba DAMO Academy.
1. Cloud-network-device convergence
The rapid development of new network technologies will fuel the evolution of cloud computing towards 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.
“In the next two years, we expect to see a surge of applications running on top of the new computing system,” Zhang said.
2. AI for science
The scientific community has 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 is forecast to not only accelerate the speed of scientific research, but also help discover new scientific laws.
3. Silicon photonic chips
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, and therefore silicon photonic chips can provide higher computing density and energy efficiency. The rise of cloud computing and AI drives the rapid development of silicon photonics technology.
In the next 3 years, DAMA sees the widespread use of silicon photonic chips in high-speed data transmission in large-scale data centers.
4. AI for renewable energy
The application of AI in the renewable energy 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.
In the next 3 years, AI is expected to pave the way for integration of renewable energy sources into the power grid and contribute to the safe, efficient and reliable operation of the power grid.
5. High-precision medicine
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 the human race to quantify, compute, predict and prevent severe diseases.
“In the next 3 years, we expect to see people-centric precision medicine become a major trend that will span multiple fields of healthcare, including disease prevention, diagnosis, and treatment,” Zhang said.
“AI will become synonymous with a highly precise compass that allows us to pinpoint diseases and their treatments,” he added.
6. Privacy-preserving computation
As more and more integrated technologies — such as dedicated chips, cryptographic algorithms, whitebox implementation, and data trusts —emerging, privacy-preserving computation will be adopted in scenarios such as processing massive amounts of data and integrating data from all domains. The adoption will boost new productivity that is powered by data from all domains.
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 scenarios such as entertainment, social networking, office, shopping, education, and healthcare.
8. Perceptive soft robotics
Perceptive soft robots are robots with physically flexible bodies and enhanced perceptibility towards 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.
In the next five years, perceptive soft robotics are seen to replace conventional robots in the manufacturing industry, and pave the way for 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 5 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
The large-scale pre-training models, also known as the foundation models, are the grounding breakthrough technique from weak AI to general AI, which relatively boosts 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. (PR)