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Intelligent World 2030

Global Outlooks 2030

As we stand on the brink of a new era, the vision of an intelligent world by 2030 is becoming increasingly vivid and tangible. This report brings together insights from leading experts across various fields to explore how AI will transform our lives, industries, and society.

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Indicator Prediction

General-purpose computing power (FP32) will reach 3.3 ZFLOPS, a 10-fold increase over 2020.

There will be 200 billion connections worldwide. IPv6 adoption will reach 90%.

There will be 1.6 billion fiber broadband subscribers.

82% of new vehicles sold will be electric vehicles.

84% of companies will have access to 10-gigabit Wi-Fi networks.

Every 10,000 workers in manufacturing companies will work with 1,000 robots.

Renewables will account for 65% of all electricity generation globally.

Privacy-enhancing computation technologies will be used in more than 50% of computing scenarios.

Directions for Exploration

Unlocking the value of health data, and shifting the focus from reactive treatment to proactive prevention

According to the WHO, 60% of related factors to illnesses are correlated to lifestyle, making healthy habits essential for well-being. With user consent, wearable devices can collect and analyze real-time health data, and offer predictive insights and medical guidance with the assistance of a unified AI architecture. This shift toward proactive prevention integrates health management into daily life, connecting disease control, hospitals, health centers, and families to reduce the risk of a more serious health condition arising.

Snapshot from the future: Focusing on real-time health status monitoring to facilitate real-time, efficient health management

Thanks to the advancements in the Internet, IoT, and AI, as well as the widespread adoption of wearable devices and home monitoring equipment, by 2040, at least a quarter of outpatient care, preventive care, long-term care, and health services will move online.

Specifically, these technologies will be used to analyze real-time health data, medical responses, and clinical outcomes to identify potential health risks. For example, AI can detect the early signs of heart disease or pre-diabetic conditions by analyzing and warning users about anomalous heart rate and blood pressure measurements, allowing for early intervention. Additionally, through interventions in nutrition, exercise, and sleep, users can be guided to gradually change their unhealthy habits and develop a healthier lifestyle, reducing the likelihood of illness. For instance, a study by Stanford University showed that continuous monitoring of heart rate and skin temperature through smartwatches and other wearables could help AI detect early signs of infection, as these powerful monitoring devices can take and analyze up to 250,000 measurements per day.

Moreover, a comprehensive health management platform could allow hospitals, doctors, users, and families to collectively access and view health data. This data-sharing mechanism ensures that doctors can stay updated on their patients' health in real time, both inside and outside the hospital, leading to more accurate diagnoses and better treatment decisions.

Snapshot from the future: Using intelligent disease prediction and prevention to enhance public health response capabilities

By integrating electronic health records (EHRs), wearable device data, lab results, and public health data from a variety of sources, AI systems can build comprehensive health databases. Using machine learning and deep learning, AI will analyze this data to identify patterns related to the incidence of disease and develop predictive models. These models will provide early warnings about the potential risk of being affected by certain diseases at both the individual and population levels, allowing for timely preventive action.

For example, researchers are using AI and big data to analyze global public health data and epidemiological information and to develop models capable of predicting outbreaks of infectious diseases such as influenza. These models can identify potential hotspots and transmission pathways, allowing health organizations to implement preventative measures before outbreaks spread.

The intelligent disease prediction and prevention system enables public health organizations to swiftly respond to epidemic threats and adopt effective control measures, significantly reducing the societal impact of outbreaks. It not only enhances public health response capabilities but also improves individual health management, and this contributes to the improvement of overall public health.

Enabling healthcare with digital intelligence to improve quality and accessibility

The integration of AI into healthcare not only safeguards public health but also drives improvements in economic and social development. By using advanced technologies, digital healthcare significantly enhances the quality and accessibility of medical services. AI and machine learning algorithms can analyze vast amounts of medical data and assist doctors in making precise diagnoses and developing personalized treatment plans. This improves treatment outcomes, enhances patient satisfaction, and reduces the likelihood of a misdiagnosis.

Digital healthcare systems also optimize resource allocation by ensuring balanced coverage across regions, including in remote and underserved areas. Through intelligent scheduling and resource management, these systems ensure that healthcare services are available to more patients, and this improves access to timely and high-quality medical care.

Snapshot from the future: Smart healthcare innovations for enhanced diagnostic efficiency and precision

In the future, digital and AI-powered medical care will lead to significant improvements in efficiency and precision. AI and big data will play a crucial role in medical imaging in particular. Using deep learning algorithms, AI can analyze large datasets of medical images, such as X-rays, CT scans, and magnetic resonance imaging (MRI) scans, and detect lesions and provide accurate diagnostic recommendations. This will not only enhance screening efficiency but also reduce the risk of misdiagnosis. For example, research shows that AI outperforms traditional methods in early breast cancer detection.

Furthermore, AI will be integrated with electronic medical records (EMRs) to continuously update patient health data in real-time. This real-time data will help AI develop personalized treatment plans, predict disease risks, and assist doctors in making more effective treatment decisions, ultimately improving patient outcomes.

AI will also play a significant role in pathology screening, where it can be used to analyze pathology slides and detect abnormal cell and tissue changes. This assists pathologists in making faster and more accurate diagnoses. AI's ability to detect cancer cells in pathology images has already been widely recognized.

Big data and AI will also play an important role in helping healthcare organizations and insurance companies manage expenses more intelligently and thus help to keep healthcare costs reasonable for patients. By analyzing vast amounts of medical data, AI can predict healthcare cost trends, identify unnecessary expenses, and detect potential fraud. This smart cost-control method not only protects patients' interests but also optimizes the use of healthcare resources.

Snapshot from the future: Enabling AI-driven, all-domain, collaborative healthcare to optimize resource allocation

The future of healthcare will be revolutionized by modern communications and information technologies. They will extend medical services to remote monitoring, consultation, and treatment. AI and foundation models will be central to this revolution. Clinical decision support systems (CDSs) are particularly important. By leveraging deep learning and machine learning, CDSs analyze medical images and EHRs to provide accurate and efficient diagnoses and personalized treatment solutions. These systems can quickly supply essential diagnostic information to doctors in remote areas, leading to more accurate decisions and treatments. For example, in Türkiye there were plans to develop a web- and mobile-based application which would allow doctors to remotely monitor patient data in real time and which would unify and enable the screening, diagnosis, treatment, and monitoring of diabetes diseases.

Personalized health management is another key area. By analyzing both historical and real-time health data, AI can predict health risks and offer tailored recommendations to help individuals adjust their lifestyles and prevent diseases. These technologies show a lot of promise in chronic disease management; for example, remote monitoring of hypertension allows for timely interventions and significantly improves health outcomes. In all-domain healthcare collaboration, continuous data monitoring and analysis enable remote doctors to provide more precise health guidance.

Large language models (LLMs) help patients by answering questions in real time, thereby easing the burden on remote doctors. These models provide medical advice, psychological support, and can also assist in making appointments with doctors, making healthcare more efficient. For instance, virtual assistants can analyze conversations with patients to offer personalized suggestions which can help patients improve their mental health.

Video communication technologies also play a vital role, as they allow doctors to conduct remote consultations and diagnostics, increasing the convenience and accessibility of medical services. For example, the Remote Hypertension Improvement Program uses video calls and remote monitoring to efficiently lower patients' blood pressure.

Overall, AI and foundation models elevate the quality and efficiency of healthcare services, enabling personalized health management and optimized resource allocation, and driving the development of more inclusive healthcare services.

Multidisciplinary integration and its role in driving innovation in medical research

The integration of multiple disciplines is a key frontier to drive medical research innovation today. Advancements in biomedical engineering, information technology, AI, and big data, are driving a significant transformation in medical research. For instance, nanotechnology in drug delivery systems enhances treatment precision and reduces side effects. Big data analysis allows researchers to uncover new disease patterns and develop novel treatment methods which advance personalized medicine. Meanwhile, AI can be used in combination with medical images and EHRs to accelerate and improve the accuracy of diagnostics. This multidisciplinary approach accelerates the research-to-clinical-practice process, drives rapid advancements in medical technology, and establishes a solid foundation for the future of healthcare.

Snapshot from the future: Multimodal data integration for more advanced precision medicine

Multimodal data integration is set to revolutionize precision medicine. By combining genomics, proteomics, metabolomics, imaging data, and clinical information, researchers can gain a more comprehensive understanding of disease mechanisms. For example, AI can predict disease risks more accurately and develop personalized treatment plans with multimodal data. A study in Nature Medicine has shown how the convergence of generative AI and LLMs in medical imaging has opened up new ways to harness the power of both visual and textual information. Integrating these advanced technologies enables multimodal data integration, representation learning, and improved clinical decision support systems. Multimodal models built upon generative AI and LLMs can integrate the visual features of medical images with contextual information from radiology reports or EHRs to facilitate various medical-image processing tasks. LLMs can process radiology reports to extract pertinent information, match them with the corresponding images, and generate natural-language summaries that can enhance communication between healthcare professionals and facilitate better decision-making when it comes to patient care. By leveraging patient-specific information, such as genetic data, medical history, and lifestyle factors, and evaluating it in conjunction with medical images, these AI models can facilitate more efficient treatment and diagnoses for patients.

Snapshot from the future: The rise of intelligent medicine and its role in driving industry transformation

The future of medicine is poised to move from a one-size-fits-all approach to a more bespoke and patient-centric approach. Key factors such as the physical condition of the patient, appropriate drug types, timing, dosage, and the treatment duration must be taken into consideration when designing drug treatment plans. These plans, once formulated, also need to be regularly updated based on the treatment effect and progress. This puts significant pressure on doctors, and they are often forced to rely on general expertise and experience rather than the patient's specific symptoms and indicators to quickly formulate a general treatment plan. However, with AI and foundation model technologies, vast amounts of pathological data can be analyzed in real time, enabling doctors to offer more personalized treatment recommendations. For example, a research institute in Singapore has developed an AI-powered platform that evaluates medication effectiveness. The platform can quickly analyze a patient's clinical data, provide the patient with a personalized prescription, and modulate tumor sizes or biomarker levels in the patient's profile based on available data.

In drug development, AI has moved beyond the proof-of-concept stage. Advanced machine learning technologies are accelerating the pace of innovation, reducing evaluation times, and enabling the exploration of new areas of medicine. In practice, AI R&D tools improve the speed of ingesting, structuring, and extracting inferences from scientific literature by a factor of 1,000. AI-driven simulations run 2 to 40 times faster, and AI models can propose new hypotheses 10 times faster than before. Autonomous AI-powered laboratories can conduct experiments 100 times faster than before. This reduction in manual data processing and information handling has increased the overall speed of drug discovery tenfold.

For example, Insilico Medicine, an AI pharmaceutical company, launched the world's first automated AI-assisted decision-making laboratory. The lab integrates AI with automation, robotics, and biological capabilities, and can complete the entire cycle of target discovery and validation within 14 days.

Looking ahead, AI will drive cost reduction and efficiency gains in the pharmaceutical industry, accelerate drug development, and bring unprecedented changes to this industry.

Using accurate data, not experience, to guide cultivation

Snapshot from the future: Precision farming based on visualized data graphs

With modern tools like sensors and mobile devices, farmers can remotely and accurately monitor soil moisture, ambient temperature, and crop conditions in real-time. This makes it possible to flexibly adjust agronomic measures, like sowing, irrigation, fertilizer, and seed adjustment, based on diverse data sets, to better align crops with the soil available. Take maize, for example. Data-powered adaptive sowing can increase crop yield by 300 to 600 kilograms per hectare of land.

Visualized data graphs for agriculture can help farmers make informed decisions regarding soil fertility, water, and nutrient delivery across key crop growth stages. These graphs can also help farmers better understand information such as local topographical characteristics, climate conditions, and crop diseases or pests so that they can better estimate crop yields, implement agricultural measures, and adjust budgets accordingly.

Visualized data graphs can also be used to monitor and manage agricultural production in real time, helping farmers make proactive, quick, and precise responses to changes in their environment. For example, in the event of extreme weathers, farmers can use this data to rapidly locate affected areas, develop solutions, and mitigate negative impacts on their yields.

Taking a "factory-like" approach to protect agricultural production from environmental conditions

Snapshot from the future: A new form of agriculture in intelligent vertical farms

In vertical farms, every step of the cultivation process, from sowing, to fertilizing, to harvesting, is closely monitored, with farmers precisely controlling light, temperature, water, and nutrient delivery based on the needs of each crop. By controlling every stage of crop growth and adjusting environmental parameters as needed, farmers are able to artificially create the ideal environment for their plants.

Vertical farms have three main advantages:

They don't need pesticides or soil, and reduce agricultural water waste.
They are not affected by climate, providing consistent and ideal conditions for fresh produce.
They provide smart agricultural models that are globally replicable.

Recent pilot programs for vertical farms have found that, if harvested every 16 days, a 7,000 square meters area can yield a staggering 900,000 kilograms of vegetables every year.

Low-carbon, 3D-printed solutions for meat

Snapshot from the future: Healthy and sustainable meat supply with 3D printing

Recent applications of 3D printing technologies have successfully improved the quality of artificial meat, making it better tasting and better looking to consumers. It has proven capable of turning both plant proteins and animal cells into artificial meat.

3D printing can use plant-based phytoproteins to build fibrous skeletons that closely mimic the texture of real meat. Alternatively, 3D printing can also be used to stack nutrient elements made of real animal cells to create the musculature and fat layers normally seen in animal tissue. Currently, 3D printing can be used to create many types of artificial meat, including pork, chicken, and beef, with the price of artificial beef quickly approaching the market price of real beef.

Adaptive home environments that understand your needs

Today, we expect more from our homes than ever before. Homes should be more than just a place to live: They should also offer superb experiences. The homes of the future will intuitively understand all of our needs. The moment we arrive home after a long exhausting day, the lights, sound systems, air filters, and television will switch on automatically. When we walk into the kitchen, the refrigerator will push healthy meal suggestions adapted to our personalized dietary needs. In the bedroom, air conditioners will check air quality and automatically adjust temperature and humidity based on what we are doing. From the comfort of the sofa or bed, we will be able to share incredible photos and videos with our loved ones, or finish some mundane paperwork anytime we want. In the event of an emergency, like a fall, where someone is unable to call for help, the home system will come to help by notifying family members, doctors, or security guards.

There are perceptible and imperceptible factors that determine how comfortable our home is. Perceptible factors are those we can instinctively feel, such as temperature, humidity, lighting, ease of access to household items, and ease of information sharing. Imperceptible factors usually include indoor air quality and safety. Intelligent automated systems can enable real-time control of both types of factors.

Snapshot from the future: Whole-house intelligence that understands usage and creates intuitive experiences

Smart home systems collect data from a wide range of smart appliances and sensors, over highly-reliable, high-speed networks that reach every corner of your home. These systems synchronize data on cloud and local storage to make smooth data flow possible. They also use AI engines to determine what is happening in your home and run appropriate applications. The AI engines, in turn, need distributed processing and computing to understand your behavior, indoor environment, and hardware systems, and then make smart decisions to configure your home appliances. These steps could be taken independently or in collaboration with other systems, to meet your needs. When implemented properly, smart home systems deliver immersive, personalized, and intelligent experiences that evolve as your usage needs change.

The variety of smart home appliances we will see in the coming years is expected to explode. They will work together to intelligently anticipate and meet your needs in different situations. Everything, from smart beds and pillows to lights and audio devices, will be able to collaborate. A sleep support solution could easily be created for the bedroom by designing a system that automatically adjusts the softness of your mattress and pillow to suit your body and sleeping habits, and changes your bedroom lighting to stimulate the production of melatonin – the hormone that helps you fall asleep. Bedroom speakers could play music to relax you, and air conditioners could keep track of temperature, humidity, and oxygen levels. Such a system could even identify snoring and curb it by rapidly adjusting the softness of your mattress and pillow. Temperature and humidity regulation could also be achieved to stop you from tossing and turning in bed . In the future, the way we interact with home appliances will also change through touch panels, apps, voice commands, and gestures. Sometimes interactions will be so subtle that we won't even be aware of them.

All members of the family will be able to store videos and photos together in a cloud-connected storage system, and access the system on any device from anywhere. 35% of homes will use cloud storage. Traditional computers at some homes will become cloud computers and work seamlessly with gadgets and home appliances with screens, delivering a consistent experience across all these devices. Cloud computers will be in use in 17% of homes.

AI-powered home cameras and optical sensing devices will be able to recognize people's movements, and identify if someone takes a fall or is in danger so that help can be quickly notified. Cameras with 3D radar optical sensors will be adopted in 8% of homes to support home nursing while ensuring privacy. These systems could also identify intruders and send alerts to police or security guards. In China, 24% of homes will be equipped with surveillance cameras; globally, the percentage is 15%.

Net-zero-carbon buildings with IoT and intelligent management systems

A net zero carbon building is "a highly energy efficient building that is fully powered from on-site and/or off-site renewable energy sources and offsets." Net zero carbon is achieved when the amount of carbon dioxide emissions released on an annual basis is zero or negative.

Snapshot from the future: Net-zero-carbon buildings

One day, net-zero-carbon buildings will be able to automatically interact with their environment through sensors.

Sensors monitor and generate data about the building in real time, including its environment and condition.
The Internet of Things connects sensors, cloud-based control systems, and core systems such as lighting, electricity meters, water meters/pumps, heaters, fire alarm systems, and water chillers.
Intelligent, cloud-based systems utilize sophisticated algorithms and real-time data to automatically decide how the building can minimize energy use. For example, a complete automated system could use IoT devices to check the number of people in a building in real time, and then decide when to switch air conditioners and lights on or off in different parts of the building. Such a system would also be able to manage elevators, hallways, and shutters, depending on actual human activity.

In addition to the environmental benefits, net zero-carbon buildings will also make people's lives more comfortable. Automated systems can keep indoor temperatures at agreeable levels, while soundproofing materials can keep outside noise down to a minimum. There will also be health benefits: Automated systems can decide how much sunlight should pass through a window, to help limit UV exposure, encourage natural sources of vitamin D, support regular sleeping patterns, and combat seasonal affective disorder.

New infrastructure provides comprehensive services for communities

One potential solution to the overwhelming amount of possessions that now fill households is offsite storage. Some proposed solutions include digitalization and cataloguing of all household items, with technologies like 3D scanning, and then storage in local shared warehouses.

Digital cataloguing and automated delivery for offsite storage

Smart doors, smart smoke detectors, falling object alerts, delivery notifications, and many other smart services are becoming increasingly widespread. This means that residents are much more closely connected with their communities and local authorities. In the future, new communities will deliver comprehensive services to residents, powered by the Internet of Things (IoT), 10-gigabit fiber networks, and other new advanced infrastructure. Services such as virtual community events and smart pet management will bring residents and their communities more closely together. Groundbreaking new design concepts will also start changing the way our homes look at the household level.

Electric vehicles for green transport

Snapshot from the future: New energy for green mobility

Some countries have made significant progress in adding electric vehicles (EVs), such as buses and taxis, to their urban public transportation systems. For example, by 2017, all of Shenzhen's 16,000 buses were electric. This made Shenzhen the world's first city with an entirely electric bus fleet. In Europe, EVs make up over 78% of Denmark's new buses, and about two-thirds of new buses in Luxembourg and the Netherlands are zero-emission.

There are two reasons behind these rapid advancements in public transportation. First, public transportation vehicles are replaced relatively frequently, which provides the opportunity to plan and implement the deployment and adoption of new energy vehicles. Government subsidies and efficient O&M solutions can also reduce the operating costs of electric fleets to levels close to or even lower than those of conventional vehicles, and this reduces the barriers to introducing new energy vehicles.

Second, these publicly-owned vehicles are centrally stored and maintained in specialized facilities that can easily be upgraded into multi-functional spaces with charging piles for EVs. Therefore, lack of charging facilities is not a major obstacle for the electrification of public transportation.

Public transportation vehicles also travel longer distances every day and generate more carbon emissions than private vehicles. Therefore, the wide adoption of electric public buses and taxis is an effective and efficient way to reduce vehicle emissions.

Snapshot from the future: New energy aircraft trials

The aviation industry accounted for about 2% of global anthropogenic CO2 emissions in 2022. If such emissions are not effectively curtailed, this percentage is expected to increase to 25% by the middle of this century.

At present, three main types of new energy aircraft are being developed: hybrid-electric, pure electric, and hydrogen-powered. In addition to increasing energy efficiency and reducing pollution and noise, new energy aircraft also present an opportunity to trial new designs, such as the blended wing body design. This design can significantly reduce aircraft's drag and energy consumption and improve their flight performance. In addition, this design can increase the amount of space in the cabin, which is a very valuable upgrade, as it increases aircraft's carrying capacity.

Autonomy opens up the mobile third space

Snapshot from the future: Autonomous driving and vehicle-road-cloud synergy in the fast lane

Low- and medium-speed public roads: Self-driving vehicles have delivered positive results in fields such as logistics and distribution, cleaning and disinfection, and patrolling.

High-speed semi-closed roads: Heavy trucks are expensive, so the price of sensors is not a limiting factor. Sensors such as lidar can be installed in these trucks for better sensing of their environment. Heavy trucks are mainly used in high-speed cargo transportation, ports, and logistics parks, which means the driving environment is less complex and routes are generally fixed. Heavy trucks are rarely seen on complex urban roads. This means that the driving environment that autonomous driving systems have to handle is not particularly complex. Truck drivers are expensive, and they frequently breach rules by overloading their vehicles and working overtime. Autonomous driving of heavy trucks would quickly help industries cut costs and work more efficiently, making this a compelling business case. According to a Deloitte report on smart logistics in China, technologies like unmanned trucks and artificial intelligence will mature in a decade or so, and will be widely used in warehousing, transportation, distribution, and last mile delivery.

Special non-public roads: Autonomous driving is playing an increasingly important role in environments like mines and ports. Some companies are working with ports to test self-driving container trucks. We have already seen unmanned trucks working in multiple fleets and even during night shifts at mines. Since 2023, 92 intelligent guided vehicles have been operating autonomously at the Second Container Terminal of Tianjin Port in China. This was made possible thanks to 5G, the Beidou navigation system, and automatic driving technologies. We are expected to see autonomous horizontal transportation at 30% of terminals by 2030.

Public roads: Autonomous driving technologies can make driving safer for the general public and help local authorities manage roads more efficiently. For example, they can quickly detect traffic incidents and access the relevant information, issue warnings about secondary accidents, select better routes to avoid traffic jams, send traffic alerts to vulnerable road users, and provide information about construction sites and other areas with temporary traffic controls. Autonomous driving can significantly reduce the number of traffic accidents.

Snapshot from the future: Urban air mobility

The research and development of electric vertical takeoff and landing (eVTOL) aircraft has attracted investment from innovative companies around the world, and their performance has seen solid improvements. Currently, the five-seat aircraft which are being manufactured by several companies have a cruising range of about 250 kilometers. Some companies are working on eVTOL aircraft with seven seats or more. Some are exploring hydrogen-fueled aerial vehicles for longer ranges (more than 600 kilometers). These new aircraft may be used in various scenarios, including emergency medical services, urban air mobility, regional air mobility, air freight transportation, and personal aircraft.

Sharing vehicles for faster, low-carbon transportation

Snapshot from the future: Mobility as a Service available on demand

According to the International Road Transport Union, Mobility as a Service (MaaS) is to put the user at the core of transport services, offering them tailor-made mobility solutions based on their individual needs. MaaS is the integration of various modes of transport into a single mobility service accessible on demand. It combines all possible modes of transport, enabling users to access services through a single application and single purchase.

A key objective of MaaS is to provide integrated and convenient public transport services and develop green transport. MaaS systems aim to integrate local transport (e.g., buses, rail, shared cars, and shared bikes) and intercity transport (e.g., planes, high-speed rail, and long-distance coaches) and provide useful local information about dining, accommodation, shopping, and local tourist attractions. These systems will build on the intelligent scheduling functions of public transport systems, and identify passenger travel models while prioritizing green transport. With online payment functions integrated, MaaS systems can offer travel booking, one-tap itinerary planning, seamless connections between different modes of transport, and one-tap payments. MaaS will improve satisfaction with transport services while also providing green transport options.

Many EU cities are building MaaS showcase projects. Different cities have different levels of integration in terms of facilities, fares, payments, information, communications, management systems, and transport services. Gothenburg, Hanover, Vienna, and Helsinki were the first cities to explore MaaS. These cities have made full use of digital technologies to optimize their transport systems, including buses, shared cars, bicycles, and urban deliveries. This will help them incubate emerging transport service providers and drive urban decarbonization.

MaaS can bring tangible benefits: Individuals can cut their transport costs while enjoying better safety and a better experience. Governments can optimize their investment in transport infrastructure for more sustainable urban management and higher citizen satisfaction. In addition, MaaS will create more opportunities for transport service providers, as they can cut service costs and expand their services. When MaaS is widely deployed, we will see integrated scheduling of transport resources, better shared resources, a user-centric experience, and low-carbon transport.

Connected vehicles for safer, faster, and larger-scale autonomous driving

Snapshot from the future: Safer, more efficient dispatch services

Over the past decade, pioneers have begun exploring the use of elevated rails to transport containers in busy ports. Containers are sent to rails similar to cable railways. The railway system dispatches the containers based on their destination and sends them to railway stations, truck warehouses, or even waterless ports in inland cities. This makes container transportation much faster at a very low cost. In the future, we will see a comprehensive transportation system that supports the coordinated scheduling of different modes of transport. This system will ensure smooth traffic, speed up the distribution of goods, and drive the development of port-related industries. When this system is up and running, transport facilities will be fully connected and different modes of transport will work together seamlessly, which will help boost logistics efficiency, form industry clusters, and drive urban development. In other words, ports, industries, and cities will work more closely than ever for shared success.

Snapshot from the future: Broadband in the air, just as at home

Moving forward, broadband coverage will extend beyond the ground into the air and beyond. Broadband connections will be available to devices at various heights, such as drones less than 1 kilometer above the ground, aerial vehicles 10 kilometers above the ground, and low-orbit spacecraft hundreds of kilometers above the ground. The integrated network will consist of small cells covering hotspots within a radius of 100 meters, macro cells with a radius of 1 to 10 kilometers, and low-orbit satellites with coverage over a radius of 300 to 400 kilometers, providing users with unbroken access to broadband of up to 10 Gbit/s, 1 Gbit/s, and 100 Mbit/s, respectively.

New digital infrastructure is the engine of digital cities

Snapshot from the future: Intelligent and digital city upgrade underpinned by intelligent computing centers

As a key pillar of urban information infrastructure, intelligent computing centers are essential to satisfy the fast-growing demand for AI computing power in cities. They play an important role in promoting AI industrialization, industry intelligence, intelligent governance, and industry clusters. They are also key to driving comprehensive digital city transformation, optimizing industry structures, and improving the competitiveness of individual cities in the digital economy era.

Intelligent computing centers will support cross-industry collaboration by providing diversified computing power and integrated services. They will also consume less energy and make computing resources more affordable and accessible. Intelligent computing centers will also be connected to AI computing networks. Computing infrastructure, especially intelligent computing centers and computing networks, can effectively promote AI industrialization and industry intelligence, and serve as the foundation for intelligent cities that underpin the digital economy. Intelligent computing centers provide cost-effective, affordable, and secure computing resources that will make AI computing power as easily available to governments, enterprises, and the public as water, electricity, gas, and other urban public utilities.

Snapshot from the future: All-optical, 10-gigabit cities

Major cities around the world are now racing to release their own 10-gigabit action plans, with over 20 provinces and cities having already released one. Beijing has announced its Optical Network Capital, 10-Gigabit City Action Plan (2023–2025) while Shanghai has unveiled its Action Plan for Further Promoting New Infrastructure Construction (2023–2026) to connect the whole city with 10-gigabit 5G-A networks and 10-gigabit optical networks. Qinghai has also released Guidelines on Dual 10 Gigabit (5G-A & F5G-A) Industry Development and Application Innovation. Saudi Arabia has also unveiled The 10 Gbps Society white paper which echoes the Middle East's vision for 10-gigabit experience.

The future architecture of an all-optical city will consist of four parts:

All-optical access: All network connections will be optical, including home, building, enterprise, and 5G base station connections. All-optical transmission networks will be extended into edge environments like large enterprises, buildings, and 5G base stations. 10-gigabit access networks will enable digital transformation across industries, support access to cloud and computing networks within one millisecond, and drive F5G-A application in diverse scenarios as well as 5G adoption in business settings. By 2030, optical transport networks will cover all government agencies, financial institutions, key universities and scientific research institutions, large hospitals, large industrial enterprises, as well as development zones and industrial parks above the county level.
All-optical anchors: Connections originating in home broadband, enterprise broadband, 5G networks, and data centers will be routed and transmitted through all-optical networks. All-optical anchors will support multiple technologies, enable service-based traffic steering, and provide one-hop connections to cloud and computing networks. By 2030, every 10,000 people will have four all-optical OTN anchors, and 25% of these anchors will deliver 100G services.
All-optical bearing: Urban optical networks support one-hop access to services. All-optical cross-connect, optical-electrical hybrid automatically switched optical network (ASON), and other technologies will be used to build multi-layer optical networks that support one-hop access to services, highly reliable networking, high-speed inter-cloud transmission, and high synergy between optical and computing networks. More than 50% of data centers will be connected with each other through optical networks, with single-wavelength bandwidth higher than 400 Gbit/s. Mesh fiber networks can deliver 99.9999% reliability thanks to their ability to withstand two or more cut fibers, without services being interrupted. All data center networks will adopt a hybrid optical-electrical design that uses all-optical switching technologies to connect switches and routers within individual data centers.
Intelligent and automated O&M: Real-time sensing of network status with proactive, preventive O&M will support elastic network resources, and automated service provisioning, resource allocation, and O&M.

Smart government services make cities more human

Snapshot from the future: Proactive, precise, data-based government services

Machine recognition technology enables contactless services. Today, in most of China's developed provinces, residents no longer need to go to government service halls to access government services. Instead, they can now simply use their smartphones. Over the next decade, more and more intelligent digital government services will surface.

1. Digital identity authentication is expected to be widely adopted. The ID cards, drivers' licenses, social security cards, and bank cards that people carry at all times will be digitalized, creating a total addressable market for global electronic identity authentication services worth US$18 billion by 2027.

2. Digital credit will underpin and restructure many public service processes and the customer experience. It will be one of the founding technologies for digital government. Most residents are already familiar with some services like electronic library cards, social security cards, and car rental services that require a credit rating.

3. Universal access to one-stop, e-government services will soon be realized. In the future, all government services will be remotely accessible and government service halls may cease to exist.

Snapshot from the future: Urban data spaces that promote the circulation and unlock the value of data elements

Data has become the fifth key factor of production in addition to land, labor, capital, and technology. It has also become a new driver of the digital economy. Efforts should be made to accelerate data circulation and transactions, as this is crucial for unlocking the value and potential of data. By 2030, the global data transaction market is expected to reach US$301.1 billion, with China's market reaching CNY515.59 billion at a fast compound annual growth rate of about 20.3%.

Digital societies produce massive amounts of data and open up new spaces for urban data. The amount of data generated by cities is growing from terabytes to petabytes, and data is becoming the new oil for cities. Through urban data spaces and technologies, especially big data, blockchain, AI, privacy computing, and security and trustworthiness technologies, cities will be able to effectively process and analyze massive amounts of data, maximize the value of data elements, and use data to inform urban management and decision making. This means data has the potential to create greater economic and social value, and become a new driver of urban development.

Intelligent environmental protection for livable cities

Snapshot from the future: Automatic waste disposal for zero waste cities

With the help of AI, the entire waste management process in a future city, from collection and transportation to sorting and processing, will be automated and intelligent. Intelligent waste recycling bins, driverless garbage trucks, automated waste sorting robots, automated garbage recycling devices, and other innovative applications will emerge one after another. Hopefully, this will help to make more and more zero waste cities possible.

Snapshot from the future: Optical detection making water sources safer

Optical technologies can also be further integrated with analytics from the IoT, AI, and cloud computing. Sensors for water quality and deep data analytics will move us closer to 24/7, efficient, real-time, automated, intelligent water quality monitoring and enable faster warnings in cases of water contamination.

Snapshot from the future: Real-time AI air quality monitoring

Most cities will likely soon opt to deploy cost-effective and reliable air quality sensors and build new monitoring networks. This will allow them to monitor air quality and weather across the entire city and take targeted measures to improve air quality and the urban environment. One company has developed a highly-integrated, real-time air monitoring system that uses integrated sensors and software to monitor the concentrations of environmental pollutants in urban environments, such as PM2.5, PM10, CO, NOx, SOx, and O3, as well as other environmental parameters like noise, temperature, humidity, air pressure, rainfall, and flooding. The system's data is wirelessly transmitted in real time to a cloud platform that in turn provides a real-time visual dashboard for effective management of the overall environment in key areas of the city.

Bringing unmanned operations to manufacturing and services to make up for labor shortages

Snapshot from the future: Collaborative robots

Collaborative robots are a type of industrial robot. They were initially designed to meet the customized and flexible manufacturing requirements of small- and medium-sized enterprises, and perfectly align with the development trends of the manufacturing industry. Collaborative robots are suitable for jobs that people don't want to do, such as highly repetitive work like sorting and packaging. Collaborative robots have several unique advantages:

Safer: Collaborative robots are compact and intelligent, and their sophisticated sensors enable them to stop in an instant.

Faster and more flexible deployment: Traditional industrial robots require professionals to plan and program their movement paths and actions, so they take a long time to deploy and are very costly. In contrast, collaborative robots feature user-friendly programming, such as programming by demonstration, natural language processing, and visual guidance. They can be placed in new positions at any time, and programing and commissioning can be completed rapidly, so they can start working very quickly.

Lower total cost of ownership (TCO) and shorter payback period: The price and annual maintenance cost of collaborative robots are significantly lower than those of traditional industrial robots. The average selling price of collaborative robots has halved over the past several years.

Snapshot from the future: Autonomous mobile robots

Autonomous mobile robots (AMRs) are a key enabler to help the manufacturing industry become flexible and intelligent. They will reshape production, warehousing, and logistics processes.

On production lines, AMRs make automated and unmanned logistics possible. This includes unmanned execution; unmanned interaction between AMRs and other equipment for material collection, feeding, and unloading; and unmanned material handling.

In warehouses, AMRs implement goods-to-person picking and execute intelligent picking, movement, and stock-in and stock-out procedures. In this model, the control system receives an order and assigns an AMR, which then lifts the shelf containing the required goods, moves it to the operator console, and unloads the goods to complete the order. After picking is completed, the robot moves the shelf back to its original position.

The distribution and picking of materials are not confined to factory buildings; AMR systems can be expanded to an entire campus. For example, when goods are unloaded, robots can automatically move them into their designated warehouses. Goods will be automatically logged into and out of warehouses, and the movement of goods between factories or warehouses will be automatically registered. These functions require the robots to support outdoor autonomous navigation, using features such as laser navigation, visual navigation, and satellite positioning.

Snapshot from the future: Industrial humanoid robots

Humanoid robots are designed to have human-like forms and functions, including anthropomorphic limbs, mobility skills, sensory perception, and learning and cognition capabilities. They will likely become the most valuable carriers of ""embodied AI"". Combined with rapidly developing general artificial intelligence and AI foundation models, humanoid robots will enable machines to interoperate and interact with their environments in a more intuitive and intelligent manner, and perform a wide variety of complex tasks just like humans.

Industrial humanoid robots can flexibly carry out different operations, move agilely, and independently learn and make decisions. Unlike traditional industrial robots, humanoid robots can complete specific tasks without requiring advanced planning. They can autonomously perceive, understand, learn, and make decisions when completing production line tasks and are capable of powerful autonomous decision making, operations, and interactions. Humanoid robots can currently work in a number of positions in factories, including material handling, quality inspection, labeling, assembly, and high-risk operations.

Humanoid robots can also work around the clock without rest, meaning they can significantly improve both production output and product quality, solve the long-standing challenges caused by labor shortages, and usher in a new era of intelligent manufacturing.

Snapshot from the future: AI-powered adaptive teaching

Conventional education uses the same model to deliver the same course content to different students. AI can transform this industry by analyzing learning models and individual differences between students. This improves the quality of education and makes it possible to teach students in accordance with their aptitudes. For example, as technologies such as big data, cloud computing, Internet of Things (IoT), virtual reality (VR), and augmented reality (AR) evolve, AI-assisted education will break down learning and teaching behavior in a more granular way and build more robust and precise education models. VR and AR technologies can be used to present materials in a more engaging manner and deliver interactions that suit students' personal preferences, helping students better master their course content.

AI liberates teachers from the repetitive and tedious grading of exam papers and administration, allowing them to focus on the creative work of educational research and one-on-one communication with students. Supported by huge amounts of data generated through educational activities, AI will help teachers better understand the effectiveness of their teaching, and provide key recommendations on the most effective teaching methods and the best way to organize course content.

In schools, AI can be deployed anywhere, and can simulate the best teachers of any subject, bringing the highest-quality education and content to the most remote schools. AI-based education offers multi-channel engagement with students, including video and audio, which can help make up for the scarcity of teaching resources in some areas (for example, in understaffed schools, a teacher may have to teach four or even five different subjects). In this way, AI promotes educational equity.

New production models geared towards personalized needs

Snapshot from the future: ICT-powered flexible manufacturing

To respond to changing market conditions and set themselves apart in the face of fierce competition, companies must take the initiative and embrace new production models. That's why an increasing number of companies are looking to concepts like flexible manufacturing. Flexible manufacturing is an advanced production model characterized by on-demand production. It helps companies become more flexible and enables them to rapidly respond to ever-changing market demand. In addition, flexible manufacturing shortens the R&D cycle, cuts R&D costs, and ensures equipment is not left idle, all while reducing inventory risks and speeding up capital turnover. Therefore, it allows companies to seize market opportunities and grow sustainably. Flexible manufacturing involves the following areas:

Flexibility of product design and production line planning: After receiving an order for a new category of product, companies need to quickly conduct R&D and design, and rapidly adjust factors such as production line equipment, working procedures, processes, and batch size. This is where ICT comes in, as simulation, modeling, VR, and other ICT technologies can be used to simulate the entire new manufacturing process. This will reduce the cost of new product development and design, and support more accurate adjustment cost projections and capacity projections.

Flexibility of process: In flexible manufacturing, companies can design products based on the personalized needs of customers, or invite customers to directly participate in product design (e.g., using modular systems to enable customers to define what a product will ultimately look like). Both models require an intelligent scheduling system. Such a system will make automatic adjustments and provide an optimal production plan based on known features such as the factory's production capacity, order complexity, and delivery deadlines. After a company receives an order, the scheduling system will automatically identify all universal components, custom components, and procedures and materials required to manufacture these components. By coordinating production tasks and the provisioning of materials and tools, the scheduling system maximizes the productivity of all equipment and workers in the factory so that no component will create a bottleneck in order delivery.

Flexibility of equipment: As the number of customizations and small-batch orders increases, factories must be able to switch between production processes in real time. Conventional manufacturing equipment can generally only be reconfigured by trained engineers using specific programming devices and languages. This makes switchover processes time-consuming, and does not support the kind of rapid responsiveness that companies need. In the future, ICT technologies such as visual programming, natural language interaction, and action capture will help factories reprogram equipment quickly and easily. This will help promptly meet companies' demand for flexible manufacturing.

Flexibility of logistics: One of the keys to flexible manufacturing is modularization, through which a large number of finished components are manufactured. This requires automated ICT technology to effectively manage warehousing and logistics, which prevents omissions and other errors in the shipment process. Take furniture producers as an example. With large-scale customization, every board, decorative strip, and handle may need its own identification code or radio frequency identification (RFID) tag to facilitate automated packing and loading, and to support traceability throughout the whole transportation and distribution process.

Resilient and intelligent supply chains that help enterprises respond to crises

Snapshot from the future: Supply chain visualization powered by digital technologies

Supply chain visualization is about using ICT technology to collect, transmit, store, and analyze upstream and downstream orders, logistics, inventories, and other relevant supply-chain information, and graphically display such information. Such visualization can effectively improve the transparency and controllability of the whole supply chain and thus greatly reduce supply chain risks.

Supply chain visualization supports the tracking of materials and equipment in upstream activities. Logistics information is displayed in real time.

With supply chain visualization, the operation data of various transportation vehicles in the logistics system is also available, and the status of these vehicles can be displayed in real time. Global Positioning System (GPS), AI, 5G-A, IoT, and other technologies are used to monitor the transportation process and the status of goods while in transit. There is also a visualized scheduling center that enables the consolidation or splitting of orders at any time, and the optimization of transportation resources and routes. This enables companies to detect and rapidly respond to any logistics emergency by promptly adjusting logistics routes to ensure the timely and safe delivery of goods.

A remote monitoring system monitors the environment in warehouses in real time. This system uses various sensors to graphically display operations and maintenance (O&M) information such as temperature, humidity, dust, and smoke. This allows the timely detection of any signs of fire or water leakage, enabling prompt intervention and preventing material losses. Goods can also be tracked in real time as they are logged into and out of warehouses. As goods are moved, IoT, RFID, and QR code technologies are used to automatically identify and register goods, and the warehousing status data of goods can be accessed remotely in real time.

Snapshot from the future: From supply chain to supply network

In the traditional supply chain model, each link in the chain depends on the previous link delivering as expected. Each link could be a bottleneck that prevents the normal flow of goods down the chain. For example, if the supply of an upstream raw material provider is disrupted, downstream manufacturers will definitely be affected, resulting in inefficient operations or even a standstill for the entire supply chain. With the adoption of ICT technologies such as cloud computing, IoT, big data, and AI, the supply chain will transform into a supply network. In this network, the upstream materials required by every link have multiple alternative sources, and they can be sourced through multiple routes. A multi-contact collaborative supply ecosystem will be created by enhancing the internal and external interconnectivity of enterprises. The failure of any single link will not paralyze the entire supply network.

The solar PV and energy storage industries will develop rapidly, expanding from a few countries to the entire world.

Snapshot from the future: Accelerated development in utility-scale, commercial & industrial (C&I), and residential scenarios

Utility-scale power plants achieve economies of scale, reduce unit energy costs, and improve energy utilization through centralized management and optimized energy configuration. Power plants that feature a synergy of wind, solar, hydro, thermal power, storage, and hydrogen are attracting increasing attention.

Technological advances have reduced the levelized cost of electricity (LCOE) for PV power by more than 90%, enabling PV power to achieve grid parity in most regions. The return on investment (ROI) for C&I and residential PV scenarios has been rapidly increasing. Consequently, all-scenario commercialization is becoming the mainstream business model.

Renewable electricity generation: Floating power plants

Snapshot from the future: Offshore wind and floating PV (FPV) are promising energy sources for the future

Offshore electricity generation can solve challenges that onshore projects confront, such as land shortages, distances from electrical load centers, reduced efficiency of solar PV systems under high temperatures, and biodiversity loss.

On average, wind speeds 10 km offshore are 25% faster than those at the shoreline. Offshore wind turbines can generate power for 3,000 hours annually, compared to 2,000 hours for onshore turbines. By 2030, offshore wind turbines are expected to have rotor diameters of 230–250 meters and capacities of 15–20 MW, which is 3 to 4 times greater than onshore turbines. Floating turbines can be installed in waters up to 60 meters deep. New high-voltage direct current (HVDC) technology offers a cost-effective solution for transmission over distances of 80–150 kilometers from the coast. The Global Wind Energy Council (GWEC) forecasts that global offshore wind capacity will increase from 75 GW today to 275 GW by 2030, with annual installations growing at 25% per year over the next five years.

Compared to land-based PV (LBPV) systems, FPV systems that are installed on water save land. The absence of obstacles on the water surface reduces shading loss and dust buildup. Additionally, the natural cooling effect of the water and higher offshore wind speeds can enhance PV performance. Studies show that FPV systems perform about 12.96% better annually than LBPV systems. The global FPV market capacity is projected to exceed 60 GW by 2030, with an estimated potential capacity of 400 GW worldwide.

The future energy world will be centered on electricity, and green hydrogen is emerging as a big player.

Snapshot from the future: The adoption of electricity will accelerate across industries, diverse energy storage technologies will evolve, and green hydrogen will see more extensive application

Sectors like industry and transportation are the main sources of carbon emissions through energy consumption. To reduce emissions, priority should be given to green transformation in traditional industrial sectors by promoting green electricity and electric manufacturing. We should focus on optimizing transportation structures, promoting green mobility, and constructing more renewable energy infrastructure. Additionally, applying technologies such as smart grids, 5G, and AI will help reduce carbon emissions and contribute to the development of green, low-carbon cities.

Besides, energy storage systems (ESSs) can store electric energy during off-peak hours and discharge that energy during peak hours for peak shaving and load balancing, thus improving the operating efficiency and reliability of power grids while cutting power system investment. Various new energy storage technologies, such as compressed-air energy storage, electrochemical energy storage, and thermal (cold) energy storage, will coexist to meet system regulation requirements.

New technologies and business models, such as hydrogen metallurgy, hydrogen production from renewables, ammonia/methanol synthesis by green hydrogen, and hydrogen-based power generation, will all be widely promoted. Electricity will interact with secondary energy sources like hydrogen through electricity-to-hydrogen conversion and electric fuel production, helping build a multi-energy complementary system that interconnects multiple energy sources with electric energy. In fields such as metallurgy, chemical industry, transportation, and power generation, hydrogen, as a reacting substance or raw material, will become essential to clean electricity.

Intelligent generation-grid-load-storage-consumption through the Energy Internet

Snapshot from the future: Virtual power plants, a paradigm shift for the power value chain

The emergence of virtual power plants (VPPs) is redrawing the boundaries between power producers and power consumers. VPPs are set to reshape the power generation value chain.

VPPs will leverage economies of scale to realize the commercial model that distributed energy producers cannot achieve alone. In order to participate in the future energy market and generate profits, distributed energy producers should be able to sense market prices in real time. Distributed new energy devices will need to respond to market changes and power grid fluctuations in real time. This requires ICT infrastructure such as interconnected networks and edge gateways or edge computing. Producers will incur the kinds of transaction costs that come with being part of the market, such as insurance and compliance costs. These additional costs represent a barrier to market entry for distributed energy producers, but by aggregating the large number of distributed energy sources, VPPs reduce costs and generate profits through economies of scale.

Energy cloud can be understood as the operating system of the energy Internet, and is typically characterized by convergence, openness, and intelligence.

Generation, grids, storage, and consumption of power need to be converged in an end-to-end manner. Generators now include a large number of distributed new energy sources, such as solar energy, wind energy, and biomass, as well as fossil fuel sources such as gas.

In addition, the energy cloud needs to interconnect with third-party systems, such as carbon trading systems. Therefore, the energy cloud should be an open ecosystem.

To enable convergence and openness, the energy cloud must be an intelligent platform. With the support of efficient, intelligent technologies such as AI and big data, the energy cloud aims to enable a frictionless flow of energy from producers to consumers as they demand it. Ultimately, it will create a green, low-carbon, safe, stable, and diverse energy system.

As generation-grid-storage-load synergy accelerates and deepens, the boundaries of the traditional value chain will be broken, and power systems will no longer adjust electricity generation simply based on plans and loads. Instead, electricity supply and demand will become more flexible and dynamic. To drive the digital and intelligent transformation of the new power system, next-generation technologies will be vigorously developed and extensively applied in areas such as digital edges, ubiquitous communications networks, compute and storage, and AI algorithms and applications. The physical world and digital space will be fully connected, device information and production processes in power systems will be converted into digital expressions, and digital mirrors of power systems will be built within the virtual space.

Efficient power use for ICT: Saving energy and cutting more emissions

Snapshot from the future: Low-carbon data centers and sites

The ICT industry needs to cut emissions and save energy while enabling other industries to reduce carbon emissions. Data centers and telecom networks are the primary sources of carbon emissions in the ICT industry.

Data centers reduce carbon emissions by purchasing green power and applying free cooling and AI. Large ICT companies have been the biggest purchasers of green power, as they strive to reduce carbon emissions in data centers and telecoms networks.

As an increasing number of high-temperature-proof servers are put into use, cooling using natural air instead of traditional chillers and air conditioners will become possible. This will reduce the energy consumption of cooling systems, thereby decreasing the PUE.

In addition to applying renewable energy and free cooling, AI is another effective way to make data centers more efficient and save energy. Sensors in data centers collect data such as temperature, power levels, pump speed, power consumption rate, and settings, which are analyzed using AI. Then, the data center operations and control thresholds are adjusted accordingly, reducing costs and increasing efficiency.

The green telecoms networks of the future will be built to support more than 100 times today's capacity, but their total energy consumption will be no higher than that of today's networks. Conventional telecoms networks are defined by their specialist functions, which makes for fragmented operation and maintenance (O&M) and means they cannot keep pace with the latest network automation and intelligence. Networks need to be reconstructed to deliver essential services through a simplified architecture that consists of three layers: basic telecoms network, cloud network, and algorithms. This simplified network architecture will greatly reduce the complexity of the algorithms in autonomous driving networks, reducing the demand for computing, and cut O&M costs, contributing to greener, low-carbon networks.

ICT enables digital trust
Digital rules redefine digital trust

ICT enables digital trust

Snapshot from the future: Intelligent agents with digital identities and trusted identifiers

The explosive development of large language models (LLMs) has increased AI penetration in all sectors. This has led to various AI-based intelligent agents emerging in both the digital and physical worlds, in turn increasing productivity, facilitating creativity, and turbocharging economic development. Digital identities build trust between people and intelligent agents so that they can engage and collaborate in new ways.

Snapshot from the future: AI security to ensure trustworthiness in the digital life

AI systems are increasingly integral to various sectors, and so when they are attacked, the impact can be huge. In the future, AI systems will increasingly need to support AIGC provenance and verification. Meanwhile, stringent technical requirements must be put in place to ensure the security of AI systems and models, as well as the trustworthiness of AI input and output data. This can help mitigate the risk of AI system misuse and safeguard the value generated by AI.

Snapshot from the future: Digital watermarking to support information provenance

In the digital world, data will be the most important asset, positioning it at greater risk of theft and misappropriation. The unprecedented capabilities provided by AIGC and similar technologies, like natural language processing and image generation, are revolutionizing the industry. If the data assets or generated data is illegally embezzled or misused, the potential harm to individuals and companies will be huge. Digital watermarking–based provenance technology is an effective method for addressing this problem.

Snapshot from the future: PEC technologies that improve computing security

PEC technologies are data security technologies used to protect and enhance privacy and security during the collection, storage, search, and analysis of private information. PEC supports efficient, high-quality services by protecting personal data from abuse, while allowing effective use of the data. This allows us to realize the data's full business, scientific, and social value.

Snapshot from the future: Quantum security that ensures trustworthiness in the digital life

Quantum computing is maturing at a tremendous pace, and is expected to be capable of breaking traditional security algorithms by 2030. Therefore, we have no choice but to transition to PQC and quantum key distribution (QKD), which are resistant to quantum computing attacks.

Rules redefine digital trust

Snapshot from the future: Unified rules that enhance data protection and mitigate data monopoly

Digital trust involves many organizations and stakeholders. Notably, in the realm of personal information protection, the EU has enacted the General Data Protection Regulation (GDPR), while other countries and regions have subsequently adopted similar laws, such as China's Personal Information Protection Law. Combating data monopoly is the only way to prevent large platforms from illegally obtaining, abusing, and trading personal privacy data. This will bolster digital security, ensure fair competition, and foster a robust digital credit ecosystem.

Snapshot from the future: Impartial standards that drive healthy ICT industry development

Governments and industry organizations must establish unified cyber security standards that are technology-neutral and equally applicable to all enterprises and all ICT products. These standards will ensure that ICT products can be independently and comprehensively verified and evaluated. As a result, organizations will be able to select products that meet their specific security requirements based on the verification and evaluation results, and foster healthy development within the ICT industry.

Snapshot from the future: A cyber security and privacy assurance system that boosts digital trust

One of Huawei's key strategies is to build and implement an end-to-end global cyber security and privacy assurance system. In compliance with applicable laws and regulations in countries where we operate and international standards, we are continuing to invest in an effective, sustainable, and reliable cyber security and privacy assurance system that addresses the requirements of both regulators and customers, as well as industry best practices. Further still, we actively engage with governments, customers, and industry partners to address cyber security and privacy challenges.