Tools & Platforms
AI key driver for services growth
Rapidly evolving artificial intelligence is playing an increasingly vital role in fostering new growth drivers and injecting strong momentum into China”s consumer market, while reshaping the global trade landscape in the digital economy era, said experts and company executives.
Speaking during the 2025 e-commerce convention sub-forum of the ongoing 2025 China International Fair for Trade in Services in Beijing, Liu Yanfang, executive director of the research institute of the China International Electronic Commerce Center, emphasized the importance of AI in driving the expansion and upgrading of online services consumption.
Liu said the recently unveiled guideline by the State Council, China’s Cabinet, on deeply implementing the “AI Plus” initiative, stressed efforts to promote the application of AI in consumption and expand new consumption scenarios, which provide guidance and create an enabling environment for e-commerce enterprises.
“The digitalization of services consumption is accelerating and becoming a new driving force for expanding domestic demand. Digital consumption — especially online services consumption — has demonstrated strong dynamism and immense growth potential,” Liu said, adding that the use of new technologies represented by AI and big data has enriched consumption scenarios and injected new vitality into services consumption.
She said AI not only gives rise to new service formats such as digital humans, virtual hosts and intelligent guidance, but also bolsters the transformation and upgrading of the traditional services industry and optimizes consumption structure.
“Looking ahead, the integration of AI with online services consumption will be deeper.”
China’s e-commerce sector witnessed steady growth in the first seven months, with online sales showing significant growth. The National Bureau of Statistics said the country’s online retail sales climbed 9.2 percent year-on-year during the period.
Alibaba Group’s business-to-business online wholesale trading platform 1688 has launched AI-powered tools to help consumers search and select products more efficiently, and improve overall operational efficiency for merchants. Currently, the platform has integrated Alibaba’s Qwen AI models and Chinese AI startup DeepSeek’s models into its systems.
It has updated AI digital employees, who take charge of releasing product information, formulating marketing activities, helping analyze market trends and providing intelligent and customized solutions.
Han Xi, vice-president of the public affairs department at Alibaba, underscored that AI is crucial for providing strong support for innovation and entrepreneurship, and dramatically boosting labor efficiency. “This technology is penetrating into all parts of e-commerce at a faster pace, including research and development, supply chain optimization, marketing and operations.”
Meanwhile, Alibaba’s Taobao and Tmall platforms, released the “China Online Consumption Brand Index” during the 2025 CIFTIS, highlighting that Chinese consumers attach great importance to product quality and brand reputation when purchasing items.
The China Federation of Logistics and Purchasing said its index tracking the e-commerce logistics market reached 112.3 in August, the highest level so far this year, indicating Chinese consumers’ rising willingness to open their wallets.
Ji Yang, an associate professor of Sun Yat-sen University in Guangzhou, Guangdong province, said the expansion of China’s middle-income group has laid a solid foundation for consumption upgrades, and consumers from the biggest cities have exhibited robust purchasing potential for high-quality products.
Qiu Sheng, vice-president of Amazon China, said AI is becoming an accelerator for product innovation as sellers can use AI to capture consumption trends in a timely manner and better understand user needs, adding that his company is helping Chinese cross-border sellers expand their overseas presence through leveraging AI digital tools.
Qiu said the cutting-edge digital technology can help merchants make business decisions more scientifically based on data analysis and trend predictions, and lower operational costs, while significantly improving resource allocation efficiency in fields like intelligent inventory management, logistics and delivery.
Hong Yong, an associate research fellow at the Chinese Academy of International Trade and Economic Cooperation, said the new type of consumption is mainly driven by technological advancements in AI, with a focus on consumers’ personalized and diversified needs.
Nurturing an AI-driven new consumption model is pivotal to expanding domestic demand, driving industrial upgrades and promoting high-quality economic growth, Hong said.
fanfeifei@chinadaily.com.cn
In the heart of San Antonio, a quiet revolution in wellness technology is unfolding at Float Wellness Spa on Fredericksburg Road. The spa has become the first in the city to introduce the Aescape AI-powered robot massager, a device that promises to blend cutting-edge artificial intelligence with the ancient art of massage therapy. Customers lie face-down on a specialized table, where robotic arms equipped with sensors scan their bodies to deliver personalized treatments, adjusting pressure and techniques in real time based on individual anatomy and preferences.
This innovation arrives amid a broader surge in AI applications within the health and wellness sector, where automation is increasingly tackling labor shortages and consistency issues in human-delivered services. According to a recent feature by Texas Public Radio, the Aescape system at Float Wellness Spa uses advanced algorithms to map muscle tension and provide targeted relief, marking a significant step for Texas in adopting such tech.
Technological Backbone and Operational Mechanics
At its core, the Aescape robot employs a combination of 3D body scanning, machine learning, and haptic feedback to simulate professional massage techniques. Users select from various programs via a touchscreen interface, and the system adapts on the fly, much like a therapist responding to subtle cues. This isn’t mere gimmickry; it’s backed by years of development, with the company raising substantial funds to refine its precision.
In a March 2025 report from Bloomberg, Aescape secured $83 million in funding from investors including Valor Equity Partners and NBA star Kevin Love, underscoring investor confidence in robotic wellness solutions. The technology draws from earlier prototypes showcased at events like CES 2024, where similar AI-driven massage robots demonstrated personalized adaptations to user needs.
Market Expansion and Local Adoption in San Antonio
The rollout in San Antonio follows successful debuts in cities like Los Angeles, as detailed in a December 2024 piece by the Los Angeles Times, which described the experience as precise yet impersonal. At Float Wellness Spa, appointments are now bookable, with sessions priced competitively to attract a mix of tech enthusiasts and those seeking convenient relief from daily stresses.
Posts on X, formerly Twitter, reflect growing public intrigue, with users like tech influencer Mario Nawfal highlighting the robot’s eight axes of motion for deep-tissue work without the awkwardness of human interaction. This sentiment aligns with San Antonio’s burgeoning tech scene, where AI innovations are intersecting with local industries, as noted in recent updates from the San Antonio Express-News.
User Experiences and Industry Implications
Early adopters in San Antonio report a mix of awe and adjustment. One reviewer in a Popular Science article from March 2024 praised the Aescape for its customized convenience, likening it to “the world’s most advanced massage” powered by AI that learns from each session. However, some note the absence of human warmth, a point echoed in an Audacy video report from August 2025, which captured the robot’s debut turning heads in the city.
For industry insiders, this represents a pivot toward scalable wellness tech. With labor costs rising and therapist shortages persistent, robots like Aescape could redefine spa economics, potentially expanding to chains like Equinox. Yet, challenges remain, including regulatory hurdles for AI in healthcare-adjacent fields and ensuring data privacy for body scans.
Future Prospects and Competitive Dynamics
Looking ahead, Aescape’s expansion signals broader trends in robotic automation. A Yahoo Finance piece from August 2025 introduced a competing system, RoboSculptor, which also leverages AI for massage, hinting at an emerging market rivalry. In San Antonio, this could spur further innovation, with local startups like those covered in Nucamp’s tech news roundup exploring AI tools in customer service and beyond.
As AI integrates deeper into personal care, ethical questions arise—will robots supplant human jobs, or augment them? For now, Float Wellness Spa’s offering provides a tangible glimpse into this future, blending Silicon Valley ingenuity with Texas hospitality. Industry watchers will be keen to monitor adoption rates, as success here could accelerate nationwide rollout, transforming how we unwind in an increasingly automated world.
From the outside, wildfire smoke may look like a drifting gray cloud. But for scientists, these plumes are dynamic, complex, and potentially dangerous. They can stretch for hundreds of miles, impacting air quality, visibility, and public health. Until now, capturing accurate data on how these smoke particles move and behave has been one of the most difficult tasks in atmospheric science.
Researchers at the University of Minnesota Twin Cities have developed a groundbreaking way to observe and analyze wildfire smoke: a swarm of AI-powered aerial robots that can detect, track, and build 3D models of smoke plumes.
Unlike traditional drones, these small flying machines work as a team. They recognize smoke, fly directly into it, and take high-resolution images from multiple angles. Their mission is to help us better understand how smoke travels—an understanding that could reshape how we predict air pollution and respond to environmental hazards.
This new study, published in the peer-reviewed journal Science of the Total Environment, opens doors to more accurate fire behavior models and better air quality predictions, not just for wildfires, but also for prescribed burns, volcanic eruptions, sandstorms, and other particle-driven events.
A Growing Crisis Meets High-Tech Tools
Between 2012 and 2021, about 50,000 prescribed burns were carried out in the United States—intentional fires set under controlled conditions to improve forest health and reduce wildfire risk. But even controlled burns carry risk. According to a 2024 report by the Associated Press, 43 of these burns spiraled out of control and became wildfires.
These numbers, while small in percentage, matter deeply. That’s because smoke particles, especially the small ones, can stay in the air for days and travel far from their source. “A key step is understanding the composition of smoke particles and how they disperse,” explained Jiarong Hong, professor of mechanical engineering at the University of Minnesota and senior author of the study. “Smaller particles can travel farther and stay suspended longer, impacting regions far from the original fire.”
Understanding how these plumes evolve over time is essential for early hazard detection, public health responses, and emergency planning. Yet, traditional tools for studying smoke—like satellites, remote sensing, and Lidar—fall short. These tools lack the detail and flexibility needed to capture fast-changing flows of smoke, especially in rough terrain or remote regions.
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That’s where the new drone swarm steps in. These AI-enabled robots are designed to adapt to the smoke’s size and shape. They gather rich data in real time—something existing technologies can’t do affordably or efficiently.
The Science Behind the Swarm
The team’s drone system includes one manager drone and four worker drones. These drones aren’t just fancy flying cameras—they’re mini laboratories in the sky.
Each drone carries a 12-megapixel camera mounted on a three-axis gimbal for capturing smoke in motion. They are powered by long-lasting 6000 mAh batteries and guided by advanced flight controllers and NVIDIA Jetson processors. These processors allow the drones to recognize smoke in real time, adjust their paths, and capture the best angles for imaging.
When launched, the drones work together to fly around a smoke plume, snapping high-resolution images from multiple directions. These images are then grouped by time intervals and fed into a computer model using something called a Neural Radiance Field (NeRF). This advanced AI model helps turn 2D images into a realistic, detailed 3D reconstruction of the smoke plume.
This step is key. With the 3D model, researchers can analyze the shape, direction, and flow of the smoke over time. It gives them crucial data like volume, angle of movement, and dispersion speed—all critical for improving fire and smoke simulation tools.
Other cutting-edge AI techniques were considered, including Dynamic NeRF (D-NeRF) and RoDynRF, which are good at modeling motion. But these systems struggle with featureless subjects like smoke and require long training times. The drone swarm approach avoids those problems by directly capturing the data in the field.
“This approach allows for high-resolution data collection across large areas—at a lower cost than satellite-based tools,” said Nikil Nrishnakumar, the study’s first author and a graduate researcher at the Minnesota Robotics Institute.
From Research to Real-World Impact
The drone swarm has already been tested in field deployments and has shown promising results. With this system, the team can generate multiple 3D reconstructions over time, creating a time-lapse view of how a smoke plume changes in real-time. It’s like watching the plume evolve in 3D—a powerful tool for scientists and emergency responders.
But the benefits of this technology reach far beyond wildfire science.
“Early identification is key,” Hong emphasized. “The sooner you can see the fire, the faster you can respond.”
The drones could be used in other dangerous scenarios as well, including volcanic eruptions, dust storms, and even urban pollution events. Because the system is modular and cost-effective, it can be scaled up or down based on the size of the area being studied. This flexibility makes it a strong candidate for use by government agencies, environmental researchers, and emergency crews.
The next steps for the team involve making the system more autonomous and scalable. They’re now integrating fixed-wing drones with Vertical Takeoff and Landing (VTOL) capability. These new drones can fly longer distances—over an hour at a time—and don’t need a runway to take off. That opens the door to monitoring vast forests and hard-to-reach locations.
In addition, the team plans to explore Digital Inline Holography to improve particle characterization. This method could provide even deeper insights into what types of particles are present in a smoke plume and how they interact with the environment.
“We’re not just building tools,” Nrishnakumar said. “We’re laying the groundwork for smarter, faster, and safer responses to environmental hazards.”
A New Era of Smoke Science
Many modern simulation tools like FIRETEC and QUIC-Fire already exist to model how fires spread and how smoke particles behave. These systems use complex inputs—everything from fuel type and moisture to wind speed and topography. But even the best models have one major limitation: they need real-world data to validate their predictions.
That’s why the drone swarm matters so much. It provides the missing piece—real, time-sensitive, high-resolution data that can make these simulations more accurate and useful.
Until now, simulation models have struggled to work in areas without detailed 3D maps of vegetation and terrain. They also haven’t been able to compare their predictions with real-world smoke movement, especially in complex or fast-changing environments. The drone swarm changes that by creating accurate 3D ground truth models that can be used for comparison and refinement.
As the climate warms and wildfire risks rise, these tools may become vital to protecting both ecosystems and human health. With more than 40% of the U.S. population living in areas prone to wildfire smoke, this research couldn’t come at a better time.
This project was supported by the National Science Foundation’s Major Research Instrumentation program and conducted with the help of the St. Anthony Falls Laboratory. Along with Hong and Nrishnakumar, the research team included Shashank Sharma and Srijan Kumar Pal, also from the Minnesota Robotics Institute.
In a bold move to assert its presence in the global artificial intelligence arena, the United Arab Emirates has unveiled K2 Think, an open-source AI model designed to challenge heavyweights like China’s DeepSeek and OpenAI’s offerings. Developed by the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) in collaboration with the tech firm G42, this model emerges from Abu Dhabi’s Institute of Foundation Models. With just 2.5 billion parameters, K2 Think punches above its weight, delivering reasoning capabilities that rival much larger systems, according to benchmarks cited in recent reports.
The launch, announced earlier this month, underscores the UAE’s strategic pivot away from oil dependency toward tech innovation. Researchers at MBZUAI claim K2 Think achieves competitive scores in key areas such as mathematical reasoning and code generation, often matching or exceeding models like DeepSeek’s V3.1, which has been hailed for its efficiency on Chinese hardware. This development comes amid intensifying competition, where nations vie for AI supremacy through accessible, cost-effective tools.
A Compact Powerhouse in AI Reasoning
What sets K2 Think apart is its emphasis on efficiency. Unlike resource-intensive models from U.S. giants, this Emirati creation runs on modest hardware, making it ideal for deployment in resource-constrained environments. As detailed in a CNBC article published on September 9, the model was trained using a novel approach that optimizes for speed and sustainability, potentially reducing energy costs by up to 70% compared to peers.
Industry experts note that K2 Think’s open-source nature democratizes access, allowing developers worldwide to fine-tune it for specific applications. This contrasts with proprietary systems like OpenAI’s o1-mini, which, while advanced, remain locked behind paywalls. Posts on X, formerly Twitter, from tech influencers have buzzed with excitement, highlighting how the UAE’s entry could accelerate innovation in regions underserved by Western tech.
Strategic Implications for Global AI Dynamics
The UAE’s foray into open-source AI isn’t isolated; it’s part of a broader ecosystem bolstered by investments from Microsoft-backed G42. A report from The National on September 9 emphasizes that K2 Think signals the country’s readiness to compete in a field dominated by the U.S. and China. DeepSeek, for instance, recently announced plans for an AI agent by year’s end, as per a Bloomberg piece dated September 4, intensifying the race.
For industry insiders, the real intrigue lies in K2 Think’s potential to foster AI sovereignty. By releasing the model under an open license, the UAE invites collaboration, potentially sparking a wave of localized adaptations. This mirrors China’s strategy with DeepSeek, which optimized for domestic chips and undercut costs, as noted in a Fortune analysis from August 21.
Challenges and Future Prospects
Yet, challenges remain. Critics point out that while K2 Think excels in reasoning tasks, it may lag in creative or multimodal capabilities compared to larger models. A Slashdot discussion from September 13 highlights community debates on its scalability, with some users questioning long-term support.
Looking ahead, the UAE’s investment in AI education and infrastructure, including MBZUAI’s programs, positions it for sustained growth. As Euronews reported on September 10, this model could redefine low-cost AI, encouraging a multipolar tech world where emerging players like the UAE challenge established powers.
Economic Diversification Through Tech Innovation
Economically, K2 Think aligns with the UAE’s Vision 2031, aiming to build a knowledge-based economy. Partnerships with global firms ensure technology transfer, while open-sourcing mitigates risks of over-reliance on foreign AI. X posts from AI enthusiasts, such as those praising DeepSeek’s cost efficiencies, underscore a sentiment that the UAE’s model could similarly disrupt markets.
In essence, K2 Think represents more than a technical achievement; it’s a geopolitical statement. As nations like China advance with models like DeepSeek’s upcoming agent, per recent Bloomberg insights, the UAE’s agile approach may inspire others to follow suit, fostering a more inclusive AI future.
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