GBase Tech Series: A Data Management Expert's Perspective on the World AI Conference

The 3rd World Intelligence Congress in Tianjin lasted four days, featuring numerous exciting AI-themed keynote speeches running concurrently across multiple halls. These presentations represent the pinnacle of AI technology, its development trends, and its flourishing applications across industries. GBase, as a data management expert, participated deeply throughout the event and offers the following interpretation of key trends emerging from this congress:

1. AI Technology Thrives and Blossoms Across Industries
 

This year, AI is being integrated into industries beyond the mature Internet and finance sectors, with significant progress in smart transportation, smart education, smart healthcare, and other fields closely tied to daily life. Moreover, in Industry 4.0 and smart agriculture—sectors that underpin social productivity—intelligent technology has shone. For example, in smart manufacturing, AI is deeply embedded across the entire production and sales chain. In agriculture, smart technology has boosted crop yields and food safety.

2. From Single AI Systems to Multi-System Collaborative Intelligence
 

In many industry applications, a standalone AI technology can no longer adequately solve problems. Instead, a comprehensive, multi-dimensional combination of artificial technologies—forming holistic solutions—greatly improves effectiveness. For instance, in autonomous driving, the vehicle’s own intelligent perception and cognition, combined with the intelligence of road infrastructure such as traffic lights, and assisted by GPS geographic information systems, enables safer and more reliable driving decisions. In fraud prevention, cross-industry data analysis involving business registrations, taxation, courts, and banks has become a consensus approach.

3. Deep Business Understanding Often Simplifies Technical Implementation
 

As AI technology matures, the key to solving business problems is no longer the AI technology itself. More important is a precise, in-depth grasp of domain knowledge. Accurate domain knowledge determines which data to use and which features to extract to address industry challenges. Once domain insights and patterns are captured, simple machine learning algorithms can often achieve most analytical goals.

4. Personalization Is a Shared Demand Across Virtually Every Industry
 

Across various industries’ intelligent analysis cases, diverse personalized solutions can be seen: in smart healthcare, tailored treatment and prevention plans for individual patients; in smart education, customized testing and teaching for each student; in smart agriculture, different cultivation, sowing, and fertilizing approaches for distinct soils and crops; in smart retail, differentiated supply and pricing for different products. These personalized solutions represent the mainstream of industry applications and are key to enhancing effectiveness. Personalization is typically implemented through classical profiling and prediction techniques—the core competency of any AI company or team.

5. Office Automation Is Ubiquitous Across All Industries
 

Today, AI not only boosts a company’s external value and the competitiveness of its products and services, but also plays a major role in improving work efficiency and saving labor. From milking robots in smart agriculture, to manufacturing robots in smart industry, to robot teachers in smart education—AI-powered robots are driving a labor revolution. Office automation also manifests in daily tasks such as automatic document classification, literature translation, and report generation. As a supporting tool, office automation greatly enhances worker productivity.

6. Cloud Is the Ultimate Solution for Enabling Cross-Industry Analytics
 

As more data and applications are deployed in the cloud, an increasing number of intelligent business operations are also unfolding there. In the future, data will reside in two places: endpoints and the cloud. Endpoints refer to intelligent terminals, the sources of various data. The cloud is where data is consumed; data from endpoints ultimately converges in the cloud for diverse multi-dimensional analysis.
 

In the past, many industry business scenarios could not be analyzed thoroughly, in large part due to data silos and the need for cross-domain data. The cloud ecosystem offers two key enablers to solve the data island problem: technically, network and storage resources ensure free and efficient cross-industry data exchange; commercially, monitoring and management components support standard service agreements between different enterprises.

7. Artificial Intelligence Draws Ever Closer to Human Intelligence Itself
 

From day one, robots have been imitating and learning from humans, a trend that will only advance. Early-stage robots primarily imitate human behavior, mid-level robots can learn human knowledge, and advanced robots can learn human methods. This leap from behavior to knowledge to methods gives rise to powerful AI that increasingly resembles humans. Human-like AI is expected to reach average human levels in certain specialized domains in the future, and in some fields may approach top human performance, thus guiding ordinary people to learn and improve.