2026 Latest AI Research Analysis, DEEPSEEK: What Problems Can AI Solve for Humans? What Is the Future Development Direction? What Is the Ultimate Goal of AGI Mentioned by Liang Wenfeng?
Based on the latest industry developments in 2026, the questions you care about can be understood from **three perspectives: current real-world applications, future technology trends, and Liang Wenfeng's vision for AGI**.
🤖 What problems can AI solve now? — From "Chatting" to "Getting Things Done"
AI's role is undergoing a fundamental transformation, evolving from a "Q&A consultant" to an "action assistant" capable of handling tasks directly. According to Stanford's "2026 AI Index Report," AI has approached or surpassed human baselines in benchmark tests such as multimodal reasoning and scientific discovery, with the penetration rate of generative AI reaching 53% globally in just three years.
Specifically, AI has been solving real-world problems across multiple fields:
* 🏥 Healthcare: Cutting-edge applications such as AI-assisted tumor diagnosis and treatment research, digital lung reconstruction, and computational pathology are being deployed. In retail pharmacies, robots equipped with general-purpose brains can autonomously receive orders, identify medications, and sort them.
* 🏭 Industrial Production: Embodied intelligence (robots) is moving beyond laboratory demonstrations and entering real-world industrial and service scenarios. For example, patrol robots can provide 24/7 autonomous inspection and risk alerts, allowing a single person to manage the operational safety of an entire campus.
* 💼 Office and Government Affairs: Agents are beginning to handle complex tasks. For example, government affairs agents have moved from "answering questions" to "handling matters," rapidly completing the integrated analysis of large volumes of legal and policy documents.
* 🌾 Meteorology and Agriculture: The China Meteorological Administration's intelligent early warning system "Mazu (MAZU)" has been delivered to Djibouti, helping precisely predict extreme weather. At the same time, AI can also provide customized weather impact forecasts for industries such as health, transportation, and agriculture.
* 🔬 Scientific Research: AI has begun designing proteins, modeling atmospheric changes, and achieving hourly iteration of aerospace vehicle schemes.
However, AI capabilities exhibit a "jagged frontier" characteristic: it may excel at solving Olympiad math problems, yet its accuracy in reading analog clocks is only 50.6%. At the same time, scaled deployment still faces challenges such as security and regulation, with nearly 40% of enterprises reporting Agent project failures due to security risk control issues.
🧭 What is the future direction of AI?
Synthesizing trend reports from the 2026 World Artificial Intelligence Conference (WAIC) and the Beijing Academy of Artificial Intelligence (BAAI), the future direction can be summarized as follows:
1. From the digital world to the physical world (Embodied Intelligence): This is the most central trend. AI is shifting from understanding language to understanding the laws of physics, and humanoid robots will move beyond the demonstration phase in 2026 to real-world industrial and service scenarios.
2. From single intelligence to multi-agent collaboration: The number of Agents is growing explosively, expected to increase from approximately 28.6 million in 2025 to 2.216 billion by 2030. Multi-agent systems will tackle complex task flows in "team" formations.
3. From "being able to talk" to "truly getting things done": Model capabilities are no longer focused solely on parameter scale, but on the ability to solve real-world problems, such as automatically designing proteins and creating long-horizon, closed-loop video content.
4. World Models become the consensus direction for AGI: Industry consensus is shifting from language models to multimodal world models capable of understanding the laws of physics, with predicting the next state of the world (NSP) potentially becoming the new paradigm.
🎯 What is the ultimate goal of Artificial General Intelligence (AGI) as described by Liang Wenfeng?
DeepSeek founder Liang Wenfeng defines AGI as an "AI system that can learn, reason, adapt, and solve any intellectual task across various domains just like a human." He emphasizes that AGI is the company's **primary long-term goal**, and that current revenue and products "are merely by-products on the path toward AGI."
Liang Wenfeng describes the path to AGI as a **non-skippable six-step progressive structure**:
> Language Model → Chain of Thought (CoT) → Agent → Continual Learning → Self-Iteration (Singularity) → Embodied Intelligence
* Current Stage and Bottleneck: He believes the 2026 step is **Agent**, but the biggest current gap in AI is **continual learning capability**—humans can continuously accumulate experience, while AI cannot.
* Ultimate Goal: After breaking through continual learning capability, AI will reach a "singularity" capable of self-development and self-iteration, **ultimately advancing toward embodied intelligence and entering the real world to serve humanity**. He firmly believes that "after continual learning, it will be able to develop the next version on its own."
In summary, AI is currently at a critical turning point from "being articulate" to "truly getting things done," with its ability to solve real-world problems in vertical domains already quite prominent. The core direction for the future is to enable AI to understand and integrate into the physical world, while the ultimate AGI goal pursued by Liang Wenfeng is to give AI the ability for continual learning and self-iteration, ultimately serving humanity in the real world in the form of embodied intelligence.
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