Tong Tong (pictured, center) is an artificial general intelligence (AGI) agent at the Beijing Institute for General Artificial Intelligence (BIGAI) embodied in a virtual world that emulates the complexity of the real physical social world. Here, she interacts with her mother and generates tasks based on her value function. PHOTO: ZHEN CHEN AND XIAOMENG GAO.

 Dear Commons Community,

Science has a featured article this morning entitled, “AI gets a mind of its own” that reports on the latest developments at DeepSeek and the Beijing Institute for General Artificial Intelligence.  It reports on the work of Song-Chun Zhu, recognized as one of the major figures in creating new AI models.  The photo above pictures Tong Tong,  an artificial general intelligence (AGI) agent, created by Zhu and his colleagues.

The entire Science article is below.  An important step forward in general AI development.

Tony

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Science

AI gets a mind of its own

Artificial general intelligence research is moving into a new era

 

Sometimes, less is more. In January, DeepSeek released the latest version of its chatbot, upending the artificial intelligence (AI) world. A training AI built for under $6 million, DeepSeek seems to rival the technical capabilities of other large language model (LLM) AIs, including ChatGPT, with only a fraction of the processing power. The breakthrough was a welcome development for Song-Chun Zhu, dean of the Institute for Artificial Intelligence at Peking University in Beijing, who has been challenging the current LLM-dominated AI paradigm in his efforts to create artificial general intelligence (AGI).

Zhu, a trailblazer in the AI field, graduated from Harvard University in 1996 and has published more than 400 papers covering computer vision, cognitive science, robot autonomy, and commonsense reasoning, among other topics. Now, he is the founder and director of the non-profit Beijing Institute for General Artificial Intelligence (BIGAI).

“We as a society may have misunderstood the term ‘AI’,” says Zhu. “Just like how we call a multifunctional cellphone ‘smart’, the popular AI models we use today are not truly intelligent.” That’s because today’s AI, he explains, is driven by big data built upon massive computing power. Zhu pioneered data-driven statistical approaches and created the world’s first large-scale annotated image dataset at the Lotus Hill Institute in 2005. However, he realized that big data sets and specific machine learning models alone are not enough to make true intelligence. “One of the major Chinese philosophical schools, the Yangmingism or the ‘Teachings of the Heart’, argues ‘the reality we see comes from how our minds perceive’,” Zhu says. To make AI more like humans, Zhu says, it needs to have a framework that emulates the top-down mechanisms in the brain.

According to Zhu, the future of AGI should be a kind of autonomous AI that doesn’t require vast datasets. In 2020, Zhu returned to China to establish and lead BIGAI. Its mission: To pursue a unified theory of artificial intelligence in order to create general intelligent agents for lifting humanity.

Defining AGI agents in CUV-space:

Zhu and his team’s focus at BIGAI is on creating value-driven human-like cognition that goes beyond data-driven imitation. “The difference between AGI and current LLM-based AI is just like the difference between a crow and a parrot,” he said. While parrots can mimic many words, he says, crows can achieve their goals autonomously in the real world. In an article published in 2017, Zhu discusses how statistical models, which modern LLMs are based upon, function like “stochastic parrots.” While leading two Multidisciplinary University Research Initiatives at UCLA, Zhu pursued research to make machines more crow-like, exploring the brain mechanisms that make it possible for crows—and humans—to understand the physical and social world and act accordingly.

Human intelligence evolves over time, as the body changes and experiences accrue. AGI also matures over time. To help define, evaluate, and improve AGI development, Zhu proposed to define AGI in the mathematical space of the “CUV framework” In this framework, C is the AGI’s “cognitive architecture” to think, or its simulation of the decision-making processes in the brain. U is a set of “potential functions” that represent an AGI’s ability to understand and interact with its environment. V is a set of hierarchical internal “value functions” that supply the AGI’s motivation. With this formulation, Zhu and colleagues can define AGI agents as points in this CUV space and characterize their learning and self-reflection processes.

The Tong test

In Chinese, the word “general” is translated as Tong (), a character that is also the logo of BIGAI. Artistically arranged, the character also holds the English letters “AGI.” Tong Tong is the name Zhu gave to world’s first AGI agent born at BIGAI, a digital Chinese girl that looks to be about 3 to 4 years old. Tong Tong is a step forward in AGI research, and researchers really want to know, “What is she thinking?” and “How is she learning and making decisions?” Researchers have long relied on tests to assess AI models. The Turing test was developed to determine whether a machine could mimic human intelligence through dialogue. ChatGPT and other AI built on big data can pass the Turing test, but Zhu wanted a test that could assess broad human intelligence. Thus, the Tong Test was born, which relies on the CUV framework.

What sets Tong Tong apart from ChatGPT is that she doesn’t exist in a vacuum, but is rather embodied in a virtual world that emulates the complexity of the real physical social world. The Tong test examines an AGI’s understanding of this world— its abilities—as well as the AGI’s internal motivations for behaviors—its values. For example, how an AGI responds to a crying baby sitting on a floor can say a lot about its commonsense reasoning, inference of social interactions, and self-awareness. “Those natural abilities such as emotions and languages are true embodiment of human intelligence,” Zhu says. “Tong Tong may be an AGI agent, but she is just like a real human child, able to understand and behave according to her own environment even if it changes. The goal of the Tong test is to build a systematic evaluation system to promote standardized, quantitative, and objective benchmarks and evaluation for AGI.” And Tong Tong is just the beginning; researchers at BIGAI are developing diverse AGI agents that may someday enter the physical world through robotics and other mediums to serve society in meaningful ways.

AGI safety

As Tong Tong and the Tong test continue to grow and mature, AGI safety is front of mind for Zhu. Because AGI behavior is human-like, and not all humans are benevolent, there are risks that AGI will take actions that are not in humanity’s best interests. On the other hand, AGI’s cognitive architecture may be able to incorporate a mutual theory of mind—in other words, the golden rule: do unto others as you would have them do unto you.

During a panel discussion at SafeAI 2023, Zhu and Stuart Russell from the University of California Berkeley, two leading figures in AGI, had an in-depth discussion on the risks and ethics of AGI.

When Russell raised a question about how humans could keep AGI agents in check, Zhu replied, “To prevent potential threats from future AGI agents to humanity, we can gradually loosen the capability and value space of agents. It’s similar to how we approach robots: initially, we confine them in a ‘cage’ and slowly increase their permission. Now, we already have autonomous vehicles operating on specific roads.” Zhu added that once AGI agents are proven safe and controllable, they can have more freedom, with the safeguard of understanding and transparency. “If we can explicitly represent the cognitive architecture of AGI agents, understanding how they work, we will be better equipped to control them.”

For Zhu, now is the beginning of a new era for AI to evolve into AGI. Zhu’s doctoral advisor at Harvard, mathematician and Fields medalist David Mumford, is also an advocate of creating AIs with the top-down neural architecture of the human brain. He gave Zhu a trophy to recognize his perseverance at AGI innovation. “The future of AGI will be a combination of science and philosophy,” Zhu says. “Chinese teachings of the heart are crucial to guiding AGI to obtain true beneficial human behavior.”