The Future of AI and the Three Challenges Ahead

According to reports, the main forum of the “Artificial Intelligence Big Model Technology Summit Forum” hosted by the Chinese Artificial Intelligence Society opened in Xiaoshan, Hangzhou. At the forum, Zheng Qinghua, President of Tongji University, pointed out when discussing the future research direction of AI that currently, Al technology is not suitable for scenarios such as boundary uncertainty, strong adversarial game, high real-time response, high complexity of the environment, and incomplete information. This is the pilot for the development of weak Al to strong AI and super Al. Meanwhile, Zheng Qinghua pointed out that currently, we are facing three challenges. The first challenge lies in the limitations of current methods in obtaining common sense, implicit, and abstract knowledge, which are difficult to mine; The second challenge lies in the integration of memory and cognitive knowledge, while the current methods face limitations such as strong perception but weak cognitive ability and high computational costs; The third challenge lies in interpretable knowledge reasoning. The current methods are limited to issues such as difficulty in causal inference, weak anti factual reasoning ability, and poor interpretability.

Zheng Qinghua, President of Tongji University: Three Major Challenges Faced by Current AI Technology

Artificial Intelligence (AI) has significantly transformed many aspects of our daily lives, from communication to entertainment and everything in between. With each passing day, AI continues to become more advanced, and experts believe that it is only a matter of time before AI surpasses human intelligence, leading to a plethora of exciting yet daunting possibilities. Recently, Zheng Qinghua, the President of Tongji University, highlighted some of the challenges facing AI research and development during the “Artificial Intelligence Big Model Technology Summit Forum” hosted by the Chinese Artificial Intelligence Society in Xiaoshan, Hangzhou. This article will examine these challenges and explore the future of AI research and development.

Introduction

The field of AI has come a long way since it was first introduced. With advances in hardware and software, AI has the potential to revolutionize the way we live, work, and communicate. However, as Zheng Qinghua points out, there are still significant challenges that need to be addressed.

The Three Challenges Facing AI

Zheng Qinghua highlighted three significant challenges facing the field of AI that need to be addressed to achieve weak to strong AI and super AI.

Challenge One: Obtaining Common Sense Knowledge

The first challenge facing AI is obtaining common sense knowledge that is difficult to mine. While computers can perform specific tasks with lightning speed and precision, they are still unable to understand the world with the same level of nuance as humans. Humans draw on their experiences and understanding of the world to make assumptions and predictions based on incomplete or ambiguous information. Machines, on the other hand, require explicit instructions to complete a task, and they lack the ability to generalize based on intuition, which means they struggle to make correct predictions in situations where they have not been explicitly trained.

Challenge Two: Integrating Memory and Cognitive Knowledge

The second challenge facing AI is integrating memory and cognitive knowledge. While current AI models are strong in perception, they lack cognitive ability and require significant computational resources. The integration of memory and cognitive knowledge would allow AI to reason using incomplete information, a process known as abductive reasoning, which could lead to transformative breakthroughs.

Challenge Three: Interpretable Knowledge Reasoning

The third challenge that AI faces is interpretable knowledge reasoning, which refers to the ability to make decisions based on reasoning that can be understood and explained by humans. Many AI models today are often considered to be black boxes, which means it is difficult to determine how the AI arrived at its decision. This problem is particularly problematic in fields such as finance and healthcare where explainable AI is essential.

Conclusion

AI is not just a technology; it is a revolutionary force that will continue to transform our world in ways we cannot yet imagine. While there are still significant challenges facing AI, the ultimate goal of AI research and development is to create machines that can think and learn like humans. Addressing the challenges highlighted by Zheng Qinghua will be a critical step towards achieving this goal.

FAQs

1. What is weak AI?
Weak AI refers to artificial intelligence systems that are designed to perform specific tasks, similar to the way humans perform a task. These systems differ from strong AI in that they do not possess the same cognitive abilities as humans and are not capable of generalizing beyond their specific task.
2. What is common sense?
Common sense refers to the basic understanding of the world that humans possess, such as the ability to navigate a room, know to go around objects and not run into them, or recognize objects in our environment.
3. What is interpretable AI?
Interpretable AI refers to AI systems that can explain their decisions, allowing humans to understand how the AI arrived at a particular decision. This is particularly important in fields such as finance and healthcare, where the consequences of an incorrect decision can be severe.