From autonomous driving to intelligent healthcare, from financial analysis to educational assistance, the application of artificial intelligence (AI) has widely penetrated into various fields of daily life and gradually become a key force driving social progress and industrial upgrading. However, behind the rapid development of AI technology, the lack of continuous learning ability of AI models has gradually become a bottleneck that restricts their further development and application. The "learning dilemma" of AI models Currently, most mainstream AI models are based on neural network architectures, which mimic the working mode of human brain neurons and optimize their own performance through training with large amounts of data. However, a fact that cannot be ignored is that after the initial training is completed, the learning ability of these models becomes relatively fixed, making it difficult for them to have sustained self-learning and evolutionary capabilities like the human brain. This means that whenever faced with a new data environment or changing demands, technology companies have to invest huge amounts of money to retrain the entire model. A study by Shibhansh Dohare's team at the University of Alberta in Canada found that many AI models experience "neuron death" after multiple retraining sessions, where a large number of neurons fall into a zero state and lose their learning ability. If you compare it to your brain, it's like 90% of the neurons are dead, "said Dohare." The rest isn't enough for you to learn. "This discovery is not limited to the field of image recognition, but also widely exists in multiple AI application fields such as natural language processing and reinforcement learning, highlighting the common dilemma of AI models in terms of continuous learning ability. The dual challenges faced by enterprises are undoubtedly brought about by the limited learning ability of AI models for technology companies. Firstly, with the explosive growth of data volume and rapid changes in market demand, enterprises need to constantly update their AI models to maintain competitiveness. Taking the retail industry as an example, consumer shopping preferences and purchasing behavior data are accumulating at an unprecedented speed. In order to accurately capture these changes and provide users with a more personalized shopping experience, e-commerce platforms have to continuously optimize their AI based recommendation systems. This means that they need to regularly adjust model parameters, introduce new algorithms and data sources to ensure that recommended content meets users' immediate needs while also being forward-looking and innovative. In the field of intelligent manufacturing, the application of AI models in production line quality control also faces the need for continuous updates. With the continuous expansion of production scale and the increasing complexity of products, the amount of data on the production line has sharply increased. In order to maintain high consistency and stability of product quality, enterprises need to flexibly adjust the monitoring range and prediction accuracy of AI models according to changes in the production environment. This not only requires enterprises to have strong data processing capabilities, but also to maintain efficiency and flexibility in the iterative upgrading of AI models. However, retraining the model is not only costly but also time-consuming, especially when dealing with large-scale datasets. In addition, in a rapidly iterating business environment, time costs cannot be ignored. If a company cannot respond to market changes and update its AI models in a timely manner, it may miss valuable market opportunities and even be surpassed by competitors. Therefore, how to improve the efficiency of model updates and reduce costs while ensuring model accuracy has become an urgent problem to be solved in the technology industry. How to improve the learning ability of AI models? Faced with the bottleneck of AI models' learning ability, researchers are actively seeking solutions. Among them, the Dohare team's research proposed a new algorithm that randomly activates some "dead" neurons after each training round to restore their learning ability. Although this algorithm has shown initial effectiveness, further testing and optimization are needed in larger systems. Mark van der Wilk from Oxford University said that the algorithm looks promising, but it still needs to be tested in larger systems. The solution for continuous learning of AI models is simply a problem worth billions of dollars He said, 'A true and comprehensive solution will allow you to continuously update models, significantly reducing the cost of training these models.' In addition, modular design and incremental learning are also seen as effective strategies to enhance the continuous learning ability of AI models. Modular design achieves flexibility and efficiency in task processing by breaking down AI models into multiple independent modules. When faced with new data, enterprises only need to update the relevant modules without retraining the entire model. Incremental learning techniques allow models to learn new knowledge while retaining old knowledge, achieving the accumulation and inheritance of knowledge. In the long run, cooperation and sharing among technology companies will become a key driving force for the sustainable development of AI technology. By building an open ecosystem, enterprises can share data, algorithms, and model resources, reduce research and development costs, and accelerate technological innovation. At the same time, this collaborative model also helps to form unified standards and norms, promoting the popularization and application of AI technology. (New Society)