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In the long history of artificial intelligence (AI), there is one period that is particularly remembered – and that is the first winter of AI.
This is a time of major challenges in technological development, a sharp reduction in funding, and setbacks in research goals.
However, it was these difficult years that laid the foundation for the resurgence of AI and the prosperity it is today. In this article, we'll take you back in history and explore how AI rose from the trough and redefined the future of technology.
Bottlenecks in computing power
In the 70s of the 20th century, the future of AI seemed bright. Scientists confidently predict that in the near future, machines will be able to simulate all of human intelligence. However, the reality is far more complex than predicted. Computer hardware at the time, especially the Cray-1 supercomputer, which was considered to be the cutting edge of technology, was far from being able to cope with the amount of computation required by AI programs to handle complex problems. Scientists have realized that the computation required behind even the simplest intelligent behavior is staggering. This bottleneck in computing power has become the first major obstacle to the development of AI.
The ebb and flow of capital investment
Just as AI researchers are working to break through technical barriers, a document called the Lighthill Report emerges. The report was a harsh critique of AI technology at the time, particularly robotics, language processing, and image recognition. The report notes that ambitious goals in these areas simply cannot be achieved, and research has failed completely. The report had a significant impact on public and government perceptions, leading to the UK government not making significant investments in AI subsequently. Subsequently, the scientific community conducted in-depth interrogation of artificial intelligence, and the research value of AI has been widely questioned. This skepticism has not only affected public perception, but has also led to a massive withdrawal of funds. Governments and research institutions began to reduce or stop funding for AI projects, and the AI field ushered in its first winter.
The difficulty of technological breakthroughs
While the future of AI seems bright, the actual progress of technology is faltering. Early AI systems faced significant challenges in processing unstructured data, such as image and sound recognition. These tasks required a huge amount of computation for a computer, and the technology of the time was far from being able to meet such requirements. In addition, although neural network algorithms are expected to be high, their over-reliance on computing power and empirical data volume has slowed down the progress of research. In 1969, Maven Minsky co-authored the book "Perceptron", which, despite its scientific and objective content, was also devastating to neural network technology, and almost no one devoted further research to it in the following decade.
A new direction for practicalization
However, just when the field of AI is in trouble, the emergence of expert systems offers a glimmer of hope. These systems are able to simulate the decision-making process of experts and solve problems in specific areas. In 1978, Carnegie Mellon University began developing XCON, a software program that would help customers automate the selection of computer accessories, and in 1980 it was put into use in the factory. It is a well-established expert system with more than 2,500 set rules, processing more than 80,000 orders over the next few years with over 95% accuracy and saving more than $25 million per year. This success not only proves the effectiveness of expert systems in solving problems in specific fields, but also opens up new paths for the practical application and commercialization of AI.
Early attempts at driverless vehicles
During the first cold winter of AI, some forward-looking attempts continued. In 1979, Stanford University built the first truly driverless car ever built, the Stanford Cart. It relies on visual sensors to autonomously move through a room littered with chairs without human intervention. While the speed is really slow and often takes several hours to complete, this attempt demonstrates the potential of AI in specific fields and lays the groundwork for future autonomous driving technology.
From winter to revival
Despite the serious challenges, researchers in the field of AI have not given up. They continue to explore in the field of basic research, using emerging technologies and methods to improve the performance and efficiency of AI. With the rapid improvement of computing power and the rise of big data, AI has begun to gradually come out of the cold winter and usher in a new spring. Today, our laptops and mobile phones are able to easily implement facial recognition, and driverless cars are driving on the road, which is a clear proof of the rise of AI from the cold winter.
epilogue
The first winter of AI was a challenging and questioning time. However, it is this difficult history that has tempered the tenacity and innovative spirit of AI researchers, laying the foundation for the revival of AI and the prosperity it is today. This history teaches us that even in the toughest of times, we should not give up on exploration and innovation. Today, we enjoy the convenience and wonder of AI, which is the result of perseverance at that time......
If you're curious about the evolution and future of AI, stay tuned. We'll provide you with the latest AI technology trends, in-depth analysis, and practical guidance. Join us as we explore the infinite possibilities of AI!
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