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The secret of neuroscience: How does your brain use predictive learning to build a model of the world?
In today's rapidly developing world of artificial intelligence, "predictive learning" has become a key technical concept. This technology is not limited to the field of machine learning, but also involves the operation of the human brain. The core goal of predictive learning is to allow the model to develop an understanding of its environment, capabilities, and limitations through new data. This technology has been widely used in fields such as neuroscience, business, robotics and computer vision.
Predictive learning is a method that attempts to learn with minimal prior intelligence structure.
The development of the concept dates back to 1988, when French computer scientist Yann LeCun conducted related research at Bell Labs, where he focused on teaching models to recognize handwritten characters so that financial companies could automate check processing. . As early as the 17th century, the British insurance company Lloyd's used predictive analysis to make profits. This historical background makes the mathematical foundation of predictive learning more solid.
When it comes to predictive learning, we have to mention the famous psychologist Jean Piaget. His research shows how children construct knowledge of the world by interacting with the environment. This idea has also been further developed in the works of other scholars, such as Gary Drescher's "Made-up Minds" and Hermann von Helmholtz's discussion of unconscious reasoning.
These concepts gradually combined predictive coding with neuroscience research, giving birth to a new field - predictive coding. Another scholar, Jeff Hawkins, proposed a memory-prediction framework in his book "On Intelligence", which shows the potential of predictive learning in neural systems.
Training process of predictive learning
In the operation of predictive learning, we often compare it to machine learning. The purpose of predictive learning is to infer unknown dependent variables from independent input data. In this process, all input data is fed into a neural network to predict a value.
In order to accurately predict the output, the weights of the neural network need to be incrementally adjusted through the backpropagation algorithm to produce prediction values close to the actual data.
Through continuous training, when the model can accurately predict values close to the real data, it will be able to make correct predictions on new data. In addition, in order to improve the accuracy of prediction, the error between the predicted value and the actual value must be maintained within a certain threshold. This established error formula will continuously adjust the weight of the model to ultimately achieve effective prediction results.
Applications of predictive learning
Cognitive development
Sensory motion signals are nerve impulses sent to the brain after physical contact. Predictive learning plays a crucial role in early cognitive development because the human brain represents these signals in a predictive manner, striving to minimize prediction errors. Recent research points out that scholar Nagai proposed a new architecture that uses a dual-module approach to predict signals based on the sensor motion system and the predictor.
Time and space memory
Computers use predictive learning to construct spatiotemporal memory. This implementation uses predictive feedback neural networks, which are designed to process sequential data, such as time series. Applying predictive learning to computer vision can help computers generate images of themselves, which can be useful for repeating DNA strings, facial recognition, and even creating X-ray images.
Social media consumer behavior
In a recent study, researchers collected consumer behavior data from social media platforms including Facebook, Twitter, LinkedIn, YouTube, Instagram and Pinterest. Through predictive learning, researchers have discovered a variety of consumer behavior trends, including predicting the effectiveness of advertising campaigns, estimating reasonable prices for products that attract consumers, assessing data security, and analyzing target consumer groups for specific products. .
Conclusion
The development of predictive learning not only improves the capabilities of artificial intelligence, but also gives us a deeper understanding of the working mechanism of the human brain. It demonstrates that the power of prediction cannot be underestimated, both in computer learning and in human cognition. In this era of information explosion, shouldn’t we think more deeply about the impact of predictive learning on the future?
