The Development History of General Large Models
The development history of general large models is a process that spans multiple technological revolutions and shifts in research paradigms. Here is a detailed review of its development history:
Early Neural Networks and Perceptron Models:
The development of deep learning can be traced back to the 1940s, when the "M-P neuron model" was first proposed, inspired by the biological brain, to mimic the structure and working principle of neurons.
In the 1950s, psychologist Frank Rosenblatt proposed the Perceptron model, one of the earliest neural network models, which could learn and recognize simple patterns, such as binary classification problems.
Multilayer Perceptrons and Backpropagation Algorithm:
To overcome the limitations of the Perceptron, researchers proposed the concept of Multilayer Perceptrons (MLP), which increased the complexity of the model by adding hidden layers between Perceptrons.
In 1986, Rumelhart, Hinton, and Williams proposed the Backpropagation algorithm, making the training of multilayer neural networks possible.
Convolutional Neural Networks and the Revolution of Deep Learning:
In 1989, LeCun and others proposed the Convolutional Neural Network (CNN), suitable for processing high-dimensional data such as images.
In 2012, Krizhevsky, Sutskever, and Hinton proposed AlexNet, a deep convolutional neural network, which significantly improved the classification accuracy in the ImageNet image classification competition that year, triggering a revolution in the field of deep learning.
The Rise of Pre-trained Language Models:
In 2006, Geoffrey Hinton proposed a way to alleviate the difficulty in training deep networks due to the vanishing gradient problem through layer-by-layer unsupervised pre-training, providing an important optimization path for the effective learning of neural networks.
In 2017, Google introduced the Transformer model structure, which greatly enhanced sequence modeling capabilities by introducing self-attention mechanisms.
The Era of Large Models:
In 2018, OpenAI released the GPT series of models, representing the pioneers of generative pre-trained models. From GPT-1 to GPT-3.5, each generation of models has seen significant improvements in scale, complexity, and performance.
At the end of 2022, the language model ChatGPT released by OpenAI attracted widespread social attention, capable of handling tasks across multiple scenarios, purposes, and disciplines.
Development of Multimodal Large Models:
With technological advancements, multimodal large models have begun to emerge, capable of understanding and generating various types of data, including text, images, and audio. In March 2023, OpenAI officially announced the multimodal large model GPT-4, which added image functionality.
Future Outlook for General Large Models
Multimodal AI Systems: Future AI large models will not be limited to processing a single type of data but will expand to handle multimodal data, such as images, videos, and audio.
Computational Base Upgrade: The training cluster scale of generative AI has entered the ten-thousand card level, and in the future, it will move towards the hundred-thousand card level, providing more powerful energy for machine brains.
Intelligence as a Service: Large language models have greatly expanded the cognitive boundaries of machines, and intelligence will become a public service like electricity in the future.
Emotional Intelligence Breakthrough: Breakthroughs in fields such as streaming speech recognition, multimodal AI, and emotional computing have laid the foundation for AI companionship, and large models with both EQ and IQ will open up the AI companionship market in the next 2-3 years.
Deep Integration in Industrial Manufacturing: Multimodal large models are expected to complement and integrate with the currently widely used specialized small models, deeply empowering various aspects of industrial manufacturing.
Edge Model Optimization: With the development of AI-native operating systems, the operating system may evolve into a mode where APIs are directly called, reducing dependence on traditional graphical user interfaces.
Embodied Intelligence Development: The combination of robot technology and large models provides a "body" for machine brains, enhancing the learning efficiency and ability to perform complex tasks of robots.
Open Source Large Model Prosperity: It is expected that AI open source will flourish in the next 2-3 years, and small developers can call upon the capabilities of large models to improve development efficiency.
Human-Machine Alignment Strengthening: As AI models become more human-like in their capabilities, how to align the capabilities and behaviors of AI models with human intentions becomes increasingly important.
In summary, the future development of general large models is full of infinite possibilities. They will play a more important role in various fields and will also face challenges such as data privacy protection, interpretability issues, and universality issues, which require continued exploration and resolution.
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