Large models reversed the thinking of previous artificial intelligence research, giving up interpretability and beginning to embrace complex networks and large-scale parameters.
These make the capabilities of modern neural networks surpass those of previous generations, but they also bring many problems.
- Huge costs: Training models require massive amounts of data and computing power, often tens of millions, which raises the threshold for using AI;
- Not scalable: Once the model is trained, it is difficult to expand and can only be fine-tuned through limited means.
Rethinking the evolutionary history of artificial intelligence technology may give us some inspiration.
Just as Huashan martial arts has a dispute between air sect and sword sect, artificial intelligence also has a dispute over routes. It can be roughly divided into two major schools: the reasoning school vs. the probability school.
The reasoning school believes that machine learning can be used to summarize and summarize knowledge in advance to achieve a level of intelligence that surpasses human intelligence.
The probabilistic school of thought believes that humans cannot correctly express the complete knowledge of the entire world, and that more primitive data should be directly fed into the machine, allowing the machine to discover the rules on its own.
In an era when computing power is scarce, the reasoning school has the upper hand. After all, relying on people's prior knowledge can save the time of machine learning.
Later, with the abundance of computing resources and data, the probabilistic approach relied on ultra-large-scale neural networks and has now become mainstream.
From hundreds of billions to trillions, the network model is approaching the limit of what human civilization can achieve, so where is the future?
In sharp contrast to the large models are ordinary children. They observe and receive data from the world and train the brain network, but it is much more efficient than software. What's the difference?
The most critical differences are 3 points:
- The human brain is dynamic. Neural connections are constantly being created and destroyed. They do not stop after training is completed, but are constantly expanding.
- The human brain can generalize. Humans can not only learn bare data, but also learn rules, and can even reason about rules and learn high-level concepts that transcend rules.
- The human brain can be partitioned. The human brain is divided into multiple areas, some focus on memory storage, some focus on rational reasoning, and some focus on emotional management.
Perhaps, if the next generation of artificial intelligence wants to be more efficient, it should learn from the human brain. Adopt dynamic link model and partition structure to strengthen generalization ability. Only in this way can it be possible to design a super brain that can continuously learn and bring benefits to all mankind.
