Machine Learning (ML) is omnipresent today. High processing speed and connectivity have helped in a significant upsurge of AI technology. ML models, due to their profound complexity, are regularly generating technology such as recommendation systems, predictive algorithms, and image & voice recognition software. These technologies are probably influencing your life today more than you realise.
However, AI isn't flawless. Just like humans, they are prone to errors and memory failures. One such phenomenon in Neural Networks (NN) is forgetting, akin to severe amnesia in humans.
How exactly does forgetting occur in Neural Networks? It happens during the training phase - Neural Networks form a dynamic scaffold of connections between individual nodes based on the input data. When new data is introduced, new bridges are built and some of the old ones are lost, causing the system to "forget" some of the tasks it was initially trained for. This might lead to an increase in error rates or total amnesia, referred to as Neural Network Catastrophic Forgetting or Interference.
Catastrophic forgetting currently isn't a major concern in deep learning, primarily because most Neural Networks are undergoing supervised training. The engineers meticulously filter the data that is fed to the system to evade biases and complications that might arise from raw data.
However, as ML becomes more sophisticated, introducing autonomous continuous learning will become a reality. This would let networks learn from new data without human supervision. One of the main concerns here is the unfiltered learning - we won't know what type of data the network is using to learn. This can potentially lead to AI catastrophic forgetting if the system tries to learn from data that’s quite different from its basic training.
The solution to avoiding Catastrophic Forgetting doesn't mean just steering clear of autonomous networks. Even similar datasets can sometimes lead to interference. The hidden layers of a neural network are ambiguous – leading to uncertainties about the possibility of crucial connection breakdown and eventual faults.
The question then arises - how to counter Catastrophic Forgetting? While complete avoidance of Catastrophic Interference is unlikely, several techniques can mitigate its risk, including Node Sharpening and Latent Learning. Strategically, it's wise to duplicate the network before re-training as a safety measure. Another common practice is to train a neural network with all the data simultaneously as sequential learning tends to cause issues when new data disrupts already learned knowledge.
Addressing Catastrophic Forgetting in reinforcement learning is one amongst the many challenges that AI experts are solving presently. Despite the immense potential of AI, its understanding and testing are still ongoing. Whether machine or human, Intelligence isn't a straightforward subject, yet significant strides are being made in comprehending it.
Machine Learning is an intriguing field, not only for its applications, but also for the insight it offers on our human nature. It brings us back to Alan Turing's postulate, his benchmark for AI was to devise a machine so convincing, it couldn't be distinguished from a human.