|AI|CONTINUAL LEARNING|DEEP LEARNING LIMITS|
Understanding the limits of deep learning and the quest for true continual adaptation
“The wise adapt themselves to circumstances, as water moulds itself to the pitcher.” — Chinese Proverb
“Adapt or perish, now as ever, is nature’s inexorable imperative.” — H. G. Wells
Artificial intelligence in recent years has made great progress. All of these systems use artificial neurons in some form. These algorithms are inspired by their biological counterparts. For example, the neuron aggregates information from previous neurons, and if the signal exceeds a certain threshold it passes the information to other neurons. This idea is represented by the matrix of weights and the activation function. Other examples can be found in convolutional networks (inspired by the visual cortex) or genetic algorithms. During the training process, the connections between various neurons (represented by the weights) are strengthened or diminished, similar to the strength of neuronal synapses. This process is the basis of the…