So how do we get from today's neural networks to true AI?

[sujonkumar6300]

Neural network architectures. It is true that neural networks have the advantage of being able to learn the ideal parameters for an algorithm automatically from data, which gives them an advantage over manual configuration strategies. However, what is not mentioned is that the architecture of that neural network, which is the basis of training to solve a specific task, cannot be generated automatically from data but must be created by hand; this is one of the major limitations of this field. There is already uk consumer email list some research to solve this problem [1], but we need more. The problem with learning neural network architectures from data is that it currently takes too much time to experiment with different architectures on a large data set. We need to overcome this limitation.

Unsupervised learning. We cannot always be on top of our neural networks, correcting them at every instance and giving feedback on their performance. The goal of unsupervised learning is to build general systems that can be trained from a small amount of data. We need to be able to build models that behave similarly to the human brain, capable of learning from a few examples, and that can automatically correct themselves and continuously learn from more complex data [2].