In this blog, we will talk about Neural network which is the base of deep learning which gave machine learning and ultra edge in the current AI revolution. Let's go ahead and tell us more about Neural networks. Neuron is a computational unit which takes the input('s), does some calculations and produces the output. Above, within the figure is the one we tend to use in Neural Network. It will produce the result (which would be a continuous value -infinity to infinity).
The question of whether a computer can think like a person is once again a hot topic. Somewhat to my surprise, this philosophical question seems to have direct practical implications for AI, especially language understanding. The following analysis has been helpful to me and might be of some value to others.
There's plenty of debate these days about whether or not robots and AI will take our jobs. I think that they will, but not in the ways we think, and that the difference is important. So we come to the current state of this debate, namely, "the nature of work will change." Kevin Kelly has written eloquently about the post-productivity economy. He sees the future coming, but thinks we'll redefine work when the things that robots and algorithms do are cheap and abundant.
This paper presents three new algorithms for the automatic construction of models from Object Oriented Probabilistic Relational Models. The first two algorithms are based on the knowledge based model construction approach while the third is based on an Object Oriented Bayesian Network instance tree triangulation method. We discuss the strengths and limitations of each of the algorithms and compare their performance against the knowledge based model construction and Structured Variable Elimination algorithms developed for Probabilistic Relational Models.
Last fall, University of Virginia computer science professor Vicente Ordóñez noticed a pattern in some of the guesses made by image-recognition software he was building. "It would see a picture of a kitchen and more often than not associate it with women, not men," he says. That got Ordóñez wondering whether he and other researchers were unconsciously injecting biases into their software. So he teamed up with colleagues to test two large collections of labeled photos used to "train" image-recognition software. Two prominent research-image collections--including one supported by Microsoft and Facebook--display a predictable gender bias in their depiction of activities such as cooking and sports.