In the previous article, I briefly mentioned how using AI sequentially is a nightmare. Whenever an AI is used as input for another, the individual errors of each model quickly add up to unacceptable levels or, simply put, catastrophic failure. Moreover, as you add more nodes to the chain, the problem gets exponentially worse. In this article, I expand on the matter, explaining the intuition to why sequential models fail and how we can remedy some of these issues. The following discussion is of paramount interest to anyone developing complex AI pipelines, such as using object detection to find objects of interest and applying some other model to these objects.

Hope you are doing well. Hacking a Neural Network is simply fooling a Neural Network. Neural Networks are increasingly being used in various security and moderating systems across different fields. It is very important that they mainatain their integrity across different types of attacks. In this article, I am going to explain how we can modify an image (without changing it too much) to force the Neural Network to mis-classify it (that too with a very high degree of certainity).

Backpropagation (backward propagation of errors) -- is a widely used algorithm in training feedforward networks. It computes the gradient of the loss function with respect to the weights of the network. The main idea of it is to break big functions in small parts and use partial derivatives to get function derivative with using the Chain Rule. And because solving this can be a very hard task, here comes backpropagation and gradient descent(updating weights by a small amount based on the gradient to move in the way of loss minimization). Let's say we have 3 variables x -2, y 5, z -4, the result will be f -12, and our target for training is -13.

Only here you will get step-by-step algorithms explanation to build strong and deep understanding of the process.

This paper proposes a new generative model called neural belief reasoner (NBR). It differs from previous models in that it specifies a belief function rather than a probability distribution. Its implementation consists of neural networks, fuzzy-set operations and belief-function operations, and query-answering, sample-generation and training algorithms are presented. This paper studies NBR in two tasks. The first is a synthetic unsupervised-learning task, which demonstrates NBR's ability to perform multi-hop reasoning, reasoning with uncertainty and reasoning about conflicting information. The second is supervised learning: a robust MNIST classifier. Without any adversarial training, this classifier exceeds the state of the art in adversarial robustness as measured by the L2 metric, and at the same time maintains 99% accuracy on natural images. A proof is presented that, as capacity increases, NBR classifiers can asymptotically approach the best possible robustness.