adversarial machine learning

What Is Adversarial Machine Learning?

The algorithm in question was GoogLeNet, a rotary neural network architecture that won the ImageNet visual recognition challenge on a large scale.


Rival examples take advantage of the way artificial intelligence algorithms work to disrupt the behavior of artificial intelligence algorithms. In recent years rival machine learning has become an active field of research as the role of AI continues to grow in many of the applications we use. 


There is concern that vulnerabilities in computerized learning systems could be exploited for malicious purposes.


Working on adversarial deep or machine learning a rival machine has resulted in results that range from funny benevolent and embarrassing like following a mostly wrong turtle to potentially harmful examples like a car accident driving itself at a stop sign as a speed limit.


How learning is called "seeing" the world

Before we get to how rival examples work we must first understand how adversarial deep or machine learning algorithms analyze images and videos. Consider classifying AI images as the one mentioned at the beginning of this article.


Before being able to perform its functions a learning model known as the "training" phase is passed in which it is provided with many images along with their appropriate labels (e.g., panda cat dog, etc.). 


The model examines the pixels in the images and adjusts its many internal parameters to be able to link each image with its label. After training the model should be able to examine images he has not yet seen and linked them to their proper labels. 


Basically, you can think of an adversarial machine learning model as a mathematical function that takes the pixel values ​​as input and produces the label of the image.


Artificial neural networks, a kind of adversarial machine learning algorithm, are particularly suitable for dealing with cluttered and incomprehensible data like images, sounds, and texts because they contain many parameters and can adapt flexibly to different patterns in their training data. 


When they are stacked on top of each other and become "deep neural networks", their ability for classification and prediction tasks increases.


Deep learning the branch of adversarial machine learning that uses deep neural networks is now the bleeding end of artificial intelligence


Deep learning algorithms often match and often exceed the performance of humans in tasks that were previously outside the realm of computers such as computer vision and natural language processing.


It is worth noting however that algorithms of deep learning and adversarial machine learning are basically machines that weigh numbers. They can find delicate and complex patterns in pixel values, word sequences, and sound waves but they do not see the world as human beings.

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