Data image augmentation is a technique used in computer vision and deep learning to increase the amount and diversity of data available for training a model. This paper presents an overview of data image augmentation and provides a tutorial on how to perform data image augmentation in Python using the Keras.preprocessing.image The paper also includes a discussion on the benefits and limitations of data image augmentation and provides tips on how to use it effectively. In recent years, computer vision and deep learning have made significant strides in accurately classifying and detecting objects in images. One of the key factors that contribute to the success of these techniques is the availability of large and diverse datasets for training models.

Today's AI cannot replace humans in cybersecurity but shows promise for driving efficiency and addressing talent shortage, a new report by ProtectWise has shown. Conducted by Osterman Research, the study explores usage trends and sentiments toward AI among more than 400 U.S. security analysts in organizations with 1000 or more employees. Nearly three quarters of respondents have already implemented at least one product that uses AI, but findings uncovered mixed results and a learning curve that needs to be addressed in order to use AI at higher levels of sophistication and effectiveness. " A lot of hype and confusion exists around AI and its role in the cybersecurity industry," said Gene Stevens, CTO, ProtectWise. "In its current state, AI is a tool for driving efficiencies and addressing staffing needs, but it is not going to replace human intelligence any time soon. AI is well positioned today to create machine-accelerated humans: an army of hunters and responders who use a wide array of expert systems to help unearth and prioritize critical threats. In the future, AI will only become more valuable as the industry develops products that improve ease of use and capitalize on AI's efficiency differentiators ."