Skip to content

AI Security Tools: Fundamentals and Applications

Oct 2026
Adversarial Robustness in Machine Learning

Abstract

Learning ObjectivesAfter studying this chapter, readers will be able to: Understand the concept of Artificial Intelligence.  Explain the importance of AI security. Identify security risks associated with AI systems.  Describe the AI security lifecycle. Recognize common AI attacks with real-world examples. Overview of Artificial Intelligence What is Artificial Intelligence?Artificial Intelligence (AI) is a branch of computer science that focuses on designing intelligent systems capable of performing tasks that normally require human intelligence.These tasks include learning from data, reasoning, problem-solving, understanding natural language, recognizing images, making decisions, and adapting to new situations.Unlike traditional software, which follows predefined rules, AI systems learn patterns from data and improve their performance over time using algorithms and models. DefinitionArtificial Intelligence is the capability of a computer system to simulate human intelligence by learning, reasoning, perceiving, and making decisions based on data.Evolution of AI Year Milestone 1950 Alan Turing proposed the Turing Test 1956 John McCarthy coined the term "Artificial Intelligence" 1980 Expert Systems gained popularity

Read PDF

Similar papers

#computer vision Review Sep 2017

Agile Software Development Methods: Review and Analysis

This publication proposes a definition and a classification of agile software development approaches and analyses ten software development methods that can be characterized as being "agile" against the defined criterion.

P. Abrahamsson, O. Salo, Jussi Ronkainen et al. · 727 citations · ⚡54
#computer vision Jun 2008

The impact of agile practices on communication in software development

The study shows that agile practices improve both informal and formal communication, but indicates that, in larger development situations involving multiple external stakeholders, a mismatch of adequate communication mechanisms can sometimes even hinder the communication.

M. Pikkarainen, Jukka Haikara, O. Salo et al. · 401 citations · ⚡48
#machine learning Review Open access Oct 2014

Software development in startup companies: A systematic mapping study

The results indicate that software engineering work practices are chosen opportunistically, adapted and configured to provide value under the constrains imposed by the startup context.

Nicolò Paternoster, Carmine Giardino, M. Unterkalmsteiner et al. · 394 citations · ⚡54

Trajectory Balance: Improved Credit Assignment in GFlowNets

It is proved that any global minimizer of the trajectory balance objective can define a policy that samples exactly from the target distribution, and empirically demonstrate the benefits of the trajectories balance objective for GFlowNet convergence, diversity of generated samples, and robustness to long action sequenc...

Esmeralda S. Whitammer, Moksh Jain, Emmanuel Bengio et al. · 302 citations · ⚡60

Related blog posts

Microsoft Research Blog Oct 22, 2026

Flint-interactive: A language for creating interactive data visualizations

Flint-interactive gives AI agents a language for creating interactive visualizations. Click a chart, filter data, highlight related content, and explore beyond the view the author originally chose. The post Flint-interactive: A language for creating interactive data visualizations appeared first on Microsoft Research.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.