Artificial Intelligence

Artificial Intelligence (AI) is an advanced computer systems that can simulate human capabilities such as learning, analysis and deciding based on predetermined set of rules or conditions.

Artificial Intelligence (AI) is already gaining widespread momentum across various disciplines and is already prevalent in many information security areas as well. Furthermore, AI technology is highly related with deep learning, process automation, neural networks, machine learning, natural language processing, and so on disciples. As result, cybersecurity professionals should have a solid understanding about AI and related technologies so that they can actually implement it to safeguard data. Besides, understanding AI technologies will help us to identify beneficial use cases and implementation challenges related to consumers, businesses, and cybersecurity as whole.

For cybersecurity professionals, exploring AI helps us to understand its uses, application areas, and its security implications at large. As a branch of computer science, AI attempts to make machines to think intelligently like human beings that is in alignment with Alan Turing theory.

Artificial Intelligence (AI) is the ability of software-powered machines or robots to perform tasks that require the application of knowledge or reasoning. While traditional computing devices commonly perform procedural and specific tasks in the form of programs, AI is more about the simulation and imitation of human intelligence. As opposed to the traditional computing paradigm, an AI is expected to make decisions and perform complex actions without receiving explicit instructions just only through  reasoning,  natural language processing, knowledge representation, learning,  planning,  perception and the ability to move and manipulate objects.

Some variations of Artificial intelligence may include:

• Deep Learning: collection of algorithms used in machine learning that are used to model high-level abstractions in data via the usage of model architectures
• Machine Learning: subspecialty of artificial intelligence that focuses on improving computer’s performance through continuously incorporating new data into its statistical model
• Artificial Neural Networks (ANN): focuses on describing algorithms and programs that mimic human or biological neural networks so that the machine can learn based on input and output as information flows through the networks
• Natural Language Processing: a field of computer science that studies the interaction between humans and computers for verbal communications

Artificial intelligence (AI) areas of applications includes the following:

• Healthcare
• Information security
• Autonomous cars
• Home automation
• Face recognition
• Sales and marketing
• Visual assistance
• So much more

Some common data models of artificial intelligence (AI) include the following:

• Logistic regression
• Linear discriminant analysis
• K-Nearest Neighbors
• Decision trees
• Naive Bayes
• Learning vector quantization
• Support vector machines
• Bagging and random forest
• Deep neural networks
• Linear regression

Frameworks are established software to assist AI programmers in creating algorithms without the need to recreate basic programming constructs. Some of the most common AI frameworks in the industry include the following:

• Apache Mahout
• Microsoft Cognitive Toolkit
• Torch
• Theano
• Amazon Machine Learning
• Caffe
• TensorFlow
• Net
• Scikit-learn
• Weka