AI@Socitm

Artificial Intelligence (AI) is a transformative field of computer science that seeks to create intelligent systems capable of performing tasks that typically require human intelligence, machines that can independently perform tasks, learn from experience, and adapt. It encompasses technologies like machine learning, natural language processing, and robotic process automation.

Below is a list of different types of AI along with brief examples of how they are used, each type of AI has been designed to address specific challenges and applications ranging from dealing with repetitive specialized tasks, to the ability to understand natural language and natural language generation.

Autonomous Systems refer to AI-driven systems capable of performing tasks or making decisions without human intervention.

Examples: Self-driving cars and unmanned planes & drones.

Computer Vision is about teaching machines to interpret and understand visual information from images or videos.

Examples: Object recognition, facial recognition, autonomous vehicles, and medical imaging.

Deep Learning is a specialized branch of Machine Learning that involves neural networks with multiple layers (deep neural networks).

Examples: Image and speech recognition, natural language processing, and playing strategic games.

Expert Systems are AI systems designed to mimic the decision-making abilities of human experts in specific domains. They use knowledge representations and rule-based reasoning to provide expert-level advice and solutions.

Examples: Knowledge representations and rule-based reasoning to provide expert-level advice and solutions such as automated diagnosis

Machine Learning uses algorithms that enable computers to learn from data and improve their performance on a specific task without being explicitly programmed. It encompasses several types of learning, such as supervised learning, unsupervised learning, and reinforcement learning.

Examples: Learning and identifying plants, recognition of handwriting to convert it to text.

Narrow AI refers to AI systems designed to perform specific tasks or solve problems with high competence but limited scope. These systems excel at a narrow set of functions and lack general intelligence.

Examples: Virtual assistants like Siri and Alexa, image recognition systems, and recommendation algorithms used in online shopping.

NLP enables computers to understand, interpret, and generate human language. It plays a crucial role in applications like language translation, sentiment analysis, text summarization, and chatbots.

Examples: ChatGPT, Microsoft CoPilot and Chatbots

Reinforcement Learning involves training AI agents to make decisions by interacting with an environment. The agent learns through a system of rewards and punishments, aiming to maximize cumulative rewards over time.

Examples: Teaching cars to drive around a predetermined track, Non player characters in computer games.

RPA is a technology that uses software robots or “bots” to automate repetitive, rule-based tasks and processes in business operations. These bots are not physical robots; instead, they are software applications that mimic human interactions with computer systems.

Examples: Processing large volumes of invoices following a set of rules