Key AI Technology Terms
Updated: Sep 2
Welcome to the first article in our series, "Tech Terminology Simplified - A Guide for Non-Technical Corporate Professionals."
In this installment, we'll delve into the captivating domain of Artificial intelligence (AI). A groundbreaking technology that is transforming industries and creating immersive experiences.
Artificial intelligence (AI) is a rapidly growing field that is transforming the way we live and work. From speech recognition to image analysis, AI technologies are being used in a wide range of applications, and their impact is only expected to grow in the coming years. However, for those who are new to the field, the jargon and technical terms used in AI can be overwhelming. To help you navigate this complex landscape, we've put together a list of key AI technology terms that anyone interested in the field should know. Whether you're a student, a professional, or just someone curious about the future of technology, this guide will provide you with a solid foundation of AI terminology to build upon.
Machine learning (ML) - an analytical method of utilizing AI to solve problems. Contrary to popular belief, machines cannot learn: they store and compute — but in increasingly complex ways. ML utilizes mathematical models to analyze data and identify patterns that may be difficult for humans to detect. While ML can suggest actions based on its analysis, it still requires human intervention before directing systems to take any actions.
Deep learning - a variant of machine learning algorithms, which is particularly well-suited for dealing with complex, unstructured data such as images, audio, and text. In contrast to ML, deep learning uses neural networks, which are composed of multiple layers of interconnected nodes, to learn from data and improve performance over time.
Generative AI generates innovative new creations that are similar to the original but do not duplicate them. The use of generative AI has the potential to increase creative content production, such as video, and accelerate R&D cycles in a variety of fields.
The Internet of Things (IoT) refers to the network of physical objects (like devices, vehicles, and buildings) that contain embedded sensors, software, and connectivity to enable them to collect and exchange data.
The IoT ecosystem includes a range of devices and systems, including smart home appliances, wearable health monitors, industrial machinery, environmental sensors, and more. The data collected by these devices can be analyzed and used to optimize processes, improve efficiency, and enhance user experiences.
Natural language processing (NLP) is a branch of AI that focuses on enabling computers to understand, interpret, and generate human language. It involves the use of computer algorithms and machine learning techniques to process and analyze large amounts of natural language data, including written text, spoken words, and other forms of communication.
NLP has a wide range of applications, including chatbots, virtual assistants, sentiment analysis, language translation, and speech recognition. It plays a crucial role in modern interactive voice response (IVR) systems and is used by many businesses to improve customer communication and experience.
Conversational AI agents or Advanced virtual assistants are powered by NLP and deep learning techniques. By monitoring and analyzing human behavior, they construct and manage data models, and utilize predictive analytics to suggest actions, thereby automating tasks that were once the sole domain of humans, while also providing assistance to them.
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