Technical information

OVERVIEW

AI technology is a group of hardware and software components that work together to build intelligent systems capable of challenging tasks. It enables machine learning models capable of advanced machine learning applications by using specialist hardware such as GPUs and FPGAs, and also software frameworks such as Tensorflow and PyTorc

HARDWARE

Hardware components used in AI contain field-programmable gate arrays (FPGAs) and graphics processing units (GPUs) are specialised computing devices used for parallel processing, training, and inference. These devices are made to hasten the computing of sophisticated algorithms that are common in AI applications. They offer the processing capacity necessary for machine learning algorithms to absorb knowledge from massive datasets and carry out operations like computer vision and natural language processing.Sensors that collect data from the environment or devices that give feedback to the environment, such as cameras for computer vision, microphones for voice recognition, and acceleration sensors for motion detection, are examples of additional hardware components. Whereas some AI applications run on normal laptops or servers, others require specialised hardware, such as autonomous cars and robots equipped with cameras, lidars, and other sensors that give environmental data.

SOFTWARE

The software components used in AI include deep computing frameworks, machine learning algorithms, and software tools for deployment. While deep learning frameworks can network algorithms to form complex neural networks, AI algorithms learn from data, create models, and identify patterns.Python, R, Java, and C++ are some popular AI programming languages, each having its own unique strengths in machine learning and AI development. Deep learning models are frequently created using libraries including Tensorflow, PyTorch, and Keras, while tools such as Jupyter Notebook provide an interactive environment for prototyping and development.