Artificial intelligence operates in various environments, including cloud-based servers, local devices, and edge computing platforms. The mechanism involves data input, processing through algorithms, and generating intelligent outputs such as predictions or actions. Machine learning algorithms, neural networks, and other AI techniques contribute to the learning and decision-making processes.

Dear Doctor

"The first wave of early AI techniques is known as 'symbolic AI' or expert systems. Here, human experts create precise rule-based procedures – known as 'algorithms' – that a computer can follow, step by step, to decide how to respond intelligently to a given situation. Fuzzy logic is a variant of the approach that allows for different levels of confidence about a situation, which is useful for capturing intuitive knowledge, so that the algorithm can make good decisions in the face of wide-ranging and uncertain variables that interact with each other. Symbolic AI is at its best in constrained environments which do not change much over time, where the rules are strict and the variables are unambiguous and quantifiable. While these methods can appear dated, they remain very relevant and are still successfully applied in several domains, earning the endearing nickname 'good old fashioned AI'.

The second wave of AI comprises more recent 'data-driven' approaches which have developed rapidly over the last two decades and are largely responsible for the current AI resurgence. These automate the learning process of algorithms, bypassing the human experts of first wave AI. Artificial neural networks (ANNs) are inspired by the functionality of the brain. Inputs are translated into signals which are passed through a network of artificial neurons to generate outputs that are interpreted as responses to the inputs. Adding more neurons and layers allow ANNs to tackle more complex problems. Deep learning simply refers to ANNs with several layers. Machine learning (ML) refers to the transformation of the network so that these outputs are considered useful – or intelligent – responses to the inputs. ML algorithms can automate this learning process by making gradual improvements to individual ANNs, or by applying evolutionary principles to yield gradual improvements in large populations of ANNs.

The third wave of AI refers to speculative possible future waves of AI. While first and second wave techniques are described as 'weak' or 'narrow' AI in the sense that they can behave intelligently in specific tasks, 'strong' or 'general' AI refers to algorithms that can exhibit intelligence in a wide range of contexts and problem spaces. Such artificial general intelligence (AGI) is not possible with current technology and would require paradigm shifting advancement. Some potential approaches have been considered, including advanced evolutionary methods, quantum computing and brain emulation. Other forms of speculative future AI such as self-explanatory and contextual AI can seem modest in their ambitions, but their potential impact – and barriers to implementation – should not be underestimated."

Machine learning, neural networks, and other AI techniques contribute to the learning and decision-making processes as mentioned by @Sundus F Hantoosh and @ Ali Abdulkadhim Taher. Appreciated