Understanding Artificial Intelligence And Its Applications

A simplified introduction to Artificial Intelligence.

Artificial Intelligence is the study and design of intelligent agents that perceive their environment and make decisions that maximise their chances of success in the performance of a specific task. Before an intelligent agent can make the right decision, it needs to be informed.

Raw data is data that has not been processed for use; thus, it has no value. For data to obtain value, it must be processed. When raw data is processed it becomes information. Information is valuable because it helps us make the right actionable decisions.

AI has many applications, of which some are :

Automation

Automation is the use of various control systems for operations such as utilising machinery, processing in factories, and steering and stabilisation of vehicles with minimal human intervention.

Robotics

Robotics is a branch of AI that deals with creating intelligent and efficient robots to perform monotonous tasks.

Knowledge Base Management Systems

A knowledge base is an easily accessible data storage hub that contains information about a particular product or service. A knowledge base management system is a system that reasons and uses a knowledge base to solve complex problems.

Neural Network

A neural network is a system modelled on the human brain and nervous system. Neural networks combine simple units to give an increased computational power. Neural networks work well with triggers and trigger algorithms to automatically execute a command at the occurrence of a specific event. Neural networks employ triggers using an if, then, else, chain of command where a certain condition is given for a command to be executed. If the condition is satisfied then, the command is executed. Else another command is executed.

Artificial Intelligence and its applications make use of various forms of technology like sensors, probes, scanners, and measuring devices to collect data and interpret it using software. After interpretation, the artificial intelligence system makes an informed decision and acts upon it.

A classic example of AI and the periphery technologies it uses is a Hospital Monitoring System. The automated system monitors the patient using wireless sensor networks to collect physical health parameters. The system interprets the collected data, makes informed decisions and acts when necessary. In the event where a critical reading is encountered, the system alerts healthcare officials by sending the collected data through a wireless network via email or text so, they attend to the patient in distress.

Another example of how AI is applied is Self-Driving Cars / Autonomous Vehicles. Self-driving cars use a combination of sensors, cameras, radars and artificial intelligence algorithms to travel between destinations without a human operator. Autonomous vehicles create and maintain an internal map of their surroundings using cameras, and sensors like radar and laser. The software then processes the inputs received from the sensors and sends instructions to the vehicles’ actuators which control acceleration, braking, and steering. The vehicle uses the map and obstacle algorithms to navigate smoothly. Self-driving cars are either connected or not. Connected self-driving cars use LTE to communicate with other vehicles or infrastructure, such as next-generation traffic lights.

Hospital Monitoring Systems and Autonomous vehicles are also examples of IoT — Internet of Things. Internet of Things is the network of physical devices, vehicles, home appliances, and other items embedded with electronics, software, sensors, actuators, and connectivity which enables these things to connect and exchange data, creating opportunities for more direct integration of the physical world into computer-based systems, resulting in efficiency improvements, economic benefits and reduced human exertions.

IoT and AI are closely linked. All IoT-related services always follow five necessary steps: sense, transmit, store, analyse, act. Artificial Intelligence is heavily involved in the “analyse” and the “act” steps. Machine learning is used to detect patterns in the data collected, so it can learn from the trends in order to adjust the ways in which it analyses the data. After analysis, the system would decide the best course of action to take and act accordingly.

We’re still in the early stages of perfecting some of the emerging technologies mentioned above. Imagine what could be achieved when these technologies are perfected.