Artificial Intelligence— Technological ABC Simplified for Laymen
- Artificial Intelligence is the technology used to develop machines that can mimic human intelligence.
- AI has existed for several years, but has rapidly emerged for a widespread adoption in the past 2-3 years.
- The programs are developed with complex algorithms, breaking down highly intricate human intellect-tasks into units of smaller operations.
Artificial Intelligence is a technology which enables machines to perform tasks that require human brain and intelligence, such as problem-solving, acquiring learned traits, responding to complex inputs and making logic-based outputs. What started with something as simple as a calculator and a computational device, has now advanced to deciphering encrypted program and self-executing complex tasks like driving a car.
The Recent Wave of Artificial Intelligence
Artificial Intelligence, as a technology, has existed for a couple of decades now, but the current spurt in adoption was never seen before. Today, most tasks that are repetitive and cumbersome in nature, are automated for efficient performance and time optimization. AI is adopted in a vast number of systems, including—
- Camera filters
- Games and non-player characters
- Message filters
- Recommendation systems on platforms like YouTube, Instagram, Amazon
- Advanced search engines and search engine optimization-based outputs
- Chatbots and assistants like Alexa, Siri, Google Assistant
- Speech-to-text and language translation
- On-command generative tools such as ChatGPT, Bing
- Automatic cars and airplanes
- Internet of Things (IoT)
- Advanced programming software, database such as Excel, Tableau etc.
How Does AI Work: Technological Breakdown
Artificial intelligence, as the name suggests, tries to mimic human intelligence by combining a lot of different computer techniques, softwares and algorithms. Each of these have various goals and functions to enable the “intelligence” of the machine.
Algorithms are designed to execute these goals. The most basic of these is calculation and computation, problem-solving, reasoning using logics and probability etc. Then there are algorithms for knowledge representation, i.e., generation of output based on the input given. Organizing, planning, probabilistic decision-making, encryption etc. use highly advanced mathematical operations to implement cryptography, data gathering, storage and game theory.
Then comes the aspect which forms the base for most of the AI development today– learning or machine learning. It involves providing volumes of data to the machines and store in their memory. Then there are certain systems of classification of the data, which algorithmically generate response from the machine based on each subset. There are in-built programs that help improve performance of the system automatically by careful analysis of data.
There are also programs that help machines learn and respond to natural human languages. This is called Natural Language Processing (NLP). This allows the system to read, write and communicate in human languages such as English.
Machine perception is another function which enables the system to receive, analyze and use inputs from more generalized signals, with the help of various sensors such as cameras, microphone, radar, sonar, tactile receptors etc.
Protocols Used by AI Software
To accomplish the goals and functions that would enable an AI system to deliver, certain protocols are developed in algorithms. These protocols make the system function in the specified way by the protocol, in response to a given input. These include—
- Logic: For every action, a mathematical function is described which is executed with the help of logic. It is used for computation and knowledge representation.
- Probability and reasoning: Whenever there is an incomplete or an uncertain set of information provided to the system, it has to work on a decision-making protocol. It is a decision making process to a little extent. It works on the probability and economic theory to reach an output.
- Statistics: Statistical data analysis is a useful tool for machine learning and prompting results to certain queries.
- Classifiers: These are basic algorithms that instruct the machine to derive to a conclusion in the instance of a set of conditions met. When a conclusion is picked, then some controller instructions dictate an action.
Search and optimization:
Many problems are solved by searching and sifting through data for all the possible solutions. It includes sifting through a tree of possible states to reach a goal state. Mathematical operations help reach a solution numerically in case of complicated problems. These mathematical operations optimize computation by assigning information to each corresponding numeric solution obtained.