Artificial intelligence is a technology that enables machines and computer applications to learn from experience through iterative processing and algorithmic training.
AI is a type of intelligence that is used to solve problems, find solutions, answer questions, make predictions, or make strategic recommendations.
AI has become extremely important to modern businesses and other types of organizations because it can do all of these things. By combining large sets of data with intelligent, iterative processing algorithms, AI systems learn from patterns and features in the data that they analyze.
Every time an AI system processes data, it tests and measures its own performance and gains new knowledge.Because AI never needs a break, it can quickly complete hundreds, thousands, or even millions of tasks, learning a great deal in a short period of time and becoming extremely capable at whatever it’s being trained to do.
However, understanding how AI truly works requires understanding that AI is more than just a single computer program or application, but an entire discipline, or science.
There are many different components to an AI system, which you can think of as sub-fields of the overarching science of artificial intelligence.
Each of the following fields is commonly utilized by AI technology:
- Machine Learning: A specific application of AI that lets computer systems, programs, or applications learn automatically and develop better results based on experience, all without being programmed to do so. Machine Learning allows AI to find patterns in data, uncover insights, and improve the results of whatever task the system has been set out to achieve.
- Deep Learning: A specific type of machine learning that allows AI to learn and improve by processing data. Deep Learning uses artificial neural networks which mimic biological neural networks in the human brain to process information, find connections between the data, and come up with inferences, or results based on positive and negative reinforcement.
- Neural Networks: A process that repeatedly analyses data sets to find associations and interpret meaning from undefined data. Neural Networks function similarly to neural networks in the human brain, allowing AI systems to take in large data sets, discover patterns in the data, and answer questions about it.
- Cognitive computing is another important component of AI systems designed to mimic human-machine interactions, allowing computer models to mimic how a human brain works when performing a complex task, such as analyzing text, speech, or images.
- Natural Language Processing (NLP) is an important part of the AI process because it allows computers to recognize, analyze, interpret, and truly understand human language, whether written or spoken. Natural Language Processing is essential for any AI-powered system that interacts with humans, whether through text or spoken input.
- Computer Vision — One of the most common applications of AI technologies is the ability to review and interpret image content using pattern recognition and deep learning. Computer Vision allows AI systems to identify visual data components, such as the captchas you’ll find all over the web that learn by asking humans to help them identify cars, crosswalks, bicycles, mountains, and so on.
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What Technology does it Require?
AI isn’t new, but its widespread application and utility have skyrocketed in recent years thanks to considerable improvements in technology.
In fact, the explosive growth of AI’s scale and value is closely related to recent technological improvements, including:
- Larger, More Accessible Data Sets — AI thrives on data, and has grown in importance alongside the rapid increase of data, along with better access to data. Without developments like “The Internet of Things”, which produces a huge amount of data from connected devices, AI would have far fewer potential applications.
- Graphical Processing Units — GPUs are one of the key enablers of AI’s rising value, as they are critical to providing AI systems with the power to perform millions of calculations needed for interactive processing. GPUs provide the computing power needed for AI to rapidly process and interpret big data.
- Intelligent Data Processing — New and more advanced algorithms allow AI systems to analyze data faster and at multiple levels simultaneously, helping those systems analyze data sets far faster so they can better and more quickly understand complex systems and predict rare events.
- Application Programming Interfaces — APIs allow AI functions to be added to traditional computer programs and software applications, essentially making those systems and programs smarter by enhancing their ability to identify and understand patterns in data.
Thus many Sectors like Education, Healthcare, Manufacturing, and many more can be Exploited with the help of AI.
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