Artificial Intelligence is gaining more and more limelight with each passing day. AI or Artificial Intelligence is a technology that allows machines to perform tasks like humans by learning from experience. 

Given how popular technology is becoming, there is no surprise that there are varying opinions regarding AI. Both the present and future applications of AI are up for discussion, as well as the possible implications of it. However, most people merely associate AI with automation without understanding much about how AI works. AI describes different technologies that enable machines to learn intelligently. 

How is AI applied?

There are still misconceptions about AI because of which people only relate it to self-driving cars and robots. With such a thought process, the biggest practical application of artificial intelligence remains unrecognized, i.e., processing the huge amounts of data that are regularly generated. 

With the strategic application of AI to specific processes, gathering insights and automating tasks occur at a rapid rate and a large scale. Such speed and scalability would be unimaginable without AI. Artificial Intelligence systems parse through the huge amounts of data that humans create. These systems then do intelligent searches in complex data, while interpreting both images and text for discovering patterns. Finally, the system acts on its learnings. 

Basic components of AI

Some of the revolutionary technologies of Artificial Intelligence include natural language processing, predictive analytics, and deep learning. These are also words that many people have heard about. These are the modern technologies that help computer systems in learning from experience, making predictions, and understanding the meaning of human language. To know about the real-world applications of AI, it is essential to understand the jargon associated with the technology. AI technologies disrupt and revolutionize the interaction between humans and data as well as decision making. Everyone should have a basic understanding of technology. Undergoing AI training is a great way to learn about artificial intelligence and how it works. 

Learning through experience

One significant application of AI is Machine Learning or ML. It enables computer systems to learn from experience automatically and improve from it without being programmed explicitly to do so. The main focus of ML is to develop algorithms for analyzing data and making predictions. It is commonly used for predicting what movies you might like on streaming platforms or the best route for your cab ride. Apart from that, machine learning also has applications in life sciences, healthcare, and pharma industries for diagnosing disease, accelerating drug development, and interpreting medical images. 

Machines that educate themselves

Machine learning has a subset known as deep learning in which artificial neural networks are employed for processing data and learning from it. The human brain has its biological neural networks which are mimicked by artificial neural networks. A single output is determined from several inputs with multiple artificial neural networks working together. The machines carry out negative and positive reinforcement of their task and learn from it. This requires consistent reinforcement and processing for progressing. Speech recognition is one more form of deep learning. It is through this that voice assistants in smartphones can understand voice instructions from users. 

Creating associations through a neural network

As mentioned before, deep learning is enabled by neural networks. These are nothing but computer systems that are modeled after the human brain’s neural connections. A perceptron is what you would call an artificial equivalent of a neuron. Similar to bundles of neurons creating neural networks in the human brain, artificial neural networks are created in computer systems by stacks of perceptrons. 

Learning in neural networks is done through the processing of examples for training. Large data sets form the best examples for the system. For instance, for a machine to recognize whether a given image is that of a cat or not, it should previously process several images of cats as its input. 

Through this process, data is analyzed many times for finding associations and providing meaning to the previously undefined data. The machine is taught with the help of various learning models, such as positive reinforcement.

Making inferences with cognitive computing

One more AI component is cognitive computing. The purpose of this technology is imitating interactions between machines and humans, and improving these interactions. Cognitive computing aims to create a computer model that recreates the thought process of humans. It does so by understanding the meaning of images and human language. Artificial intelligence along with cognitive computing strives to enable machines to have human-like behavior and abilities to process information.

Understanding language through Natural Language Processing

With the help of Natural Language Processing or NLP, computers can recognize, produce, and interpret human speech and language. The main objective of NLP is to teach systems to develop an understanding of human language based on context and produce logical responses, thus enabling seamless interaction between humans and machines. 

Understanding images

Pattern identification and deep learning can be implemented for interpreting image content. The technique that does that is known as computer vision, which helps interpret pictures, tables, and graphs within PDF documents, along with other media and text. It is an important field in Artificial Intelligence as it allows the identification, processing, and interpretation of visual data. This technology has applications that have already started revolutionizing industries like healthcare and Research & Development. With the help of Machine Learning and Computer Vision, x-ray scans can be evaluated, allowing patients to be diagnosed faster.

Technologies that support AI

  • GPUs or Graphical Processing Units provide the necessary computing power for processing huge data and millions of calculations faster. 
  • IoT or Internet of Things refers to the network of devices connected to the internet
  • Optimization of intelligent data processing is done through an advanced algorithm for quicker data analysis at multiple levels. This helps in predicting rare events, unique situations, and comprehending systems. 
  • APIs or Application Processing Interfaces can be integrated, which allows aspects of AI to be plugged into the current software and its normal function to be augmented with AI.