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Here is What Real Artificial Intelligence Looks Like!

Artificial Intelligence (AI) is the simulation of human intelligence by machines that are programmed to think and behave like humans. It also refers to all traits of machines that tend to learn from experience and take part in future problem-solving based on such experiences.

AI comes with its fascinating characteristics and applications that unveil the maximum efficiency of the technology.

Top Characteristics of Artificial Intelligence

Having evolved and come a long way justifying the hype around it, AI exhibits some prominent characteristics that are revolutionizing human life and living.

Artificial Neural Networks

Artificial Neural Networks (ANN) also termed Neural Networks (NNs) work on the collection of connected nodes called artificial neurons simulating those that are in the human brain cells. Each of these connections transmits signals from one neuron to another after processing them. Using a nonlinear function, each neuron output generates a real number as a signal at connections called edges.

Algorithms aggregate different layers of neurons for different transformations. Signals travel from the first layer to the last one several times in such network systems.

There are two types of networks: one is called the Feedforward Neural Network, or the Acyclic Network where signals travel only in a single direction. Perceptrons, multi-layer perceptrons, and radial basis networks are all Feedforward Neural Networks. The second type is a Recurrent Neural Network that enables opinions, and small memories of previous input events.

ANN’s are best suited to solve complex problems in real-life situations by revealing hidden relationships between patterns and predictions. Some of the use cases include targeted marketing, modeling highly volatile data in the finance sector, predicting events such as fraud detection, or diagnosing harmful diseases. ANN and Deep Learning are enhancing the reliability of airport operations to automate repetitive tasks of air traffic control and perform all manually intensive processes.

Deep Learning (DL)

Deep Learning is an ML technique that automates computers and machines to think like the human brain. Machines draw on huge amounts of unstructured data from different sources and learn from them. As compared to Artificial Neural Networks, DL architecture includes multiple hidden layers between input and output data. This framework performs automatic features after data extraction and classification learning.

Deep Learning lets self-driving or autopilot cars recognize signs and signals and make decisions to drive or stop. Decisions such as recognizing a stop sign, identifying a pedestrian, or avoiding a lamppost are powered by Deep Learning. Personalized feeds on social media platforms use DL image recognition, online text recognition, and more, giving apt suggestions.

Data Ingestion

With the exponential growth in data, AI not just gathers but analyzes data using previous experiences. Data Ingestion is the process of the transportation of that knowledge from assorted sources to a data-storage medium where it is accessed, used, and analyzed.

Artificial Intelligence gathers insights by analyzing large amounts of data. The data analysis here is powered by Artificial Neural Networks. Neural networks analyze large amounts of data and provide logical inferences from it. The final data is used to formulate AI models that participate in a wide range of AI activities.

Natural language processing

Natural Language Processing (NLP) is the field of linguistics artificial intelligence and computer science. It enables computers to understand human language as voice data or in text form and understand it just like human beings.

NLP uses Artificial Intelligence to take it as input, process it, and translate it in a way machines understand it. These machines use programs to read and microphones to hear just like humans do. They then process the input using program algorithms like humans use their brains. In the end, the input is converted into the form of codes that the machines can understand.

NLP lets applications translate text from one language to another, summarize large volumes of text, and respond to spoken commands in real-time. Voice-operated GPS is among the most common forms of NLP. Others are customer service chatbots, digital assistants, speech-to-text dictation software, or speech recognition software. NLP is playing an active role in business solutions to streamline operations and increase employee productivity using Text summarization or Machine translation.

Intelligent Robotics

Robotics is the combination of engineering, science, and technology that produces programmable machines or robots that mimic human actions. Originally built to handle monotonous tasks, robots now carry out tasks in the domestic, commercial, and military realms. They have a different level of autonomy to carry out tasks without any external influence and range from being human-controlled bots to fully autonomous bots.

The evolving gaming industry is introducing robots that have physical activity and imagination for the best gaming experience. These gaming robots have complete mobility, just like a member of the family, adding to the enthusiasm of gamers.


Machine perception lets it take inputs from the sensors like cameras, wireless signals, and microphones, process the data, and deduce all aspects of it. Speech recognition, facial recognition, or object recognition are some machine perceptions. Computer vision is the singular source that provides visual input for analysis.

Biometric mapping lets machines recognize individual faces where the technology uses knowledge against an existing database of faces to find its match. Employee authentication, ID verification, and criminal identification are done by calculating facial features from saved images.

Quantum Computing

Quantum computing focuses on building highly complex algorithms for computational tasks. The concept comes from quantum-enhanced AI algorithms. Google AI Quantum is a pioneer in error-corrected quantum computers. Its objective is to develop solutions for pressing problems such as sustainable energy or reduced emissions for varied applications from quantum-assisted optimization and superconducting qubit processors.

Concluding it

Perhaps the most important characteristic of Artificial Intelligence is its ability to rationalize, take actions, and achieve specific goals. However, Artificial Intelligence can be applied to any machine that exhibits traits associated with a human mind, such as learning and solving problems. AI is gaining importance in the fields of consumer products, robotics, and almost all areas of human life.

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