Article: Artificial Intelligence

What is Quantum Computing?

In computing, Artificial Intelligence (AI) is the ability of a machine to perform tasks that ordinarily require human intelligence, such as visual perception, natural language understanding, and decision-making. AI research deals with the question of how to create computers that are capable of intelligent behavior.

In practical terms, AI applications can be deployed in a wide variety of areas, including health care, manufacturing, finance, transportation, and education. AI is also being increasingly used to assist humans in a wide variety of tasks such as search, big data analysis, and robot control.

AI technology has had a profound impact on the global economy and society and is expected to accelerate in the years ahead. As AI becomes more sophisticated and capabilities increase, there will be a growing need for AI experts and resources capable of solving more complex problems.

What is AI?

The term “Artificial Intelligence” (AI) is used to refer to a wide range of computer-based technologies that are designed to perform tasks that would typically require human intelligence, such as understanding natural language and recognizing objects.

Why now?

Artificial intelligence has been around for a long time, but it is only in recent years that it has become a game-changing technology. This is due to the exponential growth of three components: algorithms, hardware, and data.

Hardware

Algorithms have been around for decades, but it is only now that we have the hardware capability to process these complex calculations and make the algorithms work.

Data

Data is created with everything we do, whether we capture it or not. With every click, with every interaction, with every smartphone’s tap, we are creating data. Today we are in the order of the zettabytes (trillions of gigabytes), and almost all this data was created in the last few years.

Compute

The exponential growth of data is generating a data deluge (a gap between the data being created and the capacity to process it), that is why we need intelligent systems to capture, store, filter and consume it.

Opportunity

With these volumes of data growing, enterprises have the opportunity to leverage investments made in data to augment existing decision making with data-driven insights faster than ever with AI.

Applications

There are a number of areas where AI can be applied, including Machine Learning (models learning from the data), Natural Language Processing (recognizing natural human language to engage directly with a user or a customer), Expert systems (making decisions in real-life situations), Computer Vision (recognizing objects), Speech recognition (conversational and transcribing speech to text and text to speech), Planning and Optimizing systems and Robotics or Cobots (that can see, hear and react using sensors, like humans).

Models

Machine Learning (ML) is one of the most popular subfields of Artificial Intelligence. ML gives computers the ability to learn from data, without explicitly being programmed to perform a task. We have 3 different types of ML: Supervised Learning (we train the model based on history, using labeled data sets), Unsupervised Learning (it clusters unlabeled data and detects patterns), and Reinforcement Learning (learning through trial and error, with reward and punishment).