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Over the last few months, artificial intelligence (AI) has dominated headlines everywhere. And now, it looks like this new-age technology has taken over big tech companies, enterprises, startups, schools, and life itself. From music, art, and films to homework, news, and more, AI is the next big thing.

Suffice it to say, it is important to keep up with why AI is this interesting, what it is made up of, and why exactly it is a big deal.

In this article, we will navigate the vast landscape of artificial intelligence terminology across machine learning (ML), natural language processing (NLP), deep learning, quantum computing, and a lot more.

While numerous scientists and engineers laid the groundwork for AI since the 1940s, American Computer scientist John McCarthy coined the term Artificial Intelligence in 1955. A year later, he, along with other scientists, held the first AI conference at Dartmouth University.

In the 1980s, there was a shift towards neural networks and machine learning approaches. Researchers explored algorithms inspired by the structure and functioning of the human brain, enabling machines to learn from data and improve their performance over time.

The late 1980s and early 1990s witnessed a period known as the "AI Winter" when interest and funding significantly declined in this area due to unmet expectations. However, the field experienced a resurgence in the late 1990s with advancements in areas such as data mining, natural language processing, and computer vision.

In recent years, the availability of vast amounts of data and advancements in computational power have fuelled breakthroughs in AI. Deep learning, a subfield of machine learning that utilises neural networks with multiple layers, has led to significant advancements in image and speech recognition, natural language processing, and other AI applications.

An algorithm is a definite set of instructions that allow a computer to perform a certain task. AI algorithms help a computer understand how to perform certain tasks and achieve the desired results on its own. In other words, algorithms set the process for decision-making.

Machine learning is a subset of AI. It effectively enables machines to learn using algorithms, data and statistical models to make better decisions. While AI is a broad term that refers to the ability of computers to mimic human thought and behaviours, ML is an application of AI used to train computers to do specific tasks using data and pattern recognition.

A subset of ML, deep learning trains computers to do what humans canlearn by example. Computer models can be taught to perform tasks by recognising patterns in images, text, or sound, sometimes surpassing humans in their ability to make connections. Computer scientists leverage large sets of data and neural network architectures to teach computers to perform these tasks.

DL is employed in cutting-edge technology like driverless cars to process a stop sign or differentiate between a human and a lamp post.

Yet another application of ML, natural language processing helps machines understand, interpret, and process human language to perform routine tasks. It uses rules of linguistics, statistics, ML, and DL to equip computers to fully understand what a human is communicating through text or audio and perform relevant tasks. AI virtual assistants and AI voice recognition systems like voice-operated GPS are examples of NLP.

Computer vision is a form of AI that trains computers to recognise visual input. For instance, a machine will be able to analyse and interpret images, videos and other visual objects to perform certain tasks that are expected of it.

An example is medical professionals using this technology to scan MRIs, X-rays or ultrasounds to detect health problems in humans.

Robotics is a branch of engineering, computer science, and AI that designs machines to perform human-like tasks without human intervention. These robots can be used to perform a wide variety of tasks that are either too complex and difficult for humans or are repetitive or both. For example, building a robotic arm to assemble cars in an assembly line is an example of a robot.

Data science uses large sets of structured and unstructured data to generate insights that data scientists and others can use to make informed decisions. Often, data science employs ML practices to find solutions to different challenges and solve real-world problems.

For instance, financial institutions may employ data science to analyse a customers financial situation and bill-paying history to make better decisions on lending.

An extension of data science is data mining. It involves extracting useful and pertinent information from a large data set and providing valuable insights. It is also known as knowledge discovery in data (KDD). Data mining has numerous applications, including in sales and marketing, education, fraud detection, and improving operational efficiency.

Quantum computing uses theories of quantum physics to solve complex problems that classic computing cannot solve. It is used to run complex simulations in a matter of seconds by converting real-time language into quantum language.

Google has a quantum computer that they claim is 100 million times faster than an average computer. Quantum computing can be used in a variety of fields ranging from cybersecurity to pharmaceuticals to solve big problems with fewer resources.

A chatbot employs AI and NLP to simulate human conversations. It can operate through text or voice conversations. Chatbots use AI to analyse millions of conversations, learn from human responses and mimic them to provide human-like responses. This tech has found great usage in customer service and as AI virtual assistants.

All AI and ML tools are human-trained. This means that any inherent human bias can reflect AI bias. AI bias is a term that refers to the tendency of machines to adopt human biases because of how and by whom they are coded or trained. Algorithms can often reinforce human biases.

For instance, a facial recognition platform may be able to recognise Caucasian people better than people of colour because of the data set it has been fed. It is possible to reduce AI bias through more testing in real-life circumstances, accounting for these biases and improving how humans operating these systems are educated.

Today, AI represents the human capacity to create, innovate, and push the boundaries of what was once thought impossible.

So, whether you are an AI enthusiast, a curious learner, or a decision-maker shaping the future, it is essential to equip yourself with the right knowledge to survive in a world that is being increasingly powered by AI and its tools.

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AI 101: 10 artificial intelligence terms to keep up with this new-age ... - YourStory