It is difficult to read the news or have a discussion nowadays without broaching the subject of AI. From virtual assistants that can understand and respond to natural language, to self-driving cars that can navigate complex environments, AI is already having a profound impact on our daily lives.

Getting started with all things AI can seem daunting, let alone keeping track of the rapid pace of change in the field. To this end, NewsBites.AI has prepared a primer to introduce readers who may not have an understanding of the different technologies that relate to AI. Whether you’re new to AI or looking to deepen your understanding of this exciting field, this primer is a great place to start.

If there are any other AI terms that you think should be included in our primer then please suggest them in the comments below!

Machine Learning (ML)

Machine learning is a way to teach computers to learn and make decisions without being told exactly what to do every step of the way. It works by showing the computer lots of examples or data, so it can learn to recognize patterns and make predictions on its own. For example, if you show a computer many pictures of dogs and cats, it can learn to tell the difference between them and make predictions about what animal is in a new picture it has never seen before. This makes machine learning a really powerful tool for solving problems that involve large amounts of data.

An example of machine learning is the recommendation system used by Netflix to suggest movies and TV shows based on what users have watched before. The more a user watches, the more data the algorithm has to work with, and the better its recommendations become.

Deep Learning (DL)

Deep learning is a type of technology that helps computers learn and make decisions on their own by using something called a neural network. Neural networks are made up of interconnected nodes, similar to the neurons in the human brain.

An example of deep learning is image recognition technology, such as the ability of Facebook to identify people in photos and tag them automatically.

Natural Language Processing (NLP)

Natural language processing is a type of artificial intelligence that helps computers understand human language, like the way we talk or write. This can include things like understanding the meaning of words and sentences, and even detecting emotions or sarcasm. With natural language processing, computers can do things like chat with us in a natural way, translate languages, or even write stories or articles. This makes it a really important area of AI that’s changing the way we interact with technology.

An example of NLP is chatbots that can answer customer service inquiries on a website or app, such as the chatbots used by Domino’s Pizza or the National Health Service in the UK.

Computer Vision (CV)

Computer vision is a type of technology that helps computers understand and analyze images and videos. It works by using algorithms and machine learning to recognize patterns and identify objects in pictures and videos.

For example, if you show a computer a picture of a cat, computer vision can help it recognize that it’s a cat and not a dog or a bird. This can be really useful in lots of different fields, like self-driving cars that need to “see” the road, or security cameras that need to identify people. Computer vision is an exciting area of technology that’s making machines smarter and more useful in our daily lives.

Another example of computer vision is facial recognition technology, such as the ability of smartphones to unlock using facial recognition or the use of security cameras to identify individuals in a crowd.

Robotics

Robotics is a field of study that focuses on the design, development, and operation of robots.

An example of robotics is the use of drones for aerial photography or delivering packages.

Autonomous Systems

Autonomous systems are machines that can operate by themselves without needing people to control them. This means they can make decisions and carry out tasks on their own, without someone telling them what to do every step of the way.

For example, self-driving cars are autonomous systems because they can drive on their own and make decisions like when to speed up, slow down, or change lanes. This makes autonomous systems really useful for tasks that are dangerous or hard for people to do, like exploring space or deep-sea diving. However, it’s important to make sure that autonomous systems are safe and don’t cause harm to people or the environment.

Reinforcement Learning

Reinforcement learning is a type of machine learning that uses rewards and punishments to train machines to make decisions.

An example of reinforcement learning is the training of a game-playing AI, such as DeepMind’s AlphaGo, which learned to play the game of Go by playing against itself and improving based on the outcomes of its moves.

Neural Networks

Neural networks are a set of algorithms that allow machines to learn from and make predictions based on input data.

An example of neural networks is speech recognition technology, such as the ability of virtual assistants like Siri or Alexa to understand and respond to spoken commands.

Artificial General Intelligence (AGI)

Artificial general intelligence is the concept of creating machines that possess human-like intelligence, including the ability to reason, learn, and solve complex problems.

While AGI does not yet exist, an example of its potential application is the creation of robots that can work alongside humans in a variety of settings, such as factories or hospitals.

Explainable AI (XAI)

Explainable AI is the ability to explain how AI models arrive at their decisions or recommendations. An example of explainable AI is the use of machine learning algorithms to diagnose medical conditions, such as skin cancer.

By making the decision-making process transparent, doctors can understand how the algorithm arrived at its diagnosis and make more informed treatment decisions.

Edge Computing

Edge computing is a distributed computing paradigm that brings computation and data storage closer to where it’s needed, allowing for faster and more efficient processing.

An example of edge computing is the use of smart sensors in agriculture to monitor crops and adjust irrigation and fertilization in real-time, based on local weather conditions.

The Internet of Things (IoT)

The Internet of Things (IoT) refers to the network of physical objects that are embedded with sensors, software, and connectivity, allowing them to collect and exchange data with other devices and systems over the internet. These devices can range from everyday objects like home appliances and wearable fitness trackers, to industrial equipment like sensors in manufacturing plants.

An example of IoT in action is a smart home system. A smart home may include devices like thermostats, light switches, and security cameras, all connected to the internet and controlled by a single app on the user’s smartphone or tablet. By collecting data on things like temperature, lighting, and movement, the smart home system can automatically adjust settings to optimize comfort, energy efficiency, and security. For example, the system may turn off lights and lower the temperature when the user leaves for work, and turn on lights and increase the temperature when the user is on their way home.

Cloud Computing

Cloud computing is the delivery of computing services over the internet, such as storage, processing power, and software applications. Instead of running programs or storing data on a local computer or server, cloud computing allows users to access resources and services from anywhere with an internet connection.

An example of cloud computing is the use of Google Drive or Dropbox to store and share files, which can be accessed from any device with an internet connection.

Big Data

Big data refers to the massive amounts of data that are generated by businesses, organizations, and individuals every day. This data is too large and complex to be processed by traditional data processing methods, and requires specialized tools and techniques to extract meaningful insights.

An example of big data is the data generated by social media platforms, which includes information about users’ likes, shares, and comments. By analyzing this data, businesses can gain valuable insights into their customers’ preferences and behavior.

Data Science

Data science is a field of study that combines statistical analysis, programming, and domain expertise to extract insights from data. Data scientists use a variety of tools and techniques, including machine learning and data visualization, to uncover patterns and relationships in data that can be used to inform business decisions.

An example of data science is the use of predictive analytics to forecast demand for a product or service, based on historical sales data and other relevant factors. By analyzing this data, businesses can make more informed decisions about production, inventory management, and marketing strategies.