Introduction To Artificial Intelligence

 Artificial intelligence is a general term for a combination of fields that set the roots of AI, including its computational and decision-making power. Below are the seven important ones: 

1) Machine Learning is when machines are trained using various mathematical algorithms and datasets to make decisions based on that data. In this, the accuracy of the output depends on the size of the dataset the machine is trained using. 

The process starts with historical data collection, inputting instructions, and direct experiences to build a model for future reference. As the model gains experience, its accuracy improves with time, and it can make decisions without specifically being programmed. Many trending programming languages like Python and R are used for AI and ML. 

There are three types of learning algorithms:

• Supervised Learning: This means using labeled data for machine learning and decision-making. Let’s take an example of building a model that recognizes the fruit images and gives their names. Such model types will be trained using a large dataset of fruit images with their names labeled on them. 

• Unsupervised Learning: An unlabelled dataset is used to train the model. The purpose of unsupervised learning is to help the machine recognize patterns, relationships, and similarities between the data. For example- if we feed the model with a bunch of apple and banana images. The model has never seen any banana or apple in the past, but looking at the shapes and colors, it would classify the pictures into two groups- one of bananas and the other of apples. 

• Reinforcement learning: Reinforcement learning is when a machine learns via try-and-get-errors methodology. It is learning through the feedback when a machine makes an action by interacting with the environment. The input can be positive or negative, using which machine takes the next move—for example- an ML-backed chess game model. 

2) Natural Language Processing (NLP): The computer can learn, understand, and process human languages via voice or text. Virtual assistants, chatbots, and spam detection are the biggest examples of NLP. The models are trained using machine learning, linguistics, and deep learning models to understand the meanings, intent, and sentiment. As humans have different accents and intonations, recognizing speech can be challenging, which is why NLP techniques are used. 

3) Computer Vision(CV): It is the branch that assists computers in recognizing and understanding images and visual information. Computer vision algorithms allow the computer to perform object detection, facial recognition, and image classification tasks. Its applications range from autonomous vehicles to medical imaging. 

4) Neural Networks/ Deep Learning: Neural networks are also called artificial neural networks (ANN) and simulated neural networks (SNN). They mimic the human brain in how it passes information from one neuron to another. ANNs have node layers- Input (takes the input), Hidden (processes and transports the data), and Output Layer (gives the final result or prediction.) Each node, also called an artificial neuron, has other neurons with a threshold and weight. Whenever a node’s output touches the threshold value, it gets activated and sends the information to the next node. Neural networks need training for both learning and improving accuracy. Deep Learning is a neural network with 3 or more layers.” Visual recognition in self-driving cars so that they can recognize traffic signals, road signs, and people is an example of a neural network. 

5) Robotics: It is a field of engineering that deals with robot conception, design, construction, and invention. Robots are programmatic machines that can take a series of actions. People can control them using a remote or their respective control systems. Robotics aims to create intelligent machines that can assist humans in various fields, including manufacturing, healthcare, and transportation. Humanoid robots are the latest developed and better-known examples of robots. 

6) Expert Systems: These AI programs simulate the judgment and behavior of humans or an organization that is expert or experienced in a particular field. Expert systems are used in various fields like financial planning, customer service, and medical diagnosis. They are intended to complement human beings and not “replace” them. “CaDet” is a diagnostic expert system that helps medical professionals detect cancer. 

7) Fuzzy Logic: Fuzzy logic lets computers make decisions in the form of “degrees of truth,” which lies between the digital values 0 and 1 (False and True). Have you ever solved a psychological assignment asking, “How often do you get angry?” and the options are very less(0), less(0.25), sometimes(0.5), often(0.75), and very much(1)? This is an example of fuzzy logic and how it considers all the possibilities between 0 and 1 instead of the basic true and false logic. Companies like Nissan use fuzzy logic to control break systems in dangerous situations. 

Some Trending Examples of AI:

Think of your day today and count how many times you used AI. ChatGPT at work, Snapchat filters to click a selfie, Alexa to play music, and Google Maps to visit any location. You must use at least one or two of these almost daily. So, this is how common artificial intelligence is these days, and now, we are about to read some common and trending AI examples that we use in our everyday lives!

• ChatGPT: ChatGPT is one of the most used trending limited memory large language models (LLM) developed by OpenAI. As the name says, ChatGPT is a chatbot that can help humans through texts. If you want to write an email, research your next blog, need help with your school project, or surf for new Instagram reel ideas, ChatGPT can take care of it. Thus, be it work, education, or business, this chat AI helps individuals in day-to-day operations and makes their tasks much quicker and easier. 

• Google Maps: Who hasn’t used Google Maps ever? No-one. No matter where you live or work, you must have used Google Maps at least twice or thrice. Google Maps is the first thing we open on our mobile whenever we want to plan a trip or visit a new location. It would be interesting to know that even this amazing innovation that helps us see any area on our mobile, all its street, indicates the traffic, tells duration, directions, and whatnot, uses AI algorithms to provide all these real-time insights. It analyzes large amounts of data, including traffic history and user inputs, to predict everything. 
• Smart Assistants: Alexa, Siri, and Google assistants that almost help us daily to play music, call someone, answer questions, do voice chats, or do anything on our mobiles or other electronic devices are other examples of AI. These also use ML and NLP techniques to understand and interpret human commands. 

• Self-Driving Cars: Self-driving cars rely heavily on AI to analyze, decide, and control their movements. It has embedded ML algorithms, deep learning techniques, cameras, and sensors to detect moving objects, interpret traffic signals, and navigate through complex roads. 

• Wearables: In this era where people focus much more on fitness and health, smartwatches are another AI example widely used to track activities, SpO2 levels, heart rate, and sleeping patterns. Moreover, they provide personalized insights to improve overall health. 

• Snapchat Filters and My AI: Snapchat is one such app that reached its peak by discovering the filters. Interestingly, these filters also use AI algorithms and computer vision for facial recognition and tracking movements to interact with the users.