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Olatunde Emmanuel
Olatunde Emmanuel

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Core Concepts of AI

Machine Learning

Machine learning (ML) is a part of artificial intelligence (AI) and computer science. The main goal is to develop statistical algorithms. These algorithms help computers learn from data. They also help computers adjust to new information. Finally, they help computers perform better without specific programming.

Types of Machine Learning

ML involves training computer systems with many examples to understand problem-solving and predictive abilities, like how humans learn. The goal is to improve the system's accuracy over time.

  1. **Supervised Learning*: In supervised learning, algorithms learn from labeled datasets. Each data point has a tag showing what it is or what it leads to. This method uses a preset "answer key" to teach algorithms how to understand data. Supervised learning involves making predictions and classifications. Supervised learning algorithms adjust their settings based on the labeled data provided to them. This helps them make accurate predictions based on new, unseen data.
  2. Unsupervised Learning: In unsupervised learning, algorithms work without labels on datasets. They look for patterns and structures within the data without direct guidance. These algorithms explore the data, grouping similar data points or uncovering hidden relationships. They find patterns and insights from large, messy sets of data without needing prior knowledge of the process of sorting the data.
  3. Reinforcement Learning: Algorithms learn the best actions by practicing reinforcement learning. They interact with their environment and get feedback in the form of rewards or penalties based on what they do. Unlike guided learning, where data needs labels, reward-based learning doesn't need labeled data. Instead, algorithms learn by trial and error to maximize total rewards over time. We use this method in situations where decisions happen in a certain order, like in games, robots, and when guiding self-driving cars.

Neural Networks.

Neural networks, also known as ANNs, are computer models inspired by the human brain. They consist of interconnected nodes arranged in layers: input, hidden, and output. Nodes process input data by considering their importance (weights) and a certain threshold. They become active if the result is higher than a set limit. These networks improve with training data, getting better at tasks like recognizing patterns. Through simulation and training, they adjust and improve their responses.

Neural networks help with tasks like Google searches and talking to your phone. They make finding stuff and understanding what you say faster and better than if people did it by hand. They play a crucial role in machine learning and deep learning, helping with fast data analysis and problem-solving.

Structure of Neural Network

Basic Structure and Functionality of Neural Networks

An Artificial Neural Network (ANN) has three main parts. Its basic structure includes:

1.Input Layer

  • Receives the input data in the form of vectors.
  • Contains neurons corresponding to the features of the input data.
  • Each neuron stands for a different feature. The number of neurons in this layer matches the dimensions of the input data.
  • The input layer sends the input data to the next layer. It doesn't do any computations itself.

2.Hidden Layers

  • Intermediate layers between the input and output layers.
  • Comprise many neurons.
  • Each hidden layer performs computations on the input data using weights and biases.
  • Activation functions apply to produce outputs that pass to the next layer.
  • The number of hidden layers can change depending on how complicated the problem is. The number of neurons in each hidden layer can also vary based on the network's design.

3.Output Layer

  • The final layer of the neural network.
  • It consists of neurons that produce the network's output.
  • The number of neurons depends on the type of problem the network is solving.
  • Apply activation functions to produce the final output of the network.

This basic structure lays the groundwork for ANNs. They can take on different shapes and setups depending on the problem at hand and how well they need to perform.

Deep Learning

Deep learning is like a smart cousin of regular machine learning. We use deep neural networks, which function in our brains, to understand information from raw data. These networks excel at identifying patterns, organizing data into categories, and making forecasts. They earn the label "deep" due to their many layers collaborating to enhance results.

Deep learning powers many advanced technologies we rely on, such as self-driving cars. It also enables smart chatbots, like ChatGPT. Face recognition on your phone is another application. Deep learning helps in detecting medical issues too. It even assists devices in understanding spoken commands. It's like having super-smart helpers that can handle all sorts of tricky tasks.

Deep learning is enhancing functionality by empowering computers to think like humans. Its ability to learn and solve complex problems simplifies and adds excitement to life.

Natural Language Processing (NLP)

Natural Language Processing (NLP) combines language with computers.

  • It assists machines in grasping and generating human speech and text.
  • It enables tasks like translation, voice commands, and summarizing text.
  • It uses machine learning to analyze language data.
  • Its goal is to help computers understand text better. NLP helps digital assistants and business tools become smarter and work better. It is important for them.

Computer Vision

Computer vision assists computers in understanding digital images and videos. It is a type of artificial intelligence. It uses cameras, data, and algorithms instead of human eyes and brains. Computers learn to recognize patterns in images by looking at lots of examples.
Different industries like energy, manufacturing, and automotive use computer vision. Computer vision helps with taking photos, changing images, understanding what's in a picture, and getting information from the real world.


Robotics involves combining science, engineering, and technology to make robots. People build, operate, and use these robots to do tasks in different industries. Robotics involves:

  • Mechanical engineering.
  • Computer science.
  • Electrical engineering.
  • Control systems.
  • Software programming and other related fields.

The main goal of robotics is to

  • create machines that can help people.
  • perform tasks that are risky, monotonous, or unpleasant.
  • enhance efficiency and precision, particularly in manufacturing.

With advancements in artificial intelligence, robots are becoming capable of handling complex situations.
Robotics involves many tasks. These include:

  • Building mechanical parts
  • Designing electrical components
  • Writing software.

The goal is to make smart machines that can work in different places.

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