Artificial intelligence (AI) is like a dynamic whirlwind that is increasingly taking control of our daily lives. But what does AI actually mean? In this blog, we dive into the different subfields of AI, each with their own unique applications and puzzles to solve. A disclaimer in advance: it will be quite a dusty article. All right, let's get started!

First of all: What does AI consist of?

AI consists of several subfields, including Machine Learning, Deep Learning, Natural Language Processing (NLP), Computer Vision, Robotics, Expert Systems, Neural Networks, Fuzzy Logic, Genetic Algorithms, and Multi-Agent Systems. Each subfield has its own focus and applications, from image recognition and speech processing to autonomous robots and optimization issues.

Let's dive into these subfields. What does it mean? How is this applied?

The AI Subfields

1. Machine Learning (ML)

Machine Learning is a fundamental subfield of AI that focuses on developing algorithms and statistical models that allow computers to learn from data and make predictions without explicit programming.

Applications:

• Spam filtering: Algorithms recognize and block unwanted emails.
• Predictive maintenance: predicting when machines need maintenance to prevent failures.
• Health care: diagnosis of diseases by analysing medical images and patient data.

2. Deep Learning

Deep Learning is a specialized form of Machine Learning that uses multi-layered neural networks. It is highly effective at processing large amounts of unstructured data such as images and sound.

Applications:

• Image recognition: Identifying objects in images used in self-driving cars.
• Voice recognition: Virtual assistants such as Siri and Alexa understand and process speech.
• Automatic translations: services such as Google Translate provide accurate translations.

3. Natural Language Processing (NLP)

Natural language processing (NLP) is a branch of artificial intelligence that focuses on the interaction between computers and human language. NLP analyses, understands, and generates human language. NLP combines linguistics, computer science, and AI to analyze text and speech. The process includes several steps, such as tokenization, where text is divided into smaller parts such as words or phrases, and sentiment analysis, which determines the emotional tone of a text.‍

Applications:

• Chatbots: Automated customer service and support.
• Sentiment analysis: Analyzing social media to measure public opinion.
• Automatic Summaries: Generate summaries of long documents.

4. Computer Vision

Computer Vision is a branch of artificial intelligence that focuses on allowing computers to understand and interpret visual information. It includes techniques such as image recognition, object detection, and facial recognition, allowing applications such as self-driving cars, medical image analysis, and augmented reality.

Computer Vision applications replicate human vision by combining sensor device input, artificial intelligence, machine learning, and Deep Learning. These applications use cloud-trained algorithms with significant amounts of visual data or images. By recognizing patterns in this data, they can analyze the content of other images.

‍Applications:‍

• Facial recognition: Security systems that identify people.
• Medical Image Analysis: Analysis of X-rays and MRI scans for diagnosis.
• Industrial Supervision: Quality Control in Production Lines.

5. Robotics

Robotics is a field in technology and engineering that deals with the design, construction and programming of robots. These machines can perform tasks ranging from simple repetitive actions to complex operations, often in environments that are dangerous or difficult for people to access. To do this, Robotics combines AI with mechanical and electronic techniques to create autonomous or semi-autonomous machines.

Applications:

• Industrial automation: robots that automate production processes.
• Health care: surgical robots that help doctors with surgeries.
• Domestic robots: devices such as robotic vacuums and lawnmowers.

6. Expert Systems

Expert systems are AI programs that make decisions or solve problems within a specific field. The heart of the expert system is the basic knowledge that human experts themselves put into this system. This information from human experts is then used to provide accurate and reliable answers, often used in diagnostics, planning and counseling.

Applications:

• Medical diagnosis: assistance in making diagnoses.
• Financial planning: advising on investments.
• Legal advice: help with legal issues.

7. Neural Networks

Neural networks are a type of artificial intelligence inspired by the human brain. They consist of layers of connected units or “neurons” that work together to recognize and learn patterns in data. Each neuron receives input, processes it, and transmits an output. This is the basis for many Deep Learning techniques & artificial intelligence.

Applications:‍

• Speech recognition: Transcribe spoken language to text.
• Image classification: Recognizing objects in images.
• Predictive Modeling: Predicting future trends.

8. Fuzzy Logic

Fuzzy Logic is also known as vague logic. This is an approach to logic that takes into account the degree of truth rather than traditional logic. It is designed to work with problems that contain uncertainty and vague information. Think of air conditioning: if it has to be 20 degrees in the room, the fuzzy logic calculates an air conditioning speed at 60%.

‍Applications:

• Control systems: Improving the performance of appliances such as washing machines.
• Decision making: Helping to make decisions in complex situations.
• Automatic transmissions: Optimizing vehicle switching performance.

9. Genetic Algorithms

Genetic Algorithms are search algorithms inspired by the process of natural selection. They are used to find solutions to optimization problems.

Applications:

• Optimization: Solving complex optimization problems.
• Machine Learning Model Selection: Choosing the best model for a specific task.
• Design automation: Automating the design of complex systems.

10. Multi-Agent Systems

Multi-Agent Systems consist of multiple intelligent agents that interact with each other and their environment. They are used for tasks that require coordination and collaboration.

Applications:

• Economic Market Simulations: Understanding Market Dynamics.
• Coordinated Robotics: Robots that work together to achieve a common goal.
• Transport and Logistics Management: Optimizing routes and deliveries.

Conclusion

Dusty, huh? In short: AI is a huge mix of subfields, each with their own speciality. From Machine Learning and Deep Learning to Robotics and Natural Language Processing, each subfield adds something extra to the AI world and can be used in our daily lives. By taking advantage of the powers of each subfield, we can use AI across a range of sectors.