Understanding the Basics of AI | Core Concepts of AI and Types of AI

An Overview of Artificial Intelligence

Artificial intelligence (AI) is a technology and a major branch of computer science trained on human intelligence. It is designed to perform tasks that typically require human thinking. Tasks such as pattern recognition, decision-making, and language understanding are a few examples of Artificial intelligence. The journey of AI began in the 1950s with scientists like Alan Turing and John McCarthy. John McCarthy was the first one to introduce the term Artificial Intelligence and is also known as the father of AI.

Since then AI has evolved from simple rule-based systems to the complex, data-driven machine learning and Artificial Intelligence models we see today. As AI integrates deeply into our daily lives, from personal assistants to financial algorithms, from our school work to office work, understanding its basic concepts has become essential. This knowledge not only helps us to understand current technological trends but also allows us to integrate AI into our daily lives, into our society, employment, and ethical considerations.

Some basic concepts of AI include Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), Computer vision, and Neural Networks. Machine Learning (ML) is further classified into Supervised Learning, Unsupervised Learning, and Reinforcement learning. In this article, we are going to introduce to talk about some important concepts of AI. However, Artificial intelligence is classified into three types, 1. Narrow AI, 2. General AI, 3. Super Intelligent AI.

“Artificial intelligence will automate everything from transportation to healthcare,  freeing up more time for us to be creative and pursue our passions.” —Elon Musk

Artificial Intelligence

Artificial intelligence (AI) is a major field of Computer Science designed to create Machines or Algorithms capable of performing tasks that typically require human intelligence. These tasks include visual perception, speech recognition, decision-making, and language translation. People use AI in education and in their daily lives and their because of the vast benefits of AI. Some benefits of AI are: 1. Automated Tasks 2. Decision Making 3. Reduction in errors 4. Availability 5. Writing tasks 6. Managing risks 7. Medical Advancements 8. Business Advancements 9. Cost reduction 10. Work Speed.

Basic Concepts Of AI

1. Machine Learning (ML)

Machine Learning is the core concept of AI and this is where AI gets intelligence in its name. The main purpose of Machine Learning is to develop algorithms that can learn from given data. Take an example of the human learning process likewise, ML enables computers to learn from data, doing predictions, and make best decisions. Machine Learning is the opposite of programming where rules and conditions are defined to make decisions. But Machine Learning enables algorithms to learn from the data and make decisions.

ML is used in many areas of our lives such as in social media apps. Apps like TikTok, Instagram, and Netflix use ML to personalize user interests. This also raises sensitive issues like data privacy of users.

ML is classified into three types:

2. Neural Networks

Neural networks are inspired by human biological networks found in the human brain. These are known as Artificial Neural Networks (ANNs). They consist of multiple layers, each layer consists of nodes that are arranged together. Neural networks consist of neurons. The purpose of these neurons is to get input data and then process the data and finally give an output. These networks process the information by considering their importance or weight.

There are three types of layers in ANNs named as: 1. Input Layer 2. Hidden Layer 3. Output Layer. Input Layer receives the information. The number of neurons responding depends upon the weight or importance of the information. Input layers just receives the information and pass the information to the next layer for further processing.

Hidden Layers are the layers lying between Input and Output Layers. The amount of neurons processing the information also depends upon the weight of data or the complexity of problem. Hidden layers solve the problem and pass the information to next layer. Output Layer is the last and final layer of ANNs. The function of output layers is to produce output.

There are different types of neural networks such as Feedforward Neural Networks, Recurrent Neural Networks, Convolutional Neural Networks. ANNs are used in tasks like image recognition, speech recognition, etc.

3. Deep Learning (DL)

Deep Learning (DL) uses ANNs to process information. Deep Learning is a subset of Machine learning. DL uses various layers of ANNs and process larger amount of information. Deep learning involves training of Neural Networks to learn from data and producing better results. DL learns from data and adapts itself to handle complex situations just like humans.

Deep Learning is often used to handle complex tasks and to process very large datasets. For instance, Deep Learning is used in automated vehicle driving to make complex decisions and identifying hurdles and objects.

4. Natural Language Processing (NLP)

The basic purpose of NLP is to make computers capable of Understanding and generating human written content. NLP is a branch of AI that helps computers to read, understand, and generate human language. NLP combines Linguistics and computer science to understand language structure. Modern NLP techniques utilize Deep Learning and Machine Learning to understand and mimic human language.

NLP algorithms are trained on large datasets. Every day, a large amount of data is available on internet (especially on internet) that humans use to interact with each other. This data is very crucial for NLP algorithms to mimic human-like language. Computer experts train algorithms on this data.

Some basic key steps of NLP include:

1. Segmentation: Dividing text into individual sentences.
2. Tokenizing: Splitting sentences into individual words or tokens.
3. Stop Words: Identifying and marking common words like prepositions and verbs that are often filtered out.
4. Stemming: Reducing words to their root form by removing prefixes or suffixes.
5. Lemmatization: Converting words to their base or dictionary form, recognizing that different forms can have the same meaning.
6. Speech Tagging: Assigning parts of speech tags (e.g., noun, verb) to each word.
7. Named Entity Tagging: Identifying and classifying key entities (e.g., names, dates) in the text.

5. Computer Vision

Just like NLP, Computer Vision is another major branch of AI. Computer Vision helps computers to visualize and understand the visual world. AI use Computer Vision to interpret images and videos. Computer Vision models are trained on large datasets, where they learn to recognize objects, detect irregularity, or even predict outcomes based on the visual inputs. As the system processes more data, it continuously improves its accuracy and reliability.

Examples: Auto-pilot modes in self-driving cars utilizes computer Vision to analyze the visual world. Moreover, tasks like Facial Recognition, analysis of images, and object recognition, all use Computer Vision. Computer vision can help robots to interact with real world like humans.

6. Expert Systems

Expert systems is also a field of AI that helps AI to solve complex tasks. Expert system is a program that helps AI solve complex problems and make decisions that typically require human thinking. Expert systems are used where we need to handle large amounts of data and want a decision quick and reliable. However, this program struggles to maintain and keep updating the information.

An Expert System consists of two components:

1. A Knowledge Base: A structured repository of facts and rules about a specific domain, gathered from human experts, that an expert system uses to make decisions.

2. An Inference Engine: The component of an expert system that applies logical rules to the knowledge base to deduce new information or make decisions.

7. Robotics

When integrated with AI, robots make a huge difference in their performance. Nowadays, many businesses are automated with AI robots, and many industries use robots to manufacture products. The integration of AI in robotics can result in the development of more sophisticated, advanced, and autonomous systems that can operate in complex environments.

In the context of AI, robots are designed to process information and take actions based on that information. For example, a robot equipped with Computer Vision can analyze the digital world. A robot equipped with NLP can make it capable of understanding and responding to human language. Machine Learning algorithms help robots improve their performance over time by learning from experience and information, similar to how a human learns through practice.

Comparison In Table

Types of Artificial Intelligence (AI)

Generally, AI is classified into three types:

2. General AI (AGI)

Artificial General Intelligence is an advanced type of AI that can understand, learn, and apply knowledge across a wide range of tasks, much like a human. AGI would be able to reason, solve problems, and think creatively in different situations without being specifically trained for each one.

For example: It is like a robot that can perform any intellectual task a human can do, such as solving a complex math problem or writing a novel.

3. Superintelligent AI (ASI)

Superintelligent AI or Artificial Superintelligence refers to an AI that surpasses human intelligence in all aspects such as creativity, problem solving, social intelligence, and more. ASI would be able to outperform humans in every field and could potentially make decisions that are far beyond human understanding.

For example: This level of AI is still a theoretical concept and doesn’t have a real World example. But it is often shown in science fiction movies as a future AI that could revolutionize or even challenge human civilization.

“AI will enable computers to process information more like a human brain, leading to incredible advancements in technology.” — AI innovator and computer scientist, Fei-Fei Li

Examples Of AI In Real Life

Virtual Assistants: AI-Powered assistants like Siri, Alexa, and Google Assistant help with tasks such as setting reminders, playing music, and answering questions.

Recommendation Systems: Platforms like Netflix, YouTube, and Amazon use AI to recommend movies, videos, and products based on your preferences and past behavior.

Healthcare: AI is used in medical imaging to detect diseases, in predictive analytics to forecast patient outcomes, and in personalized medicine to tailor treatments to individual patients.

Self-Driving Cars: Companies like Tesla and Waymo use AI to enable autonomous driving, improving safety and efficiency on the roads.

Chatbots: Many websites use AI chatbots to provide customer service, answer queries, and assist with transactions.

Smart Home Devices: AI powers devices like smart thermostats, security cameras, and robot vacuum cleaners, making homes more efficient and secure.

Social Media: AI algorithms curate your feed, suggest friends, and detect inappropriate content on platforms like Facebook, Instagram, and Twitter.

Financial Services: AI is used for fraud detection, credit scoring, and personalized financial advice in banking and investment services.

Conclusion

Through the History of AI, it has transformed and automated different aspects of our lives by enhancing decision making and improving accuracy across all fields. From ML, DL, and ANNs to NLP, AI technologies offer powerful tools for understanding and interacting with the world. The integration of programs like Computer Vision, Expert Systems, and Robotics in AI has made our lives more efficient and reliable. As AI develops day by day, its potential to transform industries and improve everyday life grows.