AI Lesson 1 – Introduction to AI | Dataplexa

AI Course

I. AI Foundations
1. Introduction to AI 2. History of AI 3. AI vs ML vs DL 4. Search Algorithms 5. Intelligent Agents 6. Informed & Uninformed Search 7. Constraint Satisfaction 8. Adversarial Search 9. Logic in AI 10. Planning in AI 11. Knowledge Representation 12. Probabilistic AI 13. Bayesian Networks 14. Markov Models 15. AI Ethics 16. AI Applications 17. Intro to PyTorch 18. Intro to TensorFlow 19. AI Workflows 20. Data for AI
II. Machine Learning
21. Introduction to ML 22. ML Workflow 23. Supervised Learning 24. Unsupervised Learning 25. Reinforcement Learning 26. Linear Regression 27. Logistic Regression 28. SVM 29. Decision Trees 30. Random Forest 31. Gradient Boosting 32. XGBoost 33. K-Means 34. Hierarchical Clustering 35. Dimensionality Reduction 36. Feature Engineering 37. Feature Selection 38. Model Evaluation 39. Overfitting vs Underfitting 40. Cross Validation
III. Deep Learning
41. Intro to Deep Learning 42. Neural Networks 43. Activation Functions 44. Backpropagation 45. Loss & Optimizers 46. Regularization 47. CNN Basics 48. CNN Architectures 49. RNN, LSTM, GRU 50. Seq2Seq 51. Transformers 52. Attention 53. Positional Encoding 54. Training DL 55. Hyperparameter Tuning 56. Transfer Learning 57. Autoencoders 58. GAN Basics 59. GAN Applications 60. Model Serving
IV. Natural Language Processing
61. Intro to NLP 62. Text Processing 63. Tokenization 64. Stopwords & Lemmatization 65. BoW & TF-IDF 66. Word Embeddings 67. Sentence Embeddings 68. NLP Classification 69. Sequence Modeling 70. RNN in NLP 71. Transformers in NLP 72. BERT Basics 73. BERT Fine-tuning 74. RoBERTa & DistilBERT 75. Sentiment Analysis 76. NER 77. Machine Translation 78. Text Generation 79. NLP Evaluation 80. NLP Use Cases
V. Computer Vision
81. Intro to CV 82. Image Processing 83. Filters & Edges 84. Convolutions 85. Object Detection 86. Object Tracking 87. Image Segmentation 88. Face Recognition 89. Feature Detection 90. OCR 91. Image Augmentation 92. Generative Vision 93. Vision Transformers 94. Multimodal Models 95. CV Use Cases
VI. LLM Engineering
96. Intro to LLMs 97. Tokenization 98. LLM Architecture 99. Pretraining 100. Fine-tuning 101. RLHF 102. Prompt Engineering 103. Embedding Models 104. Vector Databases 105. LLM Agents 106. Guardrails & Safety 107. AI in Production 108. End-to-End Project

Introduction to Artificial Intelligence

Artificial Intelligence, commonly referred to as AI, is no longer a futuristic concept limited to science fiction. It is already part of everyday life, quietly working behind the scenes in the apps, devices, and services we use daily. From search engines deciding which results appear first, to recommendation systems suggesting what to watch next, AI systems are designed to make decisions, recognize patterns, and act intelligently based on data.

At its core, Artificial Intelligence is about building machines and software that can perform tasks which normally require human intelligence. These tasks include understanding language, recognizing images, making decisions, learning from experience, and solving problems. Unlike traditional software that follows rigid, predefined rules, AI systems are often built to adapt and improve as they process more information.

What Does Artificial Intelligence Mean?

Artificial Intelligence stands for the ability of a machine to simulate aspects of human intelligence. This does not mean machines think or feel like humans, but rather that they can perform specific intelligent actions efficiently. An AI system observes its environment, processes information, and produces an output that is intended to achieve a goal.

For example, when you type a query into a search engine, the system evaluates millions of web pages, ranks them based on relevance, and presents the most useful results within milliseconds. This process involves pattern recognition, ranking logic, and decision-making — all of which fall under the umbrella of AI.

How AI Is Different From Traditional Software

Traditional software operates strictly on rules written by developers. If a condition is met, a specific action is performed. AI systems, on the other hand, are often built to handle uncertainty and variation. Instead of relying solely on fixed rules, they learn patterns from data and use those patterns to make predictions or decisions.

This ability to learn from data allows AI systems to perform well even in situations where it is difficult to write explicit rules. Tasks such as speech recognition, image classification, and recommendation systems would be nearly impossible to solve using only traditional programming approaches.

A Simple Example of AI-Style Decision Making

To understand the basic idea behind AI systems, consider a very simple rule-based decision program. While this example does not represent modern machine learning, it demonstrates how a system can take input and produce an intelligent-looking output. 


def recommend(activity):
    if activity == "tired":
        return "Play relaxing music"
    elif activity == "focused":
        return "Play concentration playlist"
    else:
        return "Play popular songs"

print(recommend("focused"))
Play concentration playlist

In this example, the program makes a decision based on the input provided. While real AI systems rely on data-driven models rather than fixed conditions, the fundamental idea remains the same: using information to select an appropriate action.

Types of Artificial Intelligence

Artificial Intelligence is commonly categorized into three broad types based on capability. These categories help us understand what current systems can do and what remains theoretical.

  • Narrow AI: Designed to perform a specific task. Almost all AI systems used today, such as voice assistants, recommendation engines, and image recognition models, fall into this category.
  • General AI: A hypothetical form of AI capable of performing any intellectual task a human can do. This level of intelligence has not yet been achieved.
  • Super AI: A theoretical concept where AI surpasses human intelligence across all domains. This remains a topic of research and debate.

All real-world AI systems you interact with today — search engines, recommendation systems, voice assistants — are examples of Narrow AI, built to solve specific problems effectively.

Practice Questions

Practice 1: What does AI stand for?



Practice 2: What key feature differentiates AI systems from traditional software?



Practice 3: Most AI systems used today belong to which category?



Quiz

Quiz 1: What best describes Artificial Intelligence?





Quiz 2: Which type of AI is commonly used today?





Quiz 3: What enables AI systems to adapt and improve?





What’s Coming Next

In the next lesson, we will explore the history of Artificial Intelligence, tracing how early ideas evolved into the powerful AI systems we use today.

Course Index Next →