What Artificial Intelligence Really Is
Artificial intelligence, conventional software, automation, algorithms, models, narrow AI, artificial general intelligence and the historical development of the field.
Artificial Intelligence AI-101
Artificial Intelligence Foundations
A Non-Technical Introduction to Modern Artificial Intelligence
Build a clear and practical understanding of how modern artificial intelligence works without programming, advanced mathematics or prior technical training.
US$145
An electronic First BCI University Certificate of Completion in PDF format is included after successful completion.
AI-101
Course overview
Artificial Intelligence Foundations is a structured beginner-level course for learners who want to understand artificial intelligence beyond headlines, product demonstrations and technical jargon.
The course explains how data, machine learning, neural networks, generative AI, computer vision, audio systems, robotics and autonomous systems fit together. Students also examine the limitations of AI and the importance of privacy, security, fairness, verification and meaningful human oversight.
The program is designed for intelligent adult learners and does not require programming experience, advanced mathematics or previous study in computer science.
Learning objectives
What you will learn
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01
Explain how artificial intelligence differs from conventional software, automation and rule-based systems.
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02
Describe how datasets, training, validation, testing and inference support AI development.
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03
Distinguish supervised, unsupervised and reinforcement learning.
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04
Explain neural networks, deep learning and transformers at a conceptual level.
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05
Describe how generative AI and large language models produce content.
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06
Recognize common problems including hallucination, overfitting, bias, data leakage and distribution shift.
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07
Understand the foundations of computer vision, audio AI, robotics and autonomous systems.
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08
Evaluate an AI system by examining its purpose, data, method, output, risks and human oversight.
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09
Discuss privacy, security, fairness, transparency and accountability in responsible AI use.
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10
Identify pathways for continued AI learning and professional development.
Ten structured modules
Course curriculum
Data: The Foundation of Artificial Intelligence
Structured and unstructured data, collection, labeling, quality, representativeness, privacy, bias and the complete data lifecycle.
Machine Learning Fundamentals
Supervised, unsupervised and reinforcement learning, model evaluation, generalization, overfitting and practical error analysis.
Neural Networks and Deep Learning
Layers, weights, representations, convolutional networks, recurrent networks, transformers and the strengths and limitations of deep learning.
Natural Language Processing and Generative AI
Language models, tokens, context, prompting, generation, hallucinations, verification, retrieval and introductory AI-agent concepts.
Computer Vision, Audio AI and Multimodal Systems
Image recognition, object detection, speech systems, synthetic media, multimodal analysis, authenticity and consent.
Robotics and Autonomous Systems
Sensors, perception, planning, control, actuators, autonomy, safety boundaries and human intervention.
Artificial Intelligence in the Real World
AI applications in healthcare, finance, education, retail, logistics, manufacturing, government, agriculture and small business.
Responsible Artificial Intelligence
Ethics, privacy, security, bias, fairness, explainability, accountability, governance and meaningful human oversight.
The Future of AI and Human Capability
AI agents, multimodal systems, emerging applications, professional roles, transferable human skills and lifelong learning.
Official recommended coursebook
First BCI University Artificial Intelligence Foundations
A Non-Technical Guide to Data, Machine Learning, Generative AI, Robotics, and Responsible Use
- Author
- Konstantin Titov
This book is the recommended learning material for AI-101. Its ten chapters follow the course curriculum and provide detailed explanations, realistic scenarios, knowledge checks and applied learning activities.
The book is published separately and is recommended for students who want a permanent reference throughout and after the course.
Successful completion
Assessment and completion
Assessment components
- Ten module knowledge checks
- Applied learning activities based on realistic AI scenarios
- A final assessment covering the principal course concepts
- A final analysis of one real-world AI system
Completion requirements
- Complete all ten learning modules
- Complete the required knowledge checks
- Achieve at least 70% on the final assessment
- Submit the final AI-system analysis
AI-101 Enrollment
Begin your structured study of artificial intelligence.
US$145
Purchase secure online access to AI-101. The electronic First BCI University Certificate of Completion in PDF format is included after meeting the completion requirements.
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