How Does Artificial Intelligence Work? From Symbols and the Perceptron to Transformers, ChatGPT, and Claude
An advanced conceptual course explaining where modern AI came from, how machine learning, neural networks, and transformers work, and how to realistically assess the capabilities and limitations of tools such as ChatGPT and Claude.
This course is designed for people who want to understand AI more deeply than at the level of marketing slogans. The participant will move from the history of AI and machine learning, through the mechanics of neural networks and deep learning, all the way to the breakthrough of the transformer architecture and the consequences that “Attention Is All You Need” brought for systems such as ChatGPT and Claude. The course does not teach model programming, but it provides operational understanding: which problems were solved by successive approaches, why earlier methods did not scale well enough, where hallucinations come from, why models can be convincing despite errors, and how to distinguish real capabilities from false expectations. Throughout the course, the participant will work with artifacts useful in practice: a timeline of breakthroughs, a comparative matrix of AI types, a task assessment card for AI suitability, a data flow diagram for model training, checklists for evaluating model responses, mini implementation case studies, and simple process diagrams. The course uses current, publicly described examples of model development and the risks associated with their use, including the growing scale of enterprise adoption, the persistent problem of hallucinations, and the industry’s emphasis on governance and model containment.
What you will learn
- Explains the main stages in the history of AI and identifies which promises, limitations, and breakthroughs truly changed the direction of the field.
- Distinguishes the basic types of AI and knows when we are talking about rule-based systems, classical machine learning, neural networks, deep learning, and generative models.
- Describes the history of machine learning as a response to the limitations of hand-written rules and can point out the cost of this shift: dependence on data, optimization objectives, and metrics.
- Explains the operation of a neuron, a layer, signal propagation, and learning in a neural network at a conceptual level, without resorting to unnecessary formalism.
- Explains what deep learning changed and why the scale of data, computing power, and architecture began to matter critically.
- Understands what problem the transformer solved compared with earlier sequential architectures and why the attention mechanism became a breakthrough.
- Can explain the significance of the publication “Attention Is All You Need” and connect it to the path toward modern models such as ChatGPT and Claude.
- Understands how a large language model is trained: data, tokenization, next-token prediction, fine-tuning, instructions, and quality evaluation.
- Sees the difference between a model generating statistically probable text and it “understanding the world” in the human sense.
- Recognizes typical sources of model errors, including hallucinations, overconfidence, prompt dependence, lack of freshness, and problems with uncertainty estimation.
- Uses simple decision-making artifacts to assess whether a given task is suitable for AI support and what safeguards are needed.
- Can have a more realistic conversation about AI in an organization: without techno-mysticism, without panic, and without confusing a product demo with the system’s actual capability.
Prerequisites
No programming requirements. General familiarity with digital products, knowledge work, or business analysis is helpful. The participant should be ready to read diagrams, comparisons, and simplified descriptions of technical mechanisms.
Course syllabus
- From the Turing test to AI winters: which promises were premature, and which laid the foundation for today’s models
- Symbolic AI vs. data-driven learning: two different ways of building “intelligence” and their operational costs
- The history of machine learning as an escape from hand-written rules: perceptron, regression, trees, and statistical models
- Why earlier waves of AI did not scale to complex language and general knowledge
- Mini-case: how a manager in 2012, 2017, and 2026 could misjudge the “AI breakthrough”
- Quiz: identify the era, paradigm, and the real reason for the success or failure of a given approach
- The word “AI” in one presentation can mean five different things: how not to be fooled by the label
- Rule-based system, classic ML model, neural network, generative model, agent: comparison on a single decision matrix
- Narrow AI vs AGI: where precise description ends and speculation begins
- Prediction, classification, ranking, recommendation, generation: five different tasks, five different quality criteria
- What does it mean that a model is multimodal, and why does that not automatically mean it “understands the world”?
- Quiz: match the right type of AI to the problem, process owner, and acceptable risk
- Artificial neuron without mystique: inputs, weights, activation, and the decision the model makes numerically
- What Really Happens During Learning: Loss, Error Signal, and Parameter Updates
- Why hidden layers exist: how a network builds increasingly complex feature representations
- The practical history of neural networks: from the perceptron through backpropagation to the post-2012 renaissance
- Deep Learning as a Shift in Scale: Data, GPUs, Architectures, and the Economics of Training
- Failure modes: overfitting, poor data, the wrong optimization objective, and a misleading success metric
- Quiz: indicate whether the problem stems from architecture, data, learning objective, or evaluation method
- What RNNs and LSTMs Lost To: Long Dependencies, the Bottleneck of Sequentiality, and Training Cost
- Attention as a mechanism for selecting context: how the model decides what to look at in a sentence
- Transformer step by step without equations: tokens, embeddings, position, attention, layers, and output
- “Attention Is All You Need”: what exactly was groundbreaking and why the industry adopted it so quickly
- What parallelism and scale enabled: why much larger models could be trained after the transformer
- Worked comparison: the same task before and after the transformer — where the real quality improvement appears
- Quiz: identify which transformer component is responsible for a given advantage or limitation
- LLM as a Token Prediction Machine: Why Complex Language Behavior Emerges from a Simple Training Objective
- Where the model “knows” what to write: training data, tokenization, and learning patterns without a fact database in its head
- From base model to helpful assistant: instruction tuning, feedback, and response policies
- What the product does beyond the model: conversation memory, tools, search, integrations, and the security layer
- Why models hallucinate despite impressive response fluency and why tone confidence misleads users
- ChatGPT, Claude and Similar Systems in 2025–2026: How the Market Is Shifting from Chat to Agents and Deeper Workflows
- Mini-case: the HR department, sales team, and operations analyst use the same model, but need different safeguards
- Quiz: distinguish model capabilities, product capabilities, and user expectations
- The model does not “understand” like a human: what can be inferred from behavior, and what must not be read into it
- How to evaluate an AI answer in practice: correctness, completeness, traceability, uncertainty, and cost of error
- When AI helps, and when it only speeds up the production of errors: a task qualification card for model use
- Governance for ordinary teams: who approves use, what data may be shared, and when a human must stay in the loop
- Case review: how to defuse the phrase “AI will do it for us” during a procurement or strategy meeting
- Final synthesis: how to explain in 5 minutes where modern AI came from and how it really works
- Final quiz: diagnosing myths, limitations, and proper applications of modern AI
FAQ
For managers, analysts, product specialists, marketers, educators, and anyone who wants to understand artificial intelligence more deeply than at the level of trendy slogans. If you use tools such as ChatGPT or Claude and want to know where their behavior, limitations, and advantages come from, this course is for you.
No. The course does not teach model implementation or coding. It is designed to give you operational and strategic understanding of AI: from the logic of successive breakthroughs, through the mechanics of neural networks, to the significance of transformer architectures.
You will understand which problems were solved by successive AI approaches, why earlier methods had limitations, how machine learning really differs from deep learning, and why transformers became the foundation of modern language models. This will make it easier to assess the capabilities, risks, and business sense of using AI.
Because AI has stopped being a curiosity and has become part of real work transformation. McKinsey indicates that organizations are moving from experiments to larger-scale deployments, and skills and training gaps are now among the main barriers. At the same time, demand is growing for AI fluency, meaning practical understanding of the technology, not just the ability to use tools. This course helps build exactly that foundation.
Yes. We present them as the result of a longer evolution of ideas: from symbolic AI and the perceptron, through the development of neural networks, to the transformer breakthrough initiated by the paper “Attention Is All You Need.” As a result, you do not just learn the names of tools, but understand why they work the way they do.
Yes — that is one of its main values. The course organizes the history, concepts, and mechanisms of AI, making it easier to distinguish flashy marketing from the real capabilities of models, ask better questions of technology vendors, and make more sensible educational or business decisions.
- 8 hours
- Advanced
- Certificate on completion
- Access immediately after purchase