Foundations of AI
Understanding the core principles behind modern intelligent systems.
Understanding the Core Principles Behind Modern Intelligent Systems
Artificial Intelligence may feel like a modern breakthrough, but the foundations that support today’s advanced models were laid decades ago. Long before large language models and deep learning, engineers and researchers developed practical methods for handling uncertainty, recognising patterns, and making automated decisions. These early techniques still form the backbone of industrial AI today.
At SES Engineering, we believe that understanding these fundamentals is essential for building reliable, predictable, and valuable AI solutions. This page introduces two of the key building blocks: fuzzy logic and pattern recognition.
The Origins of Artificial Intelligence
AI began as an attempt to formalise human decision-making — not by creating “thinking machines,” but by designing systems that could make structured, consistent choices based on real-world data. Early AI focused on rules, signal interpretation, and mathematical models that allowed automation to move beyond simple on/off logic.
Even today, many industrial processes still rely on these early AI concepts, and for good reason: they are robust, explainable, and proven in harsh environments.
Fuzzy Logic
Making decisions in a world that isn’t black and white
How it works
Traditional computing operates in absolutes: true or false, on or off, 0 or 1. But most real processes don’t behave that cleanly. Temperatures drift, pressures fluctuate, sensors pick up noise, and operators deal with shades of grey rather than strict thresholds. Fuzzy logic was developed to handle this uncertainty.
- Temperature might be 0.6 hot and 0.4 warm.
- A vibration signal might be 0.2 acceptable and 0.8 concerning.
- A flow rate might be partially stable rather than simply “stable” or “unstable.”
This allows for smoother, more human-like decision-making.
Where it’s used
- Industrial controllers
- Process optimisation systems
- HVAC and motor control
- Washing machines and consumer appliances
- Robotics and navigation
It is simple, predictable, and extremely effective.
Pattern Recognition
Teaching machines to recognise signals, trends, and behaviours
Pattern recognition was one of the earliest practical applications of AI. The idea is straightforward: machines can be taught to spot consistent patterns in data — even when those patterns are subtle. Today, it underpins nearly every machine learning technique, but it started with simple mathematical models used in:
- Quality control
- Image and signal analysis
- Fault detection
- Process monitoring
Why it matters
Industrial systems generate huge amounts of data, from sensor traces to production logs. Pattern recognition allows AI to:
- Identify normal vs abnormal behaviour
- Detect early signs of mechanical issues
- Recognise trends before human operators see them
- Support predictive maintenance
- Classify products or batches
These early techniques evolved into the algorithms we now use for neural networks and advanced analytics.
How SES Engineering Can Support Your Business
Practical help for organisations exploring AI
While AI has become more accessible, many businesses are still unsure where to start. SES Engineering offers a practical, straightforward way to explore how these foundational AI methods can be applied in real industrial environments.
Here’s how we can support you:
- Identifying opportunities. We help you understand where techniques like fuzzy logic and pattern recognition could improve reliability, efficiency, or visibility in your existing processes.
- Exploring small, low-risk projects. You don’t need a large investment to begin working with AI. We focus on manageable, well-defined tasks where early wins are achievable.
- Making AI understandable. Our goal is to translate the technical foundations into clear, understandable options so you can make confident decisions.
- Collaborative development. We work closely with clients, shaping ideas together and building solutions step by step. This keeps projects grounded, transparent, and practical.
This approach allows businesses of any size to start benefiting from AI — even if they’re just beginning the journey.
Where We Go From Here
Understanding the foundations of AI prepares you for the next steps: how machine learning models work, how neural networks learn, and how modern AI can be paired with classical logic to improve uptime, reduce waste, and streamline operations. Explore the next section — Modern Machine Learning — to see how these principles evolve into today’s advanced systems.