What is the difference between AI, machine learning and deep learning?

They are concentric circles. AI is the whole field: any system doing something we associate with intelligence. Machine learning is the part of AI where the system learns from data instead of following hand-written rules. Deep learning is the part of machine learning that uses many-layered neural networks. All deep learning is machine learning, and all machine learning is AI, but not the other way round.

What is an LLM and how does it differ from traditional AI?

An LLM is a model trained on vast amounts of text to predict and generate language. The practical difference from earlier systems is that it does not need training for each specific task: the same model can summarise, classify, translate or converse depending on what it is asked. That is what made conversational AI viable for a small business without a data science team.

Does my company need its own data to use AI?

To train a model from scratch, an enormous amount — but almost no company needs to do that. To use an existing model on your business, it is enough to have the information you already hold in order: catalogue, policies, procedures, and access to the systems where changing data lives. The real work is not about data but about organisation.

Does AI make mistakes? How is that controlled?

Yes, in two distinct ways. It can be wrong for lack of information, which is solved by connecting it to the right system; and it can generate an answer that sounds right and is false, which is solved by design: answering from queried rather than remembered data, and escalating to a person when it does not have the fact.

Where does a company that has never used AI start?

With one concrete, repetitive, measurable process — not with a general strategy. Handling frequent enquiries on the channel where your customers already write is the most common entry case, because volume is high, risk is low and the result shows in the first week.

Artificial Intelligence Fundamentals: Everything you need to know

2025-04-28 · 6 min read · Education

Artificial Intelligence (AI) has gone from science fiction to becoming a fundamental tool in modern business. But what exactly is AI and how does it work? In this guide, we explain the basic concepts every professional should know.

What is Artificial Intelligence?

Artificial Intelligence is a branch of computer science that seeks to create systems capable of performing tasks that normally require human intelligence. This includes learning, reasoning, perception, natural language processing, and decision-making.

Unlike traditional programs that follow predefined instructions, AI systems can learn from data, identify patterns, and make decisions with minimal human intervention.

Types of Artificial Intelligence

There are different classifications of AI according to their capabilities:

Narrow AI: Designed to perform specific tasks. This is the type of AI we currently use: virtual assistants, recommendation systems, facial recognition, etc.
General AI (AGI): Hypothetical AI with cognitive capabilities similar to humans, capable of learning and performing any intellectual task. It does not yet exist.
Superintelligence (ASI): AI that would surpass human intelligence in all aspects. It is a theoretical and futuristic concept.

Machine Learning: The Engine of AI

Machine Learning is a subset of AI that allows systems to automatically learn and improve from experience without being explicitly programmed.

There are three main types of Machine Learning:

Supervised Learning: The system learns from labeled data. For example, learning to classify emails as spam or not spam based on previous examples.
Unsupervised Learning: The system finds patterns in unlabeled data. Useful for customer segmentation or anomaly detection.
Reinforcement Learning: The system learns through trial and error, receiving rewards or penalties. Used in robotics and games.

Neural Networks and Deep Learning

Artificial neural networks are inspired by how the human brain works. They consist of layers of interconnected nodes (neurons) that process information.

Deep Learning uses neural networks with many layers to learn complex representations of data. It is the technology behind:

Image and voice recognition
Automatic translation
Text and image generation
Autonomous vehicles

Natural Language Processing (NLP)

NLP is the branch of AI that enables machines to understand, interpret, and generate human language. It is fundamental for:

Chatbots and virtual assistants
Sentiment analysis
Automatic text summarization
Machine translation
Semantic search

Large Language Models (LLMs) like GPT-4, Claude, and LLaMA have revolutionized NLP, enabling natural conversations and high-quality content generation.

Business Applications of AI

Companies are adopting AI for multiple purposes:

Customer Service: Intelligent chatbots that respond 24/7 with contextual understanding.
Sales and Marketing: Personalization, customer segmentation, and behavior prediction.
Operations: Process optimization, predictive maintenance, and automation.
Human Resources: Candidate selection, engagement analysis, and talent management.
Finance: Fraud detection, risk assessment, and algorithmic trading.

How Roma AI Applies These Fundamentals

At Roma AI, we use these fundamentals to create conversational assistants that truly understand your customers. Our systems combine:

Advanced language models for natural conversations
Integration with your business systems and data
Continuous learning to improve with each interaction
Sentiment analysis for empathetic responses

Conclusion

Understanding the fundamentals of AI is the first step to harnessing its potential in your business. It is no longer about whether to adopt AI, but how to do it strategically. At Roma AI, we are ready to help you take that step, implementing conversational AI solutions that transform the way you interact with your customers.

From theory to business: where each thing applies

The concepts above explain how the technology works. This section answers the question that usually follows: which of it is useful for what.

Technique	What it solves well	Example in a company
Supervised learning	Classifying things based on prior examples	Detecting which enquiries are complaints and which are not
Unsupervised learning	Finding groups in unlabelled data	Segmenting customers by purchasing behaviour
Reinforcement learning	Optimising decisions through trial and error	Tuning pricing or delivery routes
NLP and LLMs	Understanding and generating language	Serving customers on WhatsApp, summarising conversations
Computer vision	Interpreting images	Verifying documentation uploaded by a customer

For most companies starting out, the row that matters is the fourth. Not because it is the most advanced, but because it applies to a process that already exists and already costs money: answering questions.

What changed with language models

Until a few years ago, applying AI to a business problem required a project: gather data, label it, train a model specific to that task, and maintain it. That put AI out of reach for any company without a data science team.

Large language models changed that economics. The same pre-trained model serves to summarise, classify, translate or converse depending on what it is asked. The company trains nothing: it describes its case and connects its systems.

That shift is why a small business today can have a conversational assistant running in an afternoon — something that five years ago was a six-month project on a corporate budget.

The limits worth knowing before you start

AI is a tool with specific capabilities and specific failure modes. Four that matter when deciding where to apply it:

It does not reason about what it has not seen. A language model is extraordinarily good at recognising patterns and fairly poor at formal logic or exact arithmetic. Calculations belong in a system, not in the model.
It can sound confident and be wrong. This is the most important risk in customer service, and it is controlled by making it answer from data queried in your systems rather than from what it remembers.
It inherits the biases in its data. If historical decision data carried a bias, a model trained on it will reproduce it. This matters a great deal in hiring, credit and any decision with consequences for people.
It does not know what you did not connect. An assistant without access to your ERP cannot report an order status, however advanced the model.

How to start without a six-month project

The most common mistake is not technical but one of scope: attempting an "AI strategy" instead of solving a problem.

Pick a repetitive, measurable process. One that happens many times a week and consumes someone's time today.
Measure the before. How often it happens, how long it takes, what it costs. Without that number you cannot show it worked.
Automate the simple part first. No integrations, no sensitive data. Seeing something small work earns the internal support for what comes next.
Connect the systems afterwards. That is where the assistant moves from informing to resolving, and where the real return shows up.

Frequently asked questions

What is the difference between AI, machine learning and deep learning?: They are concentric circles. AI is the whole field: any system doing something we associate with intelligence. Machine learning is the part of AI where the system learns from data instead of following hand-written rules. Deep learning is the part of machine learning that uses many-layered neural networks. All deep learning is machine learning, and all machine learning is AI, but not the other way round.
What is an LLM and how does it differ from traditional AI?: An LLM is a model trained on vast amounts of text to predict and generate language. The practical difference from earlier systems is that it does not need training for each specific task: the same model can summarise, classify, translate or converse depending on what it is asked. That is what made conversational AI viable for a small business without a data science team.
Does my company need its own data to use AI?: To train a model from scratch, an enormous amount — but almost no company needs to do that. To use an existing model on your business, it is enough to have the information you already hold in order: catalogue, policies, procedures, and access to the systems where changing data lives. The real work is not about data but about organisation.
Does AI make mistakes? How is that controlled?: Yes, in two distinct ways. It can be wrong for lack of information, which is solved by connecting it to the right system; and it can generate an answer that sounds right and is false, which is solved by design: answering from queried rather than remembered data, and escalating to a person when it does not have the fact.
Where does a company that has never used AI start?: With one concrete, repetitive, measurable process — not with a general strategy. Handling frequent enquiries on the channel where your customers already write is the most common entry case, because volume is high, risk is low and the result shows in the first week.

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