Understanding AI: definitions, trustworthy systems, neurosymbolic AI

~4-minute read

Artificial Intelligence has become a defining technology of our time. But to what extent can we trust it?

To answer that, we need to go back to first principles, and clarify what we actually mean when we talk about “AI”.

What do we really mean by Artificial Intelligence?

At its most basic level, Artificial Intelligence (AI) refers to the theory and development of computer systems capable of performing tasks that normally require human intelligence, such as perception, language understanding, decision-making, and translation.

In practice, AI is a stack of different technologies.

At the foundation of most modern AI systems lies Machine Learning (ML): systems that learn patterns from data rather than being explicitly programmed. These methods are overwhelmingly statistical, learning weights in mathematical formulas that predict, classify, or generate outputs.

This approach has unlocked problems that resisted over 60 years of conventional programming. It also introduced a crucial limitation: probability replaces certainty.

Deep learning and the rise of LLMs

Deep Learning (DL) is a subset of machine learning built on very large neural networks. Its defining characteristic is scale. As models grow larger and consume more data, their capabilities improve, often dramatically.

Today’s largest models contain trillions of parameters, trained on vast portions of the world’s text and media, at costs reaching tens of millions of dollars.

From this paradigm emerged Large Language Models (LLMs).

LLMs work by predicting the next word in a sequence. By repeatedly doing so, they can write essays, answer questions, reason in natural language, and perform complex tasks. They power systems like ChatGPT and have driven the AI surge of the past three years.

Their outputs are often impressive, fluent and confident but that does not imply correctness.

The trust gap in modern AI

Every enterprise wants the advantages of AI. Many of the most valuable use cases sit in domains where mistakes carry serious consequences: financial services and risk analysis, accounting and regulatory compliance, insurance underwriting and claims, healthcare and life sciences etc.

The core problem is that machine learning systems are inherently probabilistic. They are, by design, approximations and they never reach 100% certainty.

A system earns trust when it is consistent, correct, explainable and safe.

It loses trust when it states things that are not true, makes dangerous or incorrect decisions, behaves inconsistently and cannot explain why it reached a conclusion.

Worse still, large neural models are black boxes. With trillions of parameters interacting non-linearly, it is impossible to provide explanations that truly reflect how a decision was made, not in a way that humans can audit, verify, or defend.

In high stake environments, “usually correct” is not good enough. What’s needed is AI that combines intelligence with reliability.

Neurosymbolic AI: combining power with precision

This is where neurosymbolic AI comes in.

Neurosymbolic systems combine two different approaches:

Statistical machine learning (“neuro”)

ML excels at processing complexity of language, documents, policies, and unstructured data that conventional software cannot handle. But it struggles with trust, explanation, and guarantees.

Symbolic software (“symbolic”)

Conventional software is deterministic. If it produces an incorrect output, that’s a bug — not a probability. Its logic can be inspected, traced, and explained. But on its own, it cannot solve many of the complex problems ML can.

Neurosymbolic AI aims to get the best of both worlds.

How neurosymbolic AI works

In simple terms the neurosymbolic AI technology that we use at UnlikelyAI can be divided into 3 steps

1. Data ingestion: We ingest complex inputs such as natural language documents, regulations, and policies.
2. Symbolic representation: That information is converted into a structured semantic form that can be processed symbolically, rather than guessed statistically.
3. Deterministic reasoning and explanations: Decisions are made with near-100% precision, and every step in the reasoning process can be traced and explained in human-understandable terms.

The result is AI that doesn’t just sound right, it is right, and can show its work.

From impressive demos to dependable systems

LLMs have shown us what’s possible.

Neurosymbolic AI shows us what’s dependable.

As AI moves deeper into high-stakes decision-making, trust is no longer an optional add on. The future of AI will not be defined by scale alone, but by systems that combine intelligence with rigor, transparency, and reliability.

That’s the promise of neurosymbolic technology.

Learn more insights on building AI systems you can actually trust featured in our previous blogs.

— UnlikelyAI Team