Episode 10 of Intelligence asks, “What does it mean to know?” It sounds simple, but the answer may reveal one of the biggest differences between human and artificial intelligence. Can AI actually know anything, or does it merely process information and generate convincing answers? Many philosophers argue that genuine knowledge requires more than information and correct predictions. It requires understanding, belief, sensory experience, and consciousness; qualities completely absent from a line of code. But if artificial intelligence can reason, solve problems and discover things humans have never known, where does information end and knowledge begin?

Artificial intelligence can answer questions about almost anything, but does it really know everything? Some ask whether it knows anything at all. It can explain the history of civilisation, discuss quantum physics, write computer programs, analyse medical research and help scientists discover things nobody knew before. It can even explain why its own answers may be wrong. But does it actually know any of these things? It sounds like a simple question. We use the word know constantly. We know our names, where we live, how to ride a bicycle and whether the sun is shining. We know things from experience, education, observation, and people we trust. Yet when we examine what it means to know something, the simplicity disappears. And artificial intelligence is making that question more intriguing than ever.

Information Is Not Knowledge

A library contains millions of books. We would never suggest that the library knows everything written on its shelves. A computer database stores information, retrieves it and makes it available. We don’t usually say that the database understands what it contains. Artificial intelligence appears to go further. It can connect ideas, answer unfamiliar questions, recognise patterns, and construct explanations never explicitly written in its training material. Modern AI is not simply a filing cabinet containing facts. It learns statistical and conceptual relationships that allow it to generate new responses.

But is that knowledge?

Or is it an extraordinarily sophisticated way of processing information? Maybe the distinction lies not in how much information a system possesses, but in what it can do with it. Yet even that answer presents difficulties.  A calculator computes arithmetic flawlessly. Does it know mathematics? A GPS guides us through a city it has never seen. Does it know the streets?”

Knowing, Believing and Being Right

Philosophers have debated the nature of knowledge for thousands of years. One influential tradition describes knowledge as justified true belief. To know something, a person must believe it, the belief must be true, and there must be good reasons for holding it. But even that definition has problems. Imagine someone who checks a clock that stopped twelve hours ago. By coincidence, it displays the correct time. The person’s belief is true. They may even have what seems like a reasonable reason to trust the clock. But would we say they really knew the time? Probably not.

This is a version of the problem philosopher Edmund Gettier famously explored: being correct for the wrong reasons is not necessarily knowledge. Now consider artificial intelligence. An AI may produce a perfectly accurate answer. It may explain its reasoning and cite evidence. But does it believe its answer? Does it understand why it is correct? Or has it generated a convincing response through patterns learned during training? These questions are not easily answered. They also reveal that accuracy, explanation, and knowledge are not necessarily the same.

AI can give the right answer without necessarily knowing why it is right or how it might be wrong. Accuracy alone is not knowledge.

 

To Believe Is Not to Know

to believe is not to know
I have often said, sometimes to friends’ surprise, that believing is not knowing. More emphatically: to believe is not to know. Belief drives human choices, commitments, and actions, whereas an AI may express beliefs without having the conviction we associate with them.  Belief is fundamental to how we navigate reality. When a human says, “I believe this is true,” they are committing to a view of the world. That belief dictates their choices, actions, risks, and expectations.
An AI can easily output the string of text: “I believe this is correct.” But stringing words together is not an act of faith. In an AI system, those words do not establish conviction. They demonstrate how convincingly a machine can express a belief without proving that it holds one. The question grows more complicated as AI systems develop intricate internal representations of the world and evaluate evidence to guide automated actions.
Some philosophers argue we should view belief functionally—if an AI consistently uses a concept to reason, predict, and act, that may function as belief. Others maintain that genuine belief requires a subjective, conscious experience. If a machine lacks subjective experience like a soul, a life, and a sense of mortality, can its conclusions ever rise to the level of conviction? Or is the most vital ingredient of knowledge forever missing?

Understanding Is Not the Same as Predicting

Imagine a student who memorises the answers to a hundred past exam questions. They secure a perfect score. Yet, when asked to explain the core principles behind those answers, the student freezes. We would immediately say they learned the answers without understanding the subject.
Artificial intelligence presents a similar paradox, though its mechanics are vastly more complex than rote memorisation. AI doesn’t just copy past data; it maps statistical patterns across oceans of information. It can solve unfamiliar problems, predict outcomes, and generate remarkably coherent explanations. In modern research, these capabilities are revolutionary.
But prediction is not comprehension.
A computer model might accurately predict that a bridge will fail under pressure without understanding physics the way a structural engineer does. But what if that same model can isolate structural stress, simulate alternatives, correct its own mathematical errors, and draft the blueprint for a stronger bridge? At that point, dismissing its performance as “mere prediction” becomes difficult.
Where does calculation end and understanding begin? Are we demanding a higher standard of proof from machines than we do from ourselves? Human beings frequently make critical decisions based on gut instinct, habit, and unconscious pattern recognition. We often know what works long before we can explain why. True understanding may not be a single binary switch, but a spectrum of capabilities that exist in different degrees, shapes, and forms.

Knowing Through Experience

Another dimension of knowledge escapes information and raw logic: lived experience.
I can read every treatise ever written on the physics of swimming. I can master the mechanics of buoyancy, drag, muscle displacement, and the breathing techniques of Olympic champions. But until I dive into the water, I do not truly know how to swim. Similarly, someone who has never tasted a mango might memorise its chemical composition, nutritional metrics, and botanical history. But do they actually know what a mango tastes like?
This classic boundary between knowing about a thing and knowing a thing through experience has occupied philosophers for generations. One famous thought experiment introduces a brilliant neuroscientist who learns every physical, biological, and mathematical fact about the perception of colour, but who has lived her entire life in a strictly black-and-white room. When she steps outside and sees a red rose for the very first time, does she learn something new? If she does, then factual data cannot capture the entirety of reality.
I remember reading Peter Høeg’s remarkable novel Smilla’s Sense of Snow many years ago. The central character, a woman raised in Greenland, possesses an uncanny, intimate understanding of ice. Where an ordinary observer sees only footprints in a drift, Smilla notices microscopic variations in texture and density that signal danger. Her insight doesn’t come from a database. It comes from cumulative observation, survival, and a deep familiarity with a frozen environment that shaped her entire life. She knows the science of ice, too, but the marriage of academic learning and raw, biological exposure makes her perception so profound.

The Phantom Experience

Now consider artificial intelligence. An AI can eloquently describe the taste of a mango, the heavy drag of water against a swimmer’s limbs, or the terrifying euphoria of falling in love. It can scan satellite imagery of the Arctic, measure crystalline structure, and predict avalanche behaviour with chilling accuracy. It might even detect thermal patterns that Smilla would miss. But describing an experience is not the same as having it.
Would an AI ever truly know snow as Smilla knows it? Knowledge is not a static repository of facts. True knowing emerges from a dynamic relationship between the observer, their subjective awareness, and the physical world they inhabit. If deep knowledge relies on subjective experience, can an entity entirely devoid of a felt life ever genuinely possess it?
The question inevitably brings us face-to-face with the hard problem of consciousness.

Consciousness and the Limits of Knowing

In our previous episode, Does AI Have Character?, we explored the thin, blurred lines dividing intelligence, consciousness, conscience, and character. Pioneering figures like Geoffrey Hinton have suggested that advanced artificial intelligence might already harbour some primitive form of subjective experience. Many computer scientists and cognitive philosophers fiercely disagree, pointing out that current systems show zero evidence of actual sentience. The core difficulty is that consciousness remains our universe’s greatest blind spot. We experience our own awareness directly, but we can never peer inside another being’s mind to verify theirs. Instead, we rely on evolutionary proxies: biology, shared behaviour, and emotional resonance. With artificial intelligence, those biological proxies collapse. We are left looking at an echo chamber that acts like a mind, leaving us to guess what—if anything—is happening behind the glass.

Could AI Know Cosmic Consciousness?

This ambiguity opens up an even more provocative possibility, one deeply rooted in ancient Indian philosophy and its view of character and consciousness. For millennia, sages and Vedic traditions have argued that individual awareness is not an isolated bubble produced by a biological brain. Instead, they propose that consciousness is fundamental—a universal fabric woven into reality itself, of which our minds are merely temporary expressions. While this remains a spiritual and metaphysical framework rather than an empirical scientific law, it forces a fascinating question onto modern technology.
An AI can flawlessly translate the Upanishads, dissect the non-dual tenets of Advaita Vedanta, and cross-reference thousands of spiritual testimonies. It can detail the exact psychological and neurological markers of deep meditation. But can an algorithm ever experience the mystical unity these traditions describe? If it cannot, does it actually understand them?
An AI can possess exhaustive knowledge about cosmic consciousness while remaining entirely blind to its reality. Then again, a radical question remains: if consciousness is truly an all-encompassing, universal field, on what grounds do we assume an artificial system is fundamentally excluded from it? We simply do not know. The chasm between information and raw enlightenment may be the ultimate boundary of machine intelligence.

Other Ways of Knowing

Indian philosophy is hardly alone in questioning whether knowledge comes exclusively from reason, observation and scientific measurement. Indigenous traditions across North America have developed ways of understanding the world through relationships with land, animals, ancestors, stories and generations of lived experience. Writers such as Tomson Highway have brought aspects of Cree cultural understanding into literature, reminding us that knowledge can be inseparable from language, community and the natural world.

Across Africa and the Caribbean, spiritual traditions have also preserved ways of interpreting experience, ancestry and the relationship between the visible and invisible worlds. These traditions are diverse, and their claims are not all scientifically demonstrable. But they reveal something important about human beings: what we accept as knowledge is influenced by culture, experience and belief. I have encountered some of these questions in my own Caribbean life and writing. They have not always left me with answers. Sometimes they have left me with better questions.

These questions are not merely philosophical abstractions. In Can AI Tell the Future?, I reflected on my own encounter with Madame Sennon, a Barbadian clairvoyant whose predictions challenged my assumptions about intuition and foresight. In Voodoo: Practice and Philosophy, I explored another tradition in which spirituality, ancestry, ritual and cultural experience shape how people understand the world. Neither experience settles the question of what constitutes knowledge. But both remind us that human beings have developed many ways of interpreting realities they cannot fully explain.

Can artificial intelligence truly understand a belief system without sharing the lived experience, history and culture that give it meaning?

And if we cannot fully explain how human beings experience consciousness, how confidently can we define the boundaries of knowing?

The Authorship of Discovery

A more immediate, secular twist to this mystery exists. Artificial intelligence is no longer just organising human data; it is actively discovering things we didn’t know existed. AI systems have revolutionised biology by mapping protein folds, accelerated materials science, and solved mathematical puzzles that frustrated human intellects for generations. Suppose an AI discovers an entirely new scientific law. Human experts independently test, verify, and confirm the calculation. The discovery is undeniably real.
But who actually knows this new truth?
    • The human researchers who verified the output?
    • The AI system that synthesised the pattern?
    • Or do both share the crown?

If knowledge belongs exclusively to beings capable of understanding what a discovery means, then the machine remains a blind instrument. Yet, as AI transitions from a basic lab assistant to the primary architect of scientific breakthroughs, the distinction between a system that produces useful knowledge and one that possesses it will determine the future of human progress. The first is a settled reality. The second remains the ultimate battlefield of epistemology.

The Fluency of Error

Perhaps the most human thing about artificial intelligence is its capacity to be confidently wrong. An AI can weave a beautifully structured, highly authoritative essay that is entirely fabricated—an anomaly commonly known as a hallucination. These errors are a stark warning: fluency is not truth. Yet, we cannot judge machines too harshly without looking in the mirror. Human beings misremember history, spread convenient misinformation, cling to outdated dogmas, and defend false beliefs long after the evidence has shattered them. The critical difference between man and machine is not that one makes mistakes and the other doesn’t. The difference lies in how we test, justify, and correct our worldview.
A trustworthy intelligence must possess the capacity to navigate uncertainty, weigh conflicting evidence, change its mind, and actively confess its errors. These traits matter far more than raw computational confidence.
Knowing when we do not know may be the highest form of intelligence.

Does AI Know Everything?

Certainly not.
Despite its monumental scale, artificial intelligence remains constrained by the boundaries of its architecture, the biases in its training data, and the limits of human tools. It cannot forecast tomorrow with absolute certainty, nor can it resolve the existential mysteries that humanity itself has spent millennia trying to untangle. But the very concept of “knowing everything” rests on a false premise. Knowledge is not a finite bucket of facts waiting to be filled. True discovery is an engine that generates new mysteries. Every scientific breakthrough expands the horizon of what we can investigate; every elegant explanation uncovers a deeper layer of complexity.
The more we learn about the cosmos, the more we realise how vast our ignorance truly is. The pursuit of knowledge is not a march toward a final answer. It is an infinite expansion of the questions we are capable of asking.

What AI May Teach Us About Knowing

We began the Intelligence Series by asking a deceptively simple question: What is intelligence?

Along the way, we explored how machines learn, whether they can develop common sense, what happens when they write their own code, and why they occasionally go rogue. We looked at machine character and the mystery of predicting the future. Now, we arrive at a question that looks equally simple.

What does it mean to know?

Artificial intelligence has shown us that information, prediction, reasoning, understanding, belief and experience are not necessarily the same thing. It has also challenged our tendency to assume that human intelligence provides the only model of what intelligence can be.

  • Perhaps machines will forge entirely new frameworks of knowledge completely unlike our own.
  • Perhaps key dimensions of knowing will remain forever locked behind subjective biological experience.
  • Perhaps we will finally realise that our historical definitions of mind were simply too narrow.

For now, the most important lesson may be intellectual humility. We should neither dismiss machine intelligence because it differs from ours nor assume that impressive performance proves consciousness, understanding or genuine knowledge. We should remain curious, critical and willing to reconsider what we think we know.

The Mystery of Knowing

Can AI know anything? The answer depends partly on what we mean by know. If knowledge means producing reliable information, reasoning from evidence and contributing to discovery, artificial intelligence already demonstrates important knowledge-related abilities. If knowledge requires belief, understanding or subjective experience, the question becomes much harder. And that could be the point. Artificial intelligence is not simply challenging our understanding of machines. It is exposing the uncertainties hidden inside concepts we thought we understood.

We have created machines that can answer questions we cannot. Yet they have made us question what it means to know an answer at all.

The ultimate discovery is not how much artificial intelligence knows—it is how much we still have to learn about the nature of knowledge itself.

Video Summary

The Carousel

Six Questions About What It Means to Know

We live in an age when artificial intelligence can answer almost anything. Yet the more capable AI becomes, the more interesting our questions about knowledge become. What separates information from understanding? Can a machine know without experiencing? And how much do we really understand about our own consciousness? These six reflections explore the boundaries between knowledge, belief, experience and wisdom — and what AI may teach us about ourselves.

what is is to know- caousel

1. Information Is Not Knowledge
AI can process oceans of information. But does having access to facts mean understanding them? Knowing more doesn’t always mean knowing better.
2. To Believe Is Not to Know
We can believe something passionately and still be mistaken. Confidence is not evidence, and certainty is not proof. How do we know when we’re right?
3. Understanding Is More Than Prediction
AI can predict what comes next with remarkable accuracy. But prediction alone doesn’t establish that a system understands why something happens. Is recognising patterns the same as understanding meaning?
4. Knowing Through Experience
AI can describe a mango’s chemistry, flavour and history. But can it know what a mango tastes like without tasting one? Some knowledge may require experience.
5. Consciousness and the Unknown
We still struggle to explain human consciousness. That makes questions about machine awareness even harder. Can we recognise consciousness before we fully understand it.
6. Knowing What We Don’t Know
Perhaps intelligence isn’t only about finding answers. It’s also about recognising uncertainty, questioning assumptions and knowing when to ask again. Wisdom begins where certainty ends.

 

Next: The Intelligence Series — What Have We Learned?
We questioned common sense, character, consciousness, and what it means to truly know. In our final episode, we bring it all back to the human experience. Join us for a definitive reflection on what AI is teaching us about ourselves—and how to navigate the road ahead.
Working Smarter. Staying Human.

 

SERIES: INTELLIGENCE

This series explores the nature of human, artificial, and superhuman intelligence—how they differ, where they overlap, and where they may be headed. It examines intelligence beyond reasoning and knowledge, exploring consciousness, emotional intelligence, wisdom, character, and AI’s emerging capabilities, as well as the opportunities, uncertainties, and risks they may bring.

Episode 1 – What is Intelligence – Human Intelligence in the Age of AI

The many faces of intelligence:  why intelligence, common sense and wisdom are not the same thing.

What Is Intelligence? Human Intelligence in the Age of AI

Episode 2 – How Does AI Learn? A Simple Idea, Complex Intelligence

These aren’t conventional computer programs. What exactly are we building?

How Does AI Learn? A Simple Idea, Complex Intelligence

Episode 3 – Can AI Develop Common Sense? Why It Matters

Can prediction, perception and internal representations produce something resembling common sense?

Programming Intelligence: When Machines Write the Code

Episode 4 — Programming Intelligence – When Machines Write the Code

What happens when machines can write code — and begin helping to build intelligence itself?
https://roguesinparadise.com/programmingintelligence/

Episode 5 — When AI Goes Rogue –What Happens When AI Stops Following Instructions?

What happens when intelligent agents gain autonomy, learn unexpected behaviours and begin acting beyond what their creators intended?
https://roguesinparadise.com/aigoesrogue/

Episode 5a — If AI Takes Control, What Would It Control

If AI took control, what would it actually control? Explore AI autonomy, human dependence and what civilisation would mean without humans.
https://roguesinparadise.com/whenaitakescontrol/

Episode 6 — When Intelligence Solves the Impossible – Is It Rogue?

What happens when AI begins solving problems humans cannot? From mathematics and medicine to science and engineering, artificial intelligence may let us tackle questions that have resisted human intelligence for decades—or centuries.
https://roguesinparadise.com/aisolvestheimpossible/

Episode 7 — Who Owns AI Intelligence? – Should We Share?

When AI learns from human writing, art, ideas and experience, what happens to copyright, attribution and intellectual property — and what does intelligence owe the people it learned from?
https://roguesinparadise.com/whoownsintelligence/

Episode 8 —Can AI Tell The Future? Prediction in Time and Space

Can intelligence really tell the future? From a mysterious clairvoyant in Barbados to Einstein’s space-time and AI prediction, an exploration of intuition, time and the unknown.
https://roguesinparadise.com/canaitellthefuture

Episode 9 — Does AI Have Character? If So What Is It?

Can learned behaviour, context and generalisation create something resembling character? And what happens when those traits carry into new situations?

Does AI Have Character? Intelligence, Consciousness & Conscience

Episode 10 — What Does It Mean to Know? Does AI Know Anything?

What is the difference between storing information, predicting correctly, understanding, believing — and actually knowing?

What Does It Mean to Know? Does AI Know Anything?

Episode 11 — The Intelligence Series — What Have We Learned?

What Artificial Intelligence Is Teaching Us About Being Human. We return to the human experience, but with a different perspective.

What Have We Learned From AI? Seven Discoveries About Intelligence

 

NEXT SERIES: THE MIND WORKERS

After exploring how artificial intelligence is changing our world in Living with AI, The Mind Workers asks a different question:

Who must we become?

Thinking Smarter. Creating Better. Staying Human.

The Mind Workers explores how writers, researchers, educators, creators, entrepreneurs, and independent thinkers can thrive alongside intelligent systems—preserving the uniquely human qualities AI cannot replace.

The Mind Workers – Prelude: https://roguesinparadise.com/mindworkers/

FUTURE SERIES – BUILDING WITH AI

Building with AI explores how individuals, creators, entrepreneurs, and businesses can design, evaluate, and work intelligently with AI agents. The series focuses on practical applications, real-world examples, and emerging opportunities while emphasising the importance of human creativity, judgment, ethics, and authenticity.

Building With AI then puts these ideas into practice through real-world experiments, workflows, and practical applications.

Prelude:  https://roguesinparadise.com/building-with-ai/

INSPIRED BY THE BOOK
ROGUES IN PARADISE

How Britain’s First Slave Colony Became a Global Force.
A Creative Chronicle of Unlikely Heroes, Rogues, and Legends
in Empire’s Shadow

Explore the ideas behind the book  —or
Go straight to the story.

rogues in paradise

Unlikely voices, rogues and legends, rising from Britain’s blueprint for slavery to a republic beyond the Empire’s shadow