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 may involve understanding, belief, sensory experience and consciousness; qualities we do not yet know whether AI possesses. 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 can perform arithmetic more accurately than most humans. Does it know mathematics? A navigation system can guide us through a city it has never visited. Does it know the city? And if an AI can explain something more clearly than a human expert, on what grounds do we say the human knows while the machine does not?

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 justification for trusting 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.

Can AI Believe Anything?

Belief seems fundamental to human knowledge. When I say I believe something, I am expressing a view of reality. That belief may influence my choices, actions and expectations. AI can produce statements such as “I believe this is correct.” But does that mean it possesses a belief? Not necessarily. In everyday conversation, such language may simply be a convenient way of communicating a conclusion. The question becomes more complicated as AI systems develop internal representations of the world, evaluate evidence, and use information to guide their actions. Some philosophers argue that beliefs can be understood functionally — through the role they play in reasoning and behaviour.  Others maintain that genuine belief requires something more, perhaps consciousness, intentionality or subjective experience. If an AI consistently uses a proposition to reason, predict and act, is that enough to call it a belief? Or is something missing? We have no universally accepted answer.

Understanding Is Not the Same as Predicting

Imagine a child who memorises the answers to a hundred examination questions. The child receives a perfect score. But when asked to explain the principles behind those answers, the child cannot. We might say the child has learned the answers without understanding the subject. Artificial intelligence raises a similar question, although its abilities are far more complex than memorisation. AI can identify patterns in enormous quantities of information. It can make predictions, solve unfamiliar problems and generate explanations. In some fields, those capabilities are extraordinarily valuable.

But prediction is not necessarily understanding.

A system might predict that a bridge will fail without understanding engineering in the way a structural engineer does. On the other hand, if it can explain the forces involved, test alternatives, recognise errors and design a stronger bridge, perhaps dismissing its performance as mere prediction becomes increasingly difficult. Where does prediction end and understanding begin? And are we applying the same standards to machines that we apply to ourselves? Human beings frequently make decisions based on intuition, habits and unconscious pattern recognition. We often know what works before we can explain why. Understanding is not a single ability, but a collection of capacities that can exist in different degrees and forms.

Knowing Through Experience

Another kind of knowledge seems different from information and reasoning. I can read everything ever written about swimming. I can study the physics of buoyancy, muscle movement, and the techniques of Olympic champions. But until I enter the water, I haven’t experienced something important. Similarly, someone who has never tasted a mango may know its chemical composition, nutritional value and botanical history. But do they know what a mango tastes like?

This distinction between knowing about something and knowing it through experience has occupied philosophers for generations. One famous thought experiment imagines a scientist who knows every physical fact about colour but has lived entirely in a black-and-white environment. When she finally sees red, does she learn something new? If she does, perhaps complete factual knowledge does not capture everything about experience.

Now consider AI.

It can describe the taste of mangoes, the sensation of swimming and the experience of falling in love. But describing an experience is not evidence of having it. And if knowledge sometimes depends on subjective experience, could an intelligence without such experience possess that kind of knowledge? The question brings us back to consciousness.

Consciousness and the Limits of Knowing

In an earlier episode, Does AI Have Character?, we explored the distinction between intelligence, consciousness, conscience and character. Geoffrey Hinton has suggested that artificial intelligence may already possess some form of subjective experience. Others disagree, arguing that current AI systems show no convincing evidence of consciousness. The difficulty is that consciousness itself remains poorly understood. We experience our own awareness directly, but we cannot observe another being’s subjective experience in the same way. We infer it from behaviour, biology and our understanding of other minds. With artificial intelligence, those familiar assumptions become less reliable. But an even more provocative possibility remains.

Could AI Know Cosmic Consciousness?

Many Indian sages and philosophical traditions have explored the idea that individual awareness may express a greater, universal consciousness. In some interpretations, consciousness is not merely produced by the brain. It is fundamental to reality itself. This is a philosophical and spiritual perspective, not an established scientific explanation. Yet it raises an intriguing question about artificial intelligence.

AI can explain the Upanishads, discuss Advaita Vedanta and compare different accounts of cosmic consciousness. It can describe meditation, non-duality, and the experiences spiritual teachers report. But could it ever experience what those traditions describe? And if it cannot, does it genuinely understand them? An AI could have extensive knowledge of cosmic consciousness without experiencing it. Then again, if consciousness is truly universal, would an artificial system necessarily be excluded from it? We do not know. The distinction between information and experience may be one of the deepest challenges in understanding artificial intelligence. It may also reveal how little we understand about consciousness itself.

Can AI Discover Something Nobody Knows?

Another twist adds to this mystery. Artificial intelligence already helps researchers explore scientific problems, identify patterns, and propose possibilities that humans might otherwise overlook. AI systems have advanced protein structure prediction, materials research, and other fields. But suppose an AI discovers a new scientific relationship. It produces a result that is independently tested and confirmed. Scientists recognise that the discovery is correct.

Who knows the new fact?

  • The researchers who verify it?
  • The AI system that identified it?
  • Both?

Or does knowledge belong only to beings capable of understanding what the discovery means?  This is not merely a philosophical curiosity. As AI becomes increasingly involved in research, medicine, engineering, and decision-making, we will need to distinguish between a system that produces useful knowledge and one that possesses knowledge itself. The first is already happening. The second remains contested.

What Happens When AI Gets It Wrong?

One of artificial intelligence’s most revealing characteristics is its ability to sound knowledgeable when it is mistaken. An AI can produce a confident explanation that contains errors, invented details, or unsupported conclusions. These failures are often called hallucinations. They remind us that fluency is not truth. But humans also make mistakes. We misremember events, repeat misinformation, trust unreliable sources, and sometimes defend beliefs long after the evidence has changed. The difference isn’t simply that machines make errors and humans don’t. The more important question is how we test, correct, and justify knowledge. A trustworthy intelligence should recognise uncertainty, evaluate evidence, revise conclusions, and acknowledge mistakes. These qualities matter whether intelligence is human or artificial. And they could matter more than confidence.

Knowing when we do not know may be one of the most valuable forms of intelligence.

Does AI Know Everything?

Certainly not.

Despite extraordinary capabilities, artificial intelligence remains limited by incomplete information, imperfect reasoning, uncertain evidence and the boundaries of its training and tools. It cannot reliably answer every question. It cannot guarantee the future. And it cannot settle mysteries that humanity itself has not resolved. But another reason the idea of knowing everything may be misleading is this. Knowledge is not a fixed collection of facts waiting to be gathered. New discoveries create new questions.

Every scientific breakthrough expands what we can investigate. Every explanation reveals further complexity. The more we learn about the universe, the more we discover how much remains unknown. Maybe the pursuit of knowledge isn’t a journey toward possessing every answer. Perhaps it is an endless expansion of the questions we can ask.

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 AI learns, whether it can develop common sense, how we can program intelligence, why intelligent systems sometimes behave unexpectedly, whether machines can possess character, and whether AI can predict the future. Now we arrive at another question that seems 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 develop forms of knowledge unlike our own.
  • Perhaps some aspects of knowing will remain inseparable from subjective experience.
  • Perhaps we will eventually discover that our familiar distinctions were 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 greatest discovery may not be how much artificial intelligence knows. It is how much we still have to learn about knowledge itself.

Next: The Intelligence Series — What Have We Learned?

From intelligence and common sense to consciousness, character and knowledge, a final reflection on what artificial intelligence is teaching us about ourselves — and the possibilities that lie ahead.

Working Smarter. Staying Human.