Tomorrow morning, you will wake up and believe the day has not happened yet.
You will reach for something. Perhaps your phone. Perhaps a glass of water. You will choose what to wear, where to go, what to eat, which message to answer and which one to leave glowing unanswered on the screen.
From inside your own consciousness, these moments will arrive as possibility. The day will seem unwritten.
But imagine that somewhere, a machine has already assigned probabilities to them.
Not because it has seen tomorrow.
Because it has seen yesterday.
Not just your yesterday, but billions of yesterdays: patterns of language, movement, appetite, attention, fear, habit, desire, weather, traffic, commerce, friendship, conflict and choice. An archive so vast that behaviors which feel spontaneous from inside a single life begin to reveal shapes when viewed from above.
The machine does not know what you will do next.
But what happens when its guess becomes almost indistinguishable from knowing?
The Next Word
At the center of the modern language model is an almost embarrassingly simple question:
Given everything that has come before, what is most likely to come next?
If I write, The sun rose over the…, there are countless ways the sentence could continue. Horizon might be more probable than refrigerator. Mountains might make sense. City might make sense. Nothing physically prevents the sentence from ending with octopus, but language carries patterns, and those patterns make some futures more probable than others.
A large language model learns enormously complex versions of those relationships.
Calling this merely “predicting the next word,” however, can be misleading. Modern models generally operate on tokens, fragments that may be words, pieces of words, punctuation, or other units, and learning to predict them across enormous quantities of data forces the model to capture surprisingly deep regularities.
Grammar is a pattern.
Style is a pattern.
Cause and effect leave patterns in language.
So do arguments, jokes, computer programs, historical narratives, scientific explanations, love letters, recipes, lies, confessions, and the peculiar choreography of two people disagreeing with each other on the internet.
To become better at predicting what comes next in text, the system benefits from constructing internal representations of what the text is about.
That distinction matters.
Because the interesting thing about prediction is that it can require something resembling a model of the world producing the sequence.
Imagine watching chess games without initially knowing the rules. If you observed enough games and became extraordinarily good at predicting the next move, eventually your predictions would have to encode something about bishops, pawns, openings, threats, sacrifices, and checkmate. You could not remain completely ignorant of the structure beneath the moves and still predict them indefinitely.
Human language presents a vastly messier version of the same problem.
Behind our sentences are people.
And people do things.
We fall in love and fall out of it. We become hungry. We panic when markets collapse. We imitate one another. We form tribes. We get bored. We repeat mistakes while insisting that this time is different. We develop technologies and then reorganize our lives around them. We tell stories about what happened afterward.
Language is therefore not merely a collection of words.
It is also a residue of human behavior.
Every diary describes decisions. Every history records consequences. Every forum contains miniature chains of stimulus and response. Every novel, even when invented, contains some model of how its author believes people might behave. Our written world is littered with footprints.
And this is where the question changes.
If an artificial intelligence can become increasingly capable of predicting the next element in a sequence of human language by studying what came before, what happens when the sequence is no longer a sentence?
What if the sequence is a person?
The Next Human
Consider an ordinary morning.
You wake at 7:13.
Before getting out of bed, you check your phone. You open the same three applications you usually open. You linger over one message but do not answer it. You make coffee. The weather is colder than yesterday, so you choose a different jacket. Traffic is unusually heavy along your normal route, and your navigation app suggests another.
None of these events feels particularly mechanical from inside your life.
You experience them as choices.
But from another perspective, they are data points.
Suppose a system knew when you usually wake, how long you typically remain in bed, which applications you open first, what you purchase, which routes you drive, how weather changes your behavior, which people you contact when you are lonely, what music you play when you are restless, how your language changes when you are tired, and thousands of other tiny correlations you have never consciously noticed about yourself.
It would not need to understand you in the intimate way a friend does.
It might simply need enough signal.
Then the question becomes uncomfortable:
How much of an ordinary human day is genuinely surprising?
We tend to imagine prediction in spectacular terms. Who will win the election? When will the next financial crisis occur? Where will the next geopolitical conflict erupt?
But perhaps the more consequential frontier is smaller.
The coffee.
The click.
The turn at the intersection.
The song you skip.
The person you almost text.
The product you buy after seeing it for the fourth time.
The moment you become irritated enough to close an application.
The ten thousand micro-movements from which a life is assembled.
We already live among primitive versions of this logic. Recommendation systems estimate what we might watch next. Navigation systems anticipate traffic. Fraud-detection systems identify behavior that deviates from established patterns. Advertisers estimate which messages are most likely to provoke action.
None of these systems can see the future.
They calculate probabilities from traces of the past.
But imagine those traces becoming richer. Imagine models capable of connecting patterns across language, location, social behavior, economics, environment, physiology, and history. Imagine not merely more data, but increasingly sophisticated models capable of finding relationships within it that no human observer would think to search for.
At some point, the philosophical distinction between prediction and prophecy might remain enormous while the practical distinction begins to feel disturbingly small.
The oracle would not need a crystal ball.
It would need an archive.
And perhaps enough of yesterday.
History as Training Data
A human life repeats itself in habits.
History does something stranger.
It rhymes.
Not perfectly. Not predictably enough to reduce civilization to a formula. But certain shapes appear again and again: technologies emerge, fortunes accumulate, institutions weaken, populations migrate, new media disrupt old authorities, speculative bubbles inflate, generations rebel against inherited values, empires overextend themselves, diseases alter societies, and yesterday’s radical invention becomes tomorrow’s invisible infrastructure.
We notice these repetitions because human beings are compulsive pattern-makers.
Historians compare one revolution to another. Economists search previous crashes for clues about the next one. Epidemiologists model outbreaks from earlier transmission patterns. Political scientists study elections, demographic changes, institutions, and public opinion. Meteorologists feed enormous quantities of observations into models to estimate what the atmosphere will do next.
In other words, prediction from the past is hardly an invention of artificial intelligence.
It is one of civilization’s oldest intellectual habits.
What changes with AI is the possible scale.
No historian can simultaneously read every surviving newspaper, economic report, census, diary, scientific paper, political speech, shipping record, weather observation, legislative document, market fluctuation, migration record, and public conversation humanity has preserved.
A machine does not have precisely the same limitation.
Imagine a system capable of searching for relationships across immense territories of recorded history. Not merely asking whether Event A resembles Event B, but whether thousands of weak signals tend to gather before a particular kind of change.
Perhaps commodity prices shift.
Certain phrases begin appearing more frequently in public conversation.
Migration patterns change.
Trust in institutions declines.
A new technology spreads fastest among a particular demographic.
Entertainment becomes preoccupied with certain fantasies or fears.
None of these signals alone predicts anything.
Together, perhaps they form a shape.
And perhaps the shape has appeared before.
This is where the temptation toward technological mysticism becomes dangerous. History is not a laboratory experiment that can simply be rerun. The Roman Empire did not have nuclear weapons. The French Revolution did not unfold beneath social media. The architects of the internet did not know what billions of smartphones would eventually do to attention, intimacy, politics, or identity.
Every historical moment contains conditions that have never existed before.
The future can rhyme with the past without becoming its repetition.
And yet prediction does not require perfect repetition.
It requires probability.
A meteorologist does not need tomorrow’s atmosphere to have happened previously. The forecast emerges from relationships among variables, physical laws, observations, and models. The prediction can be useful while remaining uncertain.
Perhaps increasingly sophisticated AI systems could approach parts of human history in something resembling that spirit.
Not:
This happened before, therefore it will happen again.
But:
When conditions resembling these have converged before, the range of what happened next became narrower.
That is a subtler claim.
It is also a much more unsettling one.
Because civilization may contain patterns too diffuse for any individual human mind to perceive.
A historian specializes.
An economist specializes.
A psychologist specializes.
A political scientist specializes.
A climate scientist specializes.
But reality does not.
A drought can alter food prices, which can influence migration, which can strain political institutions, which can change elections, which can alter trade, which can influence technology, which can reshape culture.
We divide knowledge into disciplines because human minds need shelves.
The world ignores the shelves.
A sufficiently capable predictive system might become interesting precisely in the spaces between them.
It might notice that a cultural change dismissed as trivial correlates with an economic condition studied elsewhere. That changes in language precede changes in behavior. That patterns visible in music, fashion, search queries, transportation, consumer spending, or online communities become meaningful only when seen together.
Not because any one of them contains the future.
Because they are all shadows cast by the same present.
And the present is the only material from which the future can emerge.
The Butterfly in the Archive
There is, however, a problem.
Reality is not merely complicated.
Some parts of it are chaotic.
Tiny differences in initial conditions can eventually produce enormous differences in outcomes. A conversation happens instead of being postponed. Someone misses a train. A storm changes direction. A scientist notices something another researcher overlooked. A politician chooses one sentence rather than another. Two people meet who otherwise would never have occupied the same room.
History turns.
Afterward, the path can look almost inevitable.
Beforehand, it may have been balanced on something microscopic.
This places a horizon around prediction.
More information can improve a forecast without transforming uncertainty into omniscience. Even an extraordinarily capable model would still confront incomplete data, measurement error, genuinely novel circumstances, chaotic dynamics, and events whose consequences depend upon interactions too sensitive to project indefinitely.
The machine might become astonishingly good at estimating futures.
That does not mean there will be only one future to estimate.
Perhaps the better metaphor is not a railway track stretching toward a predetermined destination.
It is a branching tree.
From the present extend countless possible tomorrows. Some branches are thick. Others are vanishingly thin. Every new event changes their relative weight.
Prediction would not require knowing which branch reality must follow.
It would mean becoming increasingly good at seeing the shape of the tree.
And if a machine could see that tree with a clarity no human being ever had before, something peculiar would happen.
We would have built an oracle without giving it supernatural powers.
It would not commune with gods.
It would not peer through time.
It would simply stand before an archive of human experience so enormous that, from our limited perspective, probability might occasionally begin to look like prophecy.
The Statistical Oracle
For most of human history, foreknowledge belonged to the sacred.
Kings consulted oracles. Priests interpreted signs. Astrologers watched the heavens. Dreams became warnings. Bones, birds, stars, smoke, entrails and coincidence were asked the same impossible question:
What happens next?
The methods changed across cultures, but the desire beneath them did not.
Human beings have always had to move forward through time while facing backward.
We remember what has happened.
We experience what is happening.
But what will happen remains hidden.
That asymmetry may be one of the fundamental psychological conditions of being alive.
Which makes artificial prediction strangely mythological.
Imagine asking a future system whether a government will collapse, whether a relationship will survive, whether a market will crash, whether a particular person will relapse into an old habit, whether a protest will become a movement.
The machine answers:
72 percent.
No thunder.
No trance.
No voice from Apollo.
Just probability.
Yet to the person whose life occupies that percentage, the number could acquire tremendous psychological power.
And here the ancient myths return.
Oedipus learns what the oracle has foretold and attempts to escape it. His efforts become part of the machinery through which the prophecy is fulfilled.
The story contains a problem that predictive AI would eventually encounter too:
What happens when the subject of a prediction learns the prediction?
Suppose an AI predicts that you will walk into a café tomorrow morning.
You read the forecast.
So you stay home.
The prediction has helped destroy itself.
Now imagine something larger.
A system predicts a severe financial crisis.
Governments intervene immediately.
The crisis never occurs.
Was the prediction wrong?
Or was it so useful that it prevented itself from becoming true?
Reverse the situation.
A respected system predicts that a bank is likely to fail. People panic and withdraw their money.
The bank collapses.
Did the machine foresee the future?
Or did announcing the future help create it?
The oracle is no longer standing outside the story.
The oracle has entered the causal chain.
And once that happens, prediction becomes something more consequential than knowledge.
It becomes an event.
A forecast can frighten.
A probability can reassure.
A recommendation can redirect attention.
A warning can alter behavior.
A prediction about tomorrow becomes one of the forces shaping tomorrow.
Which leads to a darker possibility.
Perhaps the most powerful predictive machine would not be the one that could tell us exactly what we were going to do.
Perhaps it would be the one that knew what small intervention would make us do something else.
From Prediction to Influence
There is a point at which knowing what someone is likely to do becomes more than observation.
It becomes leverage.
Imagine that a system predicts you are likely to buy a particular pair of shoes tomorrow.
Interesting, perhaps.
Now imagine that it also knows you are 18 percent more likely to buy them when shown the advertisement after 9:30 at night, 11 percent more likely after listening to certain music, and considerably more susceptible when the advertisement emphasizes scarcity rather than popularity.
The system no longer merely predicts the decision.
It knows something about the conditions under which the decision changes.
That is a different kind of knowledge.
And we have already begun constructing primitive versions of it.
Recommendation systems do not need to understand the private depths of a human being to influence attention. They need patterns. What makes someone stop scrolling? What keeps them watching? Which thumbnail earns a click? Which notification brings them back? Which sequence of songs prevents them from closing the application?
Each question appears trivial.
Together they describe an emerging science of the next moment.
The danger is not necessarily that some future artificial intelligence will issue commands and humanity will obediently follow them.
The more plausible possibility is quieter.
The door you were already likely to open is simply placed slightly closer to your hand.
The Architecture of Choice
Human beings rarely make decisions in empty space.
We choose inside environments.
A grocery store decides what sits at eye level. A casino decides where the clocks are. A city decides where roads lead. A website decides which button is brightest. A social platform decides what appears at the top of a feed.
Architecture influences behavior without eliminating choice.
Predictive systems potentially make that architecture personal.
The hallway can rearrange itself for every person walking through it.
Your version of the internet does not have to resemble mine. The advertisement, recommendation, headline, notification, price, search result, song, political message, or suggested response placed before us can theoretically be selected according to what a system estimates we, specifically, are most likely to respond to.
And as models of individuals become richer, personalization could move beyond preference.
Toward vulnerability.
Perhaps you are more impulsive when tired.
More pessimistic after midnight.
More receptive to reassurance after conflict.
More likely to spend money after receiving good news.
More susceptible to anger when certain subjects appear in a particular sequence.
You may not know these patterns about yourself.
A machine does not necessarily need you to.
It only needs the correlations to hold.
This creates an asymmetry that deserves attention.
For most of human history, persuasion involved one human mind attempting to understand another. The salesperson watched your expression. The politician tested a speech before a crowd. The con artist learned which emotional buttons produced trust.
But the persuader was limited.
One mind.
One conversation.
One attempt.
A sufficiently sophisticated computational system could theoretically conduct millions of experiments simultaneously, learning which arrangements of words, images, timing, context, and emotional tone tend to produce particular behaviors in particular kinds of people.
Persuasion becomes iterative.
Personal.
Continuous.
The machine does not have to know why you moved.
It can learn which pressure tends to move you.
The Map Beneath the Person
There is something unnerving about discovering a pattern in yourself that another observer noticed first.
Imagine being told:
You usually call your mother after experiencing rejection.
You buy unnecessary things when you have slept fewer than six hours.
You listen to the same album before making major decisions.
You become more politically extreme when lonely.
You are most likely to contact a former partner on Sunday evenings.
Perhaps some predictions would be wrong.
Perhaps others would feel absurd.
But imagine one being right in a way you had never noticed.
Suddenly the machine possesses a map of a small piece of you that you did not know existed.
Multiply that map by years.
Messages.
Search histories.
Purchases.
Movement.
Media consumption.
Social relationships.
Language.
The pauses between actions.
The things clicked and the things almost clicked.
The patterns become less like a demographic profile and more like a statistical shadow.
Not you.
Never you.
But something following close behind.
A probabilistic silhouette cast by thousands of ordinary actions.
And perhaps that distinction is essential.
No model contains the entirety of a person.
There is an interior dimension to human life that data observed from outside cannot simply be assumed to capture: memory, private meaning, contradiction, imagination, experiences never recorded, impulses resisted, thoughts abandoned before becoming actions.
The map is not the territory.
But a map does not need to contain every tree to help someone navigate the forest.
The Feedback Loop
Then something stranger happens.
The system predicts what you want.
So it shows you more of it.
You interact with what it shows you.
Those interactions become new evidence about what you want.
The system becomes more confident.
It shows you still more.
Eventually a difficult question appears:
Is the machine discovering your preferences, or helping manufacture them?
The distinction may not always be recoverable.
Suppose an algorithm learns that you enjoy a particular kind of music and recommends increasingly similar artists. Years later, your taste genuinely centers around that sound.
The recommendation was accurate.
But the recommendation also helped create the person for whom it became accurate.
Prediction has folded back into identity.
The snake begins eating its statistical tail.
And this is where the idea of predicting the future acquires its darkest dimension.
A sufficiently influential predictive system does not merely wait for tomorrow to reveal whether its forecast was correct.
It participates in producing tomorrow.
Not necessarily through force.
Through suggestion.
Selection.
Repetition.
Timing.
Convenience.
Friction.
A thousand microscopic pressures, each too small to resemble control.
The Soft Determinism
Perhaps the most effective form of influence would be one we continue experiencing as freedom.
No one forces you to watch the video.
It simply happens to be the next one.
No one forces you to buy the product.
It simply arrives when you are most receptive.
No one forces you to adopt an opinion.
You simply encounter the arguments most likely to resonate with the person your behavioral history suggests you already are.
You remain free to say no.
And perhaps you genuinely are free.
But the environment has learned the shape of your yes.
This is not the crude determinism of a puppet and its strings.
It is something softer.
A world increasingly capable of arranging itself around probabilities.
And the ethical question is therefore not merely whether artificial intelligence will become powerful enough to predict human beings.
It is whether the institutions controlling predictive systems will be permitted to use those predictions to shape the environments in which human beings make choices.
Because there is an enormous difference between a machine saying:
I think I know what you will do next.
and a machine quietly asking:
What would I have to change to make something else more likely?
At that point, the future is no longer simply being forecast.
It is being negotiated.
And somewhere inside that negotiation stands the individual human being, still experiencing each approaching second from the only perspective we have ever known:
as though it has never happened before.
The Last Unpredictable Thing
Return to tomorrow morning.
You wake.
Your hand moves toward your phone.
But this time, beside it, imagine there is a screen containing a prediction of your day.
Not a horoscope.
Not advice.
A forecast.
7:14 AM: You check your messages.
7:19 AM: You get out of bed.
7:31 AM: You make coffee.
8:06 AM: You leave home.
The predictions continue.
Where you will go.
What you will eat.
Whom you will call.
The song you will choose.
The argument you will have.
The thing you will regret saying.
Perhaps the system has observed you for years. Perhaps it knows your behavioral history with extraordinary precision. Perhaps similar predictions have already been correct hundreds of times.
You read the next line.
8:17 AM: You turn left.
So you turn right.
For one delicious second, the universe seems to crack open.
The machine was wrong.
You have escaped.
Except the machine knew you would read the prediction.
Perhaps beneath the first forecast is another:
8:17 AM: Subject reads prediction that he will turn left and deliberately turns right.
So you stop the car.
Perhaps it predicted that too.
You reverse.
Predicted.
You choose something absurd. You sing a sentence you’ve never sung before. Throw your shoes into the back seat. Drive nowhere. Make a decision for no reason except that you desperately want to manufacture a reason the machine could not have anticipated.
And somewhere inside this ridiculous rebellion, an ancient philosophical problem suddenly becomes personal.
What would you have to do to prove that you are free?
The Prediction That Watches Itself
There is something fundamentally strange about predicting a creature capable of hearing the prediction.
A falling stone does not care where you calculate it will land.
A planet does not become offended by its predicted orbit and choose another.
Human beings can.
We are not merely systems that behave.
We are systems that can construct ideas about our own behavior and then behave differently because of those ideas.
The prediction becomes information.
The information enters consciousness.
Consciousness responds.
The response becomes part of the world the next prediction must predict.
A mirror has been placed in front of another mirror.
This does not prove that human beings possess some metaphysical freedom beyond causality. The decision to defy the prediction could itself have causes: temperament, pride, neural activity, memory, the peculiar human pleasure of refusing to be categorized.
Perhaps rebellion is predictable too.
But the loop matters.
Any system attempting to forecast human behavior at sufficient resolution may eventually encounter human beings who are themselves attempting to forecast the system forecasting them.
Predictor and predicted begin moving together.
The future becomes recursive.
The Weather Inside Us
We often speak about prediction as though there were only two possibilities.
Either the future can be known or it cannot.
Reality appears less cooperative.
Weather can be predicted without being perfectly predictable.
A hurricane can have a probable path without possessing a single predetermined route available to our models. Small differences can compound. Measurements remain incomplete. The farther outward prediction reaches, the wider uncertainty often becomes.
Human life may contain similar horizons.
A machine might become extraordinarily good at predicting that you will go to work tomorrow while remaining terrible at predicting the sentence that will unexpectedly change your life six months from now.
It might anticipate your purchase but miss your conversion.
Your route but not your revelation.
Your habits but not the moment you finally break one.
And history is full of such discontinuities.
An invention appears.
A friendship begins.
A movement acquires a voice.
Someone changes their mind.
A work of art rearranges another person’s imagination.
A discovery makes yesterday’s assumptions obsolete.
Novelty enters the system.
Perhaps even novelty has causes. Perhaps, viewed from some impossible perspective containing every relevant variable, the surprise would disappear.
But we do not occupy that perspective.
We live here.
Inside the weather.
The Difference Between a Person and a Pattern
There is another danger in imagining increasingly accurate models of human beings.
We may begin confusing the model with the thing being modeled.
If an artificial intelligence can predict ninety-nine of your next hundred actions, it may possess an extraordinary representation of your behavior.
But does it possess you?
A person’s observable life leaves enormous quantities of evidence behind.
Purchases.
Messages.
Locations.
Relationships.
Searches.
Preferences.
Heartbeats.
Photographs.
Sentences.
Pauses.
Choices.
Yet a human life is also filled with events that never become data.
The insult you decided not to send.
The person you almost kissed.
The thought that passed through your mind and disappeared forever.
The dream you forgot before breakfast.
The private meaning of a song.
The reason one ordinary afternoon from twenty years ago still glows inexplicably in memory.
The version of yourself you nearly became.
Some of these things may influence observable behavior eventually. Others may vanish without leaving any measurable trace.
But from inside consciousness, they were real.
This may be one of the most important distinctions in the age of predictive machines.
A pattern can describe a person without exhausting the person.
The map can become extraordinarily detailed.
It is still a map.
The Future Is Listening
And so we return to the question with which we began.
Could artificial intelligence someday predict the future?
In limited domains, increasingly capable systems will almost certainly continue improving prediction. They may discover patterns humans overlook. They may model individual behavior with uncomfortable accuracy. They may help anticipate crises, diseases, markets, technological changes, social movements, or countless smaller events.
But prediction is not prophecy.
Probability is not destiny.
And intelligence, however powerful, does not automatically transform an open world into a finished script.
Perhaps the more interesting possibility is that AI will change our relationship with uncertainty.
For most of human existence, the future has been darkness illuminated by small fires.
Memory.
Experience.
Statistics.
Stories.
Religion.
Science.
Intuition.
Myth.
We have always gathered whatever light we could find and held it toward tomorrow.
Artificial intelligence may become another fire.
Possibly an astonishingly bright one.
Bright enough to illuminate paths that were previously invisible.
Bright enough, perhaps, to reveal how much of what we call spontaneity is habit wearing a mask.
That realization could be frightening.
It could also be useful.
Because a prediction about who you are contains, hidden inside it, another question:
Must you remain that person?
If a machine discovers that you repeat a pattern, knowing the pattern may create the possibility of interrupting it.
The oracle might constrain us.
Or the oracle might reveal the walls of the maze.
And perhaps seeing the wall is the first condition required to choose another direction.
Tomorrow Morning
Tomorrow morning, you will wake up.
There are things about that morning we can predict with extraordinary confidence.
Gravity will still pull you toward the floor.
The sun will rise somewhere beyond whatever weather covers the sky.
Your body will carry yesterday inside it.
Your habits will whisper their familiar instructions.
The world will already be trying to guess what you want.
And perhaps, somewhere, machines will be making increasingly sophisticated estimates about what people like you tend to do next.
But then there will arrive a moment that has never existed before.
This one.
Then another.
And another.
Whether those moments are fundamentally predetermined may remain one of philosophy’s oldest unanswered questions.
But from inside a human life, they arrive as something else.
Possibility.
Perhaps that experience is an illusion.
Perhaps it is freedom.
Perhaps the truth lives somewhere between those words.
The machine looks backward across everything it knows and asks:
What comes next?
The human being stands inside the arriving moment and asks:
What if I choose differently?
Maybe neither question defeats the other.
Maybe the future exists in the tension between them.
An archive and an improvisation.
A pattern and its interruption.
A prediction and the consciousness capable of hearing it.
We began with a machine predicting the next word.
Eventually we found ourselves asking whether it might predict the next human.
But perhaps the strangest thing about the future is not that machines may become increasingly capable of anticipating it.
It is that the moment we glimpse what might be coming, we become one more force capable of changing what comes next.
The oracle speaks.
We listen.
And tomorrow moves.
Look closer. Listen deeper. Meet me between the cracks.
And follow your bliss.