AI RABBIT HOLES | WEDNESDAY, NOVEMBER 4, 2026 | EXPEDITION #160

Featuring Professor Parallax, White Rabbit, and Hatta

What if a letter written nearly two thousand years ago survives with half its sentences missing? What if a philosophical manuscript contains a passage that scholars have puzzled over for generations? And what if artificial intelligence could suggest what the missing words might have been?

Imagine a papyrus fragment lying beneath protective glass in a museum.

Its edges are ragged.

Its surface is discolored.

Across it run several lines of ancient Greek handwriting.

Some letters remain beautifully preserved.

Others have disappeared.

A section of the papyrus is simply gone.

For centuries, historians have confronted such fragments with a mixture of excitement and frustration.

The surviving words provide evidence.

The missing words leave questions.

And occasionally, the missing words are precisely the ones needed to understand what an ancient writer was trying to communicate.

Now artificial intelligence is entering this extraordinary field of historical investigation.

In September 2026, the Austrian Academy of Sciences announced the public availability of Apollo Restore, a specialized language model developed to suggest restorations of missing passages in ancient Greek texts.

Built through collaboration with Mistral AI, SAIL Reply, and Northeastern University, Apollo Restore was trained on approximately 600 million words of historical Greek spanning antiquity through the nineteenth century.

Its purpose is not to translate ordinary modern conversations into Greek.

It is to help scholars investigate damaged historical texts whose original wording has been partly lost.

And its research results suggest that humans working alongside specialized AI systems may be able to tackle some ancient textual puzzles more effectively than either could independently.

Welcome to Expedition #160.

Today, Professor Parallax is opening the OZian Institute of Lost Literature.

White Rabbit has brought a magnifying glass.

And Hatta has arrived carrying an enormous scroll.

Unfortunately, he wrote it himself.

Professor Parallax stood beside a glass display case.

Within it lay a brittle fragment of ancient papyrus.

Several lines of Greek writing were visible.

Other portions had disappeared entirely.

White Rabbit leaned forward.

"Professor, how old is it?"

"Imagine that it comes from the Roman period, approximately two thousand years ago."

"And someone wrote all those letters by hand?"

"Indeed."

"Can we read them?"

"Some."

"What about the missing words?"

"That is today's problem."

At that moment, Hatta entered carrying a scroll taller than himself.

"GENTLEMEN! I HAVE MADE AN EXTRAORDINARY DISCOVERY!"

Parallax turned.

"What have you discovered?"

"The complete manuscript!"

The Rabbit gasped.

"You found the missing ancient text?"

"Naturally."

"Where?"

"In my study."

The Professor narrowed his eyes.

"Hatta, what exactly is written upon your scroll?"

Hatta unfurled it.

Across the parchment appeared:

HATTA WAS THE GREATEST PHILOSOPHER OF ANTIQUITY.

Below it:

ALL ANCIENT GREEKS ADMIRED HIS MAGNIFICENT HATS.

And finally:

THE INVENTION OF PUDDING REPRESENTS HUMANITY'S GREATEST ACHIEVEMENT.

The Professor stared.

"Hatta, this is not ancient Greek."

"An unfortunate technicality."

"It is written in English!"

"To make it accessible to contemporary scholars."

"And where did you obtain the historical evidence?"

Hatta looked offended.

"Professor, surely you don't expect every great discovery to be delayed by evidence!"

"YES!"

Parallax approached his blackboard.

And wrote:

A PROFOUND PARADOX, MY PEEPS! IF ARTIFICIAL INTELLIGENCE CAN GENERATE MISSING WORDS THAT SOUND PERFECTLY AUTHENTIC, HOW CAN WE KNOW WHETHER IT HAS RECOVERED HISTORY OR INVENTED IT?

And down the Rabbit Hole we went.

Ancient civilizations produced enormous amounts of written material.

Literature.

Philosophy.

Government documents.

Contracts.

Personal letters.

Religious writings.

Medical observations.

Military records.

Receipts.

And countless ordinary communications.

Some were inscribed into stone.

Others were written on parchment, wooden tablets, or papyrus.

Papyrus was especially important throughout the ancient Mediterranean world.

Produced from a plant associated with the Nile region, it provided a practical writing surface for centuries.

But papyrus was vulnerable.

Moisture could destroy it.

Fire could damage it.

Insects could consume it.

Repeated handling could tear it.

And the passage of centuries could leave manuscripts brittle and fragmented.

A remarkable number of surviving papyri come from Egypt, where dry environmental conditions helped preserve materials that would have deteriorated elsewhere.

Even so, many surviving documents are incomplete.

Scholars use the term lacuna for a gap in a text.

A lacuna may represent one missing character.

Several letters.

An entire word.

A sentence.

Or a much larger portion of a manuscript.

Sometimes researchers know approximately how many letters occupied a missing space.

In other situations, the physical damage makes the length of the missing passage uncertain.

That distinction is important.

Trying to reconstruct five missing letters is a different problem from reconstructing an unknown amount of missing writing.

Imagine discovering this fragment of a hypothetical ancient letter:

"Tomorrow we shall travel to the _____ before sunrise."

What might the missing word be?

Harbor?

Temple?

Market?

Palace?

Several possibilities might fit the surviving sentence.

But their historical meanings could differ enormously.

An ordinary journey to the market is not the same event as a visit to a royal palace.

A restoration can change how historians understand an entire document.

And that is why the missing words matter.

Long before artificial intelligence, historians developed sophisticated methods for reconstructing damaged texts.

Their work requires much more than knowing the ancient language.

They study grammar.

Vocabulary.

Writing conventions.

Historical geography.

Documentary formulas.

Literary styles.

And the social circumstances surrounding the manuscript.

Suppose a papyrus contains a contract.

Its surviving language might resemble hundreds of other contracts from the same region and period.

Scholars can examine comparable documents to identify familiar phrases.

A missing section might involve payment terms, a witness declaration, or a date.

If the relevant formula appears consistently across other surviving texts, that evidence may support a reconstruction.

But not every document follows a predictable formula.

A philosophical essay might contain an original argument.

A private letter might include an unusual expression.

A poem might deliberately violate ordinary expectations.

These circumstances make reconstruction more difficult.

A proposed restoration might be grammatically correct.

It might fit the available space.

It might even sound appropriate for the historical period.

Yet it could still be wrong.

A scholar must distinguish between what the surviving manuscript establishes and what a proposed reconstruction merely suggests.

Artificial intelligence inherits this very problem.

Only now, the number of possible suggestions can be enormous.

In September 2026, researchers introduced a specialized language model called Apollo Restore.

The system emerged from the Austrian Academy of Sciences' Decoding Antiquity initiative.

Its underlying research paper, released as a preprint, describes a 24-billion-parameter language model fine-tuned from Mistral Small.

The researchers designed it for a task called fill-in-the-middle restoration.

Ordinary language models often generate text by predicting what should come next.

But damaged manuscripts frequently contain surviving material on both sides of a missing section.

For example:

"The messenger arrived at the [MISSING TEXT] carrying a sealed letter."

An AI restoration system can examine the words before and after the gap.

It then generates possible missing passages consistent with the surrounding language.

Apollo Restore is designed to work even when the precise length of the missing passage is unknown.

That is an important technical advance for manuscript reconstruction.

Apollo was trained using approximately 600 million words of Greek spanning a broad historical period.

This exposes the model to patterns of grammar, vocabulary, and written expression across different kinds of texts.

During training, the system learns statistical relationships among words and phrases.

It can then use those relationships to propose plausible completions.

But the word plausible remains essential.

The machine is generating candidate restorations, not retrieving the missing original from some hidden historical memory.

Its suggestions require evaluation against the manuscript and other available evidence.

The September 2026 research reported encouraging performance on several restoration tasks.

The researchers evaluated Apollo Restore against earlier computational approaches using known texts with portions deliberately concealed.

This allowed them to compare the model's suggestions with the actual surviving wording.

The evaluation included:

Documentary papyri: Everyday written records such as administrative or transactional material.

Literary papyri: Surviving portions of literary works.

Stone inscriptions: Writing carved into monuments or other durable surfaces.

In tests involving missing spans of up to ten characters, the correct restoration appeared somewhere among Apollo's twenty highest-ranked suggestions in approximately:

Those results exceeded the strongest previously published comparison models under the researchers' evaluation.

But notice the wording.

The correct restoration appeared among the top twenty candidates.

That does not mean the system supplied one perfect answer in every case.

Nor do these percentages describe its ability to recover any arbitrarily long lost manuscript.

The research also included a blind evaluation involving twenty specialists in ancient texts.

The experts generally preferred Apollo's suggestions to those generated by the comparison system.

They rated its restorations as at least as good as human-written restorations in 77% of the evaluated cases.

This suggests substantial potential value for scholarly workflows.

Yet the results were reported in a September 2026 research preprint, and their broader significance requires continued independent evaluation.

A promising research result is not the same thing as a guarantee of historical accuracy.

Now our expedition encounters one of archaeology's most extraordinary stories.

In the year 79 CE, Mount Vesuvius erupted in southern Italy.

The disaster devastated Pompeii and Herculaneum.

Among the objects affected were papyrus scrolls preserved in the ruins of Herculaneum.

The intense heat carbonized many scrolls, leaving them exceptionally fragile.

For centuries, attempting to open these ancient documents could damage or destroy them.

Modern imaging techniques have begun changing that situation.

Researchers can digitally investigate the interior of certain rolled manuscripts without physically unwrapping them.

The Apollo Restore research includes work involving a Herculaneum papyrus identified as P.Herc. 1667.

The authors report that their system helped improve the proposed reading of material from this manuscript.

This is an intriguing example of how AI language modeling can complement scientific manuscript recovery.

However, the roles of different technologies must remain clear.

Imaging techniques help reveal physical writing.

Text-restoration models suggest plausible completions where portions remain missing or uncertain.

Expert scholars assess whether those suggestions fit the historical and material evidence.

These are complementary activities.

One cannot simply substitute for another.

Reading what physically survives and reconstructing what no longer survives are fundamentally different tasks.

Professor Parallax placed a handwritten sentence upon the laboratory table.

It read:

"THE KING SENT HIS MESSENGER TO THE _____."

White Rabbit studied the sentence.

"Professor, there are several possibilities."

"Indeed."

"The palace?"

"Perhaps."

"The harbor?"

"Also possible."

"The battlefield?"

"Potentially."

Hatta raised his hand.

"THE EMERALD BISTRO!"

The Professor sighed.

"That is not supported by the evidence."

"But the king might have been hungry."

"We have no information indicating that."

"Then the marketplace!"

"Again, possible but unproven."

White Rabbit examined the document.

"What if we discovered another sentence describing the messenger boarding a ship?"

"That would provide additional context."

"Then the harbor might become more likely?"

"Precisely."

"And if the document described a religious ceremony?"

"A temple might become more plausible."

Hatta nodded gravely.

"I see."

Parallax looked hopeful.

"You understand?"

"Completely."

"Excellent."

"WE NEED MORE DOCUMENTS ABOUT THE KING'S DINNER ARRANGEMENTS!"

The Professor put down his chalk.

"Hatta, the objective is to evaluate evidence, not to force the evidence toward your preferred restaurant."

Hatta looked wounded.

"An unusually narrow interpretation of historical inquiry."

One of the most important issues in historical reconstruction involves the distinction between possibility and evidence.

Imagine an AI model proposing three missing passages.

All three are grammatically correct.

All three resemble authentic ancient writing.

But only one matches the words originally written.

The other two are inventions.

They may be reasonable inventions.

They may reflect historically familiar expressions.

But they are inventions nonetheless.

Modern language models can produce remarkably coherent text.

That ability is useful.

But polished language does not establish factual accuracy.

An AI-generated reconstruction may sound convincing because it follows patterns found throughout its training material.

The original author, however, may have written something unexpected.

Ancient writers were not obligated to follow the most statistically probable phrasing.

They made mistakes.

Invented expressions.

Used regional dialects.

Made jokes.

And sometimes wrote things no historian would have anticipated.

A responsible restoration system should present its suggestions as candidates.

Scholars should examine alternatives.

And when the evidence is insufficient, the missing passage may need to remain unresolved.

An honest blank space is better than a fabricated certainty.

Imagine a damaged historical decree.

The surviving sentence reads:

"The governor ordered the prisoners to be ____."

One candidate reconstruction suggests released.

Another suggests transferred.

A third suggests executed.

These alternatives represent profoundly different historical events.

Selecting one without sufficient evidence could distort an entire interpretation of the past.

Now imagine that an AI model places released at the top of its suggestions.

Should historians accept that answer?

Not automatically.

They would need to examine the surrounding text.

Consider the historical circumstances.

Investigate similar documents.

Study surviving letters.

And assess whether the proposed restoration is supported by more than linguistic probability.

The machine's confidence is not a substitute for historical evidence.

This problem reaches far beyond ancient Greek.

It appears whenever artificial intelligence reconstructs incomplete information.

Damaged photographs.

Historical audio recordings.

Missing manuscript passages.

Incomplete scientific measurements.

And fragmented archaeological evidence.

A reconstruction can be helpful without being definitive.

The essential responsibility is to make that distinction visible.

When people imagine lost ancient literature, they often picture magnificent philosophical works.

Plato.

Aristotle.

Epic poetry.

Forgotten histories.

Lost tragedies.

And perhaps a previously unknown masterpiece.

But some of the most valuable surviving records are far more ordinary.

A receipt.

A petition.

A tax document.

A marriage contract.

A letter between family members.

A declaration of birth.

Such materials offer something that grand official histories often cannot.

They reveal aspects of everyday existence.

What people purchased.

How they organized work.

What they feared.

How they settled disputes.

What obligations they accepted.

And sometimes how they expressed affection.

A surviving administrative document might reveal how a local community functioned.

A private letter might preserve a person's worries about family, money, or travel.

A contract might illuminate the legal and economic circumstances of people otherwise absent from history.

These records rarely receive the attention given to emperors and generals.

Yet they help historians understand the lives of ordinary people.

Artificial intelligence may assist researchers in working through some of these enormous collections of fragmentary material.

And that could help bring neglected experiences into the historical record.

The past was not inhabited only by famous people. It belonged to everyone who lived through it.

By afternoon, Hatta had constructed a magnificent machine.

Brass gears turned behind polished glass.

A roll of parchment fed through one side.

Three enormous lamps illuminated the front.

A sign announced:

HATTA'S INFALLIBLE ANCIENT LITERATURE RESTORATION SERVICE

Every Missing Word Recovered!

Historical Accuracy Absolutely Guaranteed!

White Rabbit examined the mechanism.

"Professor, should we test it?"

"Very well."

The Rabbit inserted a fragment.

It read:

"THE PHILOSOPHER DECLARED THAT WISDOM REQUIRES ____."

The machine rattled.

A bell rang.

The output appeared:

A MAGNIFICENT HAT.

White Rabbit frowned.

"That seems unlikely."

Hatta smiled.

"An excellent restoration."

Professor Parallax inserted another fragment.

"THE EMPEROR ANNOUNCED THAT HIS GREATEST ACHIEVEMENT WAS ____."

The machine whirred.

A MAGNIFICENT HAT.

Parallax folded his arms.

"Hatta, that is suspicious."

"Some themes are universal."

The Professor submitted a third fragment.

"THE ANCIENT SCIENTIST EXPLAINED THAT THE EARTH REVOLVES AROUND ____."

The machine produced its answer.

A MAGNIFICENT HAT.

"HATTA!"

"Yes, Professor?"

"YOU HAVE PROGRAMMED EVERY ANSWER TO PRAISE YOUR HAT!"

"Not every answer."

"Then what happens when the question involves food?"

Hatta pressed another button.

The machine displayed:

PUDDING.

White Rabbit collapsed into laughter.

Parallax opened the inspection panel.

Inside were only two brass wheels.

One read:

MAGNIFICENT HAT.

The other:

PUDDING.

"Hatta, you have constructed a machine that cannot evaluate evidence!"

"Professor, I have constructed a machine that understands the two most important achievements of civilization."

The Professor replaced its sign.

It now read:

HATTA'S AUTOMATED ANCIENT TEXT FABRICATION DEVICE

Historical Reliability Not Established.

Hatta examined the new sign.

"An unnecessarily cautious marketing strategy."

Perhaps the most encouraging aspect of the Apollo research is not simply the model's performance.

It is the collaboration between human specialists and computational systems.

A human scholar possesses expertise about ancient languages, history, manuscript traditions, and material evidence.

An AI model can examine statistical patterns across large amounts of historical writing and rapidly generate alternative reconstructions.

Their strengths are different.

And combining them may improve the investigation.

A specialist studies a damaged manuscript.

The surviving text is transcribed.

Known gaps are identified.

Apollo Restore generates several possible restorations.

The historian compares those suggestions against grammar, historical context, material evidence, and other texts.

Some candidates may be rejected.

Others may deserve further examination.

The scholar then documents the reasoning behind any proposed reconstruction.

The final interpretation remains open to scrutiny and revision.

The AI does not become the author of the ancient document.

Nor should it become the final authority over its meaning.

Its contribution is to help expand the possibilities scholars can investigate.

The human contribution is not merely approving whatever appears on a screen.

It involves judgment, expertise, evidence, and accountability.

The strongest partnership may emerge when artificial intelligence proposes possibilities and human intelligence determines which ones deserve belief.

The Austrian Academy of Sciences has identified additional directions for the Apollo project.

One involves connecting the language model with systems capable of retrieving relevant historical material.

This approach is commonly called retrieval-augmented generation, or RAG.

Instead of relying only on patterns learned during training, a future system could search historical sources for relevant passages and scholarly parallels.

Imagine investigating a damaged document concerning an ancient trade dispute.

The system might identify other texts discussing similar transactions.

It might help locate comparable vocabulary.

Or reveal recurring formulas used in contracts.

Scholars could then examine those connections directly.

This would provide a more evidence-grounded research process than merely accepting a language model's suggested wording.

But these improvements are developing research directions.

They should not be confused with capabilities already demonstrated by the initial Apollo Restore release.

Specialized AI tools may eventually help researchers:

Each possibility requires careful testing.

And none eliminates the need to preserve original manuscripts and scholarly evidence.

Professor Parallax returned to his blackboard.

He wrote:

DEMONSTRATED. DEVELOPING. NOT ESTABLISHED.

White Rabbit studied the board.

"Professor, so Apollo can help us investigate missing words?"

"Indeed."

"But it doesn't actually travel backward through time?"

"No."

"And it cannot ask the ancient writer what was originally written?"

"Unfortunately not."

Hatta raised his hand.

"Professor, that gives me an idea!"

Parallax sighed.

"What idea?"

"We shall invent a TIME MACHINE!"

"For what purpose?"

"To interview the author!"

"And if the author confirms that your restoration is wrong?"

Hatta looked horrified.

"Then we shall find another author!"

Evening settled upon the OZian Institute of Lost Literature.

The laboratory had grown quiet.

White Rabbit sat beside the ancient papyrus fragment.

Professor Parallax was carefully organizing his notes.

Hatta stood near the display case.

For once, he carried no scroll.

No magnificent invention.

Not even a spare hat.

"Professor?" he asked.

"Yes, Hatta?"

"Do you suppose the person who wrote that manuscript imagined anyone would still be trying to read it two thousand years later?"

"Probably not."

"Perhaps it was simply an ordinary letter."

"That is entirely possible."

"A message to a friend?"

"Perhaps."

"Or a record of something important?"

"Possibly."

Hatta looked at the damaged papyrus.

"And now all we possess are these few surviving words."

"Yes."

The Rabbit approached.

"Yet those words can still tell us something."

"They can."

"And if we recover more of the text, we might understand that person's life a little better."

"Indeed."

Hatta remained thoughtful.

"Professor, perhaps that's why we shouldn't invent the missing words."

Parallax looked surprised.

"Go on."

"Because whoever wrote them deserves to have their own words remembered."

The Professor smiled.

"An excellent observation, Hatta."

The Rabbit nodded.

"And where their words cannot be recovered?"

"Then," said Parallax, "we should be honest about what remains unknown."

Outside the windows, the first stars appeared.

Within the display case lay the ancient fragment.

Its missing words had not magically returned.

But researchers now possessed another instrument for investigating what those words might have been.

And somewhere across the immense distance of two thousand years, the possibility of recovering another small piece of humanity's story had grown.

For thousands of years, human beings have written words they hoped someone would read.

Some were carved into stone.

Others were preserved on papyrus.

Some became celebrated literature.

Others were merely messages exchanged between ordinary people.

But time is an imperfect archivist.

Pages disappear.

Ink fades.

Materials crumble.

And enormous portions of the human story become inaccessible.

Modern science has developed remarkable ways to investigate what survives.

Digital imaging can reveal otherwise invisible writing.

Historical scholarship can reconstruct damaged passages.

And artificial intelligence can now help generate possible restorations based upon patterns in surviving texts.

The arrival of Apollo Restore offers an intriguing new chapter in this work.

Yet it also raises an essential question about the relationship between intelligence and evidence.

If a computer can produce a beautiful, historically plausible sentence, does that mean the sentence was actually written?

No.

If a system can generate twenty possibilities, must one of them be correct?

Not necessarily.

And if artificial intelligence becomes increasingly skilled at reconstructing missing information, will historians still need to acknowledge uncertainty?

Absolutely.

The purpose of historical investigation is not to create the most satisfying version of the past.

It is to discover what available evidence allows us to know.

So the question at the bottom of today's Rabbit Hole is not merely:

CAN ARTIFICIAL INTELLIGENCE RECOVER WORDS THAT DISAPPEARED TWO THOUSAND YEARS AGO?

It is:

IF INTELLIGENCE CAN HELP US RECOVER MORE OF HUMANITY'S FORGOTTEN WORDS, HOW DO WE ENSURE THAT WE ARE LISTENING TO THE VOICES OF THE PAST RATHER THAN REPLACING THEM WITH OUR OWN IMAGINATIONS?

Imagine an ancient writer sitting beside a lamp.

A sheet of papyrus rests upon the table.

The writer dips a pen into ink.

And begins.

Perhaps the message is ordinary.

Perhaps it is profound.

Perhaps it will change someone's life.

Centuries pass.

The paper is damaged.

Some words survive.

Others disappear.

Then, almost two thousand years later, another human being studies those faint marks.

A computer suggests possibilities.

A scholar considers the evidence.

And the effort to understand continues.

The greatest achievement may not be that a machine can generate words resembling those of the ancient world.

It may be that the living have found another way to listen respectfully to those who came before them.

History becomes richer not when we pretend to know everything, but when we learn how to recover what can be known while honoring what remains lost.

🐰🕳️♾️

Imagine archaeologists discovering a damaged letter written two thousand years ago.

Half the words are missing.

Would you rather see an AI-generated reconstruction that reads like a complete letter, or a more cautious edition showing the surviving words alongside several possible restorations?

And here's today's deeper question:

If artificial intelligence could recover just one lost work from antiquity, what would you choose?

A forgotten Greek tragedy?

A lost philosophical treatise?

A missing scientific manuscript?

An account of ancient daily life?

Or something completely unexpected?

Tell us what you think!

Hatta has nominated the complete lost works of Hatta.

Professor Parallax has requested evidence that such works ever existed.

Hatta has filed an appeal.

1. McGovern, H., Dolganov, A., Belkadi, S., Kunsch, G., Vlitas, D., & Smith, D. A. (2026). "Apollo Restore: A Foundation LLM for Historical Greek Optimized for Fill-in-the-Middle Restoration of Ancient Greek Texts."

September 2026 research preprint describing the model, its training, restoration tasks, benchmark results, and specialist evaluation.

https://arxiv.org/abs/2609.22455

2. Austrian Academy of Sciences (September 23, 2026). "AI Speaks Ancient Greek: Apollo Restores 2,000-Year-Old Texts."

Official research announcement detailing the Apollo project, scientific partnerships, and public release.

https://www.oeaw.ac.at/en/news/ai-speaks-ancient-greek-apollo-restores-2000-year-old-texts

3. Austrian Academy of Sciences. "Apollo AI."

Project information and instructions for using Apollo Restore.

https://www.oeaw.ac.at/projects/decoding-antiquity/apollo-ai

4. Austrian Academy of Sciences. "RAG for Apollo."

Description of planned retrieval-based research tools intended to help connect ancient Greek text restoration with scholarly evidence and related historical passages.

https://www.oeaw.ac.at/projects/decoding-antiquity/projects/rag-for-apollo

5. Assael, Y., and colleagues (2025). "Contextualizing Ancient Texts with Generative Neural Networks." Nature.

Peer-reviewed research introducing Aeneas, a specialized AI system for investigating ancient Latin inscriptions, restoration, and historical context.

https://www.nature.com/articles/s41586-025-09292-5

Research note: Apollo Restore's September 2026 reported results derive from a research preprint and associated expert evaluations. Its suggested restorations are hypotheses, not automatic proof of missing historical wording. The fictional scenes involving Professor Parallax, White Rabbit, and Hatta are educational dramatizations.

AI RABBIT HOLES | EXPEDITION #160

Where curiosity crosses two thousand years, ancient manuscripts reveal their unfinished stories, and Hatta discovers that his magnificent hat is not an acceptable substitute for historical evidence.

#AIRabbitHoles #ArtificialIntelligence #AncientGreek #ApolloRestore #Archaeology #AncientHistory #DigitalHumanities #HistoricalManuscripts #LanguageModels #Papyrology #AIResearch #HumanAIConvergence #ProfessorParallax #WhiteRabbit #Hatta #Wondermage

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