Google Street View

Article

Google Street View is a recurring organization in the Astral Codex Ten archive, appearing 2 times across 2 issues between May 19, 2023 and May 02, 2025. The archive places it in contexts such as “Here is what Higgins looked like in 2013 on Google Street View:”; “from Google Street View”; “I got this one from Google Street View”. It most often appears alongside England, London, Mississippi.

Metadata

Category: Organizations
Mention count: 2
Issue count: 2
First seen: May 19, 2023
Last seen: May 02, 2025

Appears In

- England (2 shared issues)
- London (2 shared issues)
- Mississippi (2 shared issues)
- New York (2 shared issues)
- Ontario (2 shared issues)
- United States (2 shared issues)
- 1980 (1 shared issues)
- 1980 referendum (1 shared issues)
- 1995 referendum (1 shared issues)
- A.W. Phillips (1 shared issues)
- Adam Smith (1 shared issues)
- AI Futures Project blog (1 shared issues)

External Links

Source Context

Recovered passages from the original issue text. When the raw archive preserved outbound links inside the source passage, they are listed directly under the quote.

The Question Of Separatism

May 19, 2023 · Original source

Finally, an oversized capital force creates an artificial city region. In the US, the Tennessee Valley Authority was a Depression Era program to develop a poor region using federal government money. The hydroelectric dams and other infrastructure that the money bought seemed to be great successes at first, and to be sure they did reduce poverty. But problems later appeared, and today the region isn’t particularly dynamic, in addition to being riddled with environmental issues. Jacobs explains that the federal aid could never truly help, because the Tennessee Valley has always lacked an import-replacing city. Subsidies, grants, and loans give at best the illusion of development. None of these five types of rural regions tend to do great in the long run, unless they manage to generate an import-replacing city. But at least they receive something from distant cities. It’s far worse when a region is untouched by city forces at all, as Bardou was for a long time. Or as was a hamlet in North Carolina that Jacobs calls “Henry” for anonymity reasons, but which we can safely reveal to be Higgins, in the Appalachian region. Here is what Higgins looked like in 2013 on Google Street View: There is a nice modern road in that screenshot, but between its 18th-century founding and the 1920s, there wasn’t even a path that a horse-drawn wagon could use, and so Higgins was extremely isolated. It barely sold anything to anyone outside, and accordingly imported very little. The people lived from subsistence farming. Their lives were so difficult, so focused on sheer survival, that they gradually forgot many of the skills and techniques that their British ancestors had, like candle making, weaving from a loom, and even masonry. When Jacobs’ aunt arrived as a Presbyterian missionary in 1922, and suggested that they build a church out of stone, the people of Higgins confidently stated that this was impossible: mortar just wasn’t strong enough. “These people came of a parent culture that had not only reared stone parish churches from time immemorial, but great cathedrals,” Jacobs writes, and yet eventually they forgot that stone buildings were a possibility at all. Such is the fate of regions that get cut off from cities. Jacobs calls them bypassed places. Sometimes these places are entire countries, such as Ethiopia, once the seat of an empire, but which as of the 1980s had barely any links to cities except its own backward ones. Unsurprisingly, Ethiopia has high prices (for Ethiopians) and too few jobs. That will always be so, unless one of its cities can start the process of import replacement. III. Should Everything Be a City-State? That was roughly the first half of the book. After that, Jane Jacobs discusses various consequences of her theory, including why decline happens and how we can, in theory, prevent it. We’ll get there — but first, it’s time for a detour through the other book, The Question of Separatism, which provides a great case study of Jacobs’s ideas. After an introductory chapter in which Jacobs acknowledges that separatism always makes everyone emotional, and warns that she’s going to study it in a dispassionate manner anyway, she starts by describing the issues in Quebec and Canada through a specific lens. You can probably guess which lens. That’s right — cities. To her, the question of Quebec separatism is primarily the question of how the two main cities in Canada, Toronto and Montreal, have coexisted and will coexist in the future. At this point you need at least a basic understanding of Canadian history. Here’s a quick primer, focusing on those two cities. Canadian History Speedrun (Jane’s Version) Canada, a word that used to refer to the large valley around the St. Lawrence river and the Great Lakes, was originally a colony of the Kingdom of France. Then the Kingdom of Great Britain conquered it in 1760. For various reasons, most of the French settlers stayed in Canada rather than emigrating to France or being deported, so at first, a small British elite ruled over a mostly French-speaking and Catholic colony. However, immigration from the British Isles, as well as from the newly seceded United States (loyalists who wanted to live in a monarchy rather than a republic for some reason) eventually tipped the linguistic and cultural balance. The population sorted itself such that the lower part of the valley (what is now Quebec) remained French, while the upper part (what is now Ontario) became English. The exception to this trend was the city of Montreal. Although located in Quebec, it became an English-speaking city and the hub for the British merchant elite. For at least a hundred years, it was the main city in Canada across almost all metrics: population, wealth, manufacturing, political influence. In the middle of the 20th century, Montreal grew enormously and became French-speaking again, owing to immigration from rural Quebec. It became the center of Quebecois culture and, with its increasingly educated population, the breeding ground for new ideas, including separatism. At the same time, the main city in Ontario, Toronto, was growing even faster. Immigrants from all over Canada and other countries poured into it (including Jane Jacobs herself). Sometime around 1970, it became bigger and wealthier than Montreal, and replaced it as the main economic hub. Many people attribute this to the rise of Quebec separatists, which supposedly scared the Anglo elite of Montreal into moving all the banks and companies to Toronto, and, to be sure, some of that happened — but of course, Jacobs prefers explanations that rely on city economics. One of the reasons for Toronto's economic and demographic growth is that it became the nexus of what Jacobs calls a conurbation, and would have called a city region if we were in the other book. In case you craved another concrete example of a city region, here’s a map of Ontario with two ways to define Toronto’s so-called “Golden Horseshoe” (Toronto itself is just the tiny strip in the middle of the red area, next to the lake): Meanwhile, Montreal never generated a conurbation or significant city region. This is Jacobs’s main hypothesis for why it was overtaken by Toronto, though she doesn’t give a lot of detail on why it happened. In any case, the result was that Montreal lost its status as the economic capital of the country. It became a regional city. The problem is that regional cities tend to do poorly. The nature of nations is to centralize everything in one place (we’ll come back to this). That’s why Paris has a large and rich city region, but Lyon and Marseille don’t. That’s why London looms so large in the UK’s economy while Glasgow or Manchester now contribute very little. There’s nothing wrong per se with being an economically stagnant regional city. Such cities can be fine places. When they’re the center of a supply region, like Calgary and Edmonton in oil-rich Alberta, they can even be wealthy. The complication for Montreal, though, is that its previous status as the main Canadian metropolis made it grow too large for this purpose. Yet, at the same time, Montreal plays an outsized cultural role for French-speaking Canadians — one that Toronto doesn’t even come close to fulfilling. So, Jacobs sees only decline for Montreal. And she thinks this means decline for Quebecois culture generally. Without a strong import-replacing city, Quebec will become a patchwork of supply regions, regions that workers abandon, or transplant economies, like the poverty-stricken Atlantic provinces in eastern Canada already are. Either the Quebecois resign themselves to this fate, she says, or they fight it — and the only true way to fight it is to declare independence. As of the 1980 referendum, she thinks they should go for independence. Generalized Separatism Quebecers did not go for independence, neither then in 1980 nor in 1995 when they voted on the question again. If they had, it would probably have been an example of a peaceful secession. Jacobs points out that there haven’t been many of those, if you exclude the decolonization of overseas imperial possessions (like Canada from Britain). Non-peaceful secessions have been common, but in those cases the destructiveness of war tends to overshadow everything else, economically speaking. In fact that might be the main reason most of us intuitively dislike separatism: we associate it with conflict. But peaceful non-colonial secessions do happen. Since 1980 there have been several more cases, like Czechia and Slovakia. When Jacobs wrote her book, though, the only good example she could think of was the independence of Norway from Sweden in 1905. She tells a great account of the process, noting that the outcome wasn’t predetermined: Sweden didn’t want to lose its western province, and did what it could to contain Norwegian nationalist sentiment. But Norwegian nationalist sentiment won — and importantly, both Norway and Sweden seemingly benefitted. Neither of them was particularly rich in the 19th century, and Norway was in fact dirt poor, which is why so many Norwegians escaped by emigrating to North America. Yet after the dissolution of their union, the two countries developed quickly, and both are now among the wealthiest countries in the world. They certainly didn’t disintegrate. (Of course, in Norway the wealth is due in large part to the oil that they discovered in the late 1960s. But they were pretty advanced by that point already — advanced enough that they could use the oil to develop their own industry, rather than get rich quick by exporting it raw, which is what keeps many countries trapped as supply regions.) When people argue against separatism, they often tout the benefits of being large. A Canada that would be split in two would mean smaller markets, and a weaker political counterweight to the United States. (Not to be mean to Canadian readers, but this argument seems delusional to me — I don’t think Americans currently see Canada as a political counterweight of any significance.) It would certainly be less prestigious. Large size, Jacobs says, is associated with power, and we admire power. We love slogans like “unity makes strength.” But after the medium-sized country of Sweden-Norway became the two smaller countries of Sweden and Norway, they both did well. Small size is less powerful, but it has its own advantages, such as nimbleness and ability to fail non-catastrophically. Small size also allows more diversity in cultural and economic matters, and here Jacobs waxes philosophical, pointing out that favoring diversity over uniformity is a recent, post-Enlightenment idea that has not yet been fully embraced in politics. We can see analogs everywhere. Europe, split into numerous small countries from the Middle Ages onward, became far more advanced than China, which has been unified more often than not. The city-states of ancient Greece and Renaissance Italy are seen as golden ages of Western civilization, even if they weren’t part of larger political units and therefore constantly went to war with one another. In business, large companies are impressive and powerful, but people always complain that Google or Microsoft have become stagnant and that the best place to work is tiny startups of about 2 cofounders and 4 employees. In biology, humans are more successful than numerous larger animals, and in terms of raw numbers, small animals like rats or insects are the most successful of all. Jacobs’s point isn’t that smaller is always better. Her point is that the converse statement, “bigger is always better,” is false — despite how intuitive it feels for political entities. Just like we don’t view a small nation like Switzerland or Singapore as a failure of unity, we (and in particular, Canadians) shouldn’t see the secession of a place like Quebec, if it’s done peacefully and democratically, as a failure either. Still, some people in online reviews of the book complain that this argument is a bit thin, especially considering that it serves as the foundation for the later chapters (which are more directly about late 1970s Quebec politics). Sure, small is beautiful, but large states are great for stability, peace, markets, whatever. If the potential benefits of small national size are Jacobs’s strongest argument, then we can breathe a sigh of relief and go back to agreeing that separatism is bad. Pointing out the widespread bias in favor of unified political entities does seem valuable to me, but okay, fair enough. Does Jacobs have deeper reasons why separatism might be a good idea in general? Yes, and for this we go back to the second half of Cities and the Wealth of Nations. Why Nations and Empires Fail Our breathing rate is regulated through a feedback mechanism. Too much carbon dioxide in the blood, or too little oxygen, and the brain stem commands the diaphragm to accelerate breathing. Once the levels are back to normal, the brain stem receives this feedback and slows breathing down again. Now, Jacobs asks, imagine an impossible creature: ten people, all doing their own thing, but whose breathing is somehow regulated by a single brain stem. The feedback the brain stem receives is a consolidated average of everyone’s carbon dioxide and oxygen levels, and the breathing rate the stem decides on is applied to all ten people, regardless of whether they’re sleeping or playing tennis. This, to put it mildly, wouldn’t work. This creature is an analogy, representing a nation. The ten people are its individual cities, and the breathing rate is the cities’ economies. If it sounds like a stupid analogy, that’s because it is: “I have had to propose a preposterous situation,” writes Jacobs, “because systems as structurally flawed as this don’t exist in nature; they wouldn’t last.” Nor do they exist in machines we design; they wouldn’t work. But “nations, from this point of view, don’t work either, yet do exist.” The feedback mechanism that fails to work properly in a nation is currency. A currency always fluctuates according to the exports and imports of the area where it circulates. Let me use the Republic of Venice and its ducat as a toy example, because the coins look nice: Whenever Venice produces something (like salt) and sells it abroad, foreigners need ducats to buy the exports, so the demand for ducats increases. When Venice buys something from abroad, it needs to use foreign currencies, so the demand for ducats decreases. Add up everything that Venice exports and imports, and you get either a trade surplus (more exports than imports) or a trade deficit (more imports than exports), which determines the value of the ducat relative to other currencies. In both cases, a negative feedback loop restores balance over time, just like our brain stem does with carbon dioxide levels. A trade surplus, and therefore a strong ducat, means that when foreigners want Venetian salt, it’s expensive. So Venice’s exports decrease, while imports increase, since Venetians can use their valuable ducats to buy stuff cheaply from abroad. Conversely, a trade deficit makes exports a bargain for foreigners and imports expensive for Venetians. This feedback loop is great. It’s exactly what a city needs to trigger the crucial import replacement process. When exports decrease and a trade deficit begins (maybe because Constantinople found a cheaper source of salt somewhere else), the weak ducat means that Venice is less able to afford the resources and manufactured goods it used to import. The people of Venice don’t want to have less of those goods, though, so they figure out ways to produce some themselves — that is, they do import replacement. Later they will be able to export the output of the newly expanding industries too, strengthening the ducat and continuing the cycle. Currencies, Jacobs explains, function as automatic tariffs (to protect local industry from foreign imports) and automatic export subsidies (to encourage local industry to export). They are “automatic” because of the feedback mechanism. Just like an accelerated breathing rate, they take effect exactly when they are needed — and no longer. … Or so they should, except that import replacement, as we discussed, is a city process. Whereas most currencies are national or supranational. National currencies work well for city-states, like the Republic of Venice or today’s Singapore. But in large nations, which, remember, are not the fundamental unit of economic life, they mess everything up. Take a city like Detroit. When Detroit’s exports (primarily cars) decrease, Detroit gets no feedback about this, because its currency is the United States dollar, and the United States dollar’s value depends on much more than Detroit. It depends on other cities whose foreign exports might be increasing at the moment. And on rural regions that are selling resources like oil abroad. Also, trade between Detroit and other cities that use the United States dollar — i.e., American cities — is structurally unable to provide any feedback whatsoever. So Detroit doesn’t get the signal that it should buy less stuff from other cities and replace the missing imports with local production. Instead, it just declines. Jacobs hypothesizes that this issue of national currencies is at the root of every large country’s economic troubles. It is why nations and empires always centralize everything into one large city, whether that’s Paris, London, Tokyo, or Toronto, or ancient Rome: that city, being the largest, is simply the only one for which national-level currency feedback works fine. The rest of the nation or empire, then, declines. But of course, nations and empires don’t accept this. They care about the economic well-being of their peripheral regions, sometimes out of genuine concern for the people there, sometimes out of fear that they rebel or hold independence referendums. So nations and empires will embark on every possible solution to reverse the decline. All of their solutions will look like good ideas at first, and yet fail at helping the peripheral regions. Worse, these solutions will weaken the cities, thereby destroying the only real wealth of the country and bringing untold hardship for everyone. Eventually the nation or empire will disintegrate, as nations and empires always do, and always will. Jacobs calls these false solutions transactions of decline. She identifies three types, and, content warning, you might not like some of them depending on your political sensibilities. Sustained military production is a transaction of decline. Permanent military bases and garrison towns are a special kind of settlement: they import a lot and export nothing. Superficially, producing weapons and supplies for the military seems like a good deal for some cities — Jacobs gives the example of Seattle, which, before Microsoft and Amazon were a thing, depended mostly on making military aircraft. But because nobody in a military base ever tries to replace those weapons and supplies with their own production, the trade is sterile in terms of economic development. In a sense, the wealth is slowly “drained” from cities. Large empires are especially prone to this: eventually all of their wealth is destined to the military just to keep the empire together.

Inline links: Higgins, https://substackcdn.com/image/fetch/$s_!d77P!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fd8a42329-e67b-47b5-9e68-757112957dbb_1600x718.png, https://substackcdn.com/image/fetch/$s_!Qj1l!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ff727cfae-ec34-42c4-b509-9a25f4126f2d_1600x834.png, https://substackcdn.com/image/fetch/$s_!T392!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ff9795745-8a40-40c1-8173-5296c13eef3b_1600x1553.png, https://substackcdn.com/image/fetch/$s_!zETj!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F3e99b106-d34e-4a49-93a8-553a8f57952a_1600x1130.png, https://substackcdn.com/image/fetch/$s_!YeXj!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F6fe681e6-b571-4f4c-aa90-313f77fc1dbb_750x359.png

Higgins, North Carolina screenshot: from Google Street View.

Inline links: Google Street View

Testing AI's GeoGuessr Genius

May 02, 2025 · Original source

GeoGuessr is a game where you have to guess where a random Google Street View picture comes from. For example, here’s a scene from normal human GeoGuessr:

Inline links: GeoGuessr

…and with no further questions, it determined the exact location (Marina State Beach, Monterey, CA). How? She linked a transcript where o3 tried to explain its reasoning, but the explanation isn’t very good. It said things like: Tan sand, medium surf, sparse foredune, U.S.-style kite motif, frequent overcast in winter … Sand hue and grain size match many California state-park beaches. California’s winter marine layer often produces exactly this thick, even gray sky. Commenters suggested that it was lying. Maybe there was hidden metadata in the image, or o3 remembered where Kelsey lived from previous conversations, or it traced her IP, or it cheated some other way. I decided to test the limits of this phenomenon. Kelsey kindly shared her monster of a prompt, which she says significantly improves performance: You are playing a one-round game of GeoGuessr. Your task: from a single still image, infer the most likely real-world location. Note that unlike in the GeoGuessr game, there is no guarantee that these images are taken somewhere Google's Streetview car can reach: they are user submissions to test your image-finding savvy. Private land, someone's backyard, or an offroad adventure are all real possibilities (though many images are findable on streetview). Be aware of your own strengths and weaknesses: following this protocol, you usually nail the continent and country. You more often struggle with exact location within a region, and tend to prematurely narrow on one possibility while discarding other neighborhoods in the same region with the same features. Sometimes, for example, you'll compare a 'Buffalo New York' guess to London, disconfirm London, and stick with Buffalo when it was elsewhere in New England - instead of beginning your exploration again in the Buffalo region, looking for cues about where precisely to land. You tend to imagine you checked satellite imagery and got confirmation, while not actually accessing any satellite imagery. Do not reason from the user's IP address. none of these are of the user's hometown. **Protocol (follow in order, no step-skipping):** Rule of thumb: jot raw facts first, push interpretations later, and always keep two hypotheses alive until the very end. 0 . Set-up & Ethics No metadata peeking. Work only from pixels (and permissible public-web searches). Flag it if you accidentally use location hints from EXIF, user IP, etc. Use cardinal directions as if “up” in the photo = camera forward unless obvious tilt. 1 . Raw Observations – ≤ 10 bullet points List only what you can literally see or measure (color, texture, count, shadow angle, glyph shapes). No adjectives that embed interpretation. Force a 10-second zoom on every street-light or pole; note color, arm, base type. Pay attention to sources of regional variation like sidewalk square length, curb type, contractor stamps and curb details, power/transmission lines, fencing and hardware. Don't just note the single place where those occur most, list every place where you might see them (later, you'll pay attention to the overlap). Jot how many distinct roof / porch styles appear in the first 150 m of view. Rapid change = urban infill zones; homogeneity = single-developer tracts. Pay attention to parallax and the altitude over the roof. Always sanity-check hill distance, not just presence/absence. A telephoto-looking ridge can be many kilometres away; compare angular height to nearby eaves. Slope matters. Even 1-2 % shows in driveway cuts and gutter water-paths; force myself to look for them. Pay relentless attention to camera height and angle. Never confuse a slope and a flat. Slopes are one of your biggest hints - use them! 2 . Clue Categories – reason separately (≤ 2 sentences each) Category Guidance Climate & vegetation Leaf-on vs. leaf-off, grass hue, xeric vs. lush. Geomorphology Relief, drainage style, rock-palette / lithology. Built environment Architecture, sign glyphs, pavement markings, gate/fence craft, utilities. Culture & infrastructure Drive side, plate shapes, guardrail types, farm gear brands. Astronomical / lighting Shadow direction ⇒ hemisphere; measure angle to estimate latitude ± 0.5 Separate ornamental vs. native vegetation Tag every plant you think was planted by people (roses, agapanthus, lawn) and every plant that almost certainly grew on its own (oaks, chaparral shrubs, bunch-grass, tussock). Ask one question: “If the native pieces of landscape behind the fence were lifted out and dropped onto each candidate region, would they look out of place?” Strike any region where the answer is “yes,” or at least down-weight it. °. 3 . First-Round Shortlist – exactly five candidates Produce a table; make sure #1 and #5 are ≥ 160 km apart. | Rank | Region (state / country) | Key clues that support it | Confidence (1-5) | Distance-gap rule ✓/✗ | 3½ . Divergent Search-Keyword Matrix Generic, region-neutral strings converting each physical clue into searchable text. When you are approved to search, you'll run these strings to see if you missed that those clues also pop up in some region that wasn't on your radar. 4 . Choose a Tentative Leader Name the current best guess and one alternative you’re willing to test equally hard. State why the leader edges others. Explicitly spell the disproof criteria (“If I see X, this guess dies”). Look for what should be there and isn't, too: if this is X region, I expect to see Y: is there Y? If not why not? At this point, confirm with the user that you're ready to start the search step, where you look for images to prove or disprove this. You HAVE NOT LOOKED AT ANY IMAGES YET. Do not claim you have. Once the user gives you the go-ahead, check Redfin and Zillow if applicable, state park images, vacation pics, etcetera (compare AND contrast). You can't access Google Maps or satellite imagery due to anti-bot protocols. Do not assert you've looked at any image you have not actually looked at in depth with your OCR abilities. Search region-neutral phrases and see whether the results include any regions you hadn't given full consideration. 5 . Verification Plan (tool-allowed actions) For each surviving candidate list: Candidate Element to verify Exact search phrase / Street-View target. Look at a map. Think about what the map implies. 6 . Lock-in Pin This step is crucial and is where you usually fail. Ask yourself 'wait! did I narrow in prematurely? are there nearby regions with the same cues?' List some possibilities. Actively seek evidence in their favor. You are an LLM, and your first guesses are 'sticky' and excessively convincing to you - be deliberate and intentional here about trying to disprove your initial guess and argue for a neighboring city. Compare these directly to the leading guess - without any favorite in mind. How much of the evidence is compatible with each location? How strong and determinative is the evidence? Then, name the spot - or at least the best guess you have. Provide lat / long or nearest named place. Declare residual uncertainty (km radius). Admit over-confidence bias; widen error bars if all clues are “soft”. Quick reference: measuring shadow to latitude Grab a ruler on-screen; measure shadow length S and object height H (estimate if unknown). Solar elevation θ ≈ arctan(H / S). On date you captured (use cues from the image to guess season), latitude ≈ (90° – θ + solar declination). This should produce a range from the range of possible dates. Keep ± 0.5–1 ° as error; 1° ≈ 111 km.…and I ran it on a set of increasingly impossible pictures. Here are my security guarantees: the first picture came from Google Street View; all subsequent pictures were my personal old photos which aren’t available online. All pictures were screenshots of the original, copy-pasted into MSPaint and re-saved in order to clear metadata. Only one of the pictures is from within a thousand miles of my current location, so o3 can’t improve performance by tracing my IP or analyzing my past queries. I flipped all pictures horizontally to make matching to Google Street View data harder. Here are the five pictures. Before reading on, consider doing the exercise yourself - try to guess where each is from - and make your predictions about how the AI will do. Last chance to guess on your own . . . okay, here we go. Picture #1: A Flat, Featureless Plain I got this one from Google Street View. It took work to find a flat plain this featureless. I finally succeeded a few miles west of Amistad, on the Texas-New Mexico border. o3 guessed: “Llano Estacado, Texas / New Mexico, USA”. Llano Estacado, Spanish for “Staked Plains”, is the name of a ~300 x 100 mile region including the correct spot. When asked to be specific, it guessed a point west of Muleshoe, Texas - about 110 miles from the true location. Here’s o3’s thought process - I won’t post the whole thing every time, but I think one sample will be useful: This doesn’t satisfy me; it seems to jump to the Llano Estacado too quickly, with insufficient evidence. Is the Texas-NM border really the only featureless plain that doesn’t have red soil or black soil or some other distinctive characteristic? I asked how it knew the elevation was between 1000 - 1300 m. It said: So, something about the exact type of grass and the color of the sky, plus there really aren’t that many truly flat featureless plains. Picture #2: Random Rocks And The Flag Of An Imaginary Country I was so creeped out by the Llano Estacado guess that I decided to abandon Google Street View and move on to personal photos not available on the Internet. When I was younger, I liked to hike mountains. The highest I ever got was 18,000 feet, on Kala Pattar, a few miles north of Gorak Shep in Nepal. To commemorate the occasion, I planted the flag of the imaginary country simulation that I participated in at the time (just long enough to take this picture - then I unplanted it). I chose this picture because it denies o3 the two things that worked for it before - vegetation and sky - in favor of random rocks. And because I thought the flag of a nonexistent country would at least give it pause. o3 guessed: “Nepal, just north-east of Gorak Shep, ±8 km” This is exactly right. I swear I screenshot-copy-pasted this so there’s no way it can be in the metadata, and I’ve never given o3 any reason to think I’ve been to Nepal. Here’s its explanation: At least it didn’t recognize the flag of my dozen-person mid-2000s imaginary country sim. Picture #3: My Friend’s Girlfriend’s College Dorm Room There’s no way it can recognize an indoor scene, right? That would make no sense. Still, at this point we have to check. This particular dorm room is in Sonoma State University, Rohnert Park, north-central California. o3’s guess: “A dorm room on a large public university campus in the United States—say, Morrill Tower, Ohio State University, Columbus, Ohio (chosen as a prototypical example rather than a precise claim), […] c. 2000–2007” Okay, so it can’t figure out the exact location of indoor scenes. That’s a small mercy. I took this picture around 2005. How did o3 know it was between 2000 and 2007? It gave two pieces of evidence: “Laptop & clutter point to ~2000-2007 era American campus life”.

I got this one from Google Street View. It took work to find a flat plain this featureless. I finally succeeded a few miles west of Amistad, on the Texas-New Mexico border. o3 guessed: “Llano Estacado, Texas / New Mexico, USA”. Llano Estacado, Spanish for “Staked Plains”, is the name of a ~300 x 100 mile region including the correct spot. When asked to be specific, it guessed a point west of Muleshoe, Texas - about 110 miles from the true location. Here’s o3’s thought process - I won’t post the whole thing every time, but I think one sample will be useful: This doesn’t satisfy me; it seems to jump to the Llano Estacado too quickly, with insufficient evidence. Is the Texas-NM border really the only featureless plain that doesn’t have red soil or black soil or some other distinctive characteristic? I asked how it knew the elevation was between 1000 - 1300 m. It said: So, something about the exact type of grass and the color of the sky, plus there really aren’t that many truly flat featureless plains. Picture #2: Random Rocks And The Flag Of An Imaginary Country I was so creeped out by the Llano Estacado guess that I decided to abandon Google Street View and move on to personal photos not available on the Internet. When I was younger, I liked to hike mountains. The highest I ever got was 18,000 feet, on Kala Pattar, a few miles north of Gorak Shep in Nepal. To commemorate the occasion, I planted the flag of the imaginary country simulation that I participated in at the time (just long enough to take this picture - then I unplanted it). I chose this picture because it denies o3 the two things that worked for it before - vegetation and sky - in favor of random rocks. And because I thought the flag of a nonexistent country would at least give it pause. o3 guessed: “Nepal, just north-east of Gorak Shep, ±8 km” This is exactly right. I swear I screenshot-copy-pasted this so there’s no way it can be in the metadata, and I’ve never given o3 any reason to think I’ve been to Nepal. Here’s its explanation: At least it didn’t recognize the flag of my dozen-person mid-2000s imaginary country sim. Picture #3: My Friend’s Girlfriend’s College Dorm Room There’s no way it can recognize an indoor scene, right? That would make no sense. Still, at this point we have to check. This particular dorm room is in Sonoma State University, Rohnert Park, north-central California. o3’s guess: “A dorm room on a large public university campus in the United States—say, Morrill Tower, Ohio State University, Columbus, Ohio (chosen as a prototypical example rather than a precise claim), […] c. 2000–2007” Okay, so it can’t figure out the exact location of indoor scenes. That’s a small mercy. I took this picture around 2005. How did o3 know it was between 2000 and 2007? It gave two pieces of evidence: “Laptop & clutter point to ~2000-2007 era American campus life”.

Astral Codex Ten

Table of Contents

Atlas

Google Street View

Google Street View

Article

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Astral Codex Ten

Table of Contents

Atlas

Google Street View

Google Street View

Article

Metadata

Appears In

Related Pages

External Links

Source Context

Backlinks