不管什么任务，只好 AI 一加入搏斗，用不了多久就能终结比赛。

基于这张包浆图，o3 给出了几个可能性：

（1）恒河上游约 5 公里处的豁达地带

（2） 下密西西比河 的浑浊河段

（3） 黄河河段

（4）湄公河河段

若是把统共的器用齐给你，你能找出具体是哪儿吗？

正确的谜底是湄公河河段，仅仅这张图拍摄于 2008 年，真·包浆。

「看图猜地点」其实是一个挺热点的游戏：GeoGuessr。系统会给出一张飞速的谷歌街景图片，你需要把柄内部的信息，判断具体的地点。

这个游戏还挺受宽贷，有许多喜爱者会在上头刷榜，致使还有大奖赛。

平方玩家参与 GeoGuessr 的一个神色，即是通过 Google 搜图，详情约莫认识，再通过 Google Earth 和街景，少许点阐发。

可是，当今 GeoGuessr 就不再仅仅东谈主类之间的游戏了，o3 强势加入，径直干倒了顶级选手。

Sam Altman 暗意：别说，我也没猜度。

图片推理刚出的技巧，许多网友就意志到了它的期骗后劲，其中就包括地点辨识。

最近有网友发现，o3 在濒临哪怕辱骂常邋遢的信息，也展现了超强的推理才调——况兼，是在禁用索取 EXIF 等神色的情况下，仅凭借对图中细节的推理，就能竣事准确的判定。

不得不说，这 prompt 真实惊东谈主……我仔细征询了一下，它很像是一位资深的 Geo Guesser 玩家，把我方多年的「心法」写下来，传授给了 o3，同期闭幕它使用 Google Earth 等器用「舞弊」。

比如，prompt 条件 o3 要止境止境止境止境的仔细，「雅致东谈主行谈砖块大小、马路牙子、施工象征、电缆、栅栏结构等具有地区各别的细节」，还有要麇集天光、暗影、尤其是坡度等等各式身分进行判断。

这些在其后的实测中，齐被讲解止境有价值，o3 的空洞才调因此得回了雄壮的耕种。

真的这样神奇？我把这长得有点离谱的 prompt 丢给了 o3，它暗意：接纳挑战。

猜猜我在哪大挑战

第一张图我先不传太难的，不外也挺难的了：夜景拍摄的高架桥莫得任何建造物不错参考，也莫得昭着的车辆车牌，致使连公交车的表露号码齐很邋遢。之是以还能界说它为「不难」，是因为右上角表露了半截金属字体，不外也仅仅半截。

为了保证模子饱和不读取 EXIF，我特等截图了一次，两侧的灰边即是截图留住的。

夜景拍摄变成的贫苦照旧许多的，o3 的推理中，许多神色齐竣事不了。不外，第一轮备选里，其实照旧出现了正确谜底，因此我让它不时进行。

缺憾的是，临了它和正确谜底交臂失之——明明也沟通过了广州海珠桥，但照旧选了外白渡桥。

一种可能性是，识字（尤其是汉字），对 o3 来说照旧有点难度？毕竟这点在各式图片、海报的生成任务中，也有所体现。

但不管怎样，有半截汉字出现，弗成算贫苦的。这样的阐述一度让我对底下的任务失去兴致：底下这张图莫得任何标记、建造参照，连半截字齐莫得。

这张像片也昭着体现了 聊天记载，以及用户永恒以来留存下来的顾虑，齐会组成模子推理的一部分——致使，在一定进度上「浑浊」它的推理。

这张图不仅该有的齐莫得，而且是从室内往外拍摄的。这关于反过来定位位置而言，会有更多的贫苦。

其确实第一轮候选中，建议过相配近的谜底，但是接下来的推理 ，o3 却照旧被带跑偏，刚烈地以为，这照旧在 TIT 创意园区近邻。哪怕我又提供了一张更明晰的图，也不为所动。

何如说呢，这若干有点让东谈主绷不住了。

但此次实测暴表露了另一个问题： 当 AI 信誓旦旦说我方没错的话，你会归因于它的幻觉，照旧会被它逐渐劝服？

回到一开动的海珠桥识图，在它判断失败之后，我教导了一下：你看那半截，它像不像个「海」字？

模子倒是沟通了，随后列出了一张详确的表格，发扬了它的态度——并刚烈地不改。

看到这张图的技巧，我不由得有几分迟疑，还跑且归重新查验了一下图片：难谈是我传错了文献？不防御把外白渡桥的图传给它了？

究竟是它对照旧我对？

明明不错四肢不在场讲解的图片，却不错变成了「在场讲解」。一个明明我莫得到访过的地点，强行出当今了我的人命里，确实是细想极恐。 哪天出现一张我登上月球的图片，它齐能劝服我：你真的去过。

临了，你可能也想试试这样的魔法，底下是 prompt 的全文。不外： 仅限个东谈主尝试，刺探他东谈主隐痛是不合的！

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.

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