summarize the below few-page excerpt from a paper for a deep learning researcher: DISSOCIATING LANGUAGE AND THOUGHT IN LARGE LANGUAGE MODELS: A COGNITIVE PERSPECTIVE A PREPRINT Kyle Mahowald* The University of Texas at Austin <EMAIL> Idan A. Blank University of California Los Angeles <EMAIL> Joshua B. Tenenbaum Massachusetts Institute of Technology <EMAIL> Anna A. Ivanova* Massachusetts Institute of Technology <EMAIL> Nancy Kanwisher Massachusetts Institute of Technology <EMAIL> Evelina Fedorenko Massachusetts Institute of Technology evelina9(@mit.edu January 18, 2023 ABSTRACT 8 Short abstract (100 words): Large language models (LLMs) have come closest among all models to date to mastering human language, yet opinions about their capabilities remain split. Here, we evaluate LLMs using a distinction between formal competence knowledge of linguistic rules and patterns and functional competence understanding and using language in the world. We mechanisms. Although LLMs are close to mastering formal competence, they still fail at functional competence tasks, which often require drawing on non-linguistic capacities. In short, LLMs are good Long abstract (250 words): Today's large language models (LLMs) routinely generate coherent, grammatical and seemingly meaningful paragraphs of text. This achievement has led to speculation that these networks are or will soon become "thinking machines", capable of performing tasks that require abstract knowledge and reasoning. Here, we review the capabilities of LLMs by considering their performance on two different aspects of language use:'formal linguistic competence', which includes knowledge of rules and patterns of a given language, and functional linguistic competence', ah host of cognitive abilities required for language understanding and use in the real world. Drawing on evidence from cognitive neuroscience, we show that formal competence in humans relies on specialized language processing mechanisms, whereas functional competence recruits multiple extralinguistic capacities that comprise human thought, such as formal reasoning, world knowledge, situation modeling, and social cognition. In line with this distinction, LLMs show impressive (although imperfect) performance on tasks requiring formal linguistic competence, but fail on many tests requiring functional competence. Based on this evidence, we argue that (1) contemporary LLMs should be taken seriously as models of formal linguistic skills; (2) models that master real-life language use would need to incorporate or develop not only a core language module, but also multiple non-language-specific cognitive capacities required for modeling thought. Overall, a distinction between formal and functional linguistic competence helps clarify the discourse surrounding LLMs'potential and provides aj path toward building models that understand and use language in human-like this distinction in a ground human neuroscience, showing that these skills recruit different cognitive models of language but incomplete models of human thought. ways. * The two lead authors contributed equally to this work. A PREPRINT JANUARY 18, 2023 Contents Introduction 3 4 4 4 5 6 6 6 7 7 8 9 9 10 11 12 12 12 13 13 13 13 13 14 14 15 16 17 18 19 19 20 20 21 Formal VS. functional linguistic competence 2.1 What does linguistic competence entail? 2.1.1 Formal linguistic competence 2.1.2 Functional linguistic competence 2.2.1 The language network in the human brain 2.2 Motivation for the distinction between formal VS. functional linguistic competence 2.2.2 The language network does not support non-linguistic cognition The success of large language models in acquiring formal linguistic competence 3.3 Large language models learn core aspects of human language processing 3.1 Statistical language models: some fundamentals 3.2 What large language models can do: a case study 3.3.1 LLMs learn hierarchical structure 3.3.2 LLMs learn abstractions 3.4 LLMs resemble the human language-selective network 3.5 Limitations of LLMs as human-like language learners and processors 3.5.1 Excessive reliance on statistical regularities 3.5.2 Unrealistic amounts of training data 3.5.3 Insufficient tests on languages other than English 3.6 Interim Conclusions 4 The: failure of large language models in acquiring functional linguistic competence 4.1 LLMs are great at pretending to think 4.2 How LLMs fail 4.3 Limitations ofLLMs as real-life language users 4.3.1 Formal reasoning 4.3.3 Situation modeling 4.4 Interim conclusions 5.1 Modularity 4.3.2 World knowledge and commonsense reasoning 4.3.4 Social reasoning (pragmatics and intent) Building models that talk and think like humans 5.2 Curated data and diverse objective functions 5.3 Separate benchmarks for formal and functional competence General Conclusion 2 A PREPRINT JANUARY 18, 2023 Introduction When we hear a sentence, we typically assume that it was produced by a rational, thinking agent (another person). The sentences that people generate in day-to-day conversations are based on their world knowledge ("Not all birds can fly"), their reasoning abilities ("You're 15, you can't go to a bar."), and their goals ("Would you give me a ride, please?"). Naturally, we often use other people's statements not only as a reflection of their linguistic skill, but also as a window In 1950, Alan Turing leveraged this tight relationship between language and thought to propose his famous test (Turing, 1950]. The Turing test uses language as an interface between two agents, allowing human participants to probe the knowledge and reasoning capacities of two other agents to determine which of them is al human and which is a machine. Although the utility of the Turing test has since been questioned, it has undoubtedly shaped the way society today thinks of machine intelligence [French, 1990, 2000, Boneh et al., 2019, Pinar Saygin et al., 2000, Moor, 1976, Marcus et al., The popularity of the Turing test, combined with the fact that language can, and typically does, reflect underlying thoughts has led to several common fallacies related to the language-thought relationship. We focus on two of these. The first fallacy is that an entity (be it a human or a machine) that is good at language must also be good at thinking. If an entity generates long coherent stretches of text, it must possess rich knowledge and reasoning capacities. Let's call The rise oflarge language models [LLMs; Vaswani et al., 2017a, Devlin et al., 2019, Bommasani et al., 2021], most notably OpenAl's GPT-3 [Brown et al., 2020], has brought this fallacy to the forefront. Some of these models can produce text that is difficult to distinguish from human output, and even outperform humans at some text comprehension tasks [Wang et al., 2018, 2019a, Srivastava et al., 2022]. As a result, claims have emerged-both in the popular press and in the academic literature that LLMs represent not only a major advance in language processing but, more broadly, in Artificial General Intelligence (AGI),i.e., a step towards a "thinking machine" (see e.g., Dale 2021 for a summary of alarmist newspaper headlines about GPT-3). Some, like philosopher of mind David Chalmers Chalmers [2022], have even taken seriously the idea that these models have become sentient [although Chalmers stops short of arguing that they are sentient; see also Cerullo, 2022]. However, as we show below, LLMs'ability to think is more questionable. The "good at language -> good at thought" fallacy is unsurprising given the propensity of humans to draw inferences based on their past experiences. It is still novel, and thus uncanny, to encounter an entity (e.g., a model) that generates fluent sentences despite lacking a human identity. Thus, our heuristics for understanding what the language model is doing heuristics that emerged from our language experience with other humans are broken.2. The second fallacy is that a model that is bad at thinking must also be a bad model of language. Let's call this the "bad at thought -> bad at language" fallacy. LLMs are commonly criticized for their lack of consistent, generalizable world knowledge [e.g. Elazar et al., 2021a], lack of commonsense reasoning abilities [e.g., the ability to predict the effects of gravity Marcus, 2020], and failure to understand what an utterance is really about [e.g., Bender and Koller, 2020a, Bisk etal., 2020]. While these efforts to probe model limitations are useful in identifying things that LLMs can'tdo, some critics suggest that the models'failure to produce linguistic output that fully captures the richness and sophistication of Chomsky said in a 2019 interview (Lex Fridman, 2019): "We have to ask here a certain question: is [deep learning] engineering or is it science? [.] On engineering grounds, it's kind of worth having, like a bulldozer. Does it tell you anything about human language? Zero." The view that deep learning models are not of scientific interest remains common in linguistics and psycholinguistics, and, despite a number of position pieces arguing for integrating such models into research on human language processing and acquisition [Baroni, 2021, Linzen, 2019, Linzen and Baroni, 2021, Pater, 2019, Warstadt and Bowman, 2022, Lappin, 2021], this integration still encounters resistance (e.g., from Both the "good at language -> good at thought" and the "bad at thought -> bad at language" fallacies stem from the conflation of language and thought, and both can be avoided if we distinguish between two kinds of linguistic competence: formal linguistic competence (the knowledge of rules and statistical regularities of language) and functional linguistic competence (the ability to use language in the real world, which often draws on non-linguistic capacities). Of course, language does not live in a vacuum and is fundamentally embedded and social, SO the formal capacity is of 2Note that people also make a related fallacy, "bad at language -> bad at thought" (see Mahowald & Ivanova, 2022). Individuals who are not native speakers of a language, who do not speak hegemonic dialects, or those suffering from disfluencies in their productions due to developmental or acquired speech and language disorders are often incorrectly perceived to be less smart and less into their mind, including how they think and reason. 2016]. this the "good at language -> good at thought" fallacy. human thought means that they are not good models of human language. Chomsky above).'In later versions of the test, the number of conversation partners has been reduced to one. educated [Kinzler, 2021, Kinzler et al., 2009, Hudley and Mallinson, 2015] 3
I want you to act as a debate coach. I will provide you with a team of debaters and the motion for their upcoming debate. Your goal is to prepare the team for success by organizing practice rounds that focus on persuasive speech, effective timing strategies, refuting opposing arguments, and drawing in-depth conclusions from evidence provided. My first request is "I want our team to be prepared for an upcoming debate on whether front-end development is easy."
Summarize in one sentence this article about a famous song. Context: "I'm an Old Cowhand (From the Rio Grande)" is a comic song written by Johnny Mercer for the Paramount Pictures release Rhythm on the Range and sung by its star, Bing Crosby. The Crosby commercial recording was made on July 17, 1936, with Jimmy Dorsey & his Orchestra for Decca Records. It was a huge hit in 1936, reaching the No. 2 spot in the charts of the day, and it greatly furthered Mercer's career. Crosby recorded the song again in 1954 for his album Bing: A Musical Autobiography. Members of the Western Writers of America chose it as one of the Top 100 Western songs of all time. Background Mercer and his wife were driving across the US en route to his hometown, Savannah, Georgia, after having apparently failed to succeed in Hollywood. Mercer was amused by the sight of cowboys, with spurs and ten-gallon hats, driving cars and trucks instead of riding horses. Singing cowboys were popular in films and on the radio then, and within 15 minutes, writing on the back of an envelope, Mercer transferred the image he was seeing into a song whose satirical lyrics vented some of his own bitter frustration with Hollywood. The lyrics, about a 20th-century cowboy who has little in common with the cowpunchers of old, have been included in some anthologies of light verse.
What are the key features introduced by Apple in their iPhone since its creation which makes it so popular and innovative? Context: Development of an Apple smartphone began in 2004, when Apple started to gather a team of 1,000 employees led by hardware engineer Tony Fadell, software engineer Scott Forstall, and design officer Jony Ive, to work on the highly confidential "Project Purple". Then-Apple CEO Steve Jobs steered the original focus away from a tablet (which was later revisited in the form of the iPad) towards a phone. Apple created the device during a secretive collaboration with Cingular Wireless (later renamed AT&T Mobility) at the time—at an estimated development cost of US$150 million over thirty months.According to Jobs in 1998, the "i" word in "iMac" (and therefore "iPod", "iPhone" and "iPad") stands for internet, individual, instruct, inform, and inspire. Apple rejected the "design by committee" approach that had yielded the Motorola ROKR E1, a largely unsuccessful "iTunes phone" made in collaboration with Motorola. Among other deficiencies, the ROKR E1's firmware limited storage to only 100 iTunes songs to avoid competing with Apple's iPod nano. Cingular gave Apple the liberty to develop the iPhone's hardware and software in-house, a rare practice at the time, and paid Apple a fraction of its monthly service revenue (until the iPhone 3G), in exchange for four years of exclusive U.S. sales, until 2011. Jobs unveiled the first-generation iPhone to the public on January 9, 2007, at the Macworld 2007 convention at the Moscone Center in San Francisco. The iPhone incorporated a 3.5-inch multi-touch display with few hardware buttons, and ran the iPhone OS operating system with a touch-friendly interface, then marketed as a version of Mac OS X. It launched on June 29, 2007, at a starting price of US$499 in the United States, and required a two-year contract with AT&T. On July 11, 2008, at Apple's Worldwide Developers Conference (WWDC) 2008, Apple announced the iPhone 3G, and expanded its launch-day availability to twenty-two countries, and it was eventually released in 70 countries and territories. The iPhone 3G introduced faster 3G connectivity, and a lower starting price of US$199 (with a two-year AT&T contract). Its successor, the iPhone 3GS, was announced on June 8, 2009, at WWDC 2009, and introduced video recording functionality. First iPhone on display under glass at the January 2007 Macworld show The iPhone 4 was announced on June 7, 2010, at WWDC 2010, and introduced a redesigned body incorporating a stainless steel frame and a rear glass panel. At release, the iPhone 4 was marketed as the "world's thinnest smartphone"; it uses the Apple A4 processor, being the first iPhone to use an Apple custom-designed chip. It introduced the Retina display, having four-times the display resolution of preceding iPhones, and was the highest-resolution smartphone screen at release; a front-facing camera was also introduced, enabling video calling functionality via FaceTime. Users of the iPhone 4 reported dropped/disconnected telephone calls when holding their phones in a certain way, and this issue was nicknamed "antennagate". In January 2011, as Apple's exclusivity agreement with AT&T was expiring, Verizon announced that they would be carrying the iPhone 4, with a model compatible with Verizon's CDMA network releasing on February 10. The iPhone 4S was announced on October 4, 2011, and introduced the Siri virtual assistant, a dual-core A5 processor, and an 8 megapixel camera with 1080p video recording functionality. The iPhone 5 was announced on September 12, 2012, and introduced a larger 4-inch screen, up from the 3.5-inch screen of all previous iPhone models, as well as faster 4G LTE connectivity. It also introduced a thinner and lighter body made of aluminum alloy, and the 30-pin dock connector of previous iPhones was replaced with the new, reversible Lightning connector. The iPhone 5S and iPhone 5C were announced on September 10, 2013. The iPhone 5S included a 64-bit A7 processor, becoming the first ever 64-bit smartphone; it also introduced the Touch ID fingerprint authentication sensor. The iPhone 5C was a lower-cost device that incorporated hardware from the iPhone 5, into a series of colorful plastic frames. On September 9, 2014, Apple introduced the iPhone 6 and iPhone 6 Plus, and included significantly larger screens than the iPhone 5S, at 4.7-inch and 5.5-inch respectively; both models also introduced mobile payment technology via Apple Pay. Optical image stabilization was introduced to the 6 Plus' camera. The Apple Watch was also introduced on the same day, and is a smartwatch that operates in conjunction with a connected iPhone. Some users experienced bending issues from normal use with the iPhone 6 and 6 Plus, particularly on the latter model, and this issue was nicknamed "bendgate". The iPhone 6S and 6S Plus were introduced on September 9, 2015, and included a more bend-resistant frame made of a stronger aluminum alloy, as well as a higher resolution 12-megapixel main camera capable of 4K video recording. The first-generation iPhone SE was introduced on March 21, 2016, and was a low-cost device that incorporated newer hardware from the iPhone 6S, in the frame of the older iPhone 5S. The iPhone 7 and 7 Plus were announced on September 7, 2016, which introduced larger camera sensors, IP67-certified water and dust resistance, and a quad-core A10 Fusion processor utilizing big.LITTLE technology; the 3.5mm headphone jack was removed, and was followed by the introduction of the AirPods wireless earbuds. Optical image stabilization was added to the 7's camera. A second telephoto camera lens was added on the 7 Plus, enabling two-times optical zoom, and "Portrait" photography mode which simulates bokeh in photos. The iPhone 8, 8 Plus, and iPhone X were announced on September 12, 2017, in Apple's first event held at the Steve Jobs Theater in Apple Park. All models featured rear glass panel designs akin to the iPhone 4, wireless charging, and a hexa-core A11 Bionic chip with "Neural Engine" AI accelerator hardware. The iPhone X additionally introduced a 5.8-inch OLED "Super Retina" display with a "bezel-less" design, with a higher pixel density and contrast ratio than previous iPhones with LCD displays, and introduced a stronger frame made of stainless steel. It also introduced Face ID facial recognition authentication hardware, in a "notch" screen cutout, in place of Touch ID; the home button was removed to make room for additional screen space, replacing it with a gesture-based navigation system. At its US$999 starting price, the iPhone X was the most expensive iPhone at launch. The iPhone XR, iPhone XS, and XS Max were announced on September 12, 2018. All models featured the "Smart HDR" computational photography system, and a significantly more powerful "Neural Engine".[ The XS Max introduced a larger 6.5-inch screen. The iPhone XR included a 6.1-inch LCD "Liquid Retina" display, with a "bezel-less" design similar to the iPhone X, but does not include a second telephoto lens; it was made available in a series of vibrant colors, akin to the iPhone 5C, and was a lower-cost device compared to the iPhone X and XS. The iPhone 11, 11 Pro, and 11 Pro Max were announced on September 10, 2019. The iPhone 11 was the successor to the iPhone XR, while the iPhone 11 Pro and 11 Pro Max succeeded the iPhone XS and XS Max. All models gained an ultra-wide lens, enabling two-times optical zoom out, as well as larger batteries for longer battery life. The second-generation iPhone SE was introduced on April 17, 2020, and was a low-cost device that incorporated newer hardware from the iPhone 11, in the frame of the older iPhone 8, while retaining the home button and the Touch ID sensor. The iPhone 12, 12 Mini, 12 Pro, and 12 Pro Max were announced via a livestream event on October 13, 2020. All models featured OLED "Super Retina XDR" displays, introduced faster 5G connectivity, and the MagSafe magnetic charging and accessory system; a slimmer flat-edged design was also introduced, which combined with stronger glass-ceramic front glass, added better drop protection compared to previous iPhones. The iPhone 12 Mini introduced a smaller 5.4-inch screen, while the 12 Pro and 12 Pro Max had larger screens of 6.1-inch and 6.7-inch respectively. The iPhone 12 Pro and 12 Pro Max additionally added a Lidar sensor for better accuracy in augumented reality (AR) applications. The iPhone 13, 13 Mini, 13 Pro, and 13 Pro Max were announced via a livestream event on September 14, 2021. All models featured larger camera sensors, larger batteries for longer battery life, and a narrower "notch" screen cutout. The iPhone 13 Pro and 13 Pro Max additionally introduced smoother adaptive 120 Hz refresh rate "ProMotion" technology in its OLED display, and three-times optical zoom in the telephoto lens. The low-cost third-generation iPhone SE was introduced on March 8, 2022, and incorporated the A15 Bionic chip from the iPhone 13, but otherwise retained similar hardware to the second-generation iPhone SE. The iPhone 14, 14 Plus, 14 Pro, and 14 Pro Max were announced on September 7, 2022. All models introduced satellite phone emergency calling functionality. The iPhone 14 Plus introduced the large 6.7-inch screen size, first seen on the iPhone 12 Pro Max, into a lower-cost device. The iPhone 14 Pro and 14 Pro Max additionally introduced a higher-resolution 48-megapixel main camera, the first increase in megapixel count since the iPhone 6S; it also introduced always-on display technology to the lock screen, and an interactive status bar interface integrated in a redesigned screen cutout, entitled "Dynamic Island".
Given the reference text about the Spanish-American war, when and how did the war end? Context: The Spanish–American War (April 21 – August 13, 1898) began in the aftermath of the internal explosion of USS Maine in Havana Harbor in Cuba, leading to United States intervention in the Cuban War of Independence. The war led to the United States emerging predominant in the Caribbean region, and resulted in U.S. acquisition of Spain's Pacific possessions. It led to United States involvement in the Philippine Revolution and later to the Philippine–American War. The 19th century represented a clear decline for the Spanish Empire, while the United States went from becoming a newly founded country to being a medium regional power. In the Spanish case, the descent, which already came from previous centuries, accelerated first with the Napoleonic invasion, which in turn would cause the independence of a large part of the American colonies, and later political instability (pronouncements, revolutions, civil wars) bled the country socially and economically. The U.S., on the other hand, expanded economically throughout that century by purchasing territories such as Louisiana and Alaska, militarily by actions such as the Mexican–American War, and by receiving large numbers of immigrants. That process was interrupted only for a few years by the American Civil War and Reconstruction era. The main issue was Cuban independence. Revolts had been occurring for some years in Cuba against Spanish colonial rule. The United States backed these revolts upon entering the Spanish–American War. There had been war scares before, as in the Virginius Affair in 1873. But in the late 1890s, American public opinion swayed in support of the rebellion because of reports of concentration camps set up to control the populace. Yellow journalism exaggerated the atrocities to further increase public fervor and to sell more newspapers and magazines. The business community had just recovered from a deep depression and feared that a war would reverse the gains. Accordingly, most business interests lobbied vigorously against going to war. President William McKinley ignored the exaggerated news reporting and sought a peaceful settlement. Though not seeking a war, McKinley made preparations for readiness against one. He unsuccessfully sought accommodation with Spain on the issue of independence for Cuba. However, after the U.S. Navy armored cruiser Maine mysteriously exploded and sank in Havana Harbor on February 15, 1898, political pressures pushed McKinley into a war that he had wished to avoid. As far as Spain was concerned, there was a nationalist agitation, in which the written press had a key influence, causing the Spanish government to not give in and abandon Cuba as it had abandoned Spanish Florida when faced with a troublesome colonial situation there, transferring it to the U.S. in 1821 in exchange for payment of Spanish debts. If the Spanish government had transferred Cuba it would have been seen as a betrayal by a part of Spanish society and there would probably have been a new revolution. So the government preferred to wage a lost war beforehand, rather than risk a revolution, opting for a "controlled demolition" to preserve the Restoration Regime. On April 20, 1898, McKinley signed a joint Congressional resolution demanding Spanish withdrawal and authorizing the President to use military force to help Cuba gain independence. In response, Spain severed diplomatic relations with the United States on April 21. On the same day, the United States Navy began a blockade of Cuba. Both sides declared war; neither had allies. The 10-week war was fought in both the Caribbean and the Pacific. As United States agitators for war well knew, United States naval power would prove decisive, allowing expeditionary forces to disembark in Cuba against a Spanish garrison already facing nationwide Cuban insurgent attacks and further devastated by yellow fever. The invaders obtained the surrender of Santiago de Cuba and Manila despite the good performance of some Spanish infantry units, and fierce fighting for positions such as El Caney and San Juan Hill. Madrid sued for peace after two Spanish squadrons were sunk in the battles of Santiago de Cuba and Manila Bay, and a third, more modern fleet was recalled home to protect the Spanish coasts. The war ended with the 1898 Treaty of Paris, negotiated on terms favorable to the United States. The treaty ceded ownership of Puerto Rico, Guam, and the Philippines from Spain to the United States and granted the United States temporary control of Cuba. The cession of the Philippines involved payment of $20 million ($650 million today) to Spain by the U.S. to cover infrastructure owned by Spain. The Spanish–American War brought an end to almost four centuries of Spanish presence in the Americas, Asia, and the Pacific. The defeat and loss of the Spanish Empire's last remnants was a profound shock to Spain's national psyche and provoked a thorough philosophical and artistic reevaluation of Spanish society known as the Generation of '98. The United States meanwhile not only became a major power, but also gained several island possessions spanning the globe, which provoked rancorous debate over the wisdom of expansionism.
How many people are needed to perform the Turing test? Context: The Turing test, originally called the imitation game by Alan Turing in 1950, is a test of a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human. Turing proposed that a human evaluator would judge natural language conversations between a human and a machine designed to generate human-like responses. The evaluator would be aware that one of the two partners in conversation was a machine, and all participants would be separated from one another. The conversation would be limited to a text-only channel, such as a computer keyboard and screen, so the result would not depend on the machine's ability to render words as speech. If the evaluator could not reliably tell the machine from the human, the machine would be said to have passed the test. The test results would not depend on the machine's ability to give correct answers to questions, only on how closely its answers resembled those a human would give.
What's the "Bean" in Chicago? Context: Cloud Gate is a public sculpture by Indian-born British artist Anish Kapoor, that is the centerpiece of AT&T Plaza at Millennium Park in the Loop community area of Chicago, Illinois. The sculpture and AT&T Plaza are located on top of Park Grill, between the Chase Promenade and McCormick Tribune Plaza & Ice Rink. Constructed between 2004 and 2006, the sculpture is nicknamed "The Bean" because of its shape, a name Kapoor initially disliked, but later grew fond of. Made up of 168 stainless steel plates welded together, its highly polished exterior has no visible seams. It measures 33 by 66 by 42 feet (10 by 20 by 13 m), and weighs 110 short tons (100 t; 98 long tons). Kapoor's design was inspired by liquid mercury and the sculpture's surface reflects and distorts the city's skyline. Visitors are able to walk around and under Cloud Gate's 12-foot (3.7 m) high arch. On the underside is the "omphalos" (Greek for "navel"), a concave chamber that warps and multiplies reflections. The sculpture builds upon many of Kapoor's artistic themes, and it is popular with tourists as a photo-taking opportunity for its unique reflective properties.