{"success":true,"data":{"query":"Lead History","limit":10,"count":10,"sources":["wiki_artificial_intelligence.hat","wiki_dallas.hat","wiki_real_estate.hat","web_1779060040.hat"],"synced":[],"results":[{"source":"wiki_artificial_intelligence.hat","text":"History\nThe study of mechanical or \"formal\" reasoning began with philosophers and mathematicians in antiquity. The study of logic led directly to Alan Turing's theory of computation, which suggested that a machine, by shuffling symbols as simple as \"0\" and \"1\", could simulate any conceivable form of mathematical reasoning. This, along with concurrent discoveries in cybernetics, information theory and neurobiology, led researchers to consider the possibility of building an \"electronic brain\". They developed several areas of research that would become part of AI, such as McCulloch and Pitts design for \"artificial neurons\" in 1943, and Turing's influential 1950 paper 'Computing Machinery and Intelligence', which introduced the Turing test and showed that \"machine intelligence\" was plausible.\nThe field of AI research was founded at a workshop at Dartmouth College in 1956. The first AI program, Logic Theorist, was presented at the workshop, created by future Turing Award winner Allen Newell and future Nobel Laureate Herbert A. Simon, in collaboration with J. C. Shaw. Many of the workshop attendees became the leaders of AI research in the 1960s. They and their students produced programs that the press described as \"astonishing\": computers were learning checkers strategies, solving word problems in algebra, proving logical theorems and speaking English. Artificial intelligence laboratories were set up at a number of British and U.S. universities in the latter 1950s and early 1960s.\nResearchers in the 1960s and the 1970s were convinced that their methods would eventually succeed in creating a machine with general intelligence and considered this the goal of their field. In 1965 Herbert Simon predicted, \"machines will be capable, within twenty years, of doing any work a man can do\". In 1967 Marvin Minsky agreed, writing that \"within a generation ... the problem of creating 'artificial intelligence' will substantially be solved\". They had, however, underestimated the difficulty of the problem. In 1974, both the U.S. and British governments cut off exploratory research in response to the criticism of Sir James Lighthill and ongoing pressure from the U.S. Congress to fund more productive projects. Minsky and Papert's book Perceptrons was understood as proving that artificial neural networks would never be useful for solving real-world tasks, thus discrediting the approach altogether. The \"AI winter\", a period when obtaining funding for AI projects was difficult, followed.\nIn the early 1980s, AI research was revived by the commercial success of expert systems, a form of AI program that simulated the knowledge and analytical skills of human experts. By 1985, the market for AI had reached over a billion dollars. At the same time, Japan's fifth generation computer project inspired the U.S. and British governments to restore funding for academic research. However, beginning with the collapse of the Lisp Machine market in 1987, AI once again fell into disrepute, and a second, longer-lasting winter began.\nUp to this point, most of AI's funding had gone to projects that used high-level symbols to represent mental objects like plans, goals, beliefs, and known facts. In the 1980s, some researchers began to doubt that this approach would be able to imitate all the processes of human cognition, especially perception, robotics, learning and pattern recognition, and began to look into \"sub-symbolic\" approaches. Rodney Brooks rejected \"representation\" in general and focussed directly on engineering machines that move and survive. Judea Pearl, Lotfi Zadeh, and others developed methods that handled incomplete and uncertain information by making reasonable guesses rather than precise logic. But the most important development was the revival of \"connectionism\", including neural network research, by Geoffrey Hinton and others. In 1990, Yann LeCun successfully showed that convolutional neural networks can recognize handwritten digits, the first of many successful applications of neural networks.\nAI gradually restored its reputation in the late 1990s and early 21st century by exploiting formal mathematical methods and by finding specific solutions to specific problems. This \"narrow\" and \"formal\" focus allowed researchers to produce verifiable results and collaborate with other fields (such as statistics, economics and mathematics). By 2000, solutions developed by AI researchers were being widely used, although in the 1990s they were rarely described as \"artificial intelligence\" (a tendency known as the AI effect).\nHowever, several academic researchers became concerned that AI was no longer pursuing its original goal of creating versatile, fully intelligent machines. Beginning around 2002, they founded the subfield of artificial general intelligence (or \"AGI\"), which had several well-funded institutions by the 2010s.\nDeep learning began to dominate industry benchmarks in 2012 and was adopted throughout the field.\nFor many specific tasks, other methods were abandoned.\nDeep learning's success was based on both hardware improvements (faster computers, graphics processing units, cloud computing) and access to large amounts of data (including curated datasets, such as ImageNet). Deep learning's success led to an enormous increase in interest and funding in AI. The amount of machine learning research (measured by total publications) increased by 50% in the years 2015–2019.","score":63.16641805025819,"links":[]},{"source":"wiki_artificial_intelligence.hat","text":"Emergent goals\nOne challenge in aligning AI systems is the potential for unanticipated goal-directed behavior to emerge. As AI systems scale up, they may acquire new and unexpected capabilities, including learning from examples on the fly and adaptively pursuing goals. This raises concerns about the safety of the goals or subgoals they would independently formulate and pursue.\nAlignment research distinguishes between the optimization process, which is used to train the system to pursue specified goals, and emergent optimization, which the resulting system performs internally. Carefully specifying the desired objective is called outer alignment, and ensuring that hypothesized emergent goals would match the system's specified goals is called inner alignment.\nIf they occur, one way that emergent goals could become misaligned is goal misgeneralization, in which the AI system would competently pursue an emergent goal that leads to aligned behavior on the training data but not elsewhere. Goal misgeneralization can arise from goal ambiguity (i.e. non-identifiability). Even if an AI system's behavior satisfies the training objective, this may be compatible with learned goals that differ from the desired goals in important ways. Since pursuing each goal leads to good performance during training, the problem becomes apparent only after deployment, in novel situations in which the system continues to pursue the wrong goal. The system may act misaligned even when it understands that a different goal is desired, because its behavior is determined only by the emergent goal. Such goal misgeneralization presents a challenge: an AI system's designers may not notice that their system has misaligned emergent goals since they do not become visible during the training phase.\nGoal misgeneralization has been observed in some language models, navigation agents, and game-playing agents. It is sometimes analogized to biological evolution. Evolution can be seen as a kind of optimization process similar to the optimization algorithms used to train machine learning systems. In the ancestral environment, evolution selected genes for high inclusive genetic fitness, but humans pursue goals other than this. Fitness corresponds to the specified goal used in the training environment and training data. But in evolutionary history, maximizing the fitness specification gave rise to goal-directed agents, humans, who do not directly pursue inclusive genetic fitness. Instead, they pursue goals that correlate with genetic fitness in the ancestral \"training\" environment: nutrition, sex, and so on. The human environment has changed: a distributional shift has occurred. They continue to pursue the same emergent goals, but this no longer maximizes genetic fitness. The taste for sugary food (an emergent goal) was originally aligned with inclusive fitness, but it now leads to overeating and health problems. Sexual desire originally led humans to have more offspring, but they now use contraception when offspring are undesired, decoupling sex from genetic fitness.\nResearchers aim to detect and remove unwanted emergent goals using approaches including red teaming, verification, anomaly detection, and interpretability. Progress on these techniques may help mitigate two open problems:","score":63.16641805025819,"links":[]},{"source":"wiki_artificial_intelligence.hat","text":"ARTICLE: Artificial intelligence\nArtificial intelligence (AI) is the capability of computational systems to perform tasks typically associated with human intelligence, such as learning, reasoning, problem-solving, perception, and decision-making. It is a field of research in engineering, mathematics and computer science that develops and studies methods and software that enable machines to perceive their environment and use learning and intelligence to take actions that maximize their chances of achieving defined goals.\nHigh-profile applications of AI include advanced web search engines, chatbots, virtual assistants, autonomous vehicles, and play and analysis in strategy games (e.g., chess and Go). Since the 2020s, generative AI has become widely available to generate images, audio, and videos from text prompts.\nThe traditional goals of AI research include learning, reasoning, knowledge representation, planning, natural language processing, and perception, as well as support for robotics. To reach these goals, AI researchers have used techniques including state space search and mathematical optimization, formal logic, artificial neural networks, and methods based on statistics, operations research, and economics. AI also draws upon psychology, linguistics, philosophy, neuroscience, and other fields. Some companies, such as OpenAI, Google DeepMind and Meta, aim to create artificial general intelligence (AGI) – AI that can complete virtually any cognitive task at least as well as a human.\nArtificial intelligence was founded as an academic discipline in 1956, and the field went through multiple cycles of optimism throughout its history, followed by periods of disappointment and loss of funding, known as AI winters. Funding and interest increased substantially after 2012, when graphics processing units began being used to accelerate neural networks, and deep learning outperformed previous AI techniques. This growth accelerated further after 2017 with the transformer architecture. In the 2020s, an AI boom has coincided with advances in generative AI, which allowed for the creation and modification of media. In addition to AI safety and unintended consequences and harms from the use of AI, ethical concerns, AI's long-term effects, and potential existential risks have prompted discussions of AI regulation.","score":53.16641805025819,"links":[]},{"source":"wiki_dallas.hat","text":"History\nIndigenous tribes in North Texas included the Caddo, Tawakoni, Wichita, Kickapoo and Comanche. Spanish colonists claimed the territory of Texas in the 18th century as a part of the Viceroyalty of New Spain. Later, France also claimed the area but never established much settlement. In all, six flags have flown over the area preceding and during the city's history: those of France, Spain, and Mexico, the flag of the Republic of Texas, the Confederate flag, and the flag of the United States of America.\nIn 1819, the Adams–Onís Treaty between the United States and Spain defined the Red River as the northern boundary of New Spain, officially placing the future location of Dallas well within Spanish territory. The area remained under Spanish rule until 1821, when Mexico declared independence from Spain, and the area was considered part of the Mexican state of Coahuila y Tejas. In 1836, Texians, with a majority of Anglo-American settlers, gained independence from Mexico and formed the Republic of Texas.\nThree years after Texas achieved independence, John Neely Bryan surveyed the area around present-day Dallas. In 1839, accompanied by his dog and a Cherokee he called Ned, he planted a stake in the ground on a bluff located near three forks of the Trinity River and left. Two years later, in 1841, he returned to establish a permanent settlement named Dallas. The origin of the name is uncertain. The official historical marker states it was named after Vice President George M. Dallas of Philadelphia, Pennsylvania. However, this is disputed. Other potential theories for the origin include his brother, Commodore Alexander James Dallas, as well as brothers Walter R. Dallas and James R. Dallas. A further theory gives the ultimate origin as the village of Dallas, Moray, Scotland, similar to the way Houston, Texas, was named after Sam Houston, whose ancestors came from the Scottish village of Houston, Renfrewshire.\nThe Republic of Texas was annexed by the United States in 1845 and Dallas County was established the following year. Dallas was formally incorporated as a city on February 2, 1856. In the mid-1800s, a group of French Socialists established La Réunion, a short-lived community, along the Trinity River in what is now West Dallas.","score":29.280917192565077,"links":[]},{"source":"wiki_dallas.hat","text":"Each spring, cold fronts moving south from the North collide with warm, humid air streaming in from the Gulf Coast, leading to severe thunderstorms with lightning, torrents of rain, hail, and occasionally, tornadoes. Over time, tornadoes have probably been the most significant natural threat to the city, as it is near the heart of Tornado Alley.\nA few times each winter in Dallas, warm and humid air from the south will override cold, dry air, resulting in freezing rain or ice and causing disruptions in the city if the roads and highways become slick. Temperatures reaching 70 °F (21 °C) on average occur on at least four days each winter month. Dallas averages 26 annual nights at or below freezing, with the winter of 1999–2000 holding the record for the fewest freezing nights with 14. During this same span of 15 years, the temperature in the region has only twice dropped below 15 °F (−9 °C), though it will generally fall below 20 °F (−7 °C) in most (67%) years.\nThe U.S. Department of Agriculture places Dallas in Plant Hardiness Zone 8b. However, mild winter temperatures in the past 15 to 20 years had encouraged the horticulture of more cold-sensitive plants such as Washingtonia filifera and Washingtonia filifera var. robusta palms, nearly all of which died off during the February 2021 North American winter storm. According to the American Lung Association, Dallas has the 12th highest air pollution among U.S. cities, ranking it behind Los Angeles and Houston. Much of the air pollution in Dallas and the surrounding area comes from a hazardous materials incineration plant in the small town of Midlothian and from cement plants in neighboring Ellis County.\nThe average daily low in Dallas is 57.4 °F (14 °C), and the average daily high is 76.9 °F (25 °C). Dallas receives approximately 39.1 inches (993 mm) of rain per year. The record snowfall for Dallas was 11.2 inches (28 cm) on February 11, 2010.","score":29.280917192565077,"links":[]},{"source":"wiki_dallas.hat","text":"Religion\nChristianity is the most prevalently practiced religion in Dallas and the wider metropolitan area according to a 2014 study by the Pew Research Center (78%), and the Public Religion Research Institute's 2020 study (77%). There is a large Protestant Christian influence in the Dallas community, though the city of Dallas and Dallas County have more Catholic than Protestant residents, while the reverse is usually true for the suburban areas of Dallas and the city of Fort Worth.\nDallas has been called the \"Prison Ministry Capital of the World\" by the prison ministry community. It is a home for the International Network of Prison Ministries, the Coalition of Prison Evangelists, Bill Glass Champions for Life, Chaplain Ray's International Prison Ministry, and 60 other prison ministries.\nMethodist, Baptist, and Presbyterian churches are prominent in many neighborhoods and anchor two of the city's major private universities (Southern Methodist University and Dallas Baptist University). Dallas is also home to two evangelical seminaries: the Dallas Theological Seminary and Criswell College. Many Bible schools including Christ For The Nations Institute are also headquartered in the city. The Christian creationist apologetics group Institute for Creation Research is headquartered in Dallas. According to the Pew Research Center, evangelical Protestantism constituted the largest form of Protestantism in the area as of 2014. The largest single evangelical Protestant group were Baptists. The largest Baptist denomination was the Southern Baptist Convention, followed by the historically black National Baptist Convention USA. African-initiated Protestant churches including Ethiopian Evangelical churches can be found throughout the metropolitan area.\nThe Catholic Church is also a significant religious organization in the Dallas area and operates the University of Dallas, a liberal-arts university in the Dallas suburb of Irving. The Cathedral Santuario de la Virgen de Guadalupe in the Arts District is home to the second-largest Catholic church membership in the United States and overseas, consisting over 70 parishes in the Dallas Diocese. The Society of Jesus operates the Jesuit College Preparatory School of Dallas. Dallas is also home to numerous Eastern Orthodox and Oriental Orthodox churches including Saint Seraphim Cathedral, see of the Orthodox Church in America's Southern Diocese. The Greek Orthodox Archdiocese of America (Ecumenical Patriarchate) has one parish in the city of Dallas. There is also the St. Sarkis Armenian Church (serving as part of the Armenian Apostolic Church facility).\nJehovah's Witnesses has a large number of members throughout the Dallas metropolitan division. In addition, there are several Unitarian Universalist congregations, including First Unitarian Church of Dallas, founded in 1899. A large community of the United Church of Christ exists in the city. The most prominent UCC-affiliated church is the Cathedral of Hope, a predominantly LGBT-affirming church.\nThe Church of Jesus Christ of Latter-day Saints has a sizeable community in the Dallas-Fort Worth Metroplex. Members in the area are organized into 24 stakes. The Dallas Texas Temple, dedicated in 1984 as the first temple in Texas, is located in the city. Two more temples, the Fort Worth Texas Temple and Fairview Texas Temple, are under construction in the area. \nSince the establishment of the city's first Jewish cemetery in 1854 and its first congregation (which would eventually be known as Temple Emanu-El) in 1873, Dallasite Jews have been well represented among leaders in commerce, politics, and various professional fields in Dallas and elsewhere. Furthermore, a large Muslim community exists in the north and northeastern portions of Dallas, as well as in the northern Dallas suburbs. The oldest mosque in Dallas is Masjid Al-Islam just south of Downtown.\nDallas has a large Buddhist community. Immigrants from East Asia, Southeast Asia, Nepal, and Sri Lanka have all contributed to the Buddhist population, which is concentrated in the northern suburbs of Garland, Plano and Richardson. Numerous Buddhist temples dot the Metroplex including The Buddhist Center of Dallas, Lien Hoa Vietnamese Temple of Irving, and Kadampa Meditation Center Texas and Wat Buddhamahamunee of Arlington. A large and growing Hindu Community lives in the Dallas–Fort Worth metroplex. Most live in Collin County and the northern portions of Dallas County. Over 28 Hindu Temples exist in the area. Some notable ones include the DFW Hindu Temple, the North Texas Hindu Mandir, Radha Krishna Temple, Dallas and Karya Siddhi Hanuman Temple. There are also at least three Sikh Gurudwaras in this metropolitan area. For irreligious people, the Winter Solstice Celebration is held in the Metroplex although some of its participants are also neo-pagans and New Agers.","score":29.280917192565077,"links":[]},{"source":"wiki_real_estate.hat","text":"History of real estate\nThe natural right of a person to own property as a concept can be seen as having roots in Roman law as well as Greek philosophy. The profession of appraisal can be seen as beginning in England during the 1500s, as agricultural needs required land clearing and land preparation. Textbooks on the subject of surveying began to be written and the term \"surveying\" was used in England, while the term \"appraising\" was more used in North America. Natural law which can be seen as \"universal law\" was discussed among writers of the 15th and 16th century as it pertained to \"property theory\" and the inter-state relations dealing with foreign investments and the protection of citizens private property abroad. Natural law can be seen as having an influence in Emerich de Vattel's 1758 treatise The Law of Nations which conceptualized the idea of private property.\nOne of the largest initial real estate deals in history known as the \"Louisiana Purchase\" happened in 1803 when the Louisiana Purchase Treaty was signed. This treaty paved the way for western expansion and made the U.S. the owners of the \"Louisiana Territory\" as the land was bought from France for fifteen million dollars, making each acre roughly 4 cents. The oldest real estate brokerage firm was established in 1855 in Chicago, Illinois, and was initially known as \"L. D. Olmsted & Co.\" but is now known as \"Baird & Warner\". In 1908, the National Association of Realtors was founded in Chicago and in 1916, the name was changed to the National Association of Real Estate Boards and this was also when the term \"realtor\" was coined to identify real estate professionals.\nThe stock market crash of 1929 and the Great Depression in the U.S. caused a major drop in real estate worth and prices and ultimately resulted in depreciation of 50% for the four years after 1929. Housing financing in the U.S. was greatly affected by the Banking Act of 1933 and the National Housing Act in 1934 because it allowed for mortgage insurance for home buyers and this system was implemented by the Federal Deposit Insurance as well as the Federal Housing Administration. In 1938, an amendment was made to the National Housing Act and Fannie Mae, a government agency, was established to serve as a secondary market for mortgages and to give lenders more money in order for new homes to be funded.\nTitle VIII of the Civil Rights Act in the U.S., which is also known as the Fair Housing Act, was put into place in 1968 and dealt with the incorporation of African Americans into neighborhoods as the issues of discrimination were analyzed with the renting, buying, and financing of homes. Internet real estate as a concept began with the first appearance of real estate platforms on the World Wide Web (www) and occurred in 1999.","score":27.551529804882808,"links":[]},{"source":"wiki_real_estate.hat","text":"History\nThe first known reference to an activity-based analysis of office work modes was by American architect Robert Luchetti in the late 1970s. in 1983, Luchetti co-invented the now widely accepted concept of the office as a series of \"activity settings\". In an activity settings-based environment, multiple settings are provided which have different technical and physical attributes assembled to support the variety of performance \"modes\" that take place in a work environment.\nThe term \"Activity Based Working\" was first coined in the book the Art of Working by Erik Veldhoen, a Dutch consultant with Veldhoen + Company, and author of the book The Demise of the Office.  Activity Based Working was first implemented in the Netherlands by Interpolis in collaboration with Veldhoen + Company in the nineties. Interpolis is one of largest insurance companies in the Netherlands. The company gained wide recognition with its advertising campaign \"Interpolis. Crystal clear\", which was adopted from their vision and brought to life in their new way of working.","score":27.551529804882808,"links":[]},{"source":"wiki_real_estate.hat","text":"ARTICLE: Building\nA building or edifice is an enclosed structure with a roof, walls and often windows, usually standing permanently in one place, such as a house or factory. Buildings come in a variety of sizes, shapes, and functions, and have been adapted throughout history for numerous factors, from building materials available, to weather conditions, land prices, ground conditions, specific uses, prestige, and aesthetic reasons. To better understand the concept, see Nonbuilding structure for contrast. \n\nBuildings serve several societal needs – occupancy, primarily as shelter from weather, security, living space, privacy, to store belongings, and to comfortably live and work. A building as a shelter represents a physical separation of the human habitat (a place of comfort and safety) from the outside (a place that may be harsh and harmful at times).\nBuildings have been objects or canvasses of much artistic expression. In recent years, interest in sustainable planning and building practices has become an intentional part of the design process of many new buildings and other structures, usually green buildings.","score":27.551529804882808,"links":[]},{"source":"web_1779060040.hat","text":"PostgreSQL 18.4 Documentation Next PostgreSQL 18.4 Documentation The PostgreSQL Global Development Group Copyright © 1996–2026 The PostgreSQL Global Development Group Legal Notice Table of Contents Preface 1. What Is PostgreSQL ? 2. A Brief History of PostgreSQL 3. Conventions 4. Further Information 5. Bug Reporting Guidelines I. Tutorial 1. Getting Started 2. The SQL Language 3. Advanced Features II. The SQL Language 4. SQL Syntax 5. Data Definition 6. Data Manipulation 7. Queries 8. Data Types 9. Functions and Operators 10. Type Conversion 11. Indexes 12. Full Text Search 13. Concurrency Control 14. Performance Tips 15. Parallel Query III. Server Administration 16. Installation from Binaries 17. Installation from Source Code 18. Server Setup and Operation 19. Server Configuration 20. Client Authentication 21. Database Roles 22. Managing Databases 23. Localization 24. Routine Database Maintenance Tasks 25. Backup and Restore 26. High Availability, Load Balancing, and Replication 27. Monitoring Database Activity 28. Reliability and the Write-Ahead Log 29. Logical Replication 30. Just-in-Time Compilation ( JIT ) 31. Regression Tests IV. Client Interfaces 32. libpq — C Library 33. Large Objects 34. ECPG — Embedded SQL in C 35. The Information Schema V. Server Programming 36. Extending SQL 37. Triggers 38. Event Triggers 39. The Rule System 40. Procedural Languages 41. PL/pgSQL — SQL Procedural Language 42. PL/Tcl — Tcl Procedural Language 43. PL/Perl — Perl Procedural Language 44. PL/Python — Python Procedural Language 45. Server Programming Interface 46. Background Worker Processes 47. Logical Decoding 48. Replication Progress Tracking 49. Archive Modules 50. OAuth Validator Modules VI. Reference I. SQL Commands II. PostgreSQL Client Applications III. PostgreSQL Server Applications VII. Internals 51. Overview of PostgreSQL Internals 52. System Catalogs 53. System Views 54. Frontend/Backend Protocol 55. PostgreSQL Coding Conventions 56. Native Language Support 57. Writing a Procedural Language Handler 58. Writing a Foreign Data Wrapper 59. Writing a Table Sampling Method 60. Writing a Custom Scan Provider 61. Genetic Query Optimizer 62. Table Access Method Interface Definition 63. Index Access Method Interface Definition 64. Write Ahead Logging for Extensions 65. Built-in Index Access Methods 66. Database Physical Storage 67. Transaction Processing 68. System Catalog Declarations and Initial Contents 69. How the Planner Uses Statistics 70. Backup Manifest Format VIII. Appendixes A. PostgreSQL Error Codes B. Date/Time Support C. SQL Key Words D. SQL Conformance E. Release Notes F. Additional Supplied Modules and Extensions G. Additional Supplied Programs H. External Projects I. The Source Code Repository J. Documentation K. PostgreSQL Limits L. Acronyms M. Glossary N. Color Support O. Obsolete or Renamed Features Bibliography Index Next Preface","score":16.09969518892752,"links":[]}]},"metadata":{},"timestamp":"2026-07-08T22:49:31.336Z"}