{"success":true,"data":{"query":"Neighborhood Intel","limit":10,"count":10,"sources":["wiki_artificial_intelligence.hat","neighborhood_context.tah","wiki_en_19ff9ee7ab0b5c99.tah"],"synced":[],"results":[{"source":"wiki_artificial_intelligence.hat","text":"Applications in government\nSeveral government bodies in the United States and United Kingdom have deployed or announced the deployment of agents, at the local and national level. The city of Kyle, Texas deployed an AI agent from Salesforce in March 2025 for 311 customer service. In November 2025, the Internal Revenue Service stated that it would use Agentforce, AI agents from Salesforce, for the Office of Chief Counsel, Taxpayer Advocate Services and the Office of Appeals. That same month, Staffordshire Police announced that they would trial Agentforce agents for handling non-emergency 101 calls in the United Kingdom starting in 2026. In December 2025, the Department of Neighborhoods in Detroit, Michigan, in partnership with a local business, deployed a pilot project in two Detroit districts for an AI agent to be used for customer service calls.\nIn February 2025, Thomas Shedd, the director of the Technology Transformation Services, proposed using AI coding agents across the United States federal government. A recruiter for the Department of Government Efficiency proposed in April 2025 to use AI agents to automate the work of about 70,000 United States federal government employees, as part of a startup with funding from OpenAI and a partnership agreement with Palantir. This proposal was criticized by experts for its impracticality, if not impossibility, and the lack of corresponding widespread adoption by businesses.\nIn December 2025, the Food and Drug Administration announced that it would offer \"agentic AI capabilities\" to its staff for \"meeting management, pre-market reviews, review validation, post-market surveillance, inspections and compliance and administrative functions.\" That same month, the United States Department of Defense launched GenAI.mil, an internal platform for American military personnel to use generative AI-based applications based on Google Gemini, including \"intelligent agentic workflows\". Defense Secretary Pete Hegseth listed applications such as \"[conducting] deep research, [formatting] documents and even [analyzing] video or imagery at unprecedented speed.\" In December 2025, the United States Immigration and Customs Enforcement agency signed a contract with a company for its Enforcement and Removal Operations department to use AI agents for skip tracing.","score":0.7,"links":[]},{"source":"neighborhood_context.tah","text":"TITLE: Neighborhood Context Capsule CONCEPT: neighborhood_context ALIASES: neighborhood, area, community, nearby, local context, commute, amenities, district, place memory DOMAIN: real_estate_agent_command_center TRUST: local_private VITALITY: high PURPOSE: Use this capsule for neighborhood explainers, area summaries, buyer relocation questions, local context, commute framing, amenities, and place-based listing support. BUYER-SAFE AREA CONTEXT: - Explain location through practical facts: access routes, nearby services, parks, schools only when sourced, shopping corridors, commute options, property styles, price bands, and market activity. - Avoid demographic claims, protected-class language, ranking people, safety promises, or unsupported school quality claims. - When data is missing, say what should be checked: MLS remarks, city data, school district source, local notes, map distance, or agent memory. NEIGHBORHOOD SIGNALS: Strong neighborhood context connects the home to daily life: grocery runs, coffee, restaurants, trails, medical, schools where sourced, transit, major roads, employment centers, local events, and nearby listings. NEIGHBORHOOD ANCHORS: neighborhood_context, neighborhood, area, community, nearby, local context, commute, amenities, place memory, relocation, buyer explainer, lifestyle, practical location, local notes, district source. OUTPUT SHAPE: Return a concise area brief, three practical buyer notes, one caution about verification, and a client-safe phrasing suggestion.","score":0.44,"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":0.44,"links":[]},{"source":"wiki_artificial_intelligence.hat","text":"Power needs and environmental impacts\nTechnology companies have built electricity and artificial intelligence infrastructure to facilitate the AI boom of the 2020s. A 2025 report from the consulting firm McKinsey & Company estimated that by 2030, $2.7 trillion would be invested into AI infrastructure and data centers in the US, surpassing World War II's Manhattan Project every month.\nIn January 2024, the International Energy Agency (IEA) released Electricity 2024, Analysis and Forecast to 2026. This is the first IEA report to make projections for data centers and power consumption by AI and cryptocurrency. The report states that power demand for these uses might double by 2026, with the additional power consumption equaling that of Japan.\nPower consumption by AI is responsible for an increase in fossil fuel use, and has delayed closings of obsolete, carbon-emitting coal energy facilities. A ChatGPT search involves the use of 10 times the electrical energy as a Google search.\nA 2024 Goldman Sachs Research Paper, AI Data Centers and the Coming US Power Demand Surge, found \"US power demand (is) likely to experience growth not seen in a generation....\" and forecasts that, by 2030, US data centers will consume 8% of US power, as opposed to 3% in 2022, presaging growth for the electrical power generation industry by a variety of means. Data centers' need for more and more electrical power is such that they might max out the electrical grid. The Big Tech companies counter that AI can be used to maximize the utilization of the grid by all.\nIn 2024, The Wall Street Journal reported that big AI companies have begun negotiations with the US nuclear power providers to provide electricity to the data centers. In March 2024 Amazon purchased a Pennsylvania nuclear-powered data center for US$650 million.\nIn September 2024, Microsoft announced an agreement with Constellation Energy to re-open the Three Mile Island nuclear power plant to provide Microsoft with 100% of all electric power produced by the plant for 20 years. Reopening the plant, which suffered a partial nuclear meltdown of its Unit 2 reactor in 1979, will require Constellation to get through strict regulatory processes which will include extensive safety scrutiny from the US Nuclear Regulatory Commission. If approved (this will be the first ever US re-commissioning of a nuclear plant), over 835 megawatts of power – enough for 800,000 homes – of energy will be produced. The cost for re-opening and upgrading is estimated at US$1.6 billion and is dependent on tax breaks for nuclear power contained in the 2022 US Inflation Reduction Act. As of 2024, the US government and the state of Michigan have been investing almost US$2 billion to reopen the Palisades Nuclear reactor on Lake Michigan. Closed since 2022, the plant was planned to be reopened in October 2025.\nAfter the last approval in September 2023, Taiwan suspended the approval of data centers north of Taoyuan with a capacity of more than 5 MW in 2024, due to power supply shortages. Taiwan aims to phase out nuclear power by 2025. \nSingapore imposed a ban on the opening of data centers in 2019 due to electric power, but in 2022, lifted this ban.\nAlthough most nuclear plants in Japan have been shut down after the 2011 Fukushima nuclear accident, according to an October 2024 Bloomberg article in Japanese, cloud gaming services company Ubitus, in which Nvidia has a stake, is looking for land in Japan near a nuclear power plant for a new data center for generative AI.\nOn 1 November 2024, the Federal Energy Regulatory Commission (FERC) rejected an application submitted by Talen Energy for approval to supply some electricity from the nuclear power station Susquehanna to Amazon's data center.\nAccording to the Commission Chairman Willie L. Phillips, it is a burden on the electricity grid as well as a significant cost shifting concern to households and other business sectors.\nIn 2025, a report prepared by the IEA estimated the greenhouse gas emissions from the energy consumption of AI at 180 million tons. By 2035, these emissions could rise to 300–500 million tonnes depending on what measures will be taken. This is below 1.5% of the energy sector emissions. The emissions reduction potential of AI was estimated at 5% of the energy sector emissions, but rebound effects (for example if people switch from public transport to autonomous cars) can reduce it.","score":0.44,"links":[]},{"source":"wiki_en_19ff9ee7ab0b5c99.tah","text":"TITLE: 163rd Military Intelligence Battalion (United States) - Wikipedia\nSOURCE_URL: https://en.wikipedia.org/wiki/163rd_Military_Intelligence_Battalion_(United_States)\nSOURCE_PAGE_ID: 68632962\nTITLE: 163rd Military Intelligence Battalion (United States) - Wikipedia\nDOMAIN: wikipedia\nLANGUAGE: en\nTRUST: wikipedia_crawl4ai\nSOURCE_URL: https://en.wikipedia.org/wiki/163rd_Military_Intelligence_Battalion_(United_States)\nSOURCE_PAGE_ID: 68632962\nCRAWLED_AT: 2026-08-17T02:03:04.539Z","score":0.35,"links":[]},{"source":"wiki_en_19ff9ee7ab0b5c99.tah","text":"TITLE: 163rd Military Intelligence Battalion (United States) - Wikipedia\nSOURCE_URL: https://en.wikipedia.org/wiki/163rd_Military_Intelligence_Battalion_(United_States)\nSOURCE_PAGE_ID: 68632962\n[Jump to content](https://en.wikipedia.org/wiki/163rd_Military_Intelligence_Battalion_\\(United_States\\)#bodyContent)\nFrom Wikipedia, the free encyclopedia\n|  hide**This article has multiple issues.** Please help or discuss these issues on the . _([Learn how and when to remove these messages](https://en.wikipedia.org/wiki/Help:Maintenance_template_removal \"Help:Maintenance template removal\"))_\n | This article includes a [list of references](https://en.wikipedia.org/wiki/Wikipedia:Citing_sources \"Wikipedia:Citing sources\"), [related reading](https://en.wikipedia.org/wiki/Wikipedia:Further_reading \"Wikipedia:Further reading\"), or [external links](https://en.wikipedia.org/wiki/Wikipedia:External_links \"Wikipedia:External links\"), **but its sources remain unclear because it lacks[inline citations](https://en.wikipedia.org/wiki/Wikipedia:Citing_sources#Inline_citations \"Wikipedia:Citing sources\")**.","score":0.35,"links":[]},{"source":"wiki_en_19ff9ee7ab0b5c99.tah","text":"TITLE: 163rd Military Intelligence Battalion (United States) - 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Wikipedia\nSOURCE_URL: https://en.wikipedia.org/wiki/163rd_Military_Intelligence_Battalion_(United_States)\nSOURCE_PAGE_ID: 68632962\nTOR](https://www.jstor.org/action/doBasicSearch?Query=%22163rd+Military+Intelligence+Battalion%22+United+States&acc=on&wc=on) _( November 2021)__([Learn how and when to remove this message](https://en.wikipedia.org/wiki/Help:Maintenance_template_removal \"Help:Maintenance template removal\"))_  |\n| --- |\n_([Learn how and when to remove this message](https://en.wikipedia.org/wiki/Help:Maintenance_template_removal \"Help:Maintenance template removal\"))_\n |\n| 163rd Language Detachment163rd Military Intelligence Platoon163rd Military Intelligence Battalion |\n| --- |\n| 163 MIBN |\n| Active  | 1945–19541955–19641969–19972006 – present  |\n| Country  |\n| Branch  |  [United States Army](https://en.wikipedia.org/wiki/United_States_Army \"United States Army\")\n  * [Military Intelligence Corps](https://en.wikipedia.org/wiki/Military_Intelligence_Corps_\\(United_States_Army\\) \"Military Intelligence Corps \\(United States Army\\)\")","score":0.35,"links":[]}]},"metadata":{},"timestamp":"2026-08-23T14:43:16.042Z"}