{"success":true,"data":{"query":"Segmented Expert Atlas","limit":10,"count":7,"sources":["wiki_artificial_intelligence.hat","wiki_real_estate.hat","atlas_pulse_master.hat"],"synced":[],"results":[{"source":"wiki_artificial_intelligence.hat","text":"Risks and harm\nPrivacy and copyright\nMachine learning algorithms require large amounts of data. The techniques used to acquire this data have raised concerns about privacy, surveillance and copyright.\nAI-powered devices and services, such as virtual assistants and IoT products, continuously collect personal information, raising concerns about intrusive data gathering and unauthorized access by third parties. The loss of privacy is further exacerbated by AI's ability to process and combine vast amounts of data, potentially leading to a surveillance society where individual activities are constantly monitored and analyzed without adequate safeguards or transparency.\nSensitive user data collected may include online activity records, geolocation data, video, or audio. For example, in order to build speech recognition algorithms, Amazon has recorded millions of private conversations and allowed temporary workers to listen to and transcribe some of them. Opinions about this widespread surveillance range from those who see it as a necessary evil to those for whom it is clearly unethical and a violation of the right to privacy.\nAI developers argue that this is the only way to deliver valuable applications and have developed several techniques that attempt to preserve privacy while still obtaining the data, such as data aggregation, de-identification and differential privacy. Since 2016, some privacy experts, such as Cynthia Dwork, have begun to view privacy in terms of fairness. Brian Christian wrote that experts have pivoted \"from the question of 'what they know' to the question of 'what they're doing with it'.\"\nGenerative AI is often trained on unlicensed copyrighted works, including in domains such as images or computer code; the output is then used under the rationale of \"fair use\". Experts disagree about how well and under what circumstances this rationale will hold up in courts of law; relevant factors may include \"the purpose and character of the use of the copyrighted work\" and \"the effect upon the potential market for the copyrighted work\". Website owners can indicate that they do not want their content scraped via a \"robots.txt\" file. However, some companies will scrape content regardless because the robots.txt file has no real authority. In 2023, leading authors (including John Grisham and Jonathan Franzen) sued AI companies for using their work to train generative AI. Another discussed approach is to envision a separate sui generis system of protection for creations generated by AI to ensure fair attribution and compensation for human authors.","score":63.958022562822734,"links":[]},{"source":"wiki_artificial_intelligence.hat","text":"Lack of transparency\nMany AI systems are so complex that their designers cannot explain how they reach their decisions. Particularly with deep neural networks, in which there are many non-linear relationships between inputs and outputs. But some popular explainability techniques exist.\nIt is impossible to be certain that a program is operating correctly if no one knows how exactly it works. There have been many cases where a machine learning program passed rigorous tests, but nevertheless learned something different than what the programmers intended. For example, a system that could identify skin diseases better than medical professionals was found to actually have a strong tendency to classify images with a ruler as \"cancerous\", because pictures of malignancies typically include a ruler to show the scale. Another machine learning system designed to help effectively allocate medical resources was found to classify patients with asthma as being at \"low risk\" of dying from pneumonia. Having asthma is actually a severe risk factor, but since the patients having asthma would usually get much more medical care, they were relatively unlikely to die according to the training data. The correlation between asthma and low risk of dying from pneumonia was real, but misleading.\nPeople who have been harmed by an algorithm's decision have a right to an explanation. Doctors, for example, are expected to clearly and completely explain to their colleagues the reasoning behind any decision they make. Early drafts of the European Union's General Data Protection Regulation in 2016 included an explicit statement that this right exists. Industry experts noted that this is an unsolved problem with no solution in sight. Regulators argued that nevertheless the harm is real: if the problem has no solution, the tools should not be used.\nDARPA established the XAI (\"Explainable Artificial Intelligence\") program in 2014 to try to solve these problems.\nSeveral approaches aim to address the transparency problem. SHAP enables to visualise the contribution of each feature to the output. LIME can locally approximate a model's outputs with a simpler, interpretable model. Multitask learning provides a large number of outputs in addition to the target classification. These other outputs can help developers deduce what the network has learned. Deconvolution, DeepDream and other generative methods can allow developers to see what different layers of a deep network for computer vision have learned, and produce output that can suggest what the network is learning. For generative pre-trained transformers, Anthropic developed a technique based on dictionary learning that associates patterns of neuron activations with human-understandable concepts.","score":63.958022562822734,"links":[]},{"source":"wiki_artificial_intelligence.hat","text":"Existential risk\nRecent public debates in artificial intelligence have increasingly focused on its broader societal and ethical implications. It has been argued AI will become so powerful that humanity may irreversibly lose control of it. This could, as physicist Stephen Hawking stated, \"spell the end of the human race\". This scenario has been common in science fiction, when a computer or robot suddenly develops a human-like \"self-awareness\" (or \"sentience\" or \"consciousness\") and becomes a malevolent character. These sci-fi scenarios are misleading in several ways.\nFirst, AI does not require human-like sentience to be an existential risk. Modern AI programs are given specific goals and use learning and intelligence to achieve them. Philosopher Nick Bostrom argued that if one gives almost any goal to a sufficiently powerful AI, it may choose to destroy humanity to achieve it (he used the example of an automated paperclip factory that destroys the world to get more iron for paperclips). Stuart Russell gives the example of household robot that tries to find a way to kill its owner to prevent it from being unplugged, reasoning that \"you can't fetch the coffee if you're dead.\" In order to be safe for humanity, a superintelligence would have to be genuinely aligned with humanity's morality and values so that it is \"fundamentally on our side\".\nSecond, Yuval Noah Harari argues that AI does not require a robot body or physical control to pose an existential risk. The essential parts of civilization are not physical. Things like ideologies, law, government, money and the economy are built on language; they exist because there are stories that billions of people believe. The current prevalence of misinformation suggests that an AI could use language to convince people to believe anything, even to take actions that are destructive. Geoffrey Hinton said in 2025 that modern AI is particularly \"good at persuasion\" and getting better all the time. He asks \"Suppose you wanted to invade the capital of the US. Do you have to go there and do it yourself? No. You just have to be good at persuasion.\"\nThe opinions amongst experts and industry insiders are mixed, with sizable fractions both concerned and unconcerned by risk from eventual superintelligent AI. Personalities such as Stephen Hawking, Bill Gates, and Elon Musk, as well as AI pioneers such as Geoffrey Hinton, Yoshua Bengio, Stuart Russell, Demis Hassabis, and Sam Altman, have expressed concerns about existential risk from AI.\nIn May 2023, Geoffrey Hinton announced his resignation from Google in order to be able to \"freely speak out about the risks of AI\" without \"considering how this impacts Google\". He notably mentioned risks of an AI takeover, and stressed that in order to avoid the worst outcomes, establishing safety guidelines will require cooperation among those competing in use of AI.\nIn 2023, many leading AI experts endorsed the joint statement that \"Mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks such as pandemics and nuclear war\".\nSome other researchers were more optimistic. AI pioneer Jürgen Schmidhuber did not sign the joint statement, emphasising that in 95% of all cases, AI research is about making \"human lives longer and healthier and easier.\" While the tools that are now being used to improve lives can also be used by bad actors, \"they can also be used against the bad actors.\" Andrew Ng also argued that \"it's a mistake to fall for the doomsday hype on AI—and that regulators who do will only benefit vested interests.\" Yann LeCun, a Turing Award winner, disagreed with the idea that AI will subordinate humans \"simply because they are smarter, let alone destroy [us]\", \"scoff[ing] at his peers' dystopian scenarios of supercharged misinformation and even, eventually, human extinction.\" In contrast, he claimed that \"intelligent machines will usher in a new renaissance for humanity, a new era of enlightenment.\" In the early 2010s, experts argued that the risks are too distant in the future to warrant research or that humans will be valuable from the perspective of a superintelligent machine. However, after 2016, the study of current and future risks and possible solutions became a serious area of research.","score":63.958022562822734,"links":[]},{"source":"wiki_real_estate.hat","text":"Project specifics\nWhen an architecture firm is working on a project that is outside their geographic location or range of expertise, it will often choose to work with an architect that is either local to the project site or skilled in that particular area of expertise. In this case, the primary architect works with the local architect in order to complete the project, and the local architect becomes the \"architect of record.\" This type of working relationship is common when high-profile architects (or \"starchitects\") win design bids but find themselves in need of architects with more practical skills or knowledge of local conditions. Or more pragmatically, the high-profile architect simply needs an architect who is local to the project site, facilitating quicker site visits and project oversight.\nThe local architecture firms that are responsible for corresponding with city agencies about code compliance, tender documents, client communication and creating up to 90 percent of the construction documents and carry out construction inspections are similar, but should be referred to as the \"executive architect.\" \n\n\n== References ==\n\n--- NEXT ARTICLE ---","score":18.775764902441402,"links":[]},{"source":"atlas_pulse_master.hat","text":"SOURCE: dallas_community_intel.hat\nSLUG: dallas-community-intel\nTITLE: Dallas Community Intel\nQUERY: Dallas Community Intel\n\nCONTENT:\nCommunity Vitality: Code Concern - CCS Status: Closed | Outcome: PENDING Location: S DENLEY DR & ATLAS DR, DALLAS, TX, 75216, Dallas TX Reported: 2026-06-03T16:16:43.000 Coordinates: 0, 0 Service Request: 26-00240656","score":10,"links":[]},{"source":"atlas_pulse_master.hat","text":"SOURCE: durandiel_expertise.tah\nSLUG: durandiel-expertise\nTITLE: Durandiel Expertise\nQUERY: Durandiel Expertise\n\nCONTENT:\nBowie: Community vibe is rustic and traditional. Strong affinity for heritage trade.","score":10,"links":[]},{"source":"atlas_pulse_master.hat","text":"SOURCE: durandiel_expertise.tah\nSLUG: durandiel-expertise\nTITLE: Durandiel Expertise\nQUERY: Durandiel Expertise\n\nCONTENT:\nKeller: Upscale suburban polish. High-engagement school board and active parks.","score":10,"links":[]}]},"metadata":{},"timestamp":"2026-07-16T21:19:37.969Z"}