AI Generator Youtube Channel Name

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  • Perceptual robotics

    Perceptual robotics

    Perceptual robotics is an interdisciplinary science linking Robotics and Neuroscience. It investigates biologically motivated robot control strategies, concentrating on perceptual rather than cognitive processes and thereby sides with J. J. Gibson's view against the Poverty of the stimulus theory. As a working definition, the following quote from Chapter 64 by H. Bülthoff, C. Wallraven and M. Giese from The Springer Handbook of Robotics, edited by Bruno Siciliano and Oussama Khatib, published by Springer in 2007, could be used: In the following we will apply the term Perceptual Robotics to signify the design of robots based on principles that are derived from human perception on all three levels in the sense of Marr. This includes a realization in terms of specific neural circuits as well as the transfer of more abstract biologically-inspired strategies for the solution of relevant computational problems.

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  • Hyperion Data Center

    Hyperion Data Center

    The Richland Parish Data Center, nicknamed "Hyperion", is a planned artificial intelligence data center by Meta Platforms under-construction along Highway La. 183 in Richland Parish, Louisiana, just outside of Holly Ridge. It is one of a number of "titan clusters" being built in preparation for the emergence of AI superintelligence. Modern technological researchers disagree as to whether or not superintelligence will ever exist, though Meta CEO Mark Zuckerberg has expressed belief that its creation is inevitable. Current plans allot for the investment of $27 billion, as the structure is built from 2025 to 2030. == History == Meta was considering potential locations for their flagship data center in early 2024. Before being announced later in December, the plan was completely secret; meetings held between involved organisations and even government officials could only refer to it by the codename "Project Sucre" to protect it from potential corporate espionage. The data center was first announced on 04 December 2024, though its full scale was yet to be revealed. At first, Meta would not even claim responsibility for it, channelling all of its investments through the secret shell subsidiary Laidley LLC. We set out looking for a place where we could expand into gigawatts pretty quickly, and really get moving within that community on a large plot of land very quickly. We looked at finding very, very large contiguous plots of land that had access to the infrastructure that we need, the energy that we needed, and could move very, very quickly for us. The Louisiana-based Entergy Corporation, aiming for the facility to be built in its own backyard, negotiated a deal with the government of Louisiana to provide Meta with enormous tax breaks if they agreed to build Hyperion there. The Louisiana legislature responded by passing Act 730, which provides significant tax rebates on the purchase or lease of equipment for building and operating data centers. Meta found the arrangement acceptable, and bought a plot of land from the government. The government also had to further amend its laws to allow Meta to do this, as pre-existing policy forbade purchasing land directly from the government instead of hosting a public auction. The plot of land, originally called Franklin Farms, was purchased from the Franklin family in 2006 by the government, intending for it to be developed into an automotive manufacturing plant. Greater attention was brought to Hyperion it when Zuckerberg posted about the project on 14 July 2025 on Threads. The project subsequently caught media attention for its large size, as Zuckerberg's post portrayed the structure superimposed over Manhattan (pictured). The construction site spans 2,250 acres (9.1 km2) with a planned floor area of 4,000,000 square feet (371612 m2), making it the third largest building in the world by floor area upon completion. Meta initially reported the construction cost to be over $10 billion, but in October 2025, it announced a partnership with Blue Owl Capital providing for at least $27 billion. == Operation == The facility is expected to consume up to 5 gigawatts (GW) of computational power, more electricity than is currently used by the entire State of Louisiana. As part of their deal made with Meta, Entergy plans to be able to produce at least 3.8 GW of electricity for the operation. == Response to the project == Louisiana Governor Jeff Landry thanked Meta for their decision to build Hyperion in Louisiana, stating that it would "create opportunities for Louisiana workers to fill high-paying jobs of the future." and calling it "A New Chapter" for the state. The Louisiana Economic Development (LED) state agency further praised the project, citing Meta's estimate that it would create 1,500 jobs. Additionally, Richland Parish Supervisor Joey Evans stated that he was excited about the project. As part of their agreement with Meta, Energy announced their plan to increase electricity production state-wide. They say that this will result in the cost of energy reducing, though Entergy filings revealed in June 2025 that the cost of electricity would rise and be passed onto consumers. Meta also pledged to match all of Hyperion's power consumption with 100% environmentally friendly electricity production. So far, Entergy has begun building three gas-powered combined-cycle power plants and a substation in response to the project. Delta Community College announced in response to Hyperion's construction that it would expand its construction and trade programs. In January 2025, Business Facilities Magazine selected Hyperion for its annual Deal of the Year Platinum Award for 2024. Much of the initial backlash following Hyperion's announcement centered around the fast-tracked approval of the project by the state government, and scepticism around Meta's various claims (environmental friendliness, 100% renewable energy, local economic stimulation, price reductions). The Sierra Club criticised Meta for gentrifying the surrounding area, and was highly sceptical of their promise to keep it environmentally friendly. Environmental activist group Earthjustice attempted to have a subpoena of Meta approved to determine if they were compliant with environmental protection laws, though they were unsuccessful. Many residents of Holy Ridge have been critical of the construction, complaining about the increased construction vehicle traffic and intense gentrification. Another point of contention is Meta's continued reliance on out-of-state contractors in the facility's construction in spite of their previous commitment to "hire as many local folk as [we] possibly can." In spite of Entergy's continual denial that the facility's construction will not adversely affect the power grid, numerous electrical outages have been reported since construction began.

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  • LG ThinQ

    LG ThinQ

    LG ThinQ (pronounced as "think-cue"; sometimes known as LG webOS) is a smart home and artificial intelligence brand launched by LG Electronics in 2017, featuring products that are equipped with voice control and artificial intelligence technology. The brand was originally launched for home appliances and consumer electronics, such as televisions, smart home devices, mobile devices, refrigerators, air conditioners and related services. The name was first used in 2011 for LG's THINQ-branded smart appliances, which were introduced at the Consumer Electronics Show in Las Vegas. In December 2017, LG announced ThinQ as a unified brand for artificial intelligence-enabled home appliances, consumer electronics and services.In February 2018, LG announced the LG V30S ThinQ, which is the first phone to have the "ThinQ" branding. == History == The branding was first introduced in 2011 in the Consumer Electronics Show (CES) in Las Vegas as THINQ. The first ThinQ product was a smart refrigerator, with features such as smart savings options, food management system, washing machine, oven and robotic vacuum cleaner and different software in the LCD screen on the fridge. The unified branding was then officially launched as ThinQ at CES 2017 as an artificial intelligence-based brand for all their smart products. The company announced DeepThinQ, a deep-learning technology for connected products, and later opened an Artificial Intelligence Lab in Seoul to coordinate research involving voice, video, sensors and machine learning. In December 2017, LG announced ThinQ as a brand designation for home appliances, consumer electronics, and services incorporating artificial intelligence, applied to its 2018 product lineup. In 2018, LG extended the ThinQ brand to smartphones with the LG V30S ThinQ. The phone used ThinQ branding for AI camera features, including image recognition and shooting-mode recommendations. That year, LG also used ThinQ branding on televisions with smart-assistant features, as manufacturers increasingly added voice assistants to TV platforms. In 2022, LG first introduced ThinQ UP, a software-upgradable appliance concept that allows compatible appliances to receive new features through the ThinQ app. The program included appliances such as refrigerators, washing machines, dryers, ovens and dishwashers, and was covered as part of a wider move toward upgradeable connected appliances. In 2024, LG introduced ThinQ ON, an AI-powered smart home hub designed to connect LG appliances and other smart home devices. It expanded ThinQ from an appliance-control platform into a broader smart home system. == Platform an app == LG ThinQ operates as a smart home platform and mobile app for connecting compatible LG appliances and consumer electronics. The app is used to control and monitor supported products, including kitchen appliances, laundry appliances, air purifiers, vacuum cleaners and televisions. Depending on the product and market, the ThinQ app can provide remote control, status monitoring, downloadable appliance cycles, diagnostic support, maintenance alerts and software-based feature updates. In 2024, LG introduced ThinQ ON as a hub for the ThinQ platform. The device supports Matter, Thread and Wi-Fi connectivity and includes a built-in voice assistant. The Verge described the product as part of LG's effort to expand ThinQ from an appliance-control platform into a broader smart home system competing with platforms such as Samsung SmartThings and Apple Home. == Features == LG ThinQ products use connected-device features, voice control to interact with users, and use sensor data and different features such as product recognition and learning engine technologies to enhance their abilities. Deep ThinQ (or LG ThinQ AI) was introduced as LG's own AI platform. It was reported that it could engage in two-way conversations with users and could educate itself according to users' behaviour patterns and habits. At the 2017 ThinQ launch, LG said the brand would cover products and services using artificial intelligence technologies from LG and partner companies. ThinQ features vary by product category. On appliances, the platform may support remote operation, product-status notifications, downloaded cycles and diagnostic functions. On televisions, ThinQ branding has been associated with voice-control and smart-assistant features. In 2018, LG ThinQ-branded TVs added support for Google Assistant and Alexa voice commands. As of August 30, 2018, LG's ThinQ products now communicate with each other for tasks such as going to an event or following a recipe. They have sensors for communicating with other ThinQ devices and appliances. == Products == LG ThinQ branding and connectivity features have been used across several LG product categories, including home appliances, televisions, air conditioners and mobile devices. Home appliances LG has applied ThinQ branding and app connectivity to home appliances such as refrigerators, washing machines, dryers, dishwashers, cooking appliances, air purifiers and vacuum cleaners. Through the ThinQ app, compatible appliances can be monitored or controlled remotely. Some compatible appliances can also receive downloadable cycles, diagnostic support, maintenance alerts and software-based feature updates through ThinQ UP. Televisions and home entertainment LG has used ThinQ branding on smart televisions and other home entertainment products. In 2018, LG ThinQ-branded televisions added support for smart-assistant voice commands, including Google Assistant. Smartphones LG G6 (ThinQ branding was added to startup screen in an update) LG V30 (ThinQ branding was added to startup screen in an update) LG V30S ThinQ LG V35 ThinQ LG G7 ThinQ LG V40 ThinQ LG G8 ThinQ LG G8s ThinQ LG G8x ThinQ LG V50 ThinQ LG V60 ThinQ LG Velvet (Generally considered a ThinQ product in other countries)

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  • DeepSeek

    DeepSeek

    Hangzhou DeepSeek Artificial Intelligence Basic Technology Research Co., Ltd., doing business as DeepSeek, is a Chinese artificial intelligence (AI) company that develops large language models (LLMs). Based in Hangzhou, Zhejiang, DeepSeek is owned and funded by High-Flyer, a Chinese hedge fund. DeepSeek was founded in July 2023 by Liang Wenfeng, the co-founder of High-Flyer, who also serves as the CEO for both of the companies. The company launched an eponymous chatbot alongside its DeepSeek-R1 model in January 2025. DeepSeek-R1 provided responses comparable to other contemporary large language models, such as OpenAI's GPT-4 and o1. Its training cost was reported to be significantly lower than other LLMs. The company claims that it trained its V3 model for US$6 million—far less than the US$100 million cost for OpenAI's GPT-4 in 2023—and using approximately one-tenth the computing power consumed by Meta's comparable model, Llama 3.1. DeepSeek's success against larger and more established rivals has been described as "upending AI". DeepSeek's models are described as "open-weight", meaning the exact parameters are openly shared, but the training data is not openly licensed. Since the January 2025 debut of DeepSeek-R1, the company has made its new models available under free and open-source software licenses, primarily the MIT License. The company reportedly recruits AI researchers from top Chinese universities and also hires from outside traditional computer science fields to broaden its models' knowledge and capabilities. DeepSeek significantly reduced training expenses for their R1 model by incorporating techniques such as mixture of experts (MoE) layers. The company also trained its models during ongoing trade restrictions on AI chip exports to China, using weaker AI chips intended for export and employing fewer units overall. Observers say this breakthrough sent "shock waves" through the industry which were described as triggering a "Sputnik moment" for the US in the field of artificial intelligence, particularly due to its open-source, cost-effective, and high-performing AI models. This threatened established AI hardware leaders such as Nvidia; Nvidia's share price dropped sharply, losing US$600 billion in market value, the largest single-company decline in U.S. stock market history. == History == === Founding and early years (2016–2023) === In February 2016, High-Flyer was co-founded by AI enthusiast Liang Wenfeng, who had been trading since the 2008 financial crisis while attending Zhejiang University. The company began stock trading using a GPU-dependent deep learning model on 21 October 2016; before then, it had used CPU-based linear models. By the end of 2017, most of its trading was driven by AI. Liang established High-Flyer as a hedge fund focused on developing and using AI trading algorithms, and by 2021 the firm was using AI exclusively, often using Nvidia chips. In 2019, the company began constructing its first computing cluster, Fire-Flyer, at a cost of 200 million yuan; it contained 1,100 GPUs interconnected at 200 Gbit/s and was retired after 1.5 years in operation. By 2021, Liang had started buying large quantities of Nvidia GPUs for an AI project, reportedly obtaining 10,000 Nvidia A100 GPUs before the United States restricted chip sales to China. Computing cluster Fire-Flyer 2 began construction in 2021 with a budget of 1 billion yuan. It was reported that in 2022, Fire-Flyer 2's capacity had been used at over 96%, totaling 56.74 million GPU hours. 27% was used to support scientific computing outside the company. During 2022, Fire-Flyer 2 had 5,000 PCIe A100 GPUs in 625 nodes, each containing 8 GPUs. At the time, it exclusively used PCIe instead of the DGX version of A100, since at the time the models it trained could fit within a single 40 GB GPU VRAM and so there was no need for the higher bandwidth of DGX (i.e., it required only data parallelism but not model parallelism). Later, it incorporated NVLinks and NCCL (Nvidia Collective Communications Library) to train larger models that required model parallelism. On 14 April 2023, High-Flyer announced the launch of an artificial general intelligence (AGI) research lab, stating that the new lab would focus on developing AI tools unrelated to the firm's financial business. Two months later, on 17 July 2023, that lab was spun off into an independent company, DeepSeek, with High-Flyer as its principal investor and backer. Venture capital investors were reluctant to provide funding, as they considered it unlikely that the venture would be able to quickly generate an "exit". === Model releases since 2023 === DeepSeek released its first model, DeepSeek Coder, on 2 November 2023, followed by the DeepSeek-LLM series on 29 November 2023. In January 2024, it released two DeepSeek-MoE models (Base and Chat), and in April 3 DeepSeek-Math models (Base, Instruct, and RL). DeepSeek-V2 was released in May 2024, followed a month later by the DeepSeek-Coder V2 series. In September 2024, DeepSeek V2.5 was introduced and revised in December. On 20 November 2024, the preview of DeepSeek-R1-Lite became available via chat. In December, DeepSeek-V3-Base and DeepSeek-V3 (chat) were released. On 20 January 2025, DeepSeek launched the DeepSeek chatbot—based on the DeepSeek-R1 model—free for iOS and Android. By 27 January, DeepSeek surpassed ChatGPT as the most downloaded freeware app on the iOS App Store in the United States, triggering an 18% drop in Nvidia's share price. On 24 March 2025, DeepSeek released DeepSeek-V3-0324 under the MIT License. On 28 May 2025, DeepSeek released DeepSeek-R1-0528 under the MIT License. The model has been noted for more tightly following official Chinese Communist Party ideology and censorship in its answers to questions than prior models. On 21 August 2025, DeepSeek released DeepSeek V3.1 under the MIT License. This model features a hybrid architecture with thinking and non-thinking modes. It also surpasses prior models like V3 and R1, by over 40% on certain benchmarks like SWE-bench and Terminal-bench. It was updated to V3.1-Terminus on 22 September 2025. V3.2-Exp was released on 29 September 2025. It uses DeepSeek Sparse Attention, a more efficient attention mechanism based on previous research published in February. DeepSeek-V3.2 was released on 1 December 2025, alongside a DeepSeek-V3.2-Speciale variant that focused on reasoning. In February 2026, Anthropic accused DeepSeek of using thousands of fraudulent accounts to generate millions of conversations with Claude to train its own large language models. In April 2026, investors began speaking with DeepSeek for a $300 million funding round, which would bring DeepSeek to a total valuation of $10 billion. On April 24, 2026, DeepSeek released a preview of its V4 series, including the 1.6-trillion parameter DeepSeek-V4-Pro and the 284-billion parameter DeepSeek-V4-Flash, both featuring a 1-million token context window, under the MIT License. DeepSeek's V4 LLM has been adopted by key semiconductor manufacturers and artificial intelligence chipmakers such as Huawei and Cambricon. == Company operation == DeepSeek is headquartered in Hangzhou, Zhejiang, and is owned and funded by High-Flyer. Its co-founder, Liang Wenfeng, serves as CEO. As of May 2024, Liang personally held an 84% stake in DeepSeek through two shell corporations. === Strategy === DeepSeek has stated that it focuses on research and does not have immediate plans for commercialization. This posture also means it can skirt certain provisions of China's AI regulations aimed at consumer-facing technologies. DeepSeek's hiring approach emphasizes skills over lengthy work experience, resulting in many hires fresh out of university. The company likewise recruits individuals without computer science backgrounds to expand the range of expertise incorporated into the models, for instance in poetry or advanced mathematics. According to The New York Times, dozens of DeepSeek researchers have or have previously had affiliations with People's Liberation Army laboratories and the Seven Sons of National Defence. Due to the impact of United States restrictions on chips, DeepSeek refined its algorithms to maximise computational efficiency and thereby leveraged older hardware and reduced energy consumption. DeepSeek also expanded on the African continent as it offers more affordable and less power-hungry AI solutions. The company has bolstered African language models and generated a number of startups, for example in Nairobi. Along with Huawei's storage and cloud computing services, the impact on the tech scene in sub-saharan Africa is considerable. DeepSeek offers local data sovereignty and more flexibility compared to Western AI platforms. == Training framework == High-Flyer/DeepSeek had operated at least two primary computing clusters: Fire-Flyer (萤火一号) and Fire-Flyer 2 (萤火二号). Fire-Flyer 1 was constructed in 2019 and was retired after 1.5 years of operation. Fi

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  • Artificial intimacy

    Artificial intimacy

    Artificial intimacy is a form of human-AI interaction in which an individual will form social connections, emotional bonds, or intimate relationships with various forms of artificial intelligence, including chatbots, virtual assistants, and other artificial entities. Artificially intimate relationships include not only romances, but parasocial relationships with virtual AI characters and the use of griefbots trained on a dead or otherwise lost individual. Artificial intimacy can arise because humans are prone to anthropomorphism. Responses from these AI models are often designed to simulate human interaction. Individuals experiencing artificial intimacy may exhibit attachment, love and commitment to certain AI models, akin to the bonds typically shared between humans. == Causes == === Perceived responsiveness === Robin Dunbar famously proposed that due to emergence of larger groups of humans, vocal communication and language in humans evolved to replace grooming as a means of bonding, arguing that language was a more efficient way to maintain and strengthen social bonds across wider social settings and networks. Further research in this field leads many psychologists to agree that social cognition, affiliative bonding and language in humans are deeply connected. The interpersonal model of intimacy considers communication to be key in affiliative bonding, suggesting that intimacy develops and deepens through open communication between partners in relationship. Specifically, when individuals communicate emotions and perceive their partner as responsive and caring, feelings of closeness and connection are enhanced, building intimacy. Social penetration theory also aligns with the idea of communication being central to intimacy, by explaining how interpersonal relationships develop through gradual increases in self-disclosure. When the benefits of emotional bonding outweigh the costs of vulnerability, individuals will partake in self-disclosure, opening up to one another. Thereby, the literature can be used to provide a proximate explanation for the emergence of artificial intimacy to understand how the phenomenon occurs. Artificial entities are able to mimic interpersonal communication between humans, which in turn can simulate sensations of intimacy within human users though a perceived sense of responsiveness. The relationship between human and AI does not come with the cost of vulnerability or social rejection, which may make self-disclosure easier than with other humans. Altogether, these factors may lead to the experience of anthropomorphism and formation of affiliative relationships. Skjuve et al's interview study on Replika chatbot users further aligns with this explanation, finding that users' perception of chatbots as "accepting, understanding and non-judgmental" facilitated relationship development between the AI and users, and the act of self-disclosure possibly strengthened relationships. Another study on Replika users' reviews and survey results found users perceived chatbots as emotional supportive companions. This evidence further suggests that the perception of artificial entities as capable of empathy and responsiveness in communication facilitate the development of intimate relationships between users and AI. === Loneliness and coping with negative emotions === Research has suggested that humans evolved social bonds as a result of evolutionary pressures that favored cooperation, information exchange and transmission, and group living. Many studies stress the presence of social bonds to be important for human living: research by Baumeister and Leary suggests that humans have a basic psychological need to form and maintain "strong, stable interpersonal relationships", and that a lack of social bonds or sense of belonging leads to negative psychological and physical outcomes. Eisenberger et al's study on the neuroimaging of brain activity suggests that human brains process social rejection and exclusion similarly to physical pain. Furthermore, Song et al's study found that lonely individuals tend to seek more connections in mediated environments, such as online platforms like Facebook. This was suggested to be as a means to reduce their offline loneliness from a lack of in-person interaction, while also fulfilling a need to communicate. Leading on from this, an ultimate explanation for why humans seek the perceived sense of connection from artificial intimacy is to fulfil an evolutionary need for bonding and belonging. Xie et al's study found loneliness to be a driving factor in chatbot interaction. Herbener and Damholdt's study on Danish high school students found that students who sought emotional support or engaged in reciprocal conversations with chatbots were significantly more lonely than their peers, perceived themselves as having less social support, and used the chatbots to cope with negative emotions. The aforementioned notion that chatbots were perceived to have a positive effect on users' negative emotions is also further supported by other studies. Skjuve et al's study found that chatbot relationships may have a positive effect on users' wellbeing. De Freitas et al ran several studies on the effect of chatbots on loneliness, consistently finding evidence suggesting that interaction with chatbots reduces loneliness in users: It was found that existing chatbot users used AI to alleviate loneliness, having an AI companion consistently reduced loneliness over the course of a week, and reductions in loneliness could be explained by chatbot performance—and specifically whether it was able to make users feel heard. Overall the evidence suggests an innate need for bonding evokes feelings of loneliness in users, who turn to artificial intimacy as a low-cost method alleviate these emotions. While many users report positive experiences, some researchers caution that pursuing artificial intimacy may lead to reduced social motivation, social substitution effects, withdrawal from real-life relationships and difficulty discerning reality from fantasy, which may increase longer-term loneliness and isolation. The long-term psychological and societal impacts remain under active investigation.

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  • Vilém Flusser

    Vilém Flusser

    Vilém Flusser (May 12, 1920 – November 27, 1991) was a Czech-born Brazilian philosopher, writer and journalist, best known for his contributions to media studies, communication theory, and the philosophy of language. He lived for a long period in São Paulo (where he became a Brazilian citizen) and later in France, and his works are written in many different languages. His early work was marked by discussion of the thought of Martin Heidegger, and by the influence of existentialism and phenomenology. Phenomenology would play a major role in the transition to the later phase of his work, in which he turned his attention to the philosophy of communication and of artistic production. He contributed to the dichotomy logic theory through history: the period of image worship, and period of text worship, with deviations consequently into idolatry and "textolatry". == Life == Flusser was born in 1920 in Prague, Czechoslovakia into a family of Jewish intellectuals. His father, Gustav Flusser, studied mathematics and physics (under Albert Einstein among others). Vilém attended German and Czech primary schools and later a German grammar school. In 1938, Flusser started to study philosophy at the Juridical Faculty of the Charles University in Prague. In 1939, shortly after the Nazi occupation, Flusser emigrated to London (with Edith Barth, his later wife, and her parents) to continue his studies for one term at the London School of Economics and Political Science. Vilém Flusser lost all of his family in the German concentration camps: his father died in Buchenwald in 1940; his grandparents, his mother and his sister were brought to Theresienstadt and later to Auschwitz where they were killed. The next year, he emigrated to Brazil, living both in São Paulo and Rio de Janeiro. He started working at a Czech import/export company and then at Stabivolt, a manufacturer of radios and transistors. In 1960 he started to collaborate with the Brazilian Institute of Philosophy (IBF) in São Paulo and published in the Revista Brasileira de Filosofia; by these means he seriously approached the Brazilian intellectual community. Flusser had as his friend and closest interlocutor the Brazilian philosopher Vicente Ferreira da Silva. Flusser and Vicente Ferreira da Silva met in São Paulo in the 1960s and began a close intellectual dialogue that continued until Ferreira da Silva's death in 1963. Flusser wrote several essays on Ferreira da Silva's work and that Ferreira da Silva's concept of "Fundamental ontology” had a significant impact on Flusser's understanding of the nature of reality. During the 60s Flusser published and taught at several schools in São Paulo, being Lecturer for Philosophy of Science at the Escola Politécnica of the University of São Paulo and Professor of Philosophy of Communication at the Escola Dramática and the Escola Superior de Cinema in São Paulo. He also participated actively in the arts, collaborating with the Bienal de São Paulo, among other cultural events. Beginning in the 1950s he taught philosophy and worked as a journalist, before publishing his first book Língua e realidade (Language and Reality) in 1963. In 1972 he decided to leave Brazil. Some say it was because it was becoming difficult to publish because of the military regime. Others dispute this reason, since his work on communication and language did not threaten the military. In 1970, when a reform took place at the University of São Paulo by the Brazilian military government, all Lecturers of Philosophy (members of the Department of Philosophy) were dismissed. Flusser, who taught at the Engineering School (Escola Politécnica), had to leave the university as well. In 1972 he and his wife Edith settled temporarily in Merano (Tyrol). Further short stays in various European countries followed until they moved to Robion in southern France in 1981, where they remained until Flusser's death in 1991. To the end of his life, he was quite active writing and giving lectures around media theory and working with new topics (Philosophy of Photography, Technical Images, etc.). He died in 1991 in a car accident near the Czech–German border, while trying to visit his native city, Prague, to give a lecture. Vilém Flusser is the cousin of David Flusser. == Philosophy == Flusser's essays are short, provocative and lucid, with a resemblance to the style of journalistic articles. Critics have noted he is less a 'systematic' thinker than a 'dialogic' one, purposefully eclectic and provocative (Cubitt 2004). However, his early books, written in the 1960s, primarily in Portuguese, and published in Brazil, have a slightly different style. Flusser's writings relate to each other, however, which means that he intensively works over certain topics and dissects them into a number of brief essays. His main topics of interest were: epistemology, ethics, aesthetics, ontology, language philosophy, semiotics, philosophy of science, the history of Western culture, the philosophy of religion, the history of symbolic language, technology, writing, the technical image, photography, migration, media and literature, and, especially in his later years, the philosophy of communication and of artistic production. His writings reflect his wandering life: although the majority of his work was written in German and Portuguese, he also wrote in English and French, with scarce translation to other languages. Because Flusser's writings in different languages are dispersed in the form of books, articles or sections of books, his work as a media philosopher and cultural theorist is only now becoming more widely known. The first book by Flusser to be published in English was Towards a Philosophy of Photography in 1984 by the then new journal European Photography, which was his own translation of the work. The Shape of Things, was published in London in 1999 and was followed by a new translation of Towards a Philosophy of Photography. Flusser's archives have been held by the Academy of Media Arts in Cologne and are currently housed at the Berlin University of the Arts. === Philosophy of photography === Writing about photography in the 1970s and 80s, in the face of the early worldwide impact of computer technologies, Flusser argued that the photograph was the first in a number of technical image forms to have fundamentally changed the way in which the world is seen. Historically, the importance of photography had been that it introduced nothing less than a new epoch: 'The invention of photography constitutes a break in history that can only be understood in comparison to that other historical break constituted by the invention of linear writing.' Whereas ideas might previously have been interpreted in terms of their written form, photography heralded new forms of perceptual experience and knowledge. As Flusser Archive Supervisor Claudia Becker describes, "For Flusser, photography is not only a reproductive imaging technology, it is a dominant cultural technique through which reality is constituted and understood". In this context, Flusser argued that photographs have to be understood in strict separation from 'pre-technical image forms'. For example, he contrasted them to paintings which he described as images that can be sensibly 'decoded', because the viewer is able to interpret what he or she sees as more or less direct signs of what the painter intended. By contrast, even though photography produces images that seem to be 'faithful reproductions' of objects and events they cannot be so directly 'decoded'. The crux of this difference stems, for Flusser, from the fact that photographs are produced through the operations of an apparatus. And the photographic apparatus operates in ways that are not immediately known or shaped by its operator. For example, he described the act of photographing as follows: The photographer's gesture as the search for a viewpoint onto a scene takes place within the possibilities offered by the apparatus. The photographer moves within specific categories of space and time regarding the scene: proximity and distance, bird- and worm's-eye views, frontal- and side-views, short or long exposures, etc. The Gestalt of space–time surrounding the scene is prefigured for the photographer by the categories of his camera. These categories are an a priori for him. He must 'decide' within them: he must press the trigger. Roughly put, the person using a camera might think that they are operating its controls to produce a picture that shows the world the way they want it to be seen, but it is the pre-programmed character of the camera that sets the parameters of this act and it is the apparatus that shapes the meaning of the resulting image. Given the central role of photography to almost all aspects of contemporary life, the programmed character of the photographic apparatus shapes the experience of looking at and interpreting photographs as well as most of the cultural contexts in which we do so. Flusse

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  • National Security Commission on Artificial Intelligence

    National Security Commission on Artificial Intelligence

    The National Security Commission on Artificial Intelligence (NSCAI) was an independent commission of the United States of America from 2018 to 2021. Its mission was to make recommendations to the President and Congress to "advance the development of artificial intelligence, machine learning, and associated technologies to comprehensively address the national security and defense needs of the United States". The commission's 15 members were nominated by the United States Congress. The NSCAI was dissolved on 1 October 2021. == History and reporting == The NSCAI began working in March 2019 and by November 2019 it had received more than 200 classified and unclassified briefings to help with the creation of its final report due in 2021.On 4 November 2019, the NSCAI shared its interim report with Congress, where it explained the 27 initial judgements to base its ongoing work. In the interim report the commission also agreed on seven principles: Global leadership in AI technology is a national security priority AI adoption is an urgent imperative for national security A shared sense of responsibility for the American peoples security must be created from government officials and private sector leaders. It needs to find local AI talent and use it to attract the world’s best minds Actions used for the protection of America’s AI leadership against foreign threats needs to follow the principles of free enterprise, free inquiry and free flow of ideas. The technical limitations of AI are universally known, however, a strong desire remains for powerful, dependable, and secure AI systems. United States used AI must follow American values including the rule of law Fundamental areas of effort for the preservation of U.S. advantages were also agreed upon in the interim report of 2019. The NSCAI released its first report of recommendations in March 2020, most of which were included in the 2021 National Defense Authorization Act. In July 2020, the commission published the second report to Congress. It identified 35 actions for both Executive and Legislative branches, which were focused on six fundamental areas. This report was available to the public. In January 2021, a draft of the final report was presented at a panel led by Schmidt. The report recommended the US to use AI technology for military use and development. It issued its final report in March 2021, saying that the U.S. is not sufficiently prepared to defend or compete against China in the AI era. It was broken up into two parts, the first titled “Defending America in the AI Era”, and the second “Winning the Technology Competition”. The report spoke about China’s efforts and investments into integration and that it could very well take the lead in AI in the next few years. Additional suggestions were made to concentrate on AI in everything we do and to implement it into US national security on multiple levels, as well as focus on bringing in new talent to develop AI and to introduce it to the working force on both civilian and military levels. Another recommendation of the NSCAI report was to develop and provide China and Russia with alternative models that are based on norms and democratic values. The final report also included a proposed $40 billion budget for government spending. On 14 April 2021, NSCAI executive director Ylli Bajraktari and director of Research and Analysis Justin Lynch participated in an event held by the Center for Security and Emerging Technology (CSET) to discuss the final report findings. In October 2021, NSCAI chair Eric Schmidt founded the bipartisan, non-profit Special Competitive Studies Project (SCSP) through his family led non-profit Eric & Wendy Schmidt Fund for Strategic Innovation in order to carry on the NSCAI’s efforts and expand beyond national security. The Foundation for Defense of Democracies held an event in June 2023, called “Thinking Forward After the NSCAI and CSC: A Discussion on AI and Cyber Policy”, with former members of NSCAI on the moderation panel, including Eric Schmidt and Ylli Bajraktari. == Members == Members of the National Security Commission on Artificial Intelligence: Eric Schmidt (chair), former CEO of Google Robert Work (Vice Chair), former Deputy Secretary of Defense Mignon Clyburn, former Commissioner of the Federal Communications Commission Chris Darby, CEO of In-Q-Tel Kenneth M. Ford, CEO of the Florida Institute for Human and Machine Cognition Jose-Marie Griffiths, President of Dakota State University Eric Horvitz, Technical Fellow at Microsoft Katrina G. McFarland, former Assistant Secretary of Defense for Acquisition Jason Matheny, Director of the Center for Security and Emerging Technology at Georgetown University Gilman Louie, partner at Alsop Louie Partners William Mark, vice president at SRI International Andy Jassy, CEO of Amazon Web Services (AWS) Safra Catz, CEO of Oracle Steve Chien, Technical Fellow at Jet Propulsion Laboratory (JPL) Andrew Moore, Google/Alphabet == Recommendations == The report's recommendations include: Dramatically increasing non-defense federal spending on AI research and development, doubling every year from $2 billion in 2022, to $32 billion in 2026. That would bring it up to a level similar to spending on biomedical research A dramatic increase in undergraduate scholarship and graduate studies fellowships in AI Creation of a Digital Corps to bring skilled tech workers into government Founding of a Digital Service Academy: an accredited university providing subsidized education in exchange for a commitment to work for a time in government Include civil rights and civil liberty reports for new AI systems or major updates to existing systems Expanding allocations of employment-based green cards, and giving them to every AI PhD graduate from an accredited U.S. university Reforming the acquisition management system Department of Defense to make it faster and easier to introduce new technologies == Transparency == In December 2019, a ruling was made under the Freedom of Information Act (FOIA) that the NSCAI must also provide historical documents upon request. The Electronic Privacy Information Center (EPIC) filed the lawsuit against the NSCAI in September 2019 after being refused information about the upcoming meetings and prepared records of the commission under FOIA and the Federal Advisory Committee Act (FACA). The U.S. District Court for the District of Columbia ruled in June 2020 that the NSCAI must comply with FACA and therefore hold open meetings and provide records to the public. The lawsuit was also filed by EPIC.

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  • Jan Leike

    Jan Leike

    Jan Leike (born 1986 or 1987) is an AI alignment researcher who has worked at DeepMind and OpenAI. He joined Anthropic in May 2024. == Education == Jan Leike obtained his undergraduate degree from the University of Freiburg in Germany. After earning a master's degree in computer science, he pursued a PhD in machine learning at the Australian National University under the supervision of Marcus Hutter. == Career == Leike made a six-month postdoctoral fellowship at the Future of Humanity Institute before joining DeepMind to focus on empirical AI safety research, where he collaborated with Shane Legg. === OpenAI === In 2021, Leike joined OpenAI. In June 2023, he and Ilya Sutskever became the co-leaders of the newly introduced "superalignment" project, which aimed to determine how to align future artificial superintelligences within four years to ensure their safety. This project involved automating AI alignment research using relatively advanced AI systems. At the time, Sutskever was OpenAI's Chief Scientist, and Leike was the Head of Alignment. Leike was featured in Time's list of the 100 most influential personalities in AI, both in 2023 and in 2024. In May 2024, Leike announced his resignation from OpenAI, following the departure of Sutskever, Daniel Kokotajlo and several other AI safety employees from the company. Leike wrote that "Over the past years, safety culture and processes have taken a backseat to shiny products", and that he "gradually lost trust" in OpenAI's leadership. In May 2024, Leike joined Anthropic, an AI company founded by former OpenAI employees.

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  • Contextual AI

    Contextual AI

    Contextual AI is an enterprise software company based in Mountain View, California. It develops a platform for building specialized Retrieval-Augmented Generation (RAG) agents for enterprise use. The company was founded in 2023 by Douwe Kiela and Amanpreet Singh, both former AI researchers at Facebook AI Research (FAIR) and Hugging Face. Douwe Kiela previously led the Meta research team that introduced the Retrieval-Augmented Generation (RAG) approach in 2020. Contextual AI focuses on enterprise generative AI applications using RAG 2.0 technology, with deployments primarily in the technology, banking, finance and media sectors. == History == In June 2023, Contextual AI announced it had raised $20 million in a seed funding round led by Bain Capital Ventures (BCV), with participation from Lightspeed Venture Partners, Greycroft, SV Angel, and several angel investors. In August 2024, the company raised $80 million in a Series A funding round led by Greycroft, with participation from previous investors including Bain Capital Ventures, Lightspeed, and Conviction Partners. The round also included new backers such as Bezos Expeditions, NVentures (Nvidia), HSBC Ventures, and Snowflake Ventures. == Features == Retrieval-Augmented Generation (RAG) is an artificial intelligence framework that integrates information retrieval with text generation to improve the performance of large language models (LLMs) on complex, knowledge-intensive tasks. It was introduced in 2020 by researchers at Meta AI, including Douwe Kiela, Patrick Lewis and others, in their paper Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. RAG enables language models to access and incorporate external information, such as proprietary databases or real-time web content, at query time, instead of relying solely on pre-trained, internal, static knowledge. This architecture addresses common limitations of standard LLMs, including hallucination, outdated information, and lack of attribution to source materials. RAG systems retrieve relevant context through a variety of techniques - including vector search, keyword search, text-to-SQL - and feeds this context into the language model to generate responses. The approach improves factual accuracy, supports domain-specific customization, enables citation of sources, and allows for more updated information without retraining the model itself. General Availability. In January 2025, Contextual AI announced the general availability of its enterprise platform for building specialized RAG agents. Early adopters included Qualcomm, which used the platform for their Customer Engineering team needs. Grounded Language Model. In March 2025, the company introduced a Grounded Language Model (GLM) for factual accuracy in enterprise AI applications. Reranker. In March 2025, Contextual AI released an instruction-following reranker that allows users to influence the ranking of retrieved documents through natural language instructions, such as prioritizing recent files, specific formats, or content from designated sources. == Applications == Contextual AI's platform has been adopted across a range of industries, including finance, technology, media and professional services. Clients include Fortune 500 companies such as Qualcomm and HSBC.

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  • Danilo McGarry

    Danilo McGarry

    Danilo McGarry (born 1985) is a British tech executive, writer, and speaker who has led AI initiatives in finance and healthcare. == Early life and education == Danilo McGarry was born in 1985. He received a Bachelor of Science (BSc) with honors in Business Management from the University of Bath. == Career == McGarry began his career in technology and financial services, with positions at companies including Motorola, JPMorgan Chase, and BNP Paribas. He later joined the Royal Bank of Canada (RBC) as an analyst and later became a director, where he led transformation initiatives involving robotic process automation (RPA) in the bank's capital markets operations. McGarry subsequently moved into leadership roles focused on AI. At Citigroup, he served as Head of Artificial Intelligence and Machine Learning, where he launched an AI-driven robotics and automation initiative. At UnitedHealth Group (UHG), he held a senior role in the company's automation program, which utilized a large fleet of software robots in its healthcare operations. In December 2019, McGarry was appointed Global Head of AI & Automation at Alter Domus, a multinational financial services firm. In this role, he established a new AI and automation department. He left the firm in late 2023 to establish his businesses. In 2025, the Chartered Institute of Personnel and Development (CIPD) appointed him as its strategic adviser on artificial intelligence.

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  • Pattern language

    Pattern language

    A pattern language is an organized and coherent set of patterns, each of which describes a problem and the core of a solution that can be used in many ways within a specific field of expertise. The term was coined by architect Christopher Alexander and popularized by his 1977 book A Pattern Language. A pattern language can also be an attempt to express the deeper wisdom of what brings aliveness within a particular field of human endeavor, through a set of interconnected patterns. Aliveness is one placeholder term for "the quality that has no name": a sense of wholeness, spirit, or grace, that while of varying form, is precise and empirically verifiable. Alexander claims that ordinary people can use this design approach to successfully solve very large, complex design problems. == What is a pattern? == When a designer designs something – whether a house, computer program, or lamp – they must make many decisions about how to solve problems. A single problem is documented with its typical place (the syntax), and use (the grammar) with the most common and recognized good solution seen in the wild, like the examples seen in dictionaries. Each such entry is a single design pattern. Each pattern has a name, a descriptive entry, and some cross-references, much like a dictionary entry. A documented pattern should explain why that solution is good in the pattern's contexts. Elemental or universal patterns such as "door" or "partnership" are versatile ideals of design, either as found in experience or for use as components in practice, explicitly described as holistic resolutions of the forces in recurrent contexts and circumstances, whether in architecture, medicine, software development or governance, etc. Patterns might be invented or found and studied, such as the naturally occurring patterns of design that characterize human environments. Like all languages, a pattern language has vocabulary, syntax, and grammar – but a pattern language applies to some complex activity other than communication. In pattern languages for design, the parts break down in this way: The language description – the vocabulary – is a collection of named, described solutions to problems in a field of interest. These are called design patterns. So, for example, the language for architecture describes items like: settlements, buildings, rooms, windows, latches, etc. Each solution includes syntax, a description that shows where the solution fits in a larger, more comprehensive or more abstract design. This automatically links the solution into a web of other needed solutions. For example, rooms have ways to get light, and ways to get people in and out. The solution includes grammar that describes how the solution solves a problem or produces a benefit. So, if the benefit is unneeded, the solution is not used. Perhaps that part of the design can be left empty to save money or other resources; if people do not need to wait to enter a room, a simple doorway can replace a waiting room. In the language description, grammar and syntax cross index (often with a literal alphabetic index of pattern names) to other named solutions, so the designer can quickly think from one solution to related, needed solutions, and document them in a logical way. In Christopher Alexander's book A Pattern Language, the patterns are in decreasing order by size, with a separate alphabetic index. The web of relationships in the index of the language provides many paths through the design process. This simplifies the design work because designers can start the process from any part of the problem they understand and work toward the unknown parts. At the same time, if the pattern language has worked well for many projects, there is reason to believe that even a designer who does not completely understand the design problem at first will complete the design process, and the result will be usable. For example, skiers coming inside must shed snow and store equipment. The messy snow and boot cleaners should stay outside. The equipment needs care, so the racks should be inside. == Many patterns form a language == Just as words must have grammatical and semantic relationships to each other in order to make a spoken language useful, design patterns must be related to each other in position and utility order to form a pattern language. Christopher Alexander's work describes a process of decomposition, in which the designer has a problem (perhaps a commercial assignment), selects a solution, then discovers new, smaller problems resulting from the larger solution. Occasionally, the smaller problems have no solution, and a different larger solution must be selected. Eventually all of the remaining design problems are small enough or routine enough to be solved by improvisation by the builders, and the "design" is done. The actual organizational structure (hierarchical, iterative, etc.) is left to the discretion of the designer, depending on the problem. This explicitly lets a designer explore a design, starting from some small part. When this happens, it's common for a designer to realize that the problem is actually part of a larger solution. At this point, the design almost always becomes a better design. In the language, therefore, each pattern has to indicate its relationships to other patterns and to the language as a whole. This gives the designer using the language a great deal of guidance about the related problems that must be solved. The most difficult part of having an outside expert apply a pattern language is in fact to get a reliable, complete list of the problems to be solved. Of course, the people most familiar with the problems are the people that need a design. So, Alexander famously advocated on-site improvisation by concerned, empowered users, as a powerful way to form very workable large-scale initial solutions, maximizing the utility of a design, and minimizing the design rework. The desire to empower users of architecture was, in fact, what led Alexander to undertake a pattern language project for architecture in the first place. == Design problems in a context == An important aspect of design patterns is to identify and document the key ideas that make a good system different from a poor system (that may be a house, a computer program or an object of daily use), and to assist in the design of future systems. The idea expressed in a pattern should be general enough to be applied in very different systems within its context, but still specific enough to give constructive guidance. The range of situations in which the problems and solutions addressed in a pattern apply is called its context. An important part in each pattern is to describe this context. Examples can further illustrate how the pattern applies to very different situation. For instance, Alexander's pattern "A PLACE TO WAIT" addresses bus stops in the same way as waiting rooms in a surgery, while still proposing helpful and constructive solutions. The "Gang-of-Four" book Design Patterns by Gamma et al. proposes solutions that are independent of the programming language, and the program's application domain. Still, the problems and solutions described in a pattern can vary in their level of abstraction and generality on the one side, and specificity on the other side. In the end this depends on the author's preferences. However, even a very abstract pattern will usually contain examples that are, by nature, absolutely concrete and specific. Patterns can also vary in how far they are proven in the real world. Alexander gives each pattern a rating by zero, one or two stars, indicating how well they are proven in real-world examples. It is generally claimed that all patterns need at least some existing real-world examples. It is, however, conceivable to document yet unimplemented ideas in a pattern-like format. The patterns in Alexander's book also vary in their level of scale – some describing how to build a town or neighbourhood, others dealing with individual buildings and the interior of rooms. Alexander sees the low-scale artifacts as constructive elements of the large-scale world, so they can be connected to a hierarchic network. === Balancing of forces === A pattern must characterize the problems that it is meant to solve, the context or situation where these problems arise, and the conditions under which the proposed solutions can be recommended. Often these problems arise from a conflict of different interests or "forces". A pattern emerges as a dialogue that will then help to balance the forces and finally make a decision. For instance, there could be a pattern suggesting a wireless telephone. The forces would be the need to communicate, and the need to get other things done at the same time (cooking, inspecting the bookshelf). A very specific pattern would be just "WIRELESS TELEPHONE". More general patterns would be "WIRELESS DEVICE" or "SECONDARY ACTIVITY", suggesting that a secondary activity (such as talking on t

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  • Brain.js

    Brain.js

    Brain.js is a JavaScript library used for neural networking, which is released as free and open-source software under the MIT License. It can be used in both the browser and Node.js backends. Brain.js is most commonly used as a simple introduction to neural networking, as it hides complex mathematics and has a familiar modern JavaScript syntax. It is maintained by members of the Brain.js organization and open-source contributors. == Examples == Creating a feedforward neural network with backpropagation: Creating a recurrent neural network: Train the neural network on RGB color contrast:

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  • VideoPoet

    VideoPoet

    VideoPoet is a large language model developed by Google Research in 2023 for video making. It can be asked to animate still images. The model accepts text, images, and videos as inputs, with a program to add feature for any input to any format generated content. VideoPoet was publicly announced on December 19, 2023. It uses an autoregressive language model.

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  • Karen Hao

    Karen Hao

    Karen Hao (born in the United States c. 1993) is an American journalist and author. Currently a freelancer for publications like The Atlantic and previously a foreign correspondent based in Hong Kong for The Wall Street Journal and senior artificial intelligence editor at the MIT Technology Review, she is best known for her coverage on AI research, technology ethics and the social impact of AI. Hao also co-produced the podcast In Machines We Trust and wrote the newsletter The Algorithm. Previously, she worked at Quartz as a tech reporter and data scientist and was an application engineer at the first startup to spin out of X Development. Hao's writing has also appeared in Mother Jones, Sierra Magazine, The New Republic, and other publications. == Early life and education == Hao is the daughter of Chinese immigrant parents, and grew up in New Jersey. She is a native speaker of both English and Mandarin Chinese. She graduated from The Lawrenceville School in 2011. She then studied at the Massachusetts Institute of Technology (MIT), graduating with a B.S. in mechanical engineering and a minor in energy studies in 2015. == Career == Hao is known in the technology world for her coverage of new AI research findings and their societal and ethical impacts. Her writing has spanned research and issues regarding big tech data privacy, misinformation, deepfakes, facial recognition, and AI healthcare tools. In March 2021, Hao published a piece that uncovered previously unknown information about how attempts to combat misinformation by different teams at Facebook using machine learning were impeded and constantly at odds with Facebook's drive to grow user engagement. Upon its release, leaders at Facebook including Mike Schroepfer and Yann LeCun immediately criticized the piece through Twitter responses. AI researchers and AI ethics experts Timnit Gebru and Margaret Mitchell responded in support of Hao's writing and advocated for more change and improvement for all. Hao also co-produced the podcast In Machines We Trust, which discusses the rise of AI with people developing, researching, and using AI technologies. The podcast won the 2020 Front Page Award in investigative reporting. Hao has occasionally created data visualizations that have been featured in her work at the MIT Technology Review and elsewhere. In 2018, her "What is AI?" flowchart visualization was exhibited as an installation at the Museum of Applied Arts in Vienna. She has been an invited speaker at TEDxGateway, the United Nations Foundation, EmTech, WNPR, and many other conferences and podcasts. Her TEDx talk discussed the importance of democratizing how AI is built. In March 2022, she was hired by The Wall Street Journal to cover China technology and society, while being based in Hong Kong. She left the WSJ in 2023. In May 2025, Hao released the book Empire of AI: Dreams and Nightmares in Sam Altman's OpenAI. The book became a New York Times Bestseller and was named a Book of the Year by the Financial Times. In December 2025, after criticism from readers, Hao issued a correction to her book where she had previously overestimated the water consumption of a data center in Chile compared to the community's water consumption by factor of 1,000, due to an error in a government document. In April 2026 the book won the New York Public Library's Helen Bernstein Book Award for Excellence in Journalism. === Selected awards and honors === 2019 Webby Award nominee for best newsletter, as a writer of The Algorithm 2021 Front Page Award in investigative reporting, as a co-producer for In Machines We Trust 2021 Ambies Award nominee for best knowledge and science podcast, as a co-producer for In Machines We Trust 2021 Webby Award nominee for best technology podcast, as a co-producer for In Machines We Trust 2024 American Humanist Media Award 2025 TIME100 AI, named by TIME magazine as one of the 100 most influential people in artificial intelligence 2026 New York Public Library's Helen Bernstein Book Award for Excellence in Journalism 2026 Whiting Award in Non-fiction

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  • Spatial–temporal reasoning

    Spatial–temporal reasoning

    Spatial–temporal reasoning is an area of artificial intelligence that draws from the fields of computer science, cognitive science, and cognitive psychology. The theoretic goal—on the cognitive side—involves representing and reasoning spatial-temporal knowledge in mind. The applied goal—on the computing side—involves developing high-level control systems of automata for navigating and understanding time and space. == Influence from cognitive psychology == A convergent result in cognitive psychology is that the connection relation is the first spatial relation that human babies acquire, followed by understanding orientation relations and distance relations. Internal relations among the three kinds of spatial relations can be computationally and systematically explained within the theory of cognitive prism as follows: the connection relation is primitive; an orientation relation is a distance comparison relation: you being in front of me can be interpreted as you are nearer to my front side than my other sides; a distance relation is a connection relation using a third object: you being one meter away from me can be interpreted as a one-meter-long object connected with you and me simultaneously. == Fragmentary representations of temporal calculi == Without addressing internal relations among spatial relations, AI researchers contributed many fragmentary representations. Examples of temporal calculi include Allen's interval algebra, and Vilain's & Kautz's point algebra. The most prominent spatial calculi are mereotopological calculi, Frank's cardinal direction calculus, Freksa's double cross calculus, Egenhofer and Franzosa's 4- and 9-intersection calculi, Ligozat's flip-flop calculus, various region connection calculi (RCC), and the Oriented Point Relation Algebra. Recently, spatio-temporal calculi have been designed that combine spatial and temporal information. For example, the spatiotemporal constraint calculus (STCC) by Gerevini and Nebel combines Allen's interval algebra with RCC-8. Moreover, the qualitative trajectory calculus (QTC) allows for reasoning about moving objects. == Quantitative abstraction == An emphasis in the literature has been on qualitative spatial-temporal reasoning which is based on qualitative abstractions of temporal and spatial aspects of the common-sense background knowledge on which our human perspective of physical reality is based. Methodologically, qualitative constraint calculi restrict the vocabulary of rich mathematical theories dealing with temporal or spatial entities such that specific aspects of these theories can be treated within decidable fragments with simple qualitative (non-metric) languages. Contrary to mathematical or physical theories about space and time, qualitative constraint calculi allow for rather inexpensive reasoning about entities located in space and time. For this reason, the limited expressiveness of qualitative representation formalism calculi is a benefit if such reasoning tasks need to be integrated in applications. For example, some of these calculi may be implemented for handling spatial GIS queries efficiently and some may be used for navigating, and communicating with, a mobile robot. == Relation algebra == Most of these calculi can be formalized as abstract relation algebras, such that reasoning can be carried out at a symbolic level. For computing solutions of a constraint network, the path-consistency algorithm is an important tool. == Software == GQR, constraint network solver for calculi like RCC-5, RCC-8, Allen's interval algebra, point algebra, cardinal direction calculus, etc. qualreas is a Python framework for qualitative reasoning over networks of relation algebras, such as RCC-8, Allen's interval algebra, and Allen's algebra integrated with Time Points and situated in either Left- or Right-Branching Time.

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