AI Detection Remover

AI Detection Remover — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

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  Human-centered AI

  Human-centered AI is the initiative at the intersection of the fields of artificial intelligence (AI) and human-computer interaction (HCI) to develop AI systems in a way that prioritizes human values, needs, and general flourishing. Emphasis is placed on the recognition that artificial intelligence systems are rapidly changing, and will continue to influence, many aspects of the human experience, in areas ranging from scientific inquiry, governance and policy, labor and the economy, and creative expression, with an aim set to adapt current developments and guide future developments on a trajectory which is most beneficial to the human population at large, with the goal of augmenting human intelligence and capacities across these areas, as opposed to replacing them. Particular attention is paid to mitigating negative effects of AI automation on the livelihoods of the labor force, the use of AI in healthcare fields, and imbuing AI systems with societal values. Human-centered AI is linked to related endeavors in AI alignment and AI safety, but while these fields primarily focus on mitigating risks posed by AI that is unaligned to human values and/or uncontrollable AI self-development, human-centered AI places significant focus in exploring how AI systems can augment human capacities and serve as collaborators. == Conceptual history == The importance of the alignment of artificial intelligence development towards human values in some sense predates artificial intelligence itself, as before the modern conception of artificial intelligence as coined at the 1956 Dartmouth Workshop, the conception of robots as constructed, autonomous agents entered the cultural consciousness as early as the 1920s, with Karel Capek's Rossum's Universal Robots. The imagined issues relating to robots' aims and values requiring intentional alignment and direction with those of humans followed soon after, most widely known from science fiction author Isaac Asimov’s Three Laws of Robotics, dating to his 1942 short story “Runaround”. Two of the three eponymous laws are directly concerned with robots’ interaction with and positioned deference towards humans, and have in recent times been reexamined in the face of modern AI. In 1985, after artificial intelligence research had taken off and its effects were more acutely conceptualized, Asimov added a Rule Zero, treating robots' relationship with humanity as a whole, distinct from individual humans. While modern artificial intelligence is largely distinct from robotics, the conceptualization of both robots and AI systems as autonomous agents positions this as a foundation for conceptions of human-centered AI. Aside from robots, artificially intelligent autonomous agents in interaction with humans have been conceived of for at least 75 years. In 1950, Alan Turing published his famous "Imitation Game", often also called the Turing Test, a thought experiment that uses human-machine interaction as an assessor for the intelligence of a system. In recent times, artificial intelligence researchers such as Stanford's Erik Brynjolfsson have conceived of rapid AI development leading to a so-called "Turing Trap". == Augmentation and automation == A major stated aim of human-centered AI is to promote the development of AI in ways that augment human capabilities, rather than replacing them. To this end, organizations and initiatives that take a human-centered approach to AI development focus on frameworks that encourage collaboration between humans and artificial intelligence systems to build towards even greater progress, rather than attempting to automate tasks currently handled by humans. Such avenues include everything from data visualization for big data, allowing human engineers to better understand extremely large datasets, allowing for the design of better machine learning models to handle them, to AI-powered sensors to monitor vitals, allowing for better responsiveness from healthcare providers. Many human-centered AI initiatives often position it as a better alternative to the apparent mainstream in AI development, which is primarily concerned with automation. Driven by the pressures of the market economy, AI development that does replace tasks currently performed by humans with automated processes is incentivized, as it allows for greater profit margins; this often comes at the detriment of the human whose performance is replaced, thus leading to an environment wherein human workers are outcompeted by AI systems across various service-sector and technology-based industries. At the same time, automation and augmentation are not always incompatible; a major aim of human-centered AI is towards the automation of rote tasks that would otherwise hinder a human’s productivity or creativity, freeing them to direct their energy and intelligence towards higher-level tasks, thus achieving augmentation through automation. Empirical research in pharmaceutical sales has shown that a human-centered implementation - where work procedures, training, and incentives are designed around individuals' cognitive needs - improves augmentation performance, while implementation without such adaptation can worsen outcomes relative to a legacy system. == Research == Much of the work done on human-centered AI comes from research institutes, within universities, companies, and as freestanding organizations. The Stanford Institute for Human-Centered AI (abbreviated to HAI) is one such group, engaging academics, industry professionals, and policymakers centered in Stanford University to conduct research and inform policy in various areas in human-centered AI, including on aspects of the intelligence itself, augmentation, and on measuring the impacts of AI systems on sociopolitcal and cultural institutions. Similar groups exist at other universities, including the Chicago Human + AI (CHAI) Lab at the University of Chicago, the HCAI@GU group at the University of Gothenburg, and the Human-Centered AI (HAI) Lab at the University of Oxford. Outside of the academy, companies such as IBM have research initiatives dedicated to advancements in human-centered AI. At Kenyon College, the Integrated Program for Humane Studies (IPHS) launched a human-centered AI program in 2016 integrating artificial intelligence research with humanities and social science inquiry. This approach treats computation and humanistic scholarship as a single unified field of research rather than as separate disciplines requiring collaboration. The program's researchers have published in both AI venues (such as the International Conference on Machine Learning and Frontiers of Computer Science) and humanities journals (such as PMLA and Poetics Today), and the lab was selected in December 2025 by Schmidt Sciences for its Humanities and AI Virtual Institute to apply AI methods to cultural heritage preservation.

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  Asian Digital Finance Forum & Awards

  Asian Digital Finance Forum & Awards (also known as Asian Digital Finance Forum and Awards) is a forum and honorary awards platform convened in Colombo, Sri Lanka. It has been hosted in a hybrid format (virtual and in-person), with editions reported in 2022, 2023 and 2025. The event is organised by the Asian FinTech Academy (AFTA) in collaboration with a number of local and international institutions. == Overview == The forum has featured international academic, industry, and policy speakers and has recognised institutions and individuals for contributions related to digital finance and fintech innovation. Media coverage has described participation and recognition at the forum as spanning multiple regions, with institutions and individuals from South Asia, Southeast Asia, East Asia, the Middle East, Europe, and North America featured across different editions. == Awards and recognition == The forum and awards were held in a hybrid format with virtual and in-person proceedings at Hilton Colombo in the 2022 and 2023 editions. The Asian Digital Finance Forum & Awards presents honorary recognitions to institutions and individuals for contributions to digital finance, financial inclusion, and related regulatory, technological, and policy developments. Media coverage has described the recognitions as non-competitive and based on demonstrated leadership and impact rather than open nominations. In 2025, the forum and awards served as an anchor initiative associated with the Asia International Digital Economy & AI in Finance Summit at Port City Colombo, with an emphasis on artificial intelligence in finance, financial inclusion, and governance-related themes. === 2022 === According to reporting by Daily FT, institutions recognised at the 2022 edition included Sri Lanka’s Bank of Ceylon, Commercial Bank of Ceylon, Hatton National Bank, and People’s Bank, alongside international organisations and fintech-sector contributors. === 2023 === Coverage of the 2023 forum described recognitions awarded to India’s International Financial Services Centres Authority (IFSCA) for regulatory innovation, as well as to digital finance and payments platforms including Dialog Genie and SLT-Mobitel mCash. IDEMIA’s Asia–Pacific operations were also recognised for contributions related to biometric and digital identity technologies in financial services. === 2025 === For the 2025 edition, institutional honourees reported in the media included Nium (Singapore), recognised for cross-border payments optimisation, and Paytm (India), recognised for AI-powered financial inclusion initiatives. A Visionary Award for Next-Generation Financial Hub Development was presented to Port City Colombo in recognition of its fintech- and AI-oriented development strategy. Individual honourees reported for 2025 included Sopnendu Mohanty (Singapore), Neil Tan (Hong Kong), Purvi Munot (United Arab Emirates), and Amira Abdelaziz (Egypt), recognised for contributions spanning fintech governance, ecosystem development, inclusive wealth technology, and AI-driven financial policy and regulation. In 2025, media reports described the awards as being subject to an independent validation framework. The process was led by Dr. Sivaguru S. Sritharan, appointed as Global Validation Chair, and involved independent research, analytical review, and benchmarking against international standards, with recognitions characterised as honorary and non-competitive.

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  Whisper (speech recognition system)

  Whisper is a machine learning model for speech recognition and transcription, created by OpenAI and first released as open-source software in September 2022. It is capable of transcribing speech in English and multiple other languages, and can translate several non-English languages into English. Whisper is a weakly-supervised deep learning acoustic model, made using an encoder-decoder transformer architecture. OpenAI claims that the combination of different training data and post-training filtering used in its development has led to improved recognition of accents, background noise, and jargon compared to previous approaches. While the model does not outperform larger, more specialized models and still experiences AI hallucination, it has been showed to be useful for general sound recognition and has many applications across different industries. == Background == Speech recognition has had a long history in research; the first approaches made use of statistical methods, such as dynamic time warping, and later hidden Markov models. At around the 2010s, deep neural network approaches became more common for speech recognition models, which were enabled by the availability of large datasets ("big data") and increased computational performance. Early approaches to deep learning in speech recognition included convolutional neural networks, which were limited due to their inability to capture sequential data, which later led to developments of Seq2seq approaches, which include recurrent neural networks, which made use of long short-term memory. Transformers, introduced in 2017 by Google, displaced many prior state-of-the-art approaches across a wide range in machine learning, and started becoming the core neural architecture in fields such as language modeling and computer vision. Weakly-supervised approaches to training acoustic models were recognized in the early 2020s as promising for speech recognition approaches using deep neural networks. According to a NYT report, in 2021 OpenAI believed they exhausted sources of higher-quality data to train their large language models and decided to complement scraped web text with transcriptions of YouTube videos and podcasts, and developed Whisper to solve this task. Whisper Large V2 was released on December 8, 2022, followed by Whisper Large V3 being released in November 2023, during the OpenAI Dev Day. In March 2025, OpenAI released new transcription models based on GPT-4o and GPT-4o mini, both of which have lower error rates than Whisper. == Architecture == The Whisper architecture is based on an encoder-decoder transformer. Input audio is resampled to 16,000 Hertz (Hz) and converted to an 80-channel Log-magnitude Mel spectrogram using 25 ms windows with a 10 ms stride. The spectrogram is then normalized to a [-1, 1] range with near-zero mean. The encoder takes this Mel spectrogram as input and processes it. It first passes through two convolutional layers. Sinusoidal positional embeddings are added. It is then processed by a series of Transformer encoder blocks (with pre-activation residual connections). The encoder's output is layer normalized. The decoder is a standard transformer decoder. It has the same width and Transformer blocks as the encoder. It uses learned positional embeddings and tied input-output token representations (using the same weight matrix for both the input and output embeddings). It uses a byte-pair encoding tokenizer, of the same kind as used in GPT-2. English-only models use the GPT-2 vocabulary, while multilingual models employ a re-trained multilingual vocabulary with the same number of words. Special tokens are used to allow the decoder to perform multiple tasks: Tokens that denote language (one unique token per language). Tokens that specify task (<|transcribe|> or <|translate|>). Tokens that specify if no timestamps are present (<|notimestamps|>). If the token is not present, then the decoder predicts timestamps relative to the segment, and quantized to 20 ms intervals. <|nospeech|> for voice activity detection. <|startoftranscript|>, and <|endoftranscript|> . Any text that appears before <|startoftranscript|> is not generated by the decoder, but given to the decoder as context. Loss is only computed over non-contextual parts of the sequence, i.e. tokens between these two special tokens. == Training data == The training dataset consists of 680,000 hours of labeled audio-transcript pairs sourced from the internet using semi-supervised learning. This includes 117,000 hours in 96 non-English languages and 125,000 hours of X→English translation data, where X stands for any non-English language. Preprocessing involved standardization of transcripts, filtering to remove machine-generated transcripts using heuristics (e.g., punctuation, capitalization), language identification and matching with transcripts, fuzzy deduplication, and deduplication with evaluation datasets to avoid data contamination. Speechless segments were also included to allow voice activity detection training. For the files still remaining after the filtering process, audio files were then broken into 30-second segments paired with the subset of the transcript that occurs within that time. If this predicted spoken language differed from the language of the text transcript associated with the audio, that audio-transcript pair was not used for training the speech recognition models, but instead for training translation. The model was trained using the AdamW optimizer with gradient norm clipping and a linear learning rate decay with warmup, with batch size 256 segments. Training proceeded for 1 million updates (approximately 2-3 epochs). No data augmentation or regularization, except for the Large V2 model, which used SpecAugment, Stochastic Depth, and BPE Dropout. The training used data parallelism with float16, dynamic loss scaling, and activation checkpointing. === Post-training filtering === After training the first model, researchers ran it on different subsets of the training data, each representing a distinct source. Data sources were ranked by a combination of their error rate and size. Manual inspection of the top-ranked sources (high error, large size) helped determine if the source was low quality (e.g., partial transcriptions, inaccurate alignment). After training, it was fine-tuned to suppress the prediction of speaker names and low-quality sources were then removed. == Capacity == While Whisper does not outperform models which specialize in the LibriSpeech dataset, when tested across many datasets, it is more robust and makes 55.2% fewer errors than other models. Whisper has a differing error rate with respect to transcribing different languages, with a higher word error rate in languages not well-represented in the training data. The authors found that multi-task learning improved overall performance compared to models specialized to one task. They conjectured that the best Whisper model trained is still underfitting the dataset, and larger models and longer training can result in better models. Third-party evaluations have found varying levels of AI hallucination. A study of transcripts of public meetings found hallucinations in eight out of every 10 transcripts, while an engineer discovered hallucinations in "about half" of 100 hours of transcriptions and a developer identified them in "nearly every one" of 26,000 transcripts. A study of 13,140 short audio segments (averaging 10 seconds) found 187 hallucinations (1.4%), 38% of which generated text that could be harmful because it inserted false references to things like race, non-existent medications, or violent events that were not in the audio. == Applications == The model has been used as the base for many applications, such as a unified model for speech recognition and more general sound recognition. Whisper has also been integrated into the workflow of biomedical research. In 2025, a study on Alzheimer's disease detection used the model to transcribe spontaneous speech recordings. The transcripts that were generated by the model were combined with LLM vector embeddings and traditional classifiers to help classify the patients' health. Another application is when OVALYTICS incorporated Whisper to transcribe YouTube videos and automate content moderation systems, which improved its detection of offensive content. The model has also been used in academic libraries and cultral heritage institutions to generate transcripts and captions for their digitized audiovisual collections. In a 2025 case study, Emory University Libraries found that Whisper reduced the labor used in transcription by around 30-35%, shifting work from text creation to text correction. However, human review is still necessary to make sure accuracy, formatting, and accessibility are all standard.

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  Generative literature

  Generative literature is poetry or fiction that is automatically generated, often using computers. It is a genre of electronic literature, and also related to generative art. John Clark's Latin Verse Machine (1830–1843) is probably the first example of mechanised generative literature, while Christopher Strachey's love letter generator (1952) is the first digital example. With the large language models (LLMs) of the 2020s, generative literature is becoming increasingly common. == Definitions == Hannes Bajohr defines generative literature as literature involving "the automatic production of text according to predetermined parameters, usually following a combinatory, sometimes aleatory logic, and it emphasizes the production rather than the reception of the work (unlike, say, hypertext)." In his book Electronic Literature, Scott Rettberg connects generative literature to avant-garde literary movements like Dada, Surrealism, Oulipo and Fluxus. Bajohr argues that conceptual art is also an important reference. == Paradigms of generative literature == Bajohr describes two main paradigms of generative literature: the sequential paradigm, where the text generation is "executed as a sequence of rule-steps" and employs linear algorithms, and the connectionist paradigm, which is based on neural nets. The latter leads to what Bajohr calls a algorithmic empathy: "a non-anthropocentric empathy aimed not at the psychological states of the artists but at understanding the process of the work’s material production." == Poetry generation == The first examples of automated generative literature are poetry: John Clark's mechanical Latin Verse Machine (1830–1843) produced lines of hexameter verse in Latin, and Christopher Strachey's love letter generator (1952), programmed on the Manchester Mark 1 computer, generated short, satirical love letters. Examples of generative poetry using artificial neural networks include David Jhave Johnston's ReRites. == Narrative generation == Story generators have often followed specific narratological theories of how stories are constructed. An early example is Grimes' Fairy Tales, the "first to take a grammar-based approach and the first to operationalize Propp's famous model." Mike Sharples and Rafael Peréz y Peréz's book Story Machines gives a detailed history of story generation. Storyland by Nanette Wylde is an example of generative narrative. Jonathan Baillehache compares Storyland to Surrealist writing. Baillehache states, "When compared to earlier uses of chance operation in literature, a piece like this one resembles some of the automatic writings produced by André Breton and Philippe Soupault in their collective work The Magnetic Fields. . . The difference between Nanette Wylde’s Storyland and Breton and Soupault’s Magnetic Fields is that the former is produced according to a computational algorithm involving randomizers and user interaction, and the latter by two free-wheeling human subjects."

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  List of data science software

  This is a list of data science software and platforms used in data science, which includes programming languages, programming environments, machine learning frameworks, data engineering tools, statistical software, data analysis, plotting, MLOps systems, and more. == Programming languages == == Development environments == These interactive notebooks, IDEs, and platforms provide specialised development environments. Apache Zeppelin Architect — Eclipse (software) CoCalc Dataiku Data Science Studio FreeMat GNU Octave Google Colab DataSpell Jupyter Notebook / JupyterLab Kaggle Notebooks MATLAB O-Matrix PyCharm RStudio SAS (software) and SAS Studio Spyder Visual Studio Code == Machine and deep learning software == The Machine learning / deep learning tools support development in those fields. == Data engineering == Examples of Data engineering tools. Apache Airflow Apache Flink Apache Hadoop Apache Kafka Apache NiFi Apache Spark Dask Data build tool (dbt) == Data mining == Examples of Data mining tools. === Free and open-source === === Proprietary === == Database management == === List of RDBMS === ==== Proprietary ==== == Data warehouses == Data warehouse environments include: Amazon Redshift Snowflake Google BigQuery Microsoft Azure Synapse Teradata Vertica == Data lakes == Data lake environments include: Apache Hadoop Cloudera Databricks Delta Lake Amazon S3 Google Cloud Storage Azure Data Lake == Algorithms == Apriori algorithm – frequent itemset mining and association rule learning in market basket analysis Backpropagation – algorithm for training artificial neural networks using gradient descent Decision Trees – tree-based algorithm for classification and regression Expectation–maximization algorithm – iterative procedure for maximum likelihood estimation with latent variables Gradient descent – iterative optimization algorithm for minimizing a loss function ID3 algorithm – used to generate a decision tree from a dataset K-Means – clustering algorithm based on minimizing within-cluster distances K-Nearest Neighbors (KNN) – instance-based learning and classification method Linear regression – estimation method for predicting a dependent variable based on independent variables Logistic regression – classification algorithm for predicting a binary outcome Naive Bayes – probabilistic classifier based on Bayes' theorem Ordinary least squares – estimation method for parameters in linear regression PageRank – graph-based algorithm for link analysis and search ranking Principal component analysis – technique to reduce high-dimensional data while preserving variance Q-learning – reinforcement learning algorithm for learning optimal actions Random forest – ensemble of decision trees for improved classification or regression Sequential minimal optimization – solver for training support vector machines Stochastic gradient descent – randomized variant of gradient descent for large-scale machine learning Support Vector Machines (SVM) – algorithm for finding a hyperplane to separate classes == Statistical software == === Open-source === === Public domain === CSPro Dataplot Epi Map X-13ARIMA-SEATS === Freeware === BV4.1 MINUIT WinBUGS Winpepi === Proprietary === == Data processing == Tools for Data processing and analysis: == Data and information visualization == Software for Data visualization: == Plotting software == Software for plotting data to support processing and visualise results. == Maps and geospatial visualization == ArcGIS Carto Epi Map GeoDA Google Earth Engine Leaflet Mapbox MountainsMap QGIS == Machine learning == MLOps and model deployment: BentoML Data Version Control (DVC) Kubeflow MLflow Seldon Core Streamlit TensorFlow Serving Weights & Biases == Data repositories == Kaggle – platform for data science competitions, datasets, and notebooks. OpenML – collaborative platform for sharing datasets, algorithms, and experiments. University of California, Irvine Machine Learning Repository Zenodo – open-access repository supported by CERN and the EU. == Educational data science software == Kaggle – online platform for data science education, competitions, datasets, and collaborative learning. KNIME – open-source data analytics platform used for teaching data science, machine learning, and workflow-based analysis. RapidMiner – used in academic research and education for data mining and machine learning. Statistics Online Computational Resource (SOCR) – online tools and instructional resources for statistics education. Tanagra (machine learning) – data mining software developed for research and teaching purposes. TinkerPlots – explore and analyze data through visual modeling.

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  Gundam Build Metaverse

  Gundam Build Metaverse (Japanese: ガンダムビルドメタバース, Hepburn: Gandamu Birudo Metabāzu) is a Japanese original net animation anime mini-series produced by Sunrise Beyond, and the fifth series within the Gundam Build Series sub-series. The series celebrates the 10th anniversary of the Gundam Build franchise, including characters from the previous installments. == Plot == The story is set in the same universe of the Gundam Build series in an online metaverse space where users can use avatars to move around and interact with other users, including conducting Gunpla (Gundam plastic model) battles with them. The story centers on Rio Hōjō, a boy who lives in Hawaii, and who learns how to build Gunpla from a local hobbyist named Seria Urutsuki. In the metaverse, a figure known as Mask Lady teaches him the art of Gunpla battling, and he strives to get better at it every day. With his custom Lah Gundam, he seeks out ever stronger opponents. == Characters == === Main characters === Rio Hojo (ホウジョウ・リオ, Hōjō Rio) Voiced by: Chika Anzai A young boy from Hawaii who is an enthusiast of Gunpla Battle and is an apprentice of the mysterious Diver "Mask Lady". Rio's Gunpla is the Lah Gundam, modeled after an entry-grade RX-78-2 Gundam, from the original Mobile Suit Gundam anime series. Seria Urutsuki (ウルツキ・セリア, Urutsuki Seria) / Mask Lady (マスクレディー, Masuku Reidi) Voiced by: Rio Tsuchiya A clerk at a local hobby shop and the instructor at their Gunpla class, Seria becomes Rio's Gunpla mentor using the alias "Mask Lady". Seria's Gunpla is the ZGMF-X20A-PF Gundam Perfect Strike Freedom Rouge, based on both the MBF-02 Strike Rouge and the GAT-X105+AQM/E-YM1 Perfect Strike Gundam from Mobile Suit Gundam Seed and the ZGMF-X20A Strike Freedom Gundam from Mobile Suit Gundam Seed Destiny. === Returning characters === Fumina Hoshino (ホシノ・フミナ, Hoshino Fumina) Voiced by: Yui Makino A veteran Gunpla Battler from the early days of the sport and the Leader of "Team Try Fighters", she works as an advertiser and announcer within the Metaverse realm. Tatsuya Yuuki (ユウキ・タツヤ, Yūki Tatsuya) / Meijin Kawaguchi III (三代目メイジン・カワグチ, Sandaime Meijin Kawaguchi) Voiced by: Takuya Satō A builder and three-times Gunpla Battle world champion who inherited the name of the legendary Meijin Kawaguchi, known as "Meijin Kawaguchi III", and still the current title holder. His newest Gunpla is the Gundam Amazing Barbatos Lupus based on the ASW-G-08 Gundam Barbatos Lupus from Mobile Suit Gundam: Iron-Blooded Orphans. Riku Mikami (ミカミ・リク, Mikami Riku) / Riku (リク) Voiced by: Yūsuke Kobayashi The Founder and former leader of the legendary force, "Build Divers". His Gunpla is the Gundam 00 Diver Arc, the latest version of the original GN-0000DVR Gundam 00 Diver from Gundam Build Divers, incorporating elements from the 00 Gundam from Mobile Suit Gundam 00 and the Gundam AGE-FX from Mobile Suit Gundam AGE. Sarah (サラ, Sara) Voiced by: Haruka Terui An EL-Diver and member of the Build Divers. Momoka Yashiro (ヤシロ・モモカ, Yashiro Momoka) / Momo (モモ) Voiced by: Nene Hieda Member of Build Divers. Her gunpla is the MOMOKAPOOL (R×R), an upgraded version of her PEN-01M Momokapool from Gundam Build Divers Aya Fujisawa (フジサワ・アヤ, Fujisawa Aya) / Ayame (アヤメ) Voiced by: Manami Numakura Member of Build Divers. Her Gunpla is the F-Kunoichi Kai, an SD Gunpla based on the F91 Gundam F91 from Mobile Suit Gundam F91. Sei Iori (イオリ・セイ, Iori Sei) Voiced by: Mikako Komatsu A builder and one time Gunpla Battle World Champion. His current Gunpla is the GAT-X105B/EG Build Strike Exceed Galaxy, the latest version of the original GAT-X105B Build Strike Gundam from Gundam Build Fighters. Aria von Reiji Asuna (アリーア・フォン・レイジ・アスナ, Arīa fon Reiji Asuna) Voiced by: Sachi Kokuryu A prince from the country called Arian that exists within a space colony in another dimension, who became friends with Sei Iori and together won the Gunpla Battle World Championship. He somehow manages to log into the metaverse to reunite with his friend, piloting the SB-011 Star Burning Gundam. Sekai Kamiki (カミキ・セカイ, Kamiki Sekai) Voiced by: Kazumi Togashi A veteran builder and former member of Team Try Fighters. He is currently the Japanese National representative Champion. In the series he develops a rivalry relationship with Hiroto similar to that of Kyoya and Rommel. His current Gunpla is the Shin Burning Gundam, the latest version of the original KMK-B01 Kamiki Burning Gundam from Gundam Build Fighters Try which is based on the Burning Gundam and Master Gundam. Hiroto Kuga (クガ・ヒロト, Kuga Hiroto) / Hiroto (ヒロト, Hiroto) Voiced by: Chiaki Kobayashi A veteran diver, the one responsible for discovering more EL-Divers, and a former member of the legendary force "Avalon", who later joined the unofficial, "BUILD DiVERS" and eventually became the current Force Leader, and as well as the current title holder of "Hero of Gunpla". In the third episode he is the only Build Diver member who participates in the tournament, while his fellow force-mates are in the audience routing for him and Rio. His Gunpla is the Plutine Gundam, which is a combination of his Core Gundam II Plus, upgraded from the Core Gundam II featured in Gundam Build Divers Re:Rise equipped with the Pluto Armor. Magee (マギー, Magī) Voiced by: Taishi Murata A flamboyant veteran Diver who owns a shop in the metaverse and is an acquaintance of Seria's. Freddie (フレディ, Furedi) Voiced by: Ai Kakuma An alien anthropomorphic dog boy from planet Eldora, a support member to both Build Diver teams, who manages to access the metaverse from his home planet along his fellow Eldorans. Ogre (オーガ, Ōga) Voiced by: Wataru Hatano Kyoya Kisugi (キスギ・キョウヤ, Kisugi Kyōya) / Kyoya Kujo (クジョウ・キョウヤ, Kujō Kyōya) Voiced by: Jun Kasama Leader of the legendary force "Avalon" and the reigning and current title holder of "World Champion". He along with Hiroto Kuga, Maria Urutsuki, and Tatsuya Yuuki are currently at the top of the entire gunpla world community. His current gunpla is an recolored version of his AGE-TRYMAG Gundam TRY AGE Magnum from Gundam Build Divers Re:Rise. Susumu Sazaki (サザキ・ススム, Sazaki Susumu) Voiced by: Ryo Hirohashi Kaoruko Sazaki (サザキ・カオルコ, Sazaki Kaoruko) Voiced by: Ryo Hirohashi Mahiru Shigure (シグレ・マヒル, Shigure Mahiru) Voiced by: Rinko Natsuhi Keiko Sano (サノ・ケイコ, Sano Keiko) Voiced by: Ami Naito === Others === Maria Urutsuki (ウルツキ・マリア, Urutsuki Maria) / Mascarilla (マスカリージャ, Masukarīja) Voiced by: Ai Kakuma A mysterious masked woman with a harsh rivalry with Seria and a similar avatar as hers, she is later revealed as Seria's younger sister Maria, who began to loathe her sister after she quit on their dream to fight for the title of Lady Kawaguchi. She later obtains the title, becoming "Lady Kawaguchi VII". Jeff (ジェフさん, Jefu-san) Voiced by: Kenta Miyake A distant relative of Seria and Maria's and owner of the hobby shop where Seria lives. Mellow Neige (メロウ・ネージュ, Merō Nēju) Voiced by: Chikano Ibuki A sentient A.I. who is the current publicity face of the Gunpla Metaverse. == Episodes ==

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  The Great Automatic Grammatizator

  The Great Automatic Grammatizator (published in the U.S. as The Umbrella Man and Other Stories) is a posthumous 1998 collection of thirteen short stories written by British author Roald Dahl. The stories were selected for teenagers from Dahl's adult works. All the stories included were published elsewhere originally; their sources are noted below. The stories, with the exception of the war story "Katina", possess a deadpan, ironic, bizarre, or even macabre sense of humor. They generally end with unexpected plot twists. == Stories == "The Great Automatic Grammatizator" (from Someone Like You): A mechanically-minded man reasons that the rules of grammar are fixed by certain, almost mathematical principles. By exploiting this idea, he is able to create a mammoth machine that can write a prize-winning novel in roughly fifteen minutes. The story ends on a fearful note, as more and more of the world's writers are forced into licensing their names—and all hope of human creativity—to the machine. "Mrs. Bixby and the Colonel's Coat" (from Kiss Kiss): Mrs. Bixby cheats on her dentist husband with a rich, dashing colonel. When their relationship breaks off, the colonel offers Mrs. Bixby a gorgeous and expensive mink coat. In an attempt to explain the coat away, Mrs. Bixby sets up an elaborate trick with the help of a pawn shop—but her husband learns of the ruse and manages to turn the tables. "The Butler" (from More Tales of the Unexpected): An obnoxious and newly wealthy couple employs a butler and chef to impress dinner guests. The butler recommends that the husband buy expensive wines to please his guests, and the man slavishly follows the idea. The butler and the chef reap the rewards of this idea, while making fools of the "fashionable" couple. "Man from the South" (from Someone Like You): At a seaside resort in Jamaica, a strange old man makes a bet with an American man in his late teens. If the young man's cigarette lighter can spark ten times without fail, the American will win a brand-new Cadillac car—but failure means losing the little finger of his right hand. The high-tension wager ensues, and with only a few sparks left, a woman—who knows only too well the cost of the old man's bets—appears and stops the madness. "The Landlady" (from Kiss Kiss): A young man traveling to London on business stops at a bed and breakfast along the way, where a strange and slightly dotty landlady eagerly welcomes him. The eccentric nature of the house, and the news that only two other young men have ever stayed there, confuse and frighten the young man. In the end, the landlady—who indulges in the hobby of taxidermy—and the boy share a drink of tea that tastes of bitter almonds, and the landlady softly smiles at what may be her latest stuffing project. "Parson's Pleasure" (from Kiss Kiss): A man discovers an extremely rare piece of Chippendale furniture at the farm of some boorish ranchers. He desperately attempts to buy the piece cheap, in the hope of selling it at auction to earn a huge profit. He manages to buy the piece "for firewood", only for the ranchers to destroy it in an attempt to make it fit into his car. "The Umbrella Man" (from More Tales of the Unexpected): On a rainy day, a mother and daughter meet a gentlemanly old man on a street corner, who offers them a beautiful silk umbrella in exchange for a pound note. They trade, and the daughter notices that the "feeble" old man suddenly seems much sprier. They follow him, and discover that the gentleman is a con artist who visits various pubs, has a drink, and then steals another umbrella to continue the cycle. "Katina" (from Over to You: Ten Stories of Flyers and Flying): A group of RAF pilots stationed in Greece during World War II discover a hauntingly beautiful young girl, whose "family is beneath the rubble." She becomes their squadron's unofficial "mascot". In the end, her fragile life is taken as she stands defiantly against a rain of bullets from Nazi aircraft, shaking her fists at the heavens. "The Way Up to Heaven" (from Kiss Kiss): Mrs. Foster suffers from a chronic phobia of being late for appointments. Her husband enjoys the cruel sport of purposely delaying their activities, just to rile his wife. On the day when Mrs. Foster is due to fly to Paris to visit her grandchildren, her husband engages in his usual tricks. But as Mrs. Foster rushes from their taxi to the house to find him, she hears a strange noise—and turns triumphantly toward her cab. It is only when she returns, and calls a man to "repair the lift" that was stuck between floors in the house, that readers guess Mr. Foster's fate. "Royal Jelly" (from Kiss Kiss): New parents fear for the life of their little girl, who is sickly and dangerously underweight. The husband, a beekeeper, remembers hearing of the miraculous royal jelly used by bees to transform one particular larva into a queen. He adds the mixture to his daughter's bottles, and she puts on weight at an astonishing rate. The mother senses that something is amiss, and the husband confesses his actions—along with the fact that he himself swallowed buckets of the jelly for months in an attempt to cure his impotence. The royal jelly did the trick—but the strange side-effects include a disturbing metamorphosis for both father and daughter. "Vengeance is Mine Inc." (from More Tales of the Unexpected): Two brothers who are short of cash bemoan their fate over breakfast while reading the society column of a newspaper. They hit upon a scheme to take revenge on cruel tabloid writers in exchange for money from wealthy patrons. The unconventional plan works, and the brothers line their pockets with the spoils of their plans. "Taste" (from Someone Like You): A rich man with a beautiful young daughter hosts a dinner party, inviting a famous connoisseur of fine wines. When the rich man boasts that he has a wine that the expert cannot identify, the stakes become frighteningly high: if he can guess the name and vintage of the wine, he will win his daughter's hand. After an elaborate show, the expert guesses correctly; however, the family's maid appears and inadvertently exposes the guest as a cheat, thus saving the girl. "Neck" (from Someone Like You): A newspaper heir finds himself suddenly engaged to the voluptuous and controlling Lady Tutton. He loses all control of his life, and only his trusted butler and friends realize how broken he is by her control. A weekend trip to their estate, however, proves the perfect opportunity for Lord Tutton to engage in revenge against his wicked wife: her head is trapped in a valuable piece of wooden sculpture, and he must decide whether to use a saw or an axe to cut her free. == Publication details == Dahl, Roald (19 January 2004). The Umbrella Man and Other Stories. Speak. ISBN 9780142400876. == Reception == Groff Conklin in 1954 called the short story "The Great Automatic Grammatizator" "an awe-inspiring fantasy-satire ... an unforgettable bit of biting nonsense".

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  Federation of International Robot-soccer Association

  The Federation of International Robot-soccer Association (FIRA) is an international organisation organising competitive soccer competitions between autonomous robots. The matches are usually five-a-side. == History == In 1996 and 1997, this competition was known as MiroSot and was held in Daejeon, Korea. The 1996 competition offered a challenging arena to the younger generation and researchers working with autonomous mobile robotic systems. From 1998 through 2008, it was called the FIRA Cup, and in 2009, it became the FIRA RoboWorld Cup & Congress. The 15th RoboWorld Cup was held at Amrita Vishwa Vidyapeetham, Bangalore, India in September 2010. In 2013, it took place in Kuala Lumpur, Malaysia. The championship started on August 24, 2013, and ended on August 29. At that time, it involved five categories: Micro-Robot Soccer Tournament, Amire, Naro, Simulated Robot, Android, Robo and Humanoid Robot. It attracted teams from Singapore, Indonesia, Taiwan, India, China, South Korea, the United Kingdom, Mexico, Canada, Russia and Malaysia. 80 teams from 11 countries participated. In 2018, the competition had 277 teams participating from 12 countries. === Past Events === == FIRA RoboWorld Cup & Congress == This competition has 4 leagues: FIRA AIR, FIRA Sports, FIRA Challenges, and FIRA Youth. Each league has its own competitions, and each competition can have several events. === FIRA AIR === The FIRA AIR league has two associated competitions, Autonomous Race and Emergency Service. === FIRA Sports === The FIRA Sports league has four associated competitions, HuroCup, RoboSot, SimuroSot, and AndroSot. This the robot soccer league. HuroCup consists of single events for bipedal humanoid robots. The events are: archery, sprint, marathon, united soccer, obstacle run, long jump, spartan race, marathon, weightlifting, and basketball. There is an all-round competition for the single robot that performs the best overall. === FIRA Challenges === The FIRA Challenges league has three associated competitions, Autonomous Cars, Autonomous Cars Simulation, Innovation and Business. === FIRA Youth === The FIRA Youth league has six associated challenges, Sport Robots, HuroCup Junior, CityRacer, DRV_Explorer, Cliff Hanger, and Mission Impossible.

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  Rabbit r1

  The Rabbit r1 is an artificial intelligence personal assistant device developed by the American technology startup Rabbit Inc and co-designed by Teenage Engineering. It was announced at the 2024 Consumer Electronics Show as a handheld device intended to perform digital tasks through voice commands, touch interaction, and web-based AI agents. The r1 was marketed around Rabbit's concept of a "large action model" (LAM), which the company described as software able to operate websites and services on behalf of users. The device runs rabbitOS, an operating system based on the Android Open Source Project. Its services have included AI search, image recognition, voice interaction, music playback, rideshare and food-ordering integrations, and later experimental web-agent features such as LAM Playground and teach mode. Initial reviews were largely negative, with reviewers criticizing the device's limited functionality, bugs, and unclear advantages over a smartphone. Critics also questioned Rabbit's claims after the r1 software was shown to run on an Android phone. Rabbit continued to issue software updates after launch, including rabbitOS 2 in September 2025, which introduced a redesigned card-based interface, gesture navigation, and a "creations" feature for generating small software tools and experiences on the device. Rabbit Inc was founded by Jesse Lyu Cheng. == Hardware == Display: A 2.88-inch touchscreen for interactive user input. Input: push-to-talk button to activate voice commands; scroll wheel; Gyroscope; Magnetometer; Accelerometer; GPS. Camera: 8 MP single camera, with a resolution of 3264x2448, allowing for the connected external AI to use computer vision. Audio: Equipped with a speaker and dual microphones for audio interaction. Connectivity: Supports Wi-Fi and cellular connections via a SIM card slot to access internet services. Processor: Runs on a 2.3GHz MediaTek Helio P35 processor. Memory: Contains 4GB of RAM for operational tasks. Storage: Offers 128GB of internal storage for data. Ports: Utilizes a USB-C port for charging and data connections. == Software == The Rabbit r1 runs rabbitOS, which is based on the Android Open Source Project (AOSP), specifically Android 13. Rabbit founder Jesse Lyu described rabbitOS as a "very bespoke AOSP" after reports that the r1's software could be run on a conventional Android phone. Rabbit described the r1 as using a large action model (LAM), a type of AI agent intended to perform tasks across software interfaces rather than only answer questions. At launch, the device supported a limited set of services, including AI search, vision features, music playback, and some third-party integrations. Perplexity.ai was one of the AI services used to answer user queries. In 2024, Rabbit released several software updates that added features and attempted to address early criticism of the device. In July 2024, the company launched "beta rabbit", an advanced search and conversation mode for more complex queries. In October 2024, it released LAM Playground, a web-based agent feature intended to let the r1 operate websites on behalf of users. Reviewers found the feature experimental; Android Authority reported that it could perform some navigation tasks but struggled with CAPTCHAs, loops, and unintended behavior. In November 2024, Rabbit introduced a beta "teach mode", which allowed users to demonstrate web-based tasks in the Rabbithole web portal and later ask the r1 to repeat them. The company described teach mode as experimental, and The Verge noted that Rabbit warned users that results could be unpredictable and that CAPTCHA-protected sites could cause problems. Rabbit released rabbitOS 2 in September 2025. The update redesigned the interface around a card-based layout, added additional touchscreen gestures, and introduced "creations", a feature that lets users generate simple software tools, games, and interfaces through natural-language prompts. Coverage of the update described it as a major software overhaul rather than new hardware. == Reception == === Funding === Rabbit raised $20 million in funding from Khosla Ventures, Synergis Capital and Kakao Investment in October 2023. The company announced an additional $10 million in funding in December 2023. === Sales === Following its announcement at the 2024 Consumer Electronics Show, 130,000 units were sold. On August 13, 2024, Rabbit announced that sales of r1 had expanded to the entire European Union (except Malta) and United Kingdom. On August 21, 2024, sales of r1 expanded to Singapore. === Reviews === The r1 was met with strong criticism immediately after Rabbit began shipping the device. Some reviews questioned what the device was able to do that a smartphone could not, while comparing it to the similar Humane Ai Pin. YouTuber Marques Brownlee called the device "barely reviewable". Android Authority's Mishaal Rahman managed to install Rabbit r1's software on a Pixel 6a smartphone, after a tipster shared an APK file. The Verge echoed the claims made by Rahman. In response, Lyu published statements confirming its use of Android, but denying that the r1 is an Android app. Mashable called its Vision features impressive, but said that "these praise-worthy features are overshadowed by buggy performance". Ars Technica wrote a blog post claiming "the company is blocking access from bootleg APKs". TechCrunch gave a slightly more positive review, calling the device a "fun peep at a possible future", but could not "advise anyone to buy one now." Shortly after the launch of r1, Rabbit began a weekly cadence of software updates to address much of the criticism from the early reviews, including "battery and GPS performance, time zone selection, and more". Digital Trends said the Magic Camera feature "takes the most mundane, ordinary, and badly composed photos and makes something fun and eye-catching from them." Mashable said the "beta rabbit" feature "makes Rabbit R1 more conversational and intelligent". Later coverage noted that Rabbit continued to update the r1 after its poorly received launch. The Verge reported in September 2024 that about 5,000 of roughly 100,000 purchasers were using the device at any given moment, citing Lyu, and described the product as having launched before it was ready. In 2025, coverage of rabbitOS 2 described the update as an attempt to reset the device's software experience after the criticism of its original release. == Controversies == === GAMA project === Rabbit Inc has garnered attention due to allegations surrounding its funding and the company's past projects. The company came under scrutiny when Stephen Findeisen, known as Coffeezilla on YouTube, published a video in May 2024, alleging that Rabbit Incorporation was "built on a scam". Rabbit Incorporation, initially named Cyber Manufacturing Co, rebranded just two months before launching the Rabbit R1. The company, under its former name, raised $6 million in November 2021 for a project called GAMA, described as a "Next Generation NFT Project." Jesse Lyu, the CEO of Rabbit Incorporation, referred to GAMA as a "fun little project." Coffeezilla, who investigates influencer scams, highlighted old Clubhouse recordings of Jesse Lyu discussing the GAMA project. In these recordings, Lyu emphasized the substantial funding behind GAMA and its potential to be a revolutionary, carbon-negative cryptocurrency. Coffeezilla questioned the whereabouts of the funds raised for GAMA, estimating that approximately $1 million in refunds to investors remained unresolved. He suggested that the rebranding to Rabbit Incorporation and the shift to developing the Rabbit R1 were attempts to divert from the GAMA project's issues. In response to Coffeezilla's inquiries, Rabbit Incorporation stated that the $6 million raised was used for the GAMA project. The company said that NFTs cannot be refunded unless the owner agrees to "burn" them on the blockchain. Rabbit Incorporation also said that the GAMA project was open-sourced and returned to the community, aligning with community feedback. They also mentioned that efforts to buy back NFTs were made to counteract malicious trading and maintain market stability. === Security === In June 2024, Engadget reported that the Rabbitude team, a community reverse engineering project, had gained access to the r1's codebase revealing that r1's software contained several hardcoded API keys in its code for ElevenLabs, Microsoft Azure, Yelp, and Google Maps, potentially allowing unauthorized access to r1 responses, including those containing the users' personal information. For a short time, Rabbit immediately began revoking and rotating those secrets and confirmed that the code was leaked by an employee who had "been terminated and remains under investigation". In July 2024, the company revealed that all user chats and device pairing data were logged on the r1 with no ability to delete them. This meant that lost or stolen devices could be used to extract user

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  House of Suns

  House of Suns is a 2008 science fiction novel by Welsh author Alastair Reynolds. The novel was shortlisted for the 2009 Arthur C. Clarke Award. == Setting == Approximately six million years in the future, humanity has spread throughout the Milky Way galaxy, which appears devoid of any other organic sentient life. The galaxy is populated by numerous civilizations of humans and posthumans of widely varying levels of development. A civilization of sentient robots known as the Machine People coexists peacefully with humanity. Technologies of the era include anti-gravity, inertia damping, force fields, stellar engineering, and stasis fields. Also of note is the "Absence"—the mysterious disappearance of the Andromeda Galaxy. Large-scale human civilizations almost invariably seem to collapse and disappear within a few millennia (a phenomenon referred to as "turnover"), the limits of sub-lightspeed travel making it too difficult to hold interstellar empires together. Consequently, the most powerful entities in the galaxy are the "Lines"—familial organizations made of cloned "shatterlings". The Lines do not inhabit planets, but instead travel through space, holding reunions after they have performed a "circuit" of the galaxy; something that takes about 200,000 years. House of Suns concerns the Gentian Line, also known as the House of Flowers, composed of Abigail Gentian and her 999 clones (or "shatterlings"), male and female: exactly which of the 1,000 shatterlings is the original Abigail Gentian is unknown. The clones and Abigail travel the Milky Way Galaxy, helping young civilizations, collecting knowledge, and experiencing what the universe has to offer. Members of the Gentian Line are named after flowering plants. == Synopsis == The novel is divided into eight parts, with the first chapter of each part taking the form of a narrative flashback to Abigail Gentian's early life (six million years earlier, in the 31st century), before the cloning and the creation of the Gentian Line. Each subsequent chapter is narrated from the first-person perspective of two shatterlings named Campion and Purslane, alternating between them each chapter. Campion and Purslane are in a relationship, which is frowned upon, even punishable, by the Line. The primary storyline begins as Campion and Purslane are roughly fifty years late to the 32nd Gentian reunion. They take a detour to contact a posthuman known as ‘Ateshga’ in hopes of getting a replacement ship for Campion because his is getting old (several million years old). After being tricked by Ateshga, Campion and Purslane manage to turn the tables on him and leave his planet with a being he had been keeping captive, a golden robot called Hesperus. Hesperus is a member of the "Machine People", an advanced civilization of robots, and supposedly the only non-human sentient society in existence. The two shatterlings hope that the rescue of Hesperus will let them off the hook for their lateness, as returning him to his people (who will be at the reunion as guests of other shatterlings) will put the Gentian Line on good terms with the Machine People. However, before reaching the reunion world, Campion and Purslane encounter an emergency distress signal from Fescue, another Gentian shatterling. There was a vicious attack on the reunion world; an ambush in which the majority of the Gentian Line was wiped out. The identity of the responsible party is unknown, but the attackers used the supposedly long-vanished 'Homunculus' weapons – monstrous spacetime-bending weapons that were created ages ago, but were ordered to be destroyed by another Line. Despite Fescue's warning, Campion and Purslane approach the reunion system to look for survivors. They manage to find the remains of a ship with several Gentian members still alive, and rescue them and the four enemy prisoners they had captured. Hesperus, however, is gravely injured in the process by remaining ambushers. The group escapes and make their way to the Gentian backup meeting planet, Neume, in the hope of re-grouping with any other Gentians who may have survived the ambush. Upon reaching Neume, Campion, Purslane and the other shatterlings they rescued are greeted by the few Gentian survivors of the ambush (numbering only in the forties, compared to the hundreds that existed before the ambush). They also meet two members of the Machine People: Cadence and Cascade, guests of another shatterling. During the next few days, the interrogation of the prisoners commences. Another Gentian, Cyphel, is mysteriously murdered, which fuels the Line's concerns that there is a traitor among them. As a way of punishing Campion for transgressions against the Line, Purslane is made to give up her ship, the Silver Wings of Morning (one of the fastest and most powerful in the Line) to Cadence and Cascade, ostensibly so they can return to the Machine People with news of the ambush, in a bid to gain the Line some assistance. Hesperus, still critically wounded following the rescue of the survivors, is taken to the Neumean "Spirit of the Air", an ancient posthuman machine-intelligence, in the hopes that it will fix him. The Spirit takes Hesperus away and returns him some time later, though apparently still not functioning. The robots Cadence and Cascade make preparations to leave on Purslane's ship. They agree to take him aboard and return him to their people, who they promise may be able to help Hesperus. Purslane accompanies them to her ship, where she must be physically present to give the ship order to transfer control over to the robots. On their way to the bridge, Hesperus suddenly springs to life, grabbing Purslane and hiding her while Cadence and Cascade are whisked along to the bridge. Hersperus quickly explains that Cadence and Cascade are actually planning on hijacking the ship. Bewildered by this sudden change of events, Purslane delays in acting, not sure if she should trust Hesperus, before deciding to ask the ship to detain and eject the robots in the bridge. By then, though, it is too late. Cadence and Cascade hack into the ship's computer, taking it over, and take off from Neume with Hesperus and Purslane still aboard. Campion and several other shatterlings immediately launch a pursuit. Together Hesperus and Purslane find a hideout in a smaller ship in the hold of the Silver Wings of Morning. Using information gained from the other two robots and his own memories, Hesperus (who is now an amalgamation of both Hesperus and the Spirit of the Air) has pieced together what is going on: Cadence and Cascade have discovered that the Line was involved in the accidental extermination of a forgotten earlier race of machine people, dubbed the "First Machines". The Commonality (a confederation of the various Lines), horrified and ashamed of this pointless genocide, erased all knowledge of the event from historical records and their own memories. Unfortunately, Campion, in a previous circuit, unwittingly uncovered information pertaining to the extermination. Hesperus believes that the ambush at the reunion was seeking to destroy this evidence before it could spread, carried out by a shadow Line known as the "House of Suns", tasked with maintaining the conspiracy. Cadence and Cascade, on the other hand, are racing for a wormhole which leads to the Andromeda Galaxy, to where the few survivors of the First Machines are revealed to have retreated. They plan to release the First Machines back into the Milky Way, thus effecting a revenge against the Commonality for the genocide. As Campion and the shatterlings are pursuing Purslane's hijacked ship, transmissions from Neume confirm that a shatterling within their midst, Galingale, is the traitor and a secret member of the House of Suns. The shatterlings open fire on both Galingale's and Purslane's ships, and while they manage to capture Galingale, they are unable to stop Purslane's ship. Unable to get within weapons range, Campion pursues Purslane's ship for sixty thousand light years, during which time he and Purslane, on their separate ships, are suspended in "abeyance", a form of temporal slowdown or stasis. Despite efforts to stop the hijacked ship from reaching the concealed wormhole by local civilisations, the robot Cascade succeeds in opening the "stardam" enclosing the wormhole and travelling through it to the Andromeda Galaxy. On board Silver Wings of Morning, Hesperus reveals to Campion that while he managed to destroy Cadence before they could leave the Neume star system, Cascade survived and he and Cascade had engaged in a marathon battle, several thousand years. Hesperus was ultimately victorious, but Cascade has fused the ship controls before his defeat and they are past the point of no return. Campion, now the only shatterling still in pursuit, enters the wormhole after them and emerges in the Andromeda Galaxy, a place apparently devoid of all sentient life. In his search for Purslane and her ship, he travels to a star enca

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  Conference on Neural Information Processing Systems

  The Conference on Neural Information Processing Systems (abbreviated as NeurIPS and formerly NIPS) is a machine learning and computational neuroscience conference held annually in December. Along with ICLR and ICML, it is one of the three primary conferences of high impact in machine learning and artificial intelligence research. The conference includes three days of invited talks along with oral and poster presentations of refereed papers, followed by two days of workshops and competitions. == History == The NeurIPS meeting was first proposed in 1986 at the annual invitation-only Snowbird Meeting on Neural Networks for Computing organized by The California Institute of Technology and Bell Laboratories. NeurIPS was designed as a complementary open interdisciplinary meeting for researchers exploring biological and artificial Neural Networks. Reflecting this multidisciplinary approach, NeurIPS began in 1987 with information theorist Ed Posner as the conference president and learning theorist Yaser Abu-Mostafa as program chairman. Research presented in the early NeurIPS meetings included a wide range of topics from efforts to solve purely engineering problems to the use of computer models as a tool for understanding biological nervous systems. Since then, the biological and artificial systems research streams have diverged, and recent NeurIPS proceedings have been dominated by papers on machine learning, artificial intelligence and statistics. From 1987 until 2000 NeurIPS was held in Denver, United States. Since then, the conference was held in Vancouver, Canada (2001–2010), Granada, Spain (2011), and Lake Tahoe, United States (2012–2013). In 2014 and 2015, the conference was held in Montreal, Canada, in Barcelona, Spain in 2016, in Long Beach, United States in 2017, in Montreal, Canada in 2018 and Vancouver, Canada in 2019. Reflecting its origins at Snowbird, Utah, the meeting was accompanied by workshops organized at a nearby ski resort up until 2013, when it outgrew ski resorts. The first NeurIPS Conference was sponsored by the IEEE. The following NeurIPS Conferences have been organized by the NeurIPS Foundation, established by Ed Posner. Terrence Sejnowski has been the president of the NeurIPS Foundation since Posner's death in 1993. The board of trustees consists of previous general chairs of the NeurIPS Conference. The first proceedings was published in book form by the American Institute of Physics in 1987, and was entitled Neural Information Processing Systems, then the proceedings from the following conferences have been published by Morgan Kaufmann (1988–1993), MIT Press (1994–2004) and Curran Associates (2005–present) under the name Advances in Neural Information Processing Systems. The conference was originally abbreviated as "NIPS". By 2018 a few commentators were criticizing the abbreviation as encouraging sexism due to its association with the word nipples, and as being a slur against Japanese. The board changed the abbreviation to "NeurIPS" in November 2018. == Topics == Along with machine learning and neuroscience, other fields represented at NeurIPS include cognitive science, psychology, computer vision, statistical linguistics, and information theory. Over the years, NeurIPS became a premier conference on machine learning and although the 'Neural' in the NeurIPS acronym had become something of a historical relic, the resurgence of deep learning in neural networks since 2012, fueled by faster computers and big data, has led to achievements in speech recognition, object recognition in images, image captioning, language translation and world championship performance in the game of Go, based on neural architectures inspired by the hierarchy of areas in the visual cortex (ConvNet) and reinforcement learning inspired by the basal ganglia (Temporal difference learning). Notable affinity groups have emerged from the NeurIPS conference and displayed diversity, including Black in AI (in 2017), Queer in AI (in 2016), and others. === Named lectures === In addition to invited talks and symposia, NeurIPS also organizes two named lectureships to recognize distinguished researchers. The NeurIPS Board introduced the Posner Lectureship in honor of NeurIPS founder Ed Posner; two Posner Lectures were given each year up to 2015. Past lecturers have included: 2010 – Josh Tenenbaum and Michael I. Jordan 2011 – Rich Sutton and Bernhard Schölkopf 2012 – Thomas Dietterich and Terry Sejnowski 2013 – Daphne Koller and Peter Dayan 2014 – Michael Kearns and John Hopfield 2015 – Zoubin Ghahramani and Vladimir Vapnik 2016 – Yann LeCun 2017 – John Platt 2018 – Joëlle Pineau 2019 – Yoshua Bengio 2020 – Christopher Bishop 2021 – Peter Bartlett In 2015, the NeurIPS Board introduced the Breiman Lectureship to highlight work in statistics relevant to conference topics. The lectureship was named for statistician Leo Breiman, who served on the NeurIPS Board from 1994 to 2005. Past lecturers have included: 2015 – Robert Tibshirani 2016 – Susan Holmes 2017 – Yee Whye Teh 2018 – David Spiegelhalter 2019 – Bin Yu 2020 – Marloes Maathuis 2021 – Gabor Lugosi 2022 – Emmanuel Candes 2023 – Susan Murphy 2024 – Arnaud Doucet == NeurIPS consistency experiment == In NIPS 2014, the program chairs duplicated 10% of all submissions and sent them through separate reviewers to evaluate randomness in the reviewing process. Several researchers interpreted the result. Regarding whether the decision in NIPS is completely random or not, John Langford writes: "Clearly not—a purely random decision would have arbitrariness of ~78%. It is, however, quite notable that 60% is much closer to 78% than 0%." He concludes that the result of the reviewing process is mostly arbitrary. In NeurIPS 2021, the program chairs repeated the 2014 experiment and found similar levels of review inconsistency; 23% of duplicated submissions received different accept/reject decisions, and 50.6% of accepted papers would have been rejected under re-review. == Locations == 1987–2000: Denver, Colorado, United States 2001–2010: Vancouver, British Columbia, Canada 2011: Granada, Spain 2012 & 2013: Stateline, Nevada, United States 2014 & 2015: Montréal, Quebec, Canada 2016: Barcelona, Spain 2017: Long Beach, California, United States 2018: Montréal, Quebec, Canada 2019: Vancouver, British Columbia, Canada 2020: Vancouver, British Columbia, Canada (virtual conference) 2021: Virtual conference 2022 & 2023: New Orleans, Louisiana, United States 2024: Vancouver, British Columbia, Canada 2025: San Diego, California, United States and Mexico City, Mexico 2026: Sydney, New South Wales, Australia, with satellite events in Atlanta and Paris

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  List of Tesla Autopilot crashes

  Tesla Autopilot, a Level 2 advanced driver assistance system (ADAS), was released in October 2015 and the first fatal crashes involving the system occurred less than one year later. The fatal crashes attracted attention from news publications and United States government agencies, including the National Transportation Safety Board (NTSB) and National Highway Traffic Safety Administration (NHTSA), which has argued the Tesla Autopilot death rate is higher than the reported estimates. In addition to fatal crashes, there have been many nonfatal ones. Causes behind the incidents include the ADAS failing to recognize other vehicles, insufficient Autopilot driver engagement, and violating the operational design domain. As of October 2025, there have been hundreds of nonfatal incidents involving versions of Autopilot and sixty-five reported fatalities, fifty-four of which NHTSA investigations or expert testimony later verified and two that NHTSA's Office of Defect Investigations determined as happening during the engagement of Full Self-Driving (FSD) after 2022. Collectively, these cases culminated in a general recall in December 2023 of all vehicles equipped with Autopilot, which Tesla claims it resolved by an over-the-air software update. Immediately after closing its investigation in April 2024, NHTSA opened a recall query to determine the effectiveness of the recall. == Notable fatal crashes == === Handan, Hebei, China (January 20, 2016) === On January 20, 2016, Gao Yaning, the driver of a Tesla Model S in Handan, Hebei, China, was killed when his car crashed into a stationary truck. The Tesla was following a car in the far left lane of a multi-lane highway; the car in front moved to the right lane to avoid a truck stopped on the left shoulder, and the Tesla, which the driver's father believes was in Autopilot mode, did not slow before colliding with the stopped truck. According to footage captured by a dashboard camera, the stationary street sweeper on the left side of the expressway partially extended into the far left lane, and the driver did not appear to respond to the unexpected obstacle. Initially, Yaning was held responsible for the collision by local traffic police and, in September 2016, his family filed a lawsuit in July against the Tesla dealer who sold the car. The family's lawyer stated the suit was intended "to let the public know that self-driving technology has some defects. We are hoping Tesla when marketing its products, will be more cautious. Do not just use self-driving as a selling point for young people." Tesla released a statement which said they "have no way of knowing whether or not Autopilot was engaged at the time of the crash" since the car telemetry could not be retrieved remotely due to damage caused by the crash. In 2018, the lawsuit was stalled because telemetry was recorded locally to a SD card and was not able to be given to Tesla, who provided a decoding key to a third party for independent review. Tesla stated that "while the third-party appraisal is not yet complete, we have no reason to believe that Autopilot on this vehicle ever functioned other than as designed." Chinese media later reported that the family sent the information from that card to Tesla, which admitted Autopilot was engaged two minutes before the crash. Tesla since then removed the term "Autopilot" from its Chinese website. === Williston, Florida, US (May 7, 2016) === On May 7, 2016, Tesla driver Joshua Brown was killed in a crash with an 18-wheel tractor-trailer in Williston, Florida. By late June 2016, the NHTSA opened a formal investigation into the fatal autonomous accident, working with the Florida Highway Patrol. According to the NHTSA, preliminary reports indicate the crash occurred when the tractor-trailer made a left turn in front of the 2015 Tesla Model S at an intersection on a non-controlled access highway, and the car failed to apply the brakes. The car continued to travel after passing under the truck's trailer. The Tesla was eastbound in the rightmost lane of US 27, and the westbound tractor-trailer was turning left at the intersection with NE 140th Court, approximately 1 mi (1.6 km) west of Williston; the posted speed limit is 65 mph (105 km/h). The diagnostic log of the Tesla indicated it was traveling at a speed of 74 mi/h (119 km/h) when it collided with and traveled under the trailer, which was not equipped with a side underrun protection system. A reconstruction of the accident estimated the driver would have had approximately 10.4 seconds to detect the truck and take evasive action. The underride collision sheared off the Tesla's greenhouse, destroying everything above the beltline, and caused fatal injuries to the driver. In the approximately nine seconds after colliding with the trailer, the Tesla traveled another 886.5 feet (270.2 m) and came to rest after colliding with two chain-link fences and a utility pole. The NHTSA's preliminary evaluation was opened to examine the design and performance of any automated driving systems in use at the time of the crash, which involves a population of an estimated 25,000 Model S cars. On July 8, 2016, the NHTSA requested Tesla Inc. to hand over to the agency detailed information about the design, operation and testing of its Autopilot technology. The agency also requested details of all design changes and updates to Autopilot since its introduction, and Tesla's planned updates scheduled for the next four months. According to Tesla, "neither autopilot nor the driver noticed the white side of the tractor-trailer against a brightly lit sky, so the brake was not applied." The car attempted to drive full speed under the trailer, "with the bottom of the trailer impacting the windshield of the Model S". Tesla also stated that this was Tesla's first known Autopilot-related death in over 130 million miles (208 million km) driven by its customers while Autopilot was activated. According to Tesla there is a fatality every 94 million miles (150 million km) among all type of vehicles in the U.S. It is estimated that billions of miles will need to be traveled before Tesla Autopilot can claim to be safer than humans with statistical significance. Researchers say that Tesla and others need to release more data on the limitations and performance of automated driving systems if self-driving cars are to become safe and understood enough for mass-market use. The truck's driver told the Associated Press that he could hear a Harry Potter movie playing in the crashed car, and said the car was driving so quickly that "he went so fast through my trailer I didn't see him. [The film] was still playing when he died and snapped a telephone pole a quarter-mile down the road." According to the Florida Highway Patrol, they found in the wreckage an aftermarket portable DVD player. (It is not possible to watch videos on the Model S touchscreen display while the car is moving.) A laptop computer was recovered during the post-crash examination of the wreck, along with an adjustable vehicle laptop mount attached to the front passenger's seat frame. The NHTSA concluded the laptop was probably mounted, and the driver may have been distracted at the time of the crash. In January 2017, the NHTSA Office of Defects Investigations (ODI) released a preliminary evaluation, finding that the driver in the crash had seven seconds to see the truck and identifying no defects in the Autopilot system; the ODI also found that the Tesla car crash rate dropped by 40 percent after Autosteer installation, but later also clarified that it did not assess the effectiveness of this technology or whether it was engaged in its crash rate comparison. The NHTSA Special Crash Investigation team published its report in January 2018. According to the report, for the drive leading up to the crash, the driver engaged Autopilot for 37 minutes and 26 seconds, and the system provided 13 "hands not detected" alerts, to which the driver responded after an average delay of 16 seconds. The report concluded "Regardless of the operational status of the Tesla's ADAS technologies, the driver was still responsible for maintaining ultimate control of the vehicle. All evidence and data gathered concluded that the driver neglected to maintain complete control of the Tesla leading up to the crash." In July 2016, the NTSB announced it had opened a formal investigation into the fatal accident while Autopilot was engaged. The NTSB is an investigative body that only has the power to make policy recommendations. An agency spokesman said, "It's worth taking a look and seeing what we can learn from that event, so that as that automation is more widely introduced we can do it in the safest way possible." The NTSB opens annually about 25 to 30 highway investigations. In September 2017, the NTSB released its report, determining that "the probable cause of the Williston, Florida, crash was the truck driver's failure to yield the right of way to the car, combine

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  Generative art

  Generative art is post-conceptual art that has been created (in whole or in part) with the use of an autonomous system. An autonomous system in this context is generally one that is non-human and can independently determine features of an artwork that would otherwise require decisions made directly by the artist. In some cases the human creator may claim that the generative system represents their own artistic idea, and in others that the system takes on the role of the creator. "Generative art" often refers to algorithmic art (algorithmically determined computer generated artwork) and synthetic media (general term for any algorithmically generated media), but artists can also make generative art using systems of chemistry, biology, mechanics and robotics, smart materials, manual randomization, mathematics, data mapping, symmetry, and tiling. Generative algorithms, algorithms programmed to produce artistic works through predefined rules, stochastic methods, or procedural logic, often yielding dynamic, unique, and contextually adaptable outputs—are central to many of these practices. == History == The use of the word "generative" in the discussion of art has developed over time. The use of "Artificial DNA" defines a generative approach to art focused on the construction of a system able to generate unpredictable events, all with a recognizable common character. The use of autonomous systems, required by some contemporary definitions, focuses a generative approach where the controls are strongly reduced. This approach is also named "emergent". Margaret Boden and Ernest Edmonds have noted the use of the term "generative art" in the broad context of automated computer graphics in the 1960s, beginning with artwork exhibited by Georg Nees and Frieder Nake in 1965: A. Michael Noll did his initial computer art, combining randomness with order, in 1962, and exhibited it along with works by Bell Julesz in 1965. The terms "generative art" and "computer art" have been used in tandem, and more or less interchangeably, since the very earliest days. The first such exhibition showed the work of Nees in February 1965, which some claim was titled "Generative Computergrafik". While Nees does not himself remember, this was the title of his doctoral thesis published a few years later. The correct title of the first exhibition and catalog was "computer-grafik". "Generative art" and related terms was in common use by several other early computer artists around this time, including Manfred Mohr and Ken Knowlton. Vera Molnár (born 1924) is a French media artist of Hungarian origin. Molnar is widely considered to be a pioneer of generative art, and is also one of the first women to use computers in her art practice. The term "Generative Art" with the meaning of dynamic artwork-systems able to generate multiple artwork-events was clearly used the first time for the "Generative Art" conference in Milan in 1998. The term has also been used to describe geometric abstract art where simple elements are repeated, transformed, or varied to generate more complex forms. Thus defined, generative art was practiced by the Argentinian artists Eduardo Mac Entyre and Miguel Ángel Vidal in the late 1960s. In 1972 the Romanian-born Paul Neagu created the Generative Art Group in Britain. It was populated exclusively by Neagu using aliases such as "Hunsy Belmood" and "Edward Larsocchi". In 1972 Neagu gave a lecture titled 'Generative Art Forms' at the Queen's University, Belfast Festival. In 1970 the School of the Art Institute of Chicago created a department called Generative Systems. As described by Sonia Landy Sheridan the focus was on art practices using the then new technologies for the capture, inter-machine transfer, printing and transmission of images, as well as the exploration of the aspect of time in the transformation of image information. Also noteworthy is John Dunn, first a student and then a collaborator of Sheridan. In 1988 Clauser identified the aspect of systemic autonomy as a critical element in generative art: It should be evident from the above description of the evolution of generative art that process (or structuring) and change (or transformation) are among its most definitive features, and that these features and the very term 'generative' imply dynamic development and motion. (the result) is not a creation by the artist but rather the product of the generative process - a self-precipitating structure. In 1989 Celestino Soddu defined the Generative Design approach to Architecture and Town Design in his book Citta' Aleatorie. In 1989 Franke referred to "generative mathematics" as "the study of mathematical operations suitable for generating artistic images." From the mid-1990s Brian Eno popularized the terms generative music and generative systems, making a connection with earlier experimental music by Terry Riley, Steve Reich and Philip Glass. From the end of the 20th century, communities of generative artists, designers, musicians and theoreticians began to meet, forming cross-disciplinary perspectives. The first meeting about generative Art was in 1998, at the inaugural International Generative Art conference at Politecnico di Milano University, Italy. In Australia, the Iterate conference on generative systems in the electronic arts followed in 1999. On-line discussion has centered around the eu-gene mailing list, which began late 1999, and has hosted much of the debate which has defined the field. These activities have more recently been joined by the Generator.x conference in Berlin starting in 2005. In 2012 the new journal GASATHJ, Generative Art Science and Technology Hard Journal was founded by Celestino Soddu and Enrica Colabella jointing several generative artists and scientists in the editorial board. Some have argued that as a result of this engagement across disciplinary boundaries, the community has converged on a shared meaning of the term. As Boden and Edmonds put it in 2011: Today, the term "Generative Art" is still current within the relevant artistic community. Since 1998 a series of conferences have been held in Milan with that title (Generativeart.com), and Brian Eno has been influential in promoting and using generative art methods (Eno, 1996). Both in music and in visual art, the use of the term has now converged on work that has been produced by the activation of a set of rules and where the artist lets a computer system take over at least some of the decision-making (although, of course, the artist determines the rules). In the call of the Generative Art conferences in Milan (annually starting from 1998), the definition of Generative Art by Celestino Soddu: Generative Art is the idea realized as genetic code of artificial events, as construction of dynamic complex systems able to generate endless variations. Each Generative Project is a concept-software that works producing unique and non-repeatable events, like music or 3D Objects, as possible and manifold expressions of the generating idea strongly recognizable as a vision belonging to an artist / designer / musician / architect /mathematician. Discussion on the eu-gene mailing list was framed by the following definition by Adrian Ward from 1999: Generative art is a term given to work which stems from concentrating on the processes involved in producing an artwork, usually (although not strictly) automated by the use of a machine or computer, or by using mathematic or pragmatic instructions to define the rules by which such artworks are executed. A similar definition is provided by Philip Galanter: Generative art refers to any art practice where the artist creates a process, such as a set of natural language rules, a computer program, a machine, or other procedural invention, which is then set into motion with some degree of autonomy contributing to or resulting in a completed work of art. Around the 2020s, generative AI models learned to imitate the distinct style of particular authors. For example, a generative image model such as Stable Diffusion is able to model the stylistic characteristics of an artist like Pablo Picasso (including his particular brush strokes, use of colour, perspective, and so on), and a user can engineer a prompt such as "an astronaut riding a horse, by Picasso" to cause the model to generate a novel image applying the artist's style to an arbitrary subject. Generative image models have received significant backlash from artists who object to their style being imitated without their permission, arguing that this harms their ability to profit from their own work. The emergence of text-to-image generative AI systems has expanded debates over authorship, copyright, and artistic labor. The main issues in these debates include the eligibility of AI-generated outputs for copyright protection and the legal and ethical questions of using existing copyrighted works as training data for generative AI systems. == Types == === Music === Johann Kirnberger's Mu

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  Vehicle infrastructure integration

  The Vehicle Infrastructure Integration (VII), also known as "Connected Roadways" or "vehicle-to-everything" (V2X) technology, is a United States Department of Transportation initiative that aims to improve road safety by developing technology that connects road vehicles with their environment. This development draws on several disciplines, including transport engineering, electrical engineering, automotive engineering, telematics, and computer science. Although VII specifically covers road transport, similar technologies are under development for other modes of transport. For example, airplanes may use ground-based beacons for automated guidance, allowing the autopilot to fly the plane without human intervention. == Goals == The goal of VII is to establish a communication link between vehicles (via On-Board Equipment, or OBE) and roadside infrastructure (via Roadside Equipment, or RSE) to enhance the safety, efficiency, and convenience of transportation systems. Two potential approaches are the widespread deployment of a dedicated short-range communications (DSRC) link on the 5.9GHz band, and cellular communication (C-V2X). Either of these methods would allow vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. The initiative has three priorities: Stakeholder evaluation and acceptance of the business model and its deployment schedule, Validation of the technology, with a focus on communications systems, in relation to deployment costs, and Creation of legal structures and policies, especially concerning digital privacy, to improve the system's long-term potential for success. === Safety === Current automotive safety technology relies primarily on vehicle-based radar, lidar, and sonar systems. This technology allows, for instance, a potential reduction in rear-end collisions by monitoring obstacles in front of or behind the vehicle and automatically applying the brakes when necessary. This technology, however, is limited by the sensing range of vehicle-based radar, particularly in angled and left-turn collisions, such as a motorist losing control of the vehicle during an impending head-on collision. The rear-end collisions addressed by current technology are generally less severe than angled, left-turn, or head-on collisions. VII promotes the development of a direct communication link between road vehicles and all other vehicles nearby, allowing for the exchange of information on vehicle speed and orientation or driver awareness and intent. This real-time exchange of information may enable more effective automated emergency maneuvers, such as steering, decelerating, or braking. In addition to nearby vehicle awareness, VII promotes a communication link between vehicles and roadway infrastructure. Such a link may allow for improved real-time traffic information, better queue management, and feedback to vehicles. Existing implementations of VII use vehicle-based sensors that can recognize and respond to roadway markings or signs, automatically adjusting vehicle parameters to follow the recognized instructions. However, this information may also be acquired via roadside beacons or stored in a centralized database accessible to all vehicles. === Efficiency === With a VII system in place, vehicles will be linked together. The headway between vehicles may therefore be reduced so that there is less empty space on the road, increasing the available capacity per lane. More capacity per lane will in turn imply fewer lanes in general, possibly satisfying the community's concerns about the impact of roadway widening. VII will enable precise traffic-signal coordination by tracking vehicle platoons and will benefit from accurate timing by drawing on real-time traffic data covering volume, density, and turning movements. Real-time traffic data can also be used in the design of new roadways or modification of existing systems as the data could be used to provide accurate origin-destination studies and turning-movement counts for uses in transportation forecasting and traffic operations. Such technology would also lead to improvements for transport engineers to address problems whilst reducing the cost of obtaining and compiling data. Tolling is another prospect for VII technology as it could enable roadways to be automatically tolled. Data could be collectively transmitted to road users for in-vehicle display, outlining the lowest cost, shortest distance, and/or fastest route to a destination on the basis of real-time conditions. === Existing applications === To some extent, results along these lines have been achieved in trials performed around the globe, making use of GPS, mobile phone signals, and vehicle registration plates. GPS is becoming standard in many new high-end vehicles and is an option on most new low- and mid-range vehicles. In addition, many users also have mobile phones that transmit trackable signals (and may also be GPS-enabled). Mobile phones can already be traced for purposes of emergency response. GPS and mobile phone tracking, however, do not provide fully reliable data. Furthermore, integrating mobile phones in vehicles may be prohibitively difficult. Data from mobile phones, though useful, might even increase risks to motorists as they tend to look at their phones rather than concentrate on their driving. Automatic registration plate recognition can provide large quantities of data, but continuously tracking a vehicle through a corridor is a difficult task with existing technology. Today's equipment is designed for data acquisition and functions such as enforcement and tolling, not for returning data to vehicles or motorists for response. GPS will nevertheless be one of the key components in VII systems. == Limitations == === Privacy === VII architecture is designed to prevent identification of individual vehicles, with all data exchange between the vehicle and the system occurring anonymously. Exchanges between the vehicles and third parties such as OEMs and toll collectors will occur, but the network traffic will be sent via encrypted tunnels and will therefore not be decipherable by the VII system. Data sharing with law enforcement or Homeland Security was not included in system design as of 2006. === Technical issues === ==== Coordination ==== A major issue facing the deployment of VII is the problem of how to set up the system initially. The costs associated with installing the technology in vehicles and providing communications and power at every intersection are significant. ==== Maintenance ==== Another factor for consideration in regard to the technology's distribution is how to update and maintain the units. Traffic systems are highly dynamic, with new traffic controls implemented every day and roadways constructed or repaired every year. The vehicle-based option could be updated via the internet (preferably wireless) but may subsequently require all users to have access to internet technology. Alternatively, if receivers were placed in all vehicles and the VII system was primarily located along the roadside, information could be stored in a centralized database. This would allow the agency responsible to issue updates at any time. These would then be disseminated to the roadside units for passing motorists. Operationally, this method is currently considered to provide the greatest effectiveness but at a high cost to the authorities. ==== Security ==== Security of the units is another concern, especially in light of the public acceptance issue. Criminals could tamper, remove, or destroy VII units regardless of whether they are installed inside vehicles or along the roadside. Magnets, electric shocks, and malicious software (viruses, hacking, or jamming) could be used to damage VII systems – regardless of whether units are located inside vehicle or along the roadside. == Recent developments == Much of the current research and experimentation is conducted in the United States where coordination is ensured through the Vehicle Infrastructure Integration Consortium; consisting of automobile manufacturers (Ford, General Motors, Daimler Chrysler, Toyota, Nissan, Honda, Volkswagen, BMW), IT suppliers, U.S. Federal and state transportation departments, and professional associations. Trialing is taking place in Michigan and California. The specific applications now being developed under the U.S. initiative are: Warning drivers of unsafe conditions or imminent collisions. Warning drivers if they are about to run off the road or speed around a curve too fast. Informing system operators of real-time congestion, weather conditions and incidents. Providing operators with information on corridor capacity for real-time management, planning and provision of corridor-wide advisories to drivers. In mid-2007, a VII environment covering some 20 square miles (52 km2) near Detroit was used to test 20 prototype VII applications. Several automobile manufacturers are also conducting their own VII research and triali

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  Kórsafn

  Kórsafn (Icelandic: Choral archives) is a sound installation by Icelandic artist Björk. Developed in collaboration with the technology company Microsoft, audio design firm Listen and architecture office firm Atelier Ace, the installation was designed for the lobby of the Sister City Hotel in New York City, United States, and launched in 2020. Elaborating 17 years of choral recording taken from Björk discography, Kórsafn consisted of an evolving music composition that uses an artificial intelligence model that responds to real-time weather data, creating a continuously shifting auditory experience. == Background and concept == In 2018, Björk announced her tenth concert tour Cornucopia, which debuted as a residency show at The Shed arts center. Before the start of the show, it was confirmed she would be accompanied by The Hamrahlid Choir. In 2019, while she was performing at The Shed, Björk stayed alongside the choir at the Sister City Hotel in New York City, where they would rehearse for the performances. While there, the Atelier Ace, which owns the Sister City boutique hotels, asked her to create a sound installation for the lobby. This was the second work commissioned by the hotel, a year after a similar piece by Julianna Barwick was featured in the lobby. Kórsafn is formed from two Icelandic words, "kór" ("choral") and "safn" ("archives"). The installation features recordings of Björk’s choral works from the previous 17 years, including compositions taken from her albums Medúlla (2004) and Biophilia (2011). The artificial intelligence system was developed in collaboration with Microsoft. The software processes data gathered from sensors and by a camera placed on the roof of the Sister City Hotel building and by a barometer. It then uses algorithms to determine how the choral elements are layered, pitched, and mixed in real time. The AI generate variations in real time by reacting to the passage of flocks, clouds, airplanes and changes in pressure. Data collected from sensors on the hotel’s rooftop include wind speed, cloud cover, and precipitation levels. These inputs influence the tonal quality, volume, and rhythmic patterns of the soundscape. The sound is played through hidden speakers in the hotel's lobby, blending with the architectural environment to create an immersive experience for guests. The AI system learns over time from the changing of the seasons and weather constantly evolving the sound - keeping in harmony with the sky. Björk described the project as an "AI tango," expressing curiosity about the interplay between her choral compositions and the AI's interpretations of environmental data. She noted the significance of the Hudson Valley's rich bird migrations, which influence the generative aspects of the soundscape. Due to the COVID-19 pandemic, the hotel closed while the installation was ongoing, making a version of the sound piece available online. == Reception == Kórsafn was positively reviewed. It's Nice That author Jenny Brewer described the piece as "a high-tech alternative to the smooth jazz that usually whistles through hotel lobbies". Writing for CNET, Scott Stein observed that it "is lovely and low-key, and honestly, it just blends into the background. It's nothing wild, but it fits the hotel", adding that "after an hour, it didn't get annoying, or too repetitive". The installation garnered several recognitions. It was nominated in the Fast Company's 2020 Innovation by Design Awards in the Hospitality category. It received three Clio Awards silver prizes, in the Use of Music in Experience/Activation, Sound Design and Emerging Technology categories.

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