Eyes of Things (EoT) is the name of a project funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement number 643924. The purpose of the project, which is funded under the Smart Cyber-physical systems topic, is to develop a generic hardware-software platform for embedded, efficient (i.e. battery-operated, wearable, mobile), computer vision, including deep learning inference. On November 29, 2018, the European Space Agency announced that it was testing the suitability of the device for space applications in advance of a flight in a Cubesat. == Motivation == EoT is based on the following tenets: Future embedded systems will have more intelligence and cognitive functionality. Vision is paramount to such intelligent capacity Unlike other sensors, vision requires intensive processing. Power consumption must be optimized if vision is to be used in mobile and wearable applications Cloud processing of edge-captured images is not sustainable. The sheer amount of visual data generated cannot be transferred to the cloud. Bandwidth is not sufficient and cloud servers cannot cope with it. == Partners == VISILAB group at University of Castilla–La Mancha (Coordinator) Movidius Awaiba Thales Security Solutions & Systems DFKI Fluxguide Evercam nVISO == Awards == 2019 Electronic Component and Systems Innovation Award by the European Commission 2018 HiPEAC Tech Transfer Award 2018 EC Innovation Radar - highlighting excellent innovations Award 2018 Internet of Things (IoT) Technology Research Award Pilot by Google 2016 Semifinalist "THE VISION SHOW STARTUP COMPETITION", Global Association for Vision Information, Boston US
Gallery software
Gallery software is software that helps the user publish or share photos, pictures, videos or other digital media. Most galleries are located on Web servers, where users are allowed to register and publish their pictures. Gallery software usually features automatic image resizing, allows digital media be categorized into sets, and allows comments. == Types == Early digital media publishing and sharing was done with imageboards. The boards are by topics, sometimes called "chan". Each discussion in a "chan" are started with a piece of digital media, and follow-up discussions can contain another piece too. Software works in this way: Futallaby, Danbooru. Traditionally, galleries are managed. An administrator maintains a set of or hierarchy of albums. The users can upload their digital media in one of the existing albums defined by an administrator, or create their own albums. The users with sufficient permission can re-categorise the digital media others uploaded. Often, the site's administrator can define which album the users are allowed to categorise their media into, or delete other user's content. Examples are open source galleries Coppermine, Gallery Project. There are decentralised gallery software that does not have an administrator for managing contents. Pinterest, Flickr and DeviantArt has been successful with this model. Open source gallery software MediaGoblin works in this way. Each user can create their own "collections", to categorise theirs or other users' media. However users cannot put media into other user's collections. Each user's category is separate. There is no centralised theme or hierarchy for the media.
Dataism
Dataism is a term that has been used to describe the mindset or philosophy created by the emerging significance of big data. It was first used by David Brooks in The New York Times in 2013. The term has been expanded to describe what historian Yuval Noah Harari, in his book Homo Deus: A Brief History of Tomorrow from 2015, calls an emerging ideology or even a new form of religion, in which "information flow" is the "supreme value". In art, the term was used by Albert-Laszlo Barabasi to refer to an artist movement that uses data as its primary source of inspiration. == History == "If you asked me to describe the rising philosophy of the day, I'd say it is Data-ism", wrote David Brooks in The New York Times in February 2013. Brooks argued that in a world of increasing complexity, relying on data could reduce cognitive biases and "illuminate patterns of behavior we haven't yet noticed". In 2015, Steve Lohr's book Data-ism looked at how Big Data is transforming society, using the term to describe the Big Data revolution. In his 2016 book Homo Deus: A Brief History of Tomorrow, Yuval Noah Harari argues that all competing political or social structures can be seen as data processing systems: "Dataism declares that the universe consists of data flows, and the value of any phenomenon or entity is determined by its contribution to data processing" and "we may interpret the entire human species as a single data processing system, with individual humans serving as its chips." According to Harari, a Dataist should want to "maximise dataflow by connecting to more and more media". Harari predicts that the logical conclusion of this process is that, eventually, humans will give algorithms the authority to make the most important decisions in their lives, such as whom to marry and which career to pursue. Harari argues that Aaron Swartz could be called the "first martyr" of Dataism. In 2022, Albert-László Barabási coined the term "Dataism" to define an artistic movement that positions data as the central means of understanding nature, society, technology, and human essence. This movement underscores the necessity for art to integrate with data to stay relevant in contemporary society. Dataism responds to the intricacy and interconnectedness of modern social, economic, and technological realms, which exceed individual understanding. Advocating for the use of methodologies from various fields like science, business, and politics in art, Dataism sees this fusion as essential for art to retain its significance and influence. == Criticism == Commenting on Harari's characterisation of Dataism, security analyst Daniel Miessler believes that Dataism does not present the challenge to the ideology of liberal humanism that Harari claims, because humans will simultaneously be able to believe in their own importance and that of data. Harari himself raises some criticisms, such as the problem of consciousness, which Dataism is unlikely to illuminate. Humans may also find out that organisms are not algorithms, he suggests. Dataism implies that all data is public, even personal data, to make the system work as a whole, which is a factor that's already showing resistance today. Other analysts, such as Terry Ortleib, have looked at the extent to which Dataism poses a dystopian threat to humanity. The Facebook–Cambridge Analytica data scandal showed how political leaders manipulated Facebook's users' data to build specific psychological profiles that went on to manipulate the network. A team of data analysts reproduced the AI technology developed by Cambridge Analytica around Facebook's data and was able to define the following rules: 10 likes enables a machine to know a person like a coworker, 70 likes like a friend would, 150 likes like a parent would, 300 likes like a lover would, and beyond it may be possible to know a people better than they know themselves.
BabelNet
BabelNet is a multilingual lexical-semantic knowledge graph, ontology and encyclopedic dictionary developed at the NLP group of the Sapienza University of Rome under the supervision of Roberto Navigli. BabelNet was automatically created by linking Wikipedia to the most popular computational lexicon of the English language, WordNet. The integration is done using an automatic mapping and by filling in lexical gaps in resource-poor languages by using statistical machine translation. The result is an encyclopedic dictionary that provides concepts and named entities lexicalized in many languages and connected with large amounts of semantic relations. Additional lexicalizations and definitions are added by linking to free-license wordnets, OmegaWiki, the English Wiktionary, Wikidata, FrameNet, VerbNet and others. Similarly to WordNet, BabelNet groups words in different languages into sets of synonyms, called Babel synsets. For each Babel synset, BabelNet provides short definitions (called glosses) in many languages harvested from both WordNet and Wikipedia. == Statistics of BabelNet == As of December 2023, BabelNet (version 5.3) covers 600 languages. It contains almost 23 million synsets and around 1.7 billion word senses (regardless of their language). Each Babel synset contains 2 synonyms per language, i.e., word senses, on average. The semantic network includes all the lexico-semantic relations from WordNet (hypernymy and hyponymy, meronymy and holonymy, antonymy and synonymy, etc., totaling around 364,000 relation edges) as well as an underspecified relatedness relation from Wikipedia (totaling around 1.9 billion edges). Version 5.3 also associates around 61 million images with Babel synsets and provides a Lemon RDF encoding of the resource, available via a SPARQL endpoint. 2.67 million synsets are assigned domain labels. == Applications == BabelNet has been shown to enable multilingual natural language processing applications. The lexicalized knowledge available in BabelNet has been shown to obtain state-of-the-art results in: Semantic relatedness, Multilingual word-sense disambiguation and entity linking, with the Babelfy system, Video games with a purpose. == Prizes and acknowledgments == BabelNet received the META prize 2015 for "groundbreaking work in overcoming language barriers through a multilingual lexicalised semantic network and ontology making use of heterogeneous data sources". The Artificial Intelligence Journal paper that describes BabelNet won the Prominent Paper Award in 2017. BabelNet featured prominently in a Time magazine article about the new age of innovative and up-to-date lexical knowledge resources available on the Web.
ML.NET
ML.NET is a free software machine learning library for the C# and F# programming languages. It also supports Python models when used together with NimbusML. The preview release of ML.NET included transforms for feature engineering like n-gram creation, and learners to handle binary classification, multi-class classification, and regression tasks. Additional ML tasks like anomaly detection and recommendation systems have since been added, and other approaches like deep learning will be included in future versions. == Machine learning == ML.NET brings model-based Machine Learning analytic and prediction capabilities to existing .NET developers. The framework is built upon .NET Core and .NET Standard inheriting the ability to run cross-platform on Linux, Windows and macOS. Although the ML.NET framework is new, its origins began in 2002 as a Microsoft Research project named TMSN (text mining search and navigation) for use internally within Microsoft products. It was later renamed to TLC (the learning code) around 2011. ML.NET was derived from the TLC library and has largely surpassed its parent says Dr. James McCaffrey, Microsoft Research. Developers can train a Machine Learning Model or reuse an existing Model by a 3rd party and run it on any environment offline. This means developers do not need to have a background in Data Science to use the framework. Support for the open-source Open Neural Network Exchange (ONNX) Deep Learning model format was introduced from build 0.3 in ML.NET. The release included other notable enhancements such as Factorization Machines, LightGBM, Ensembles, LightLDA transform and OVA. The ML.NET integration of TensorFlow is enabled from the 0.5 release. Support for x86 & x64 applications was added to build 0.7 including enhanced recommendation capabilities with Matrix Factorization. A full roadmap of planned features have been made available on the official GitHub repo. The first stable 1.0 release of the framework was announced at Build (developer conference) 2019. It included the addition of a Model Builder tool and AutoML (Automated Machine Learning) capabilities. Build 1.3.1 introduced a preview of Deep Neural Network training using C# bindings for Tensorflow and a Database loader which enables model training on databases. The 1.4.0 preview added ML.NET scoring on ARM processors and Deep Neural Network training with GPU's for Windows and Linux. === Performance === Microsoft's paper on machine learning with ML.NET demonstrated it is capable of training sentiment analysis models using large datasets while achieving high accuracy. Its results showed 95% accuracy on Amazon's 9GB review dataset. === Model builder === The ML.NET CLI is a Command-line interface which uses ML.NET AutoML to perform model training and pick the best algorithm for the data. The ML.NET Model Builder preview is an extension for Visual Studio that uses ML.NET CLI and ML.NET AutoML to output the best ML.NET Model using a GUI. === Model explainability === AI fairness and explainability has been an area of debate for AI Ethicists in recent years. A major issue for Machine Learning applications is the black box effect where end users and the developers of an application are unsure of how an algorithm came to a decision or whether the dataset contains bias. Build 0.8 included model explainability API's that had been used internally in Microsoft. It added the capability to understand the feature importance of models with the addition of 'Overall Feature Importance' and 'Generalized Additive Models'. When there are several variables that contribute to the overall score, it is possible to see a breakdown of each variable and which features had the most impact on the final score. The official documentation demonstrates that the scoring metrics can be output for debugging purposes. During training & debugging of a model, developers can preview and inspect live filtered data. This is possible using the Visual Studio DataView tools. === Infer.NET === Microsoft Research announced the popular Infer.NET model-based machine learning framework used for research in academic institutions since 2008 has been released open source and is now part of the ML.NET framework. The Infer.NET framework utilises probabilistic programming to describe probabilistic models which has the added advantage of interpretability. The Infer.NET namespace has since been changed to Microsoft.ML.Probabilistic consistent with ML.NET namespaces. === NimbusML Python support === Microsoft acknowledged that the Python programming language is popular with Data Scientists, so it has introduced NimbusML the experimental Python bindings for ML.NET. This enables users to train and use machine learning models in Python. It was made open source similar to Infer.NET. === Machine learning in the browser === ML.NET allows users to export trained models to the Open Neural Network Exchange (ONNX) format. This establishes an opportunity to use models in different environments that don't use ML.NET. It would be possible to run these models in the client side of a browser using ONNX.js, a JavaScript client-side framework for deep learning models created in the Onnx format. === AI School Machine Learning Course === Along with the rollout of the ML.NET preview, Microsoft rolled out free AI tutorials and courses to help developers understand techniques needed to work with the framework.
Watch Duty
Watch Duty is real-time wildfire tracking and alert platform. It utilizes a combination of official data sources and human monitoring by experienced volunteers, including active and retired firefighters, dispatchers, and first responders. The service is operated by Sherwood Forestry Service, a 501(c)(3) non-profit organization. In 2025, Watch Duty had 48 full-time employees and approximately 250 volunteers who reported on over 13,000 wildfires. == History == Watch Duty was launched in August 2021 by John Mills, who experienced a wildfire shortly after he moved to Sonoma County, California. The California Department of Forestry and Fire Protection (CAL FIRE) was unable to provide updates more than once a day due to time constraints, and residents of the area were unable to monitor the progression of the wildfire. Mills discovered that updates were being shared on social media by volunteers following radio scanners, and developed the Watch Duty app to make the information more readily available. It launched with a volunteer staff of "citizen information officers," initially serving Sonoma County before expanding to all of California in June 2022. As of December 2024, the service covered 22 states west of the Mississippi River. During the January 2025 Southern California wildfires, Watch Duty was downloaded millions of times, ranking among the most popular free downloads on the iOS App Store. On December 1st, 2025, Watch Duty announced an expansion to all 50 U.S. states. == App == The application is centered around an interactive map based on OpenStreetMap data with a variety of overlays visualizing fire risk, active fires and evacuation zones, weather conditions, and air quality observations. Watch Duty sources wildfire information from radio scanner transmissions, firefighters, sheriffs, and CAL FIRE publications. It has policies against the publication of personally identifiable information, such as the names of fire victims. Watch Duty is free to use, doesn't require users to sign up, and doesn't display ads.
Otterly.ai
Otterly.ai is an Austrian software company, founded in 2024, that provides tools for generative engine optimization, the practice of monitoring and optimizing results in large language models. == History == Otterly.ai was co-founded in 2024 by Thomas Peham, Klaus-M. Schremser and Josef Trauner. The concept for OtterlyAI was developed in response to the increasing use of generative AI tools in digital search and content discovery. The company announced a technology partnership with SEO platform Semrush in January 2025.