Orange is an open-source data visualization, machine learning and data mining toolkit. It features a visual programming front-end for exploratory qualitative data analysis and interactive data visualization. == Description == Orange is a component-based visual programming software package for data visualization, machine learning, data mining, and data analysis. Orange components are called widgets. They range from simple data visualization, subset selection, and preprocessing to empirical evaluation of learning algorithms and predictive modeling. Visual programming is implemented through an interface in which workflows are created by linking predefined or user-designed widgets, while advanced users can use Orange as a Python library for data manipulation and widget alteration. == Software == Orange is an open-source software package released under GPL and hosted on GitHub. Versions up to 3.0 include core components in C++ with wrappers in Python. From version 3.0 onwards, Orange uses common Python open-source libraries for scientific computing, such as numpy, scipy and scikit-learn, while its graphical user interface operates within the cross-platform Qt framework. The default installation includes a number of machine learning, preprocessing and data visualization algorithms in 6 widget sets (data, transform, visualize, model, evaluate and unsupervised). Additional functionalities are available as add-ons (text-mining, image analytics, bioinformatics, etc.). Orange is supported on macOS, Windows and Linux and can also be installed from the Python Package Index repository (pip install Orange3). == Features == Orange consists of a canvas interface onto which the user places widgets and creates a data analysis workflow. Widgets offer basic functionalities such as reading the data, showing a data table, selecting features, training predictors, comparing learning algorithms, visualizing data elements, etc. The user can interactively explore visualizations or feed the selected subset into other widgets. Canvas: graphical front-end for data analysis Widgets: Data: widgets for data input, data filtering, sampling, imputation, feature manipulation and feature selection Visualize: widgets for common visualization (box plot, histograms, scatter plot) and multivariate visualization (mosaic display, sieve diagram). Classify: a set of supervised machine learning algorithms for classification Regression: a set of supervised machine learning algorithms for regression Evaluate: cross-validation, sampling-based procedures, reliability estimation and scoring of prediction methods Unsupervised: unsupervised learning algorithms for clustering (k-means, hierarchical clustering) and data projection techniques (multidimensional scaling, principal component analysis, correspondence analysis). == Add-ons == Orange users can extend their core set of components with components in the add-ons. Supported add-ons include: Associate: components for mining frequent itemsets and association rule learning. Bioinformatics: components for gene expression analysis, enrichment, and access to expression databases (e.g., Gene Expression Omnibus) and pathway libraries. Data fusion: components for fusing different data sets, collective matrix factorization, and exploration of latent factors. Educational: components for teaching machine learning concepts, such as k-means clustering, polynomial regression, stochastic gradient descent, ... Explain: provides an extension with components for the model explanation, including Shapley value analysis Geo: components for working with geospatial data. Image analytics: components for working with images and ImageNet embeddings Network: components for graph and network analysis. Text mining: components for natural language processing and text mining. Time series: widget components for time series analysis and modeling. Single-cell: support for single-cell gene expression analysis, including components for loading single-cell data, filtering and batch effect removal, marker genes discovery, scoring of cells and genes, and cell type prediction. Spectroscopy: components for analyzing and visualization of (hyper)spectral datasets. Survival analysis: add-on for data analysis dealing with survival data. It includes widgets for standard survival analysis techniques, such as the Kaplan-Meier plot, the Cox regression model, and several derivative widgets. World Happiness: support for downloading socioeconomic data from a database, including OECD and World Development Indicators. Provides access to thousands of country indicators from various economic databases. Fairness: add-on for evaluation and creation of fair machine learning models without discrimination. Widgets range from computing fairness metrics like statistical parity to post-, pre-, in-processing methods to build fair models. == Objectives == The program provides a platform for experiment selection, recommendation systems, and predictive modelling and is used in biomedicine, bioinformatics, genomic research, and teaching. In science, it is used as a platform for testing new machine learning algorithms and for implementing new techniques in genetics and bioinformatics. In education, it was used for teaching machine learning and data mining methods to students of biology, biomedicine, and informatics. == Extensions == Various projects build on Orange either by extending the core components with add-ons or using only the Orange Canvas to exploit the implemented visual programming features and GUI. OASYS — ORange SYnchrotron Suite scOrange — single cell biostatistics Quasar — data analysis in natural sciences == History == In 1996, the University of Ljubljana and Jožef Stefan Institute started development of ML, a machine learning framework in C++, and Python bindings were developed for this framework in 1997, which, together with emerging Python modules, formed a joint framework called Orange. Over the following years, most contemporary major algorithms for data mining and machine learning were implemented in C++ (Orange's core) or Python modules. In 2002, first prototypes to create a flexible graphical user interface were designed using Pmw Python megawidgets. In 2003, the graphical user interface was redesigned and re-developed for Qt framework using PyQt Python bindings. The visual programming framework was defined, and the development of widgets (graphical components of the data analysis pipeline) began. In 2005, extensions for data analysis in bioinformatics was created. In 2008, Mac OS X DMG and Fink-based installation packages were developed. In 2009, over 100 widgets were created and maintained. In 2009, Orange 2.0 beta was released, offering installation packages on the website based on the daily compiling cycle. In 2012, a new object hierarchy was imposed, replacing the old module-based structure. In 2013, a significant redesign of the graphical user interface included a new toolbox and depiction of workflows. In 2015, Orange 3.0 was released. Orange stores the data in NumPy arrays; machine learning algorithms mostly use scikit-learn. In 2015, a text analysis add-on for Orange3 was released. In 2016, Orange released version 3.3. Development scheduled a monthly cycle for stable releases. In 2016, Orange began development and release of an Image Analytics add-on, with server-side deep neural networks for image embedding In 2017, a Spectroscopy add-on for the analysis of spectral data was introduced. In 2017, Geo, an add-on for dealing with geo-location data and visualisation of geo maps was introduced In 2018, Orange began development and release of an add-on for single-cell data analysis. In 2019, Orange separated its graphical interface for development as a separate project, orange-canvas-core In 2020, Orange introduced the Explain add-on with widgets for explaining classification models and regression models, highlighting the strength and contributions specific features make towards predicting a specific class. In 2022, World Happiness, an add-on for the Orange3 data mining suite, was introduced, providing widgets for accessing socioeconomic data from various databases such as World Happiness Report, World Development Indicators, OECD. In 2022, Orange extended the Explain add-on with an Individual Conditional Expectation plot and the Permutation Feature Importance technique. In 2023, Orange introduced the Fairness add-on, including widgets to calculate bias metrics, as well as widgets for pre-, post-, and in-processing methods, allowing the creation of models less susceptible to systematic error due to the vagaries of the data set.
Elowan
Elowan is a plant-robot cyborg. Using its own internal bioelectrical signals, The plant has a robotic extension that makes it move towards light sources. Electrodes are inserted into the leaves, stem, and ground to detect the faint bioelectrical signals the plant produces. Then they are amplified so the robot can read them. So when the plant "wants" to go to light, the cyborg automatically goes to the nearest light source. Future extensions of the robot could provide: Protection, growth frameworks, and nutrients. Other factors that could make the cyborg move are temperature, soil, and gravity conditions Elowan is one in a series of plant-electronic hybrid experiments.
Interim Measures for the Management of Anthropomorphic AI Interactive Services
The Interim Measures for the Management of Anthropomorphic AI Interactive Services (Chinese: 人工智能拟人化互动服务管理暂行办法) is a document proposed by the Cyberspace Administration of China to regulate anthropomorphic artificial intelligence systems. The draft was released on December 27, 2026 for public comment period until January 25, 2026. The proposed document would prohibit AI companies and users of AI services from generating certain types of content deemed harmful to national interests or the social order, and impose various regulatory and safety requirements on providers of AI systems. The proposed regulation is motivated by concerns about the psychological and social effects of AI systems that are perceived as personalities by their users, including addiction, encouragement of self-harm, or generation of illegal content. == Description == === Scope === The regulation would apply to AI systems that are offered to the general public within China. They would not apply to company-internal or research use, or to products that are only available outside of China. For the purpose of the regulation, anthropomorphic Ai systems are defined as those that "simulate human personality traits, modes of thinking, and communication styles, and that engage in emotional interaction with humans through text, images, audio, video, or other means". === Requirements === The regulation would require AI providers to monitor users for signs of harmful use and to take various interventions when indications of harmful use are detected. It would also prohibit AI systems from certain types of behaviors and generation of certain types of content. In some circumstances where a user appears to be at risk of self harm, the system would be required to hand over control to a human operator who would manually intervene. The regulation would also require more rigorous practices for managing the provenance of training data used to develop these systems, and would require explicit opt-in consent from users before their interactions with an AI system were used as training data. Data used to train the regulated systems would be required to reflect core socialist values and traditional Chinese culture.
Department of Defense Directive 3000.09
Department of Defense Directive 3000.09 (DODD 3000.09), titled Autonomy in Weapon Systems, is the current U.S. military policy on autonomous weapons. It states: "Autonomous and semi-autonomous weapon systems will be designed to allow commanders and operators to exercise appropriate levels of human judgment over the use of force." == History == Then-Deputy Secretary of Defense Ashton Carter issued DOD's policy on autonomy in weapons systems, Department of Defense Directive (DODD) 3000.09, in November 2012. DOD updated the directive in January 2023. In February 2023, the US issued a related foreign policy proposal, Political Declaration on Responsible Military Use of Artificial Intelligence and Autonomy. == Definitions == There is no agreed definition of lethal autonomous weapon systems that is used in international fora. However, DODD 3000.09 provides definitions for different categories of autonomous weapon systems for the purposes of the U.S. military. These definitions are principally grounded in the role of the human operator with regard to target selection and engagement decisions, rather than in the technological sophistication of the weapon system. DODD 3000.09 defines LAWS as "weapon system[s] that, once activated, can select and engage targets without further intervention by a human operator." This concept of autonomy is also known as "human out of the loop" or "full autonomy." The directive contrasts LAWS with human-supervised, or "human on the loop," autonomous weapon systems, in which operators have the ability to monitor and halt a weapon's target engagement. Another category is semi-autonomous, or "human in the loop," weapon systems that "only engage individual targets or specific target groups that have been selected by a human operator." Semi-autonomous weapons include so-called "fire and forget" weapons, such as certain types of guided missiles, that deliver effects to human-identified targets using autonomous functions. The directive does not apply to autonomous or semi-autonomous cyberspace capabilities; unarmed platforms; unguided munitions; munitions manually guided by the operator (e.g., laser- or wire-guided munitions); mines; unexploded explosive ordnance; or autonomous or semi-autonomous systems that are not weapon systems, nor subject them to its guidelines. == Role of human operator == DODD 3000.09 requires that all systems, including LAWS, be designed to "allow commanders and operators to exercise appropriate levels of human judgment over the use of force." As noted in an August 2018 U.S. government white paper, "'appropriate' is a flexible term that reflects the fact that there is not a fixed, one-size-fits-all level of human judgment that should be applied to every context. What is 'appropriate' can differ across weapon systems, domains of warfare, types of warfare, operational contexts, and even across different functions in a weapon system." Furthermore, "human judgment over the use of force" does not require manual human "control" of the weapon system, as is often reported, but rather broader human involvement in decisions about how, when, where, and why the weapon will be employed. This includes a human determination that the weapon will be used "with appropriate care and in accordance with the law of war, applicable treaties, weapon system safety rules, and applicable rules of engagement." To aid this determination, DODD 3000.09 requires that "[a]dequate training, [tactics, techniques, and procedures], and doctrine are available, periodically reviewed, and used by system operators and commanders to understand the functioning, capabilities, and limitations of the system's autonomy in realistic operational conditions." The directive also requires that the weapon's human-machine interface be "readily understandable to trained operators" so they can make informed decisions regarding the weapon's use. == Weapons review process == DODD 3000.09 requires that the software and hardware of covered semi-autonomous and autonomous weapon systems, be tested and evaluated to ensure they:Function as anticipated in realistic operational environments against adaptive adversaries taking realistic and practicable countermeasures, [and] complete engagements within a timeframe and geographic area, as well as other relevant environmental and operational constraints, consistent with commander and operator intentions. If unable to do so, the systems will terminate the engagement or obtain additional operator input before continuing the engagement.Systems must also be "sufficiently robust to minimize the probability and consequences of failures." Any changes to the system's operating state—for example, due to machine learning—would require the system to go through testing and evaluation again to ensure that it has retained its safety features and ability to operate as intended. The directive also notes that "the use of AI capabilities in autonomous or semi-autonomous systems will be consistent with the DOD AI Ethical Principles." In addition to the standard weapons review process, a secondary senior-level review is required for covered autonomous and semi-autonomous systems. This review requires the Under Secretary of Defense for Policy (USD[P]), the vice chairman of the Joint Chiefs of Staff (VCJCS), and the Under Secretary of Defense for Research and Engineering (USD[R&E]) to approve the system before formal development. USD(P), VCJCS, and the Under Secretary of Defense for Acquisition and Sustainment (USD[A&S]) must then approve the system before fielding. In the event of "urgent military need," this senior-level review may be waived by the Deputy Secretary of Defense. DODD 3000.09 additionally establishes the Autonomous Weapon System Working Group—composed of representatives of USD(P); USD(R&E); USD(A&S); DOD General Counsel; the Chief Digital and AI Officer; the Director, Operational Test and Evaluation; and the chairman of the Joint Chiefs of Staff—to support and advise the senior-level review process. == Congressional notification == Per Section 251 of the FY2024 National Defense Authorization Act (NDAA; Pub. L. 118–31 (text) (PDF)), the Secretary of Defense is to notify the defense committees of any changes to DODD 3000.09 within 30 days. The Secretary is directed to provide a description of the modification and an explanation of the reasons for the modification. Section 1066 of the FY2025 NDAA (Pub. L. 118–159 (text) (PDF)) additionally requires the Secretary to "submit to the congressional defense committees a comprehensive report on the approval and deployment of lethal autonomous weapon systems by the United States," annually through December 31, 2029. Section 1061 of the FY2026 NDAA (P.L. Pub. L. 119–60 (menu; GPO has not yet published law)) amends the U.S. Code to require congressional notification of any waiver issued under DODD 3000.09. == AI safety == The second revision of DoDD 3000.09, effective January 25, 2023, requires that "The DoD will design and engineer AI capabilities to fulfill their intended functions while possessing the ability to detect and avoid unintended consequences, and the ability to disengage or deactivate deployed systems that demonstrate unintended behavior." == Criticism == As noted in the Bulletin of the Atomic Scientists, the policy requires that autonomous weapon systems that kill people or use kinetic force, selecting and engaging targets without further human intervention, be certified as compliant with "appropriate levels" and other standards, not that such weapon systems cannot meet these standards and are therefore forbidden. "Semi-autonomous" hunter-killers that autonomously identify and attack targets do not require certification.
Global call for AI red lines
The global call for AI red lines is a declaration made on 22 September 2025 calling on governments to define and internationally prohibit unacceptable AI uses and behaviors. The online declaration was announced by Nobel Peace Prize laureate Maria Ressa at the 80th United Nations General Assembly high-level week. The declaration was initially signed by 200 prominent politicians and scientists, including 10 Nobel Prize winners. The call does not specify which red lines to set, but suggests several, such as banning bioweapon design, mass surveillance or AI impersonation. == The declaration == The declaration was published online as an open letter on 22 September 2025. Nobel Peace Prize laureate Maria Ressa announced it in her opening speech at the 80th United Nations General Assembly high-level week in New York, urging governments to "define what AI should never be allowed to do" and "establish clear international boundaries to prevent universally unacceptable risks for A.I." The initiative was organized by three nonprofit organisations: the French Center for AI Safety (CeSIA), The Future Society, and the Center for Human-Compatible Artificial Intelligence (CHAI). The letter argues that humanity faces risks such as engineered pandemics, widespread disinformation, large-scale manipulation, unemployment and loss of control. Proponents argue that national laws are insufficient to address these risks and that "an international agreement on clear and verifiable red lines is necessary". They urge governments to reach an agreement by the end of 2026, and called for robust enforcement mechanisms and the creation of an independent organisation to implement it. The letter does not call for specific red lines, but suggests the possibility of banning lethal autonomous weapons, autonomous replication of AI systems and the use of AI in nuclear warfare. Other examples of possible red lines include social scoring, mass surveillance, bioweapon design, AI-generated child sexual abuse material and AI impersonation. A red line could prohibit either AI behaviors (what AI systems should be guaranteed to never do even if asked to) or AI uses. == Signatories == When published, the online declaration was signed by more than 200 prominent politicians and scientists, including 10 Nobel Prize winners. Signers include former president of Colombia Juan Manuel Santos and researchers Geoffrey Hinton and Yoshua Bengio. It also includes popular authors like Stephen Fry and Yuval Noah Harari. The letter received support from European lawmakers, including former Italian prime minister Enrico Letta, and former president of Ireland Mary Robinson. == Development of red lines == As of 2025, there is no global red line on AI. Some regional red lines exist, such as with the uses deemed "unacceptable" by the AI Act in Europe, and with the US-China agreement not to leave to AI the decision of whether to launch nuclear weapons. At the United Nations Security Council, days after the declaration, Michael Kratsios, Donald Trump's director of the White House Office of Science and Technology Policy, said "We totally reject all efforts by international bodies to assert centralized control and global governance of AI." The topic of AI red lines gained prominence in 2026 with the dispute between Anthropic and the Department of Defense (DoD), which resulted from the DoD requesting Anthropic to remove contractual red lines on fully autonomous weapons and mass domestic surveillance. The event led employees from Google and OpenAI as well as Senate Democrats to further call for red lines on military use of AI. Senator Adam Schiff proposed a bill to "codify" Anthropic's red lines.
Nextcloud
Nextcloud is a modular workspace platform designed to provide teams and businesses with a comprehensive environment for digital collaboration. Beyond central data management, it integrates office suites like Collabora Online and EuroOffice office suites. for seamless, cooperative workflows. The platform features built-in tools for chat, videoconferencing, and a privacy-focused AI assistant capable of running entirely on local LLMs. Supported by a rich ecosystem of apps, it can be hosted in the cloud or on premises and can scale up to millions of users. It has been translated into over 100 languages. == Features == Nextcloud files are stored in conventional directory structures, accessible via WebDAV if necessary. A SQLite, MySQL/MariaDB or PostgreSQL database is required to provide additional functionality like permissions, shares, and comments. Nextcloud can synchronize with local clients running Windows (Windows 8.1 and above), macOS (10.14 or later), Linux and FreeBSD. Nextcloud permits user and group administration locally or via different backends like OpenID or LDAP. Content can be shared inside the system by defining granular read/write permissions between users and groups. Nextcloud users can create public URLs when sharing files. Logging of file-related actions, as well as disallowing access based on file access rules is also available. Security options like brute-force protection and multi-factor authentication using TOTP, WebAuthn, Oauth2, and OpenID Connect are available. Nextcloud has planned new features such as monitoring capabilities, full-text search and Kerberos authentication, as well as audio/video conferencing, expanded federation and smaller user interface improvements. == History == In April 2016 Frank Karlitschek and most core contributors left ownCloud Inc. These included some of ownCloud's staff according to sources near to the ownCloud community. Karlitschek and many of these contributors went on to fork ownCloud, creating Nextcloud. The fork was preceded by a blog post of Karlitschek announcing his departure and raising questions about the management of the ownCloud, its community, and priorities between growth, money, and sustainability. There have been no official statements about the reason for the fork. However, Karlitschek mentioned the fork several times in a talk at the 2018 FOSDEM conference and in two appearances on the FLOSS Weekly podcast, emphasizing cultural mismatch between open source developers and business oriented people not used to the open source community. On June 2, within 12 hours of the announcement of the fork, the American entity "ownCloud Inc." announced that it is shutting down with immediate effect, stating that "[...] main lenders in the US have cancelled our credit. Following American law, we are forced to close the doors of ownCloud, Inc. with immediate effect and terminate the contracts of 8 employees." ownCloud Inc. accused Karlitschek of poaching developers, while Nextcloud developers such as Arthur Schiwon stated that he "decided to quit because not everything in the ownCloud Inc. company world evolved as I imagined". ownCloud GmbH continued operations, secured financing from new investors and took over the business of ownCloud Inc. In April 2018 Informationstechnikzentrum Bund (ITZBund) reported Nextcloud won the tender for "Bundescloud" (Germany government cloud) project. In August 2019 it was announced that the governments of France, Sweden and the Netherlands would use Nextcloud for file transfer. In January 2020 Nextcloud 18 "Nextcloud Hub" was released. The major change was direct integration with an Office suite (OnlyOffice) and Nextcloud announced that their goal was to compete with Office 365 and Google Docs. A partnership with Ionos was revealed – its hosting location in Germany and compliance with GDPR should support the goal of data sovereignty. In spring 2020 remote work and web conferencing usage increased due to the COVID-19 pandemic and Nextcloud released version 19 with chat and videoconferencing Talk app integrated into the application core. Communication with an optional "high performance back-end" allows self-hosting of web conferences with more than 10 participants. Collabora Online was introduced as another integrated office suite. In August 2021 Nextcloud was chosen as a collaboration platform for European cloud software GAIA-X. In a September 2021 European Commission report it was mentioned as "the most widely deployed Open Source content collaboration platform" Following the 2025 United States tariffs against the European Union, fear of overreliance on US cloud providers such as Microsoft 365 and Google Workspace increased, with Nextcloud being one of the foremost contenders to replace them. Some governmental organisations including the European Data Protection Supervisor and the German state of Schleswig-Holstein have since switched from Microsoft's Sharepoint to Nextcloud. According to Nextcloud, during the first 5 months of 2025, customer interest in the software had tripled.
Jess (programming language)
Jess is a rule engine for the Java computing platform, written in the Java programming language. It was developed by Ernest Friedman-Hill of Sandia National Laboratories. It is a superset of the CLIPS language. It was first written in late 1995. The language provides rule-based programming for the automation of an expert system, and is often termed as an expert system shell. In recent years, intelligent agent systems have also developed, which depend on a similar ability. Rather than a procedural paradigm, where one program has a loop that is activated only one time, the declarative paradigm used by Jess applies a set of rules to a set of facts continuously by a process named pattern matching. Rules can modify the set of facts, or can execute any Java code. It uses the Rete algorithm to execute rules. == License == The licensing for Jess is freeware for education and government use, and is proprietary software, needing a license, for commercial use. In contrast, CLIPS, which is the basis and starting code for Jess, is free and open-source software. == Code examples == Code examples: Sample code: