AI Detector Accuracy

AI Detector Accuracy — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

• 

  Virtual assistant

  A virtual assistant (VA) is a software agent that can perform a range of tasks or services for a user based on user input, such as commands or questions, including verbal ones. Such technologies often incorporate chatbot capabilities to streamline task execution. The interaction may be via text, graphical interface, or voice, as some virtual assistants are able to interpret human speech and respond via synthesized voices. In many cases, users can ask their virtual assistants questions, control home automation devices and media playback, and manage other basic tasks such as email, to-do lists, and calendars – all with verbal commands. In recent years, prominent virtual assistants for direct consumer use have included Apple Siri, Amazon Alexa, Google Assistant (Gemini), Microsoft Copilot and Samsung Bixby. Also, companies in various industries often incorporate some kind of virtual assistant technology into their customer service or support. Into the 2020s, the emergence of artificial intelligence based chatbots, such as ChatGPT, has brought increased capability and interest to the field of virtual assistant products and services. == History == === Experimental decades: 1910s–1980s === Radio Rex was the first voice-activated toy, patented in 1916 and released in 1922. It was a wooden toy in the shape of a dog that would come out of its house when its name is called. In 1952, Bell Labs presented "Audrey", the Automatic Digit Recognition machine. It occupied a six-foot-high relay rack, consumed substantial power, had streams of cables and exhibited the myriad maintenance problems associated with complex vacuum-tube circuitry. It could recognize the fundamental units of speech, phonemes. It was limited to the accurate recognition of digits spoken by designated talkers. It could therefore be used for voice dialing, but in most cases, push-button dialing was cheaper and faster, rather than speaking the consecutive digits. Another early tool which was enabled to perform digital speech recognition was the IBM Shoebox voice-activated calculator, presented to the general public during the 1962 Seattle World's Fair after its initial market launch in 1961. This early computer, developed almost 20 years before the introduction of the first IBM Personal Computer in 1981, was able to recognize 16 spoken words and the digits 0 to 9. The first natural language processing computer program or the chatbot ELIZA was developed by MIT professor Joseph Weizenbaum in the 1960s. It was created to "demonstrate that the communication between man and machine was superficial". ELIZA used pattern matching and substitution methodology into scripted responses to simulate conversation, which gave an illusion of understanding on the part of the program. Weizenbaum's own secretary reportedly asked Weizenbaum to leave the room so that she and ELIZA could have a real conversation. Weizenbaum was surprised by this, later writing: "I had not realized ... that extremely short exposures to a relatively simple computer program could induce powerful delusional thinking in quite normal people. This gave name to the ELIZA effect, the tendency to unconsciously assume computer behaviors are analogous to human behaviors; that is, anthropomorphisation, a phenomenon present in human interactions with virtual assistants. The next milestone in the development of voice recognition technology was achieved in the 1970s at the Carnegie Mellon University in Pittsburgh, Pennsylvania with substantial support of the United States Department of Defense and its DARPA agency, funded five years of a Speech Understanding Research program, aiming to reach a minimum vocabulary of 1,000 words. Companies and academia including IBM, Carnegie Mellon University (CMU) and Stanford Research Institute took part in the program. The result was "Harpy", it mastered about 1000 words, the vocabulary of a three-year-old and it could understand sentences. It could process speech that followed pre-programmed vocabulary, pronunciation, and grammar structures to determine which sequences of words made sense together, and thus reducing speech recognition errors. In 1986, Tangora was an upgrade of the Shoebox, it was a voice recognizing typewriter. Named after the world's fastest typist at the time, it had a vocabulary of 20,000 words and used prediction to decide the most likely result based on what was said in the past. IBM's approach was based on a hidden Markov model, which adds statistics to digital signal processing techniques. The method makes it possible to predict the most likely phonemes to follow a given phoneme. Still each speaker had to individually train the typewriter to recognize their voice, and pause between each word. In 1983, Gus Searcy invented the "Butler in a Box", an electronic voice home controller system. === Birth of smart virtual assistants: 1990s–2010s === In the 1990s, digital speech recognition technology became a feature of the personal computer with IBM, Philips and Lernout & Hauspie fighting for customers. Much later the market launch of the first smartphone IBM Simon in 1994 laid the foundation for smart virtual assistants as we know them today. In 1997, Dragon's NaturallySpeaking software could recognize and transcribe natural human speech without pauses between each word into a document at a rate of 100 words per minute. A version of Naturally Speaking is still available for download and it is still used today, for instance, by many doctors in the US and the UK to document their medical records. In 2001 Colloquis publicly launched SmarterChild, on platforms like AIM and MSN Messenger. While entirely text-based SmarterChild was able to play games, check the weather, look up facts, and converse with users to an extent. The first modern digital virtual assistant installed on a smartphone was Siri, which was introduced as a feature of the iPhone 4S on 4 October 2011. Apple Inc. developed Siri following the 2010 acquisition of Siri Inc., a spin-off of SRI International, which is a research institute financed by DARPA and the United States Department of Defense. Its aim was to aid in tasks such as sending a text message, making phone calls, checking the weather or setting up an alarm. Over time, it has developed to provide restaurant recommendations, search the internet, and provide driving directions. In November 2014, Amazon announced Alexa alongside the Echo. In 2016, Salesforce debuted Einstein, developed from a set of technologies underlying the Salesforce platform. Einstein was replaced by Agentforce, an agentic AI, in September 2024. In April 2017 Amazon released a service for building conversational interfaces for any type of virtual assistant or interface. === Large Language Models: 2020s-present === In the 2020s, artificial intelligence (AI) systems like ChatGPT have gained popularity for their ability to generate human-like responses to text-based conversations. In February 2020, Microsoft introduced its Turing Natural Language Generation (T-NLG), which was then the "largest language model ever published at 17 billion parameters." On November 30, 2022, ChatGPT was launched as a prototype and quickly garnered attention for its detailed responses and articulate answers across many domains of knowledge. The advent of ChatGPT and its introduction to the wider public increased interest and competition in the space. In February 2023, Google began introducing an experimental service called "Bard" which is based on its LaMDA program to generate text responses to questions asked based on information gathered from the web. While ChatGPT and other generalized chatbots based on the latest generative AI are capable of performing various tasks associated with virtual assistants, there are also more specialized forms of such technology that are designed to target more specific situations or needs. == Method of interaction == Virtual assistants work via: Text, including: online chat (especially in an instant messaging application or other application ), SMS text, e-mail or other text-based communication channel, for example Conversica's intelligent virtual assistants for business. Voice: for example with Amazon Alexa on Amazon Echo devices, Siri on an iPhone, Google Assistant on Google-enabled Android devices, or Bixby on Samsung devices. Images: some assistants, such as Google Assistant (which includes Google Lens) and Bixby on the Samsung Galaxy series, have the added capability of performing image processing to recognize objects in images. Many virtual assistants are accessible via multiple methods, offering versatility in how users can interact with them, whether through chat, voice commands, or other integrated technologies. Virtual assistants use natural language processing (NLP) to match user text or voice input to executable commands. Some continually learn using artificial intelligence techniques including machine learning and ambient intelligence. To activate a virtual assistant u

  Read more →
• 

  Early-exit network

  Early-exit networks are a class of dynamic neural networks designed for efficient inference by allowing models to make confident predictions at intermediate layers, rather than processing the full network. Early-exit mechanisms are methods for deep neural networks that add intermediate classifiers, allowing inference to stop at earlier layers for inputs assessed as low uncertainty. Decisions to exit are typically based on confidence measures such as softmax-derived scores, classification margins, or entropy-based criteria, with the goal of reducing computational cost. These approaches are commonly paired with specialized training procedures and system-level optimizations to improve efficiency while preserving accuracy. The main idea behind the technology is to stop excessive calculations when a good answer can already be given with a high degree of probability, which can save both computation and time. Early-exit networks have also been extended with expert-based exit criteria, where intermediate classifiers are treated as multiple “experts” whose predictions and confidence scores can be aggregated to decide whether to stop computation early. Hardware implementations are also being developed.

  Read more →
• 

  Mustafa Suleyman

  Mustafa Suleyman (born in August 1984) is a British artificial intelligence (AI) entrepreneur. He is the CEO of Microsoft AI, and the co-founder and former head of applied AI at DeepMind, an AI company which was acquired by Google. After leaving DeepMind, he co-founded Inflection AI, a machine learning and generative AI company, in 2022. == Early life and education == Suleyman's Syrian father worked as a taxi driver and his English mother was a nurse. He grew up off Caledonian Road, London, where he lived with his parents and his two younger brothers. Suleyman went to Thornhill Primary School, a state school in Islington, followed by Queen Elizabeth's School, Barnet, a boys' grammar school. Around that time, he met his DeepMind co-founder, Demis Hassabis, through his best friend, who was Demis's younger brother. Suleyman shared that he and Hassabis often discussed how they could make a positive impact on the world. Suleyman enrolled to study philosophy and theology at the University of Oxford where he was an undergraduate student at Mansfield College, Oxford, before dropping out at 19. == Career == In August 2001, while still a teenager and a "strong atheist", Suleyman helped Mohammed Mamdani establish a telephone counselling service called the Muslim Youth Helpline. The organization would later become one of the largest mental health support services. Suleyman subsequently worked as a policy officer on human rights for Ken Livingstone, the Mayor of London, before going on to start Reos Partners, a "systemic change" consultancy that uses methods from conflict resolution to navigate social problems. As a negotiator and facilitator, Mustafa worked for a wide range of clients such as the United Nations, the Dutch government, and the World Wide Fund for Nature. === DeepMind and Google === In 2010 Suleyman co-founded DeepMind Technologies, an artificial intelligence (AI) and machine learning company, and became its chief product officer. The company quickly established itself as one of the leaders in the AI sector. In 2014 DeepMind was acquired by Google for a reported £400 million, the company's largest acquisition in Europe at that time. Following the acquisition, Suleyman became head of applied AI at DeepMind, taking on responsibility for integrating the company's technology across a wide range of Google products. In February 2016 Suleyman launched DeepMind Health at the Royal Society of Medicine. DeepMind Health builds clinician-led technology for the National Health Service (NHS) and other partners to improve frontline healthcare services. Under Suleyman, DeepMind also developed research collaborations with healthcare organizations in the United Kingdom, including Moorfields Eye Hospital NHS foundation trust. In 2016, Suleyman led an effort to apply DeepMind's machine learning algorithms to help reduce the energy required to cool Google's data centres. The system evaluated the billions of possible combinations of actions that the data centre operators could take, and came up with recommendations based on the predicted power usage. The system discovered novel methods of cooling, leading to a reduction of up to 40% of the amount of energy used for cooling, and a 15% improvement in the buildings' overall energy efficiency. Since June 2019, Suleyman has served on the board of The Economist Group, which publishes The Economist newspaper. In August 2019, Suleyman was placed on administrative leave following allegations of bullying employees. The company hired an external lawyer to investigate, and shortly thereafter Suleyman left to take a VP role at parent company Google. An email circulated by DeepMind's leadership to staff after the story broke, as well as additional details published by Business Insider, said Suleyman's "management style fell short" of expected standards. In December 2019, Suleyman announced he would be leaving DeepMind to join Google, working in a policy role. === Inflection AI === Suleyman left Google in January 2022 and joined Greylock Partners as a venture partner and in March 2022, Suleyman co-founded Inflection AI, a new AI lab venture with Greylock's Reid Hoffman. The company was founded with the goal of leveraging "AI to help humans 'talk' to computers," recruited former staff from companies such as Google and Meta and raised $225 million in its first funding round. In 2023, Inflection AI launched a chatbot named “Pi” for Personal Intelligence. The bot “remembers” past conversations and seems to get to know its users over time. According to Suleyman, the long-term goal for Pi is to be a digital “Chief of Staff”, with the initial design focused on maintaining conversational dialogue with users, asking questions, and offering emotional support. === Microsoft AI === In March 2024, Microsoft appointed Suleyman as Executive Vice President (EVP) and CEO of its newly created consumer AI unit, Microsoft AI. Several members of Inflection AI's team were also appointed to the division, including co-founder Karen Simonyan. === Awards and honours === Suleyman was appointed a Commander of the British Empire (CBE) in the 2019 New Year Honours. Suleyman was named by Time as one of the 100 most influential people in artificial intelligence in 2023 and in 2024. === Views on AI ethics === Suleyman is prominent in the debate over the ethics of AI and has spoken widely about the need for companies, governments and civil society to join in holding technologists accountable for the impacts of their work. He has advocated redesigning incentives in the technology industry to steer business leaders toward prioritising social responsibility alongside their fiduciary duties. Within DeepMind he set up a research unit called DeepMind Ethics & Society to study the real-world impacts of AI and help technologists put ethics into practice. Suleyman is also a founding co-chair of the Partnership on AI – an organisation that includes representatives from companies such as Amazon, Apple, DeepMind, Meta, Google, IBM, and Microsoft. The organisation studies and formulates best practices for AI technologies, advances the public's understanding of AI, and serves as an open platform for discussion and engagement about AI and how it affects people and society. Its board of directors has equal representation from non-profit and for profit entities. In September 2023, Suleyman, in collaboration with researcher Michael Bhaskar, published The Coming Wave, Technology, Power and the 21st Century's Greatest Dilemma, a book that examines the transformative and potentially perilous impact of advanced technologies, particularly AI and synthetic biology. According to Suleyman, AI notably has the potential to bring "radical abundance", address climate change and empower people with its cheap problem-solving capabilities. But it may also improve its own design and manufacturing processes, leading to a period of dangerously rapid AI progress. And it could enable catastrophic misuse, from bioengineered pathogens to autonomous weapons, making global oversight and containment essential to avoid unintended consequences. It was shortlisted for the 2023 Financial Times Business Book of the Year Award. In June 2024, in an interview with Andrew Ross Sorkin at the Aspen Ideas Festival, Suleyman expressed the view that unless a website explicitly specifies otherwise, for "content that is already on the open web, the social contract of that content since the 90s has been that it is fair use. Anyone can copy it, recreate with it, reproduce with it. That has been freeware, if you like. That's been the understanding." The statement sparked controversy over the use of Internet data for training AI models. == Personal life == A Business Insider profile in 2017 described Suleyman as being liberal.

  Read more →
• 

  Tree (abstract data type)

  In computer science, a tree is a widely used abstract data type that represents a hierarchical tree structure with a set of connected nodes. Each node in the tree can be connected to many children (depending on the type of tree), but must be connected to exactly one parent, except for the root node, which has no parent (i.e., the root node as the top-most node in the tree hierarchy). These constraints mean there are no cycles or "loops" (no node can be its own ancestor), and also that each child can be treated like the root node of its own subtree, making recursion a useful technique for tree traversal. In contrast to linear data structures, many trees cannot be represented by relationships between neighboring nodes (parent and children nodes of a node under consideration, if they exist) in a single straight line (called edge or link between two adjacent nodes). Binary trees are a commonly used type, which constrain the number of children for each parent to at most two. When the order of the children is specified, this data structure corresponds to an ordered tree in graph theory. A value or pointer to other data may be associated with every node in the tree, or sometimes only with the leaf nodes, which have no children nodes. The abstract data type (ADT) can be represented in a number of ways, including a list of parents with pointers to children, a list of children with pointers to parents, or a list of nodes and a separate list of parent-child relations (a specific type of adjacency list). Representations might also be more complicated, for example using indexes or ancestor lists for performance. Trees as used in computing are similar to but can be different from mathematical constructs of trees in graph theory, trees in set theory, and trees in descriptive set theory. == Terminology == A node is a structure which may contain data and connections to other nodes, sometimes called edges or links. Each node in a tree has zero or more child nodes, which are below it in the tree (by convention, trees are drawn with descendants going downwards). A node that has a child is called the child's parent node (or superior). All nodes have exactly one parent, except the topmost root node, which has none. A node might have many ancestor nodes, such as the parent's parent. Child nodes with the same parent are sibling nodes. Typically siblings have an order, with the first one conventionally drawn on the left. Some definitions allow a tree to have no nodes at all, in which case it is called empty. An internal node (also known as an inner node, inode for short, or branch node) is any node of a tree that has child nodes. Similarly, an external node (also known as an outer node, leaf node, or terminal node) is any node that does not have child nodes. The height of a node is the length of the longest downward path to a leaf from that node. The height of the root is the height of the tree. The depth of a node is the length of the path to its root (i.e., its root path). Thus the root node has depth zero, leaf nodes have height zero, and a tree with only a single node (hence both a root and leaf) has depth and height zero. Conventionally, an empty tree (tree with no nodes, if such are allowed) has height −1. Each non-root node can be treated as the root node of its own subtree, which includes that node and all its descendants. Other terms used with trees: Neighbor Parent or child. Ancestor A node reachable by repeated proceeding from child to parent. Descendant A node reachable by repeated proceeding from parent to child. Also known as subchild. Degree For a given node, its number of children. A leaf, by definition, has degree zero. Degree of tree The degree of a tree is the maximum degree of a node in the tree. Distance The number of edges along the shortest path between two nodes. Level The level of a node is the number of edges along the unique path between it and the root node. This is the same as depth. Width The number of nodes in a level. Breadth The number of leaves. Complete tree A tree with every level filled, except the last. Forest A set of one or more disjoint trees. Ordered tree A rooted tree in which an ordering is specified for the children of each vertex. Size of a tree Number of nodes in the tree. == Common operations == Enumerating all the items Enumerating a section of a tree Searching for an item Adding a new item at a certain position on the tree Deleting an item Pruning: Removing a whole section of a tree Grafting: Adding a whole section to a tree Finding the root for any node Finding the lowest common ancestor of two nodes === Traversal and search methods === Stepping through the items of a tree, by means of the connections between parents and children, is called walking the tree, and the action is a walk of the tree. Often, an operation might be performed when a pointer arrives at a particular node. A walk in which each parent node is traversed before its children is called a pre-order walk; a walk in which the children are traversed before their respective parents are traversed is called a post-order walk; a walk in which a node's left subtree, then the node itself, and finally its right subtree are traversed is called an in-order traversal. (This last scenario, referring to exactly two subtrees, a left subtree and a right subtree, assumes specifically a binary tree.) A level-order walk effectively performs a breadth-first search over the entirety of a tree; nodes are traversed level by level, where the root node is visited first, followed by its direct child nodes and their siblings, followed by its grandchild nodes and their siblings, etc., until all nodes in the tree have been traversed. == Representations == There are many different ways to represent trees. In working memory, nodes are typically dynamically allocated records with pointers to their children, their parents, or both, as well as any associated data. If of a fixed size, the nodes might be stored in a list. Nodes and relationships between nodes might be stored in a separate special type of adjacency list. In relational databases, nodes are typically represented as table rows, with indexed row IDs facilitating pointers between parents and children. Nodes can also be stored as items in an array, with relationships between them determined by their positions in the array (as in a binary heap). A binary tree can be implemented as a list of lists: the head of a list (the value of the first term) is the left child (subtree), while the tail (the list of second and subsequent terms) is the right child (subtree). This can be modified to allow values as well, as in Lisp S-expressions, where the head (value of first term) is the value of the node, the head of the tail (value of second term) is the left child, and the tail of the tail (list of third and subsequent terms) is the right child. Ordered trees can be naturally encoded by finite sequences, for example with natural numbers. == Examples of trees and non-trees == == Type theory == As an abstract data type, the abstract tree type T with values of some type E is defined, using the abstract forest type F (list of trees), by the functions: value: T → E children: T → F nil: () → F node: E × F → T with the axioms: value(node(e, f)) = e children(node(e, f)) = f In terms of type theory, a tree is an inductive type defined by the constructors nil (empty forest) and node (tree with root node with given value and children). == Mathematical terminology == Viewed as a whole, a tree data structure is an ordered tree, generally with values attached to each node. Concretely, it is (if required to be non-empty): A rooted tree with the "away from root" direction (a more narrow term is an "arborescence"), meaning: A directed graph, whose underlying undirected graph is a tree (any two vertices are connected by exactly one simple path), with a distinguished root (one vertex is designated as the root), which determines the direction on the edges (arrows point away from the root; given an edge, the node that the edge points from is called the parent and the node that the edge points to is called the child), together with: an ordering on the child nodes of a given node, and a value (of some data type) at each node. Often trees have a fixed (more properly, bounded) branching factor (outdegree), particularly always having two child nodes (possibly empty, hence at most two non-empty child nodes), hence a "binary tree". Allowing empty trees makes some definitions simpler, some more complicated: a rooted tree must be non-empty, hence if empty trees are allowed the above definition instead becomes "an empty tree or a rooted tree such that ...". On the other hand, empty trees simplify defining fixed branching factor: with empty trees allowed, a binary tree is a tree such that every node has exactly two children, each of which is a tree (possibly empty). == Applications == Trees are commonly used to represent or manipulate hierarchical data in ap

  Read more →
• 

  Text simplification

  Text simplification is an aspect of natural language processing that involves modifying, organizing, or categorizing existing text to make it easier to understand while retaining its original meaning. This process is essential in today's world, where communication is increasingly complex due to advancements in science, technology, and media. Human languages are inherently intricate, with extensive vocabularies and complex structures that can be challenging for machines to handle efficiently. Researchers have found that semantic compression techniques can help streamline and simplify text by reducing linguistic diversity and simplifying the vocabulary used in a given context. == Example == Text simplification involves modifying complex sentences into simpler ones to enhance readability and comprehension. Siddharthan (2006) provides an example to illustrate this process. The original sentence contains multiple clauses and phrases, which can be broken down into simpler sentences for better understanding. Also contributing to the firmness in copper, the analyst noted, was a report by Chicago purchasing agents, which precedes the full purchasing agents report that is due out today and gives an indication of what the full report might hold. Also contributing to the firmness in copper, the analyst noted, was a report by Chicago purchasing agents. The Chicago report precedes the full purchasing agents report. The Chicago report gives an indication of what the full report might hold. The full report is due out today. An approach to text simplification involves lexical simplification via lexical substitution, a process that replaces complex words with simpler synonyms. Identifying complex words is a challenge addressed by machine learning classifiers trained on labeled data. Researchers have found that asking labelers to sort words by complexity levels yields more consistent results than the traditional method of categorizing words as simple or complex.

  Read more →
• 

  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.

  Read more →
• 

  Thinking Machines Lab

  Thinking Machines Lab Inc. is an American artificial intelligence (AI) startup founded by Mira Murati, the former chief technology officer of OpenAI. The company was founded in February 2025, and by July had completed an early-stage funding round led by Andreessen Horowitz, raising $2 billion at a valuation of $12 billion overall from investors such as Nvidia, AMD, Cisco, and Jane Street. The company is based in San Francisco and structured as a public benefit corporation. == History == By its launch in February 2025, Thinking Machines Lab was reported to have hired about 30 researchers and engineers from competitors including OpenAI, Meta AI, and Mistral AI. Its founding team members include Barret Zoph, former OpenAI VP of Research (Post-Training), Lilian Weng, former OpenAI VP, and OpenAI cofounder John Schulman, who joined after a brief stint at the lab's competitor Anthropic. In January 2026, it was reported that Barret Zoph and Luke Metz, departed the startup to return to OpenAI. Other former OpenAI employees who have been hired include Jonathan Lachman and Andrew Tulloch (although Tulloch departed after getting recruited for Meta Superintelligence Labs). Thinking Machines Lab's advisers include Bob McGrew, previously OpenAI's chief research officer, and Alec Radford, who was a lead researcher for OpenAI. On October 1, 2025, it announced Tinker, an API for fine-tuning language models. Users would submit jobs through the API for fine-tuning one of the various open-weight models supported. The Lab would run the jobs on its internal clusters and training infrastructure. == Business structure == Thinking Machines Lab grants Mira Murati a deciding vote on board matters, weighted to provide her with a majority decision-making capability. Additionally, founding shareholders possess votes weighted 100 times greater than those of regular shareholders. In July 2025, Andreessen Horowitz was reported to have led the company's initial funding round, raising "about $2 billion at a valuation of $12 billion". The government of Albania (Murati's country of origin) was also included in this round, making a $10 million investment which required an amendment to the country's 2025 budget. == Partnership == In March 2026, Thinking Machines Lab announced a strategic partnership with NVIDIA involving an undisclosed investment and a multi-year agreement to deploy one gigawatt of Vera Rubin computing capacity.

  Read more →
• 

  OpenVINO

  OpenVINO is an open-source software toolkit developed by Intel for optimizing and deploying deep learning models. It supports several popular model formats and categories, such as large language models, computer vision, and generative AI. OpenVINO is optimized for Intel hardware, but offers support for ARM/ARM64 processors. It sees great use in AI Sound Processing drivers when tied with Intel's Gaussian & Neural Accelerator (GNA). Based in C++, it extends API support for C and Python, as well as Node.js (in early preview). OpenVINO is cross-platform and free for use under Apache License 2.0. == Workflow == The simplest OpenVINO usage involves obtaining a model and running it as is. Yet for the best results, a more complete workflow is suggested: obtain a model in one of supported frameworks, convert the model to OpenVINO IR using the OpenVINO Converter tool, optimize the model, using training-time or post-training options provided by OpenVINO's NNCF. execute inference, using OpenVINO Runtime by specifying one of several inference modes. == OpenVINO model format == OpenVINO IR is the default format used to run inference. It is saved as a set of two files, .bin and .xml, containing weights and topology, respectively. It is obtained by converting a model from one of the supported frameworks, using the application's API or a dedicated converter. Models of the supported formats may also be used for inference directly, without prior conversion to OpenVINO IR. Such an approach is more convenient but offers fewer optimization options and lower performance, since the conversion is performed automatically before inference. Some pre-converted models can be found in the Hugging Face repository. The supported model formats are: PyTorch TensorFlow TensorFlow Lite ONNX (including formats that may be serialized to ONNX) PaddlePaddle JAX/Flax == OS support == OpenVINO runs on Windows, Linux and MacOS.

  Read more →
• 

  Universal portfolio algorithm

  The universal portfolio algorithm is a portfolio selection algorithm from the field of machine learning and information theory. The algorithm learns adaptively from historical data and maximizes log-optimal growth rate in the long run, per the Kelly criterion. It was introduced by the late Stanford University information theorist Thomas M. Cover. The algorithm rebalances the portfolio at the beginning of each trading period. At the beginning of the first trading period it starts with a naive diversification. In the following trading periods the portfolio composition depends on the historical total return of all possible constant-rebalanced portfolios. The universal portfolio algorithm is the predecessor of the various online portfolio selection methodologies.

  Read more →
• 

  Safe Superintelligence Inc.

  Safe Superintelligence Inc. (SSI Inc.) is an Israeli-American artificial intelligence company founded by Ilya Sutskever, the former chief scientist of OpenAI; Daniel Gross, former head of Apple’s AI efforts; and Daniel Levy, an investor and AI researcher. The company's mission is to focus on safely developing a superintelligence, a computer-based agent capable of surpassing human intelligence. == History == On May 15, 2024, OpenAI co-founder Ilya Sutskever left OpenAI after a board dispute where he voted to fire Sam Altman amid concerns about communication and trust. Sutskever and others additionally believed that OpenAI was neglecting its original focus on safety in favor of pursuing opportunities for commercialization. On June 19, 2024, Sutskever posted on X that he was starting SSI Inc, with the goal to safely develop superintelligent AI, alongside Daniel Levy, and Daniel Gross. The company, composed of a small team, is split between Palo Alto, California and Tel Aviv, Israel. In September 2024, SSI revealed it had raised $1 billion from venture capital firms including SV Angel, DST Global, Sequoia Capital, and Andreessen Horowitz. The money will be used to build up more computing power and hire top individuals in the field. In March 2025, SSI reached a $30 billion valuation in a funding round led by Greenoaks Capital. This is six times its previous $5 billion valuation from September 2024. Despite not yet generating revenue and having approximately 20 employees, the company has attracted significant investor interest, largely due to co-founder Ilya Sutskever's reputation and its focus on developing safe superintelligence. In April 2025, Google Cloud announced a partnership to provide TPUs for SSI's research. In the first half of 2025, Meta attempted to acquire SSI but was rebuffed by Sutskever. In July 2025, co-founder Gross left the company to join Meta Superintelligence Labs, and Sutskever became the CEO of SSI.

  Read more →
• 

  Minimum intelligent signal test

  The minimum intelligent signal test, or MIST, is a variation of the Turing test proposed by Chris McKinstry in which only boolean (yes/no or true/false) answers may be given to questions. The purpose of such a test is to provide a quantitative statistical measure of humanness, which may subsequently be used to optimize the performance of artificial intelligence systems intended to imitate human responses. McKinstry gathered approximately 80,000 propositions that could be answered yes or no, e.g.: Is Earth a planet? Was Abraham Lincoln once President of the United States? Is the sun bigger than my foot? Do people sometimes lie? He called these propositions Mindpixels. These questions test both specific knowledge of aspects of culture, and basic facts about the meaning of various words and concepts. It could therefore be compared with the SAT, intelligence testing and other controversial measures of mental ability. McKinstry's aim was not to distinguish between shades of intelligence but to identify whether a computer program could be considered intelligent at all. According to McKinstry, a program able to do much better than chance on a large number of MIST questions would be judged to have some level of intelligence and understanding. For example, on a 20-question test, if a program were guessing the answers at random, it could be expected to score 10 correct on average. But the probability of a program scoring 20 out of 20 correct by guesswork is only one in 220, i.e. one in 1,048,576; so if a program were able to sustain this level of performance over several independent trials, with no prior access to the propositions, it should be considered intelligent. == Discussion == McKinstry criticized existing approaches to artificial intelligence such as chatterbots, saying that his questions could "kill" AI programs by quickly exposing their weaknesses. He contrasted his approach, a series of direct questions assessing an AI's capabilities, to the Turing test and Loebner Prize method of engaging an AI in undirected typed conversation. Critics of the MIST have noted that it would be easy to "kill" a McKinstry-style AI too, due to the impossibility of supplying it with correct answers to all possible yes/no questions by ways of a finite set of human-generated Mindpixels: the fact that an AI can answer the question "Is the sun bigger than my foot?" correctly does not mean that it can answer variations like "Is the sun bigger than (my hand | my liver | an egg yolk | Alpha Centauri A | ...)" correctly, too. However, the late McKinstry might have replied that a truly intelligent, knowledgeable entity (on a par with humans) would be able to work out answers such as (yes | yes | yes | don't know | ...) by applying its knowledge of the relative sizes of the objects named. In other words, the MIST was intended as a test of AI, not as a suggestion for implementing AI. It can also be argued that the MIST is a more objective test of intelligence than the Turing test, a subjective assessment that some might consider to be more a measure of the interrogator's gullibility than of the machine's intelligence. According to this argument, a human's judgment of a Turing test is vulnerable to the ELIZA effect, a tendency to mistake superficial signs of intelligence for the real thing, anthropomorphizing the program. The response, suggested by Alan Turing's essay Computing Machinery and Intelligence, is that if a program is a convincing imitation of an intelligent being, it is in fact intelligent. The dispute is thus over what it means for a program to have "real" intelligence, and by what signs it can be detected. A similar debate exists in the controversy over great ape language, in which nonhuman primates are said to have learned some aspects of sign languages but the significance of this learning is disputed.

  Read more →
• 

  Stochastic Neural Analog Reinforcement Calculator

  The Stochastic Neural Analog Reinforcement Calculator (SNARC) is a neural network machine designed by Marvin Minsky. Prompted by a letter from Minsky, George Armitage Miller gathered the funding (a few thousand dollars) for the project from the Office of Naval Research of the U.S. Department of Defense in the summer of 1951 with the work to be carried out by Minsky, who was then a graduate student in mathematics at Princeton University. At the time, a physics graduate student at Princeton, Dean S. Edmonds, volunteered that he was good with electronics and therefore Minsky brought him onto the project. During undergraduate years, Minsky was inspired by the 1943 Warren McCulloch and Walter Pitts paper on artificial neurons, and decided to build such a machine. The learning was Skinnerian reinforcement learning, and Minsky talked with Skinner extensively during the development of the machine. They tested the machine on a copy of Shannon's maze, and found that it could learn to solve the maze. Unlike Shannon's maze, this machine did not control a physical robot, but simulated rats running in a maze. The simulation is displayed as an "arrangement of lights", and the circuit was reinforced each time the simulated rat reached the goal. The machine surprised its creators. "The rats actually interacted with one another. If one of them found a good path, the others would tend to follow it." The machine itself is a randomly connected network of approximately 40 Hebb synapses. These synapses each have a memory that holds the probability that signal comes in one input and another signal will come out of the output. There is a probability knob that goes from 0 to 1 that shows this probability of the signals propagating. If the probability signal gets through, a capacitor remembers this function and engages an electromagnetic clutch. At this point, the operator will press a button to give a reward to the machine. This activates a motor on a surplus Minneapolis-Honeywell C-1 gyroscopic autopilot from a B-24 bomber. The motor turns a chain that goes to all 40 synapse machines, checking if the clutch is engaged or not. As the capacitor can only "remember" for a certain amount of time, the chain only catches the most recent updates of the probabilities. Each neuron contained 6 vacuum tubes and a motor. The entire machine is "the size of a grand piano" and contained 300 vacuum tubes. The tubes failed regularly, but the machine would still work despite failures. This machine is considered one of the first pioneering attempts at the field of artificial intelligence. Minsky went on to be a founding member of MIT's Project MAC, which split to become the MIT Laboratory for Computer Science and the MIT Artificial Intelligence Lab, and is now the MIT Computer Science and Artificial Intelligence Laboratory. In 1985 Minsky became a founding member of the MIT Media Laboratory. According to Minsky, he loaned the machine to students in Dartmouth, and subsequently lost, except for a single neuron. A photo of Minsky's last neuron can be seen here. The photo shows 6 vacuum tubes, one of which is a Sylvania JAN-CHS-6H6GT/G/VT-90A.

  Read more →
• 

  Drush

  Drush (DRUpal SHell) is a computer software shell-based application used to control, manipulate, and administer Drupal websites. == Details == Drush was originally developed by Arto Bendiken for Drupal 4.7. In May 2007, it was partly rewritten and redesigned for Drupal 5 by Franz Heinzmann. Drush is maintained by Moshe Weitzman with the support of Owen Barton, greg.1.anderson, jonhattan, Mark Sonnabaum, Jonathan Hedstrom and Christopher Gervais.

  Read more →
• 

  Library classification

  A library classification is a system used within a library to organize materials, including books, sound and video recordings, electronic materials, etc., both on shelves and in catalogs and indexes. Each item is typically assigned a call number, which identifies the location of the item within the system. Materials can be arranged by many different factors, typically in either a hierarchical tree structure based on the subject or using a faceted classification system, which allows the assignment of multiple classifications to an object, enabling the classifications to be ordered in many ways. == Description == Library classification is an important and crucial aspect in library and information science. It is distinct from scientific classification in that it has as its goal to provide a useful ordering of documents rather than a theoretical organization of knowledge. Although it has the practical purpose of creating a physical ordering of documents, it does generally attempt to adhere to accepted scientific knowledge. Library classification helps to accommodate all the newly published literature in an already created order of arrangement in a filial sequence. Library classification can be defined as the arrangement of books on shelves, or description of them, in the manner which is most useful to those who read with the ultimate aim of grouping similar things together. Library classification is meant to achieve these four purposes: ordering the fields of knowledge in a systematic way, bring related items together in the most helpful sequence, provide orderly access on the shelf, and provide a location for an item on the shelf. Library classification is distinct from the application of subject headings in that classification organizes knowledge into a systematic order, while subject headings provide access to intellectual materials through vocabulary terms that may or may not be organized as a knowledge system. The characteristics that a bibliographic classification demands for the sake of reaching these purposes are: a useful sequence of subjects at all levels, a concise memorable notation, and a host of techniques and devices of number synthesis. == History == Library classifications were preceded by classifications used by bibliographers such as Conrad Gessner. The earliest library classification schemes organized books in broad subject categories. The earliest known library classification scheme is the Pinakes by Callimachus, a scholar at the Library of Alexandria during the third century BC. During the Renaissance and Reformation era, "Libraries were organized according to the whims or knowledge of individuals in charge." This changed the format in which various materials were classified. Some collections were classified by language and others by how they were printed. After the printing revolution in the sixteenth century, the increase in available printed materials made such broad classification unworkable, and more granular classifications for library materials had to be developed in the nineteenth century. In 1627 Gabriel Naudé published a book called Advice on Establishing a Library. At the time, he was working in the private library of Président à mortier Henri de Mesmes II. Mesmes had around 8,000 printed books and many more Greek, Latin and French written manuscripts. Although it was a private library, scholars with references could access it. The purpose of Advice on Establishing a Library was to identify rules for private book collectors to organize their collections in a more orderly way to increase the collection's usefulness and beauty. Naudé developed a classification system based on seven different classes: theology, medicine, jurisprudence, history, philosophy, mathematics, and the humanities. These seven classes would later be increased to twelve. Advice on Establishing a Library was about a private library, but within the same book, Naudé encouraged the idea of public libraries open to all people regardless of their ability to pay for access to the collection. One of the most famous libraries that Naudé helped improve was the Bibliothèque Mazarine in Paris. Naudé spent ten years there as a librarian. Because of Naudé's strong belief in free access to libraries to all people, the Bibliothèque Mazarine became the first public library in France around 1644. Although libraries created order within their collections from as early as the fifth century BC, the Paris Bookseller's classification, developed in 1842 by Jacques Charles Brunet, is generally seen as the first of the modern book classifications. Brunet provided five major classes: theology, jurisprudence, sciences and arts, belles-lettres, and history. Classification can now be seen as a provider of subject access to information in a networked environment. == Types == There are many standard systems of library classification in use, and many more have been proposed over the years. However, in general, classification systems can be divided into three types depending on how they are used: === Universal schemes === Covers all subjects, e.g. the Dewey Decimal Classification (DDC), Universal Decimal Classification (UDC), and Colon Classification (CC). === Specific classification schemes === Covers particular subjects or types of materials, e.g. Iconclass (art), British Catalogue of Music Classification, and Dickinson classification (music), or the NLM Classification (medicine). === National schemes === Specially created for certain countries, e.g. Swedish library classification system, SAB (Sveriges Allmänna Biblioteksförening). The Library of Congress Classification was designed around the collection of the US Library of Congress and has an American, European, and Christian bias. Nevertheless, it is used widely in large academic and research libraries. In terms of functionality, classification systems are often described as: === Enumerative === Subject headings are listed alphabetically, with numbers assigned to each heading in alphabetical order. === Hierarchical === Subjects are divided hierarchically, from most general to most specific. === Faceted/analytico-synthetic === Subjects are divided into mutually exclusive orthogonal facets. There are few completely enumerative systems or faceted systems; most systems are a blend but favouring one type or the other. The most common classification systems, LCC and DDC, are essentially enumerative, though with some hierarchical and faceted elements (more so for DDC), especially at the broadest and most general level. The first true faceted system was the colon classification of S. R. Ranganathan. == Methods or systems == Classification types denote the classification or categorization according to the form or characteristics or qualities of a classification scheme or schemes. Method and system has similar meaning. Method or methods or system means the classification schemes like Dewey Decimal Classification or Universal Decimal Classification. The types of classification is for identifying and understanding or education or research purposes while classification method means those classification schemes like DDC, UDC. === English language universal classification systems === The most common systems in English-speaking countries are: Dewey Decimal Classification (DDC) Library of Congress Classification (LCC) Universal Decimal Classification (UDC) Other systems include: Book Industry Standards and Communications (BISAC), originally developed for use by U.S. booksellers, has become increasingly popular in libraries. Bliss bibliographic classification used in some British libraries Colon classification (CC) Garside classification used in most libraries of University College London Gladstone Library Classification, devised by W.E. Gladstone and used exclusively at Gladstone's Library Harvard-Yenching Classification, an English classification system for Chinese language materials === Non-English universal classification systems === German Regensburger Verbundklassifikation (RVK) A system of book classification for Chinese libraries (Liu's Classification) library classification for user New Classification Scheme for Chinese Libraries Nippon Decimal Classification (NDC) Chinese Library Classification (CLC) Korean Decimal Classification (KDC) Russian Library-Bibliographical Classification (BBK) Swedish library classification system (SAB) === Universal classification systems that rely on synthesis (faceted systems) === Bliss bibliographic classification Colon classification Cutter Expansive Classification Universal Decimal Classification Newer classification systems tend to use the principle of synthesis (combining codes from different lists to represent the different attributes of a work) heavily, which is comparatively lacking in LC or DDC. == Practice == Library classification is associated with library (descriptive) cataloging under the rubric of cataloging and classification, sometimes grouped together as technical serv

  Read more →
• 

  Intelligent Robotics Group

  The Intelligent Robotics Group (IRG) is a research organization within the Intelligent Systems Division at the NASA Ames Research Center in California's Silicon Valley. IRG conducts applied research in the area of robotics and autonomy and is one of the principal organizations at NASA responsible for robotics expertise, along with groups at the Jet Propulsion Laboratory and Johnson Space Center. The group's portfolio includes robotics in support of human exploration, perception and navigation, user interfaces, software architectures, and simulation. IRG developed the Astrobee free-flying robots on the International Space Station and was a primary contributor to the VIPER lunar rover in the areas of flight software, navigation, simulation, and mission operations. IRG has also conducted many robotic field test campaigns in support of spaceflight mission concept developments. These experiences led to the commercialization of the GigaPan system in collaboration with Carnegie Mellon University.

  Read more →