AIXI is a theoretical mathematical formalism for artificial general intelligence. It combines Solomonoff induction with sequential decision theory. AIXI was first proposed by Marcus Hutter in 2000 and several results regarding AIXI are proved in Hutter's 2005 book Universal Artificial Intelligence. AIXI is a reinforcement learning (RL) agent. It maximizes the expected total rewards received from the environment. Intuitively, it simultaneously considers every computable hypothesis (or environment). In each time step, it looks at every possible program and evaluates how many rewards that program generates depending on the next action taken. The promised rewards are then weighted by the subjective belief that this program constitutes the true environment. This belief is computed from the length of the program: longer programs are considered less likely, in line with Occam's razor. AIXI then selects the action that has the highest expected total reward in the weighted sum of all these programs. == Etymology == According to Hutter, the word "AIXI" can have several interpretations. AIXI can stand for AI based on Solomonoff's distribution, denoted by ξ {\displaystyle \xi } (which is the Greek letter xi), or e.g. it can stand for AI "crossed" (X) with induction (I). There are other interpretations. == Definition == AIXI is a reinforcement learning agent that interacts with some stochastic and unknown but computable environment μ {\displaystyle \mu } . The interaction proceeds in time steps, from t = 1 {\displaystyle t=1} to t = m {\displaystyle t=m} , where m ∈ N {\displaystyle m\in \mathbb {N} } is the lifespan of the AIXI agent. At time step t, the agent chooses an action a t ∈ A {\displaystyle a_{t}\in {\mathcal {A}}} (e.g. a limb movement) and executes it in the environment, and the environment responds with a "percept" e t ∈ E = O × R {\displaystyle e_{t}\in {\mathcal {E}}={\mathcal {O}}\times \mathbb {R} } , which consists of an "observation" o t ∈ O {\displaystyle o_{t}\in {\mathcal {O}}} (e.g., a camera image) and a reward r t ∈ R {\displaystyle r_{t}\in \mathbb {R} } , distributed according to the conditional probability μ ( o t r t | a 1 o 1 r 1 . . . a t − 1 o t − 1 r t − 1 a t ) {\displaystyle \mu (o_{t}r_{t}|a_{1}o_{1}r_{1}...a_{t-1}o_{t-1}r_{t-1}a_{t})} , where a 1 o 1 r 1 . . . a t − 1 o t − 1 r t − 1 a t {\displaystyle a_{1}o_{1}r_{1}...a_{t-1}o_{t-1}r_{t-1}a_{t}} is the "history" of actions, observations and rewards. The environment μ {\displaystyle \mu } is thus mathematically represented as a probability distribution over "percepts" (observations and rewards) which depend on the full history, so there is no Markov assumption (as opposed to other RL algorithms). Note again that this probability distribution is unknown to the AIXI agent. Furthermore, note again that μ {\displaystyle \mu } is computable, that is, the observations and rewards received by the agent from the environment μ {\displaystyle \mu } can be computed by some program (which runs on a Turing machine), given the past actions of the AIXI agent. The only goal of the AIXI agent is to maximize ∑ t = 1 m r t {\displaystyle \sum _{t=1}^{m}r_{t}} , that is, the sum of rewards from time step 1 to m. The AIXI agent is associated with a stochastic policy π : ( A × E ) ∗ → A {\displaystyle \pi :({\mathcal {A}}\times {\mathcal {E}})^{}\rightarrow {\mathcal {A}}} , which is the function it uses to choose actions at every time step, where A {\displaystyle {\mathcal {A}}} is the space of all possible actions that AIXI can take and E {\displaystyle {\mathcal {E}}} is the space of all possible "percepts" that can be produced by the environment. The environment (or probability distribution) μ {\displaystyle \mu } can also be thought of as a stochastic policy (which is a function): μ : ( A × E ) ∗ × A → E {\displaystyle \mu :({\mathcal {A}}\times {\mathcal {E}})^{}\times {\mathcal {A}}\rightarrow {\mathcal {E}}} , where the ∗ {\displaystyle } is the Kleene star operation. In general, at time step t {\displaystyle t} (which ranges from 1 to m), AIXI, having previously executed actions a 1 … a t − 1 {\displaystyle a_{1}\dots a_{t-1}} (which is often abbreviated in the literature as a < t {\displaystyle a_{ Vicarious was an artificial intelligence company based in the San Francisco Bay Area, California. They use the theorized computational principles of the brain to attempt to build software that can think and learn like a human. Vicarious describes its technology as "a turnkey robotics solution integrator using artificial intelligence to automate tasks too complex and versatile for traditional automations". Alphabet Inc acquired the company in 2022 for an undisclosed amount. == Founders == The company was founded in 2010 by D. Scott Phoenix and Dileep George. Before co-founding Vicarious, Phoenix was Entrepreneur in Residence at Founders Fund and CEO of Frogmetrics, a touchscreen analytics company he co-founded through the Y Combinator incubator program. Previously, George was Chief Technology Officer at Numenta, a company he co-founded with Jeff Hawkins and Donna Dubinsky while completing his PhD at Stanford University. == Funding == The company launched in February 2011 with funding from Founders Fund, Dustin Moskovitz, Adam D’Angelo (former Facebook CTO and co-founder of Quora), Felicis Ventures, and Palantir co-founder Joe Lonsdale. In August 2012, in its Series A round of funding, it raised an additional $15 million. The round was led by Good Ventures; Founders Fund, Open Field Capital and Zarco Investment Group also participated. The company received $40 million in its Series B round of funding. The round was led by individuals including Mark Zuckerberg, Elon Musk, and others. An additional undisclosed amount was later contributed by Amazon.com CEO Jeff Bezos, Yahoo! co-founder Jerry Yang, Skype co-founder Janus Friis and Salesforce.com CEO Marc Benioff. == Recursive Cortical Network == Vicarious is developing machine learning software based on the computational principles of the human brain. One such software is a vision system known as the Recursive Cortical Network (RCN), it is a generative graphical visual perception system that interprets the contents of photographs and videos in a manner similar to humans. The system is powered by a balanced approach that takes sensory data, mathematics, and biological plausibility into consideration. On October 22, 2013, beating CAPTCHA, Vicarious announced its model was reliably able to solve modern CAPTCHAs, with character recognition rates of 90% or better when trained on one style. However, Luis von Ahn, a pioneer of early CAPTCHA and founder of reCAPTCHA, expressed skepticism, stating: "It's hard for me to be impressed since I see these every few months." He pointed out that 50 similar claims to that of Vicarious had been made since 2003. Vicarious later published their findings in peer-reviewed journal Science. Vicarious has indicated that its AI was not specifically designed to complete CAPTCHAs and its success at the task is a product of its advanced vision system. Because Vicarious's algorithms are based on insights from the human brain, it is also able to recognize photographs, videos, and other visual data. Oracle AI Database (commonly referred to as Oracle Database, Oracle DBMS, Oracle Autonomous Database, or simply as Oracle) is a proprietary multi-model database management system produced and marketed by Oracle Corporation. It is a database commonly used for running online transaction processing (OLTP), data warehousing (DW) and mixed (OLTP & DW) database workloads. Oracle AI Database uses SQL for database updating and retrieval. Oracle Database runs on-premises, on Oracle engineered systems such as Oracle Exadata, on Oracle Cloud Infrastructure, and as a managed Autonomous Database service. It is also offered inside Microsoft Azure, Google Cloud, and Amazon Web Services data centers through Oracle's multicloud offerings. The current long-term support release, Oracle AI Database 26ai, became available in the cloud and on Oracle engineered systems in October 2025 and on-premises for Linux x86-64 in January 2026. == History == Larry Ellison and his two friends and former co-workers, Bob Miner and Ed Oates, started a consultancy called Software Development Laboratories (SDL) in 1977, later Oracle Corporation. SDL developed the original version of the Oracle software. The name Oracle comes from the code-name of a Central Intelligence Agency-funded project Ellison had worked on while formerly employed by Ampex; the CIA was Oracle's first customer, and allowed the company to use the code name for the new product. Ellison wanted his database to be compatible with IBM System R, but that company's Don Chamberlin declined to release its error codes. By 1985 Oracle advertised, however, that "Programs written for SQL/DS or DB2 will run unmodified" on the many non-IBM mainframes, minicomputers, and microcomputers its database supported "Because all versions of ORACLE are identical". Later releases introduced capabilities associated with successive eras of the product, including PL/SQL stored procedures and triggers in Oracle7 (1992), Real Application Clusters in Oracle9i (2001), grid infrastructure and automatic management in Oracle 10g (2003), the multitenant architecture and In-Memory Column Store in Oracle Database 12c (2013), and AI Vector Search and JSON Relational Duality in Oracle Database 23ai (2024). In October 2025 Oracle rebranded the 23ai line as Oracle AI Database 26ai. (see Release History) == Architecture == An Oracle Database system consists of an instance and a database. The instance is a set of memory structures and background processes; the database is the set of files that store data. The instance exists only in memory, and a single instance is associated with one multitenant container database. The principal memory structures are the System Global Area, which is shared, and the Program Global Areas, which are private to individual processes. The shared pool, database buffer cache, and redo log buffer are components of the System Global Area, and the optional In-Memory Column Store also resides there. Background processes operate on the database files and use these memory structures; they include the database writer, the log writer, the checkpoint process, and the system and process monitor processes. Server processes handle connections from client programs and run their SQL statements. Storage is organized logically and physically. Logically, data is held in tablespaces composed of segments, extents, and data blocks. Physically, the database comprises datafiles, control files, and online redo log files, with archived redo logs supporting media recovery. == High Availability and Scalability == Oracle Database includes several technologies for high availability, disaster recovery, and scale. Oracle Real Application Clusters allows multiple instances on separate servers to access one shared database concurrently; it was introduced with Oracle9i in 2001. Oracle Data Guard maintains standby databases synchronized with a primary database, and Active Data Guard additionally allows read-only workloads on a standby while it applies changes. Oracle GoldenGate performs logical replication and data integration across heterogeneous systems. Native sharding, introduced in Oracle Database 12c Release 2, distributes one logical database across independent shards. Oracle Exadata is an engineered system that pairs database servers with storage servers and offloads operations such as filtering to the storage tier; it is available on-premises, in Oracle Cloud Infrastructure, and through Cloud@Customer. == Notable Features == AI Vector Search adds a vector data type, vector indexes, and vector distance operators to the database. These allow similarity search over machine-learning embeddings to be expressed in SQL and combined with queries over relational, JSON, spatial, and graph data. It became generally available in Oracle Database 23ai. JSON Relational Duality exposes the same data both as relational tables and as JSON documents through duality views, so that an application can read and write either representation of the data. It became generally available in Oracle Database 23ai. In-Memory Column Store maintains a column-oriented copy of selected tables in memory in addition to the row-oriented format, and the optimizer can use the columnar copy for analytic queries. It was introduced in Oracle Database 12c Release 1.Partitioning divides large tables and indexes into independently managed pieces. Advanced Compression and Hybrid Columnar Compression are compression features for transactional and warehouse data respectively. == Data Types == Oracle AI Database supports a variety of data types and data models within a single system. These include traditional relational data types as well as semi-structured, unstructured, and specialized data formats, enabling different types of data to be stored and queried together. == Releases and versions == Oracle products follow a custom release-numbering and -naming convention. The "ai" in the current release, Oracle AI Database 26ai, stands for "Artificial Intelligence". Previous releases (e.g. Oracle Database 19c, 10g, and Oracle9i Database) have used suffixes of "c", "g", and "i" which stand for "Cloud", "Grid", and "Internet" respectively. Prior to the release of Oracle8i Database, no suffixes featured in Oracle AI Database naming conventions. There was no v1 of Oracle AI Database, as Ellison "knew no one would want to buy version 1". For some database releases, Oracle also provides an Express Edition (XE) that is free to use. Oracle AI Database release numbering has used the following codes: The Introduction to Oracle AI Database includes a brief history on some of the key innovations introduced with each major release of Oracle AI Database. See My Oracle Support (MOS) note Release Schedule of Current Database Releases (Doc ID 742060.1) for the current Oracle AI Database releases and their patching end dates. == Patch updates and security alerts == Prior to Oracle Database 18c, Oracle Corporation released Critical Patch Updates (CPUs) and Security Patch Updates (SPUs) and Security Alerts to close security vulnerabilities. These releases are issued quarterly; some of these releases have updates issued prior to the next quarterly release. Starting with Oracle Database 18c, Oracle Corporation releases Release Updates (RUs) and Release Update Revisions (RURs). RUs usually contain security, regression (bug), optimizer, and functional fixes which may include feature extensions as well. RURs include all fixes from their corresponding RU but only add new security and regression fixes. However, no new optimizer or functional fixes are included. == Competition == In the market for relational databases, Oracle AI Database competes against commercial products such as IBM Db2 and Microsoft SQL Server. Oracle and IBM tend to battle for the mid-range database market on Unix and Linux platforms, while Microsoft dominates the mid-range database market on Microsoft Windows platforms. However, since they share many of the same customers, Oracle and IBM tend to support each other's products in many middleware and application categories (for example: WebSphere, PeopleSoft, and Siebel Systems CRM), and IBM's hardware divisions work closely with Oracle on performance-optimizing server-technologies (for example, Linux on IBM Z). Niche commercial competitors include Teradata (in data warehousing and business intelligence), Software AG's ADABAS, Sybase, and IBM's Informix, among many others. In the cloud, Oracle AI Database competes against the database services of AWS, Microsoft Azure, and Google Cloud Platform. Increasingly, the Oracle AI Database products compete against open-source software relational and non-relational database systems such as PostgreSQL, MongoDB, Couchbase, Neo4j, ArangoDB and others. Oracle acquired Innobase, supplier of the InnoDB codebase to MySQL, in part to compete better against open source alternatives, and acquired Sun Microsystems, owner of MySQL, in 2010. Database products licensed as open The ITU-WHO Focus Group on Artificial Intelligence for Health (AI for Health) was an inter-agency collaboration from 2018 between the World Health Organization and the ITU, which in 2019 created a benchmarking framework to assess the accuracy of AI in health. The organization convened an international network of experts and stakeholders from fields like research, practice, regulation, ethics, public health, etc, that developed guideline documentation and code. The documents have addressed ethics, assessment/evaluation, handling, and regulation of AI for health solutions, covering specific use cases including AI in ophthalmology, histopathology, dentistry, malaria detection, radiology, symptom checker applications, etc. FG-AI4H has established an ad hoc group concerned with digital technologies for health emergencies, including COVID-19. All documentation is public. The idea for the Focus Group came out of the Health Track of the 2018 AI for Good Global Summit. Administratively, FG-AI4H was created by ITU-T Study Group 16. Under ITU-T's framework, participation in Focus Groups is open to anyone from an ITU Member State. The secretariat is provided by the Telecommunication Standardization Bureau (under Director Chaesub Lee). It was first created at the July 2018 meeting with a lifetime of two years, at the July 2020 meeting, this was extended for another two years, where the focus group also submitted its deliverables to its parent body. It was also presented at the NeurIPS 2020 health workshop. In July 2023 "the work was grandfathered in the Global Initiative on AI for Health (GI-AI4H)". == AI for Health Framework == The outline of the benchmarking framework was published in a 2019 commentary in The Lancet. The output of the Focus Group AI for Health were structured in the AI for Health Framework. Depending on their primary domain being health or ICT, the individual components of the AI for Health Framework were ratified by the corresponding United Nations Specialized Agency, as WHO Guidelines and ITU Recommendations respectively. Standards drawn up by FG-AI4H were titled as: AI4H ethics considerations AI4H regulatory [best practices | considerations] AI4H requirements specification AI software life cycle specification Data specification AI training best practices specification AI4H evaluation considerations AI4H scale-up and adoption AI4H applications and platforms Use cases of the ITU-WHO Focus Group on AI for Health Dr.Fill is a computer program that solves American-style crossword puzzles. It was developed by Matt Ginsberg and described by Ginsberg in an article in the Journal of Artificial Intelligence Research. Ginsberg claims in that article that Dr.Fill is among the top fifty crossword solvers in the world. == History == Dr.Fill participated in the 2012 American Crossword Puzzle Tournament, finishing 141st of approximately 650 entrants with a total score of just over 10,000 points. The appearance led to a variety of descriptions of Dr.Fill in the popular press, including The Economist, the San Francisco Chronicle and Gizmodo. A description of Dr.Fill appeared on the front page of the March 17, 2012 New York Times. Dr.Fill's score in 2013 improved to 10,550, which would have earned it 92nd place. Videos of the program solving the problems from the tournament are available on YouTube. The score in 2014 improved further to 10,790, which would have tied for 67th place. A video of the program solving the first six puzzles from that tournament, together with a talk given by Ginsberg describing its performance, can be found on YouTube. Dr.Fill has largely continued to improve since the 2014 event. In 2015, it scored 10,920 points and finished in 55th place. In 2016, it scored 11,205 points and finished in 41st place. In 2017, it scored 11,795 and finished in 11th place. In 2018, it scored 10,740 points, dropping to 78th place. Dr.Fill returned to "form" in 2019, once again scoring 11,795 and finishing in 14th place. The 2020 ACPT was cancelled due to COVID-19, and Dr.Fill participated as a non-competitor in the Boswords tournament instead. The program outperformed the humans, scoring 11,218 points (fast solves with a total of one mistake) while the best scoring human scored 10,994 points (slower solves but no mistakes). The 2021 ACPT was virtual, again due to COVID-19. The Dr.Fill effort was joined by the Berkeley NLP Group, creating a hybrid system named Berkeley Crossword Solver, and Dr.Fill won the main event, scoring 12,825 points with Erik Agard, the highest scoring human, scoring 12,810 points. The tournament was won by Tyler Hinman (12,760 points), who completed the championship puzzle perfectly in three minutes. Dr.Fill also completed that puzzle perfectly, but in 49 seconds. After winning the tournament, Ginsberg announced on August 8, 2021, that both he and Dr.Fill would be retiring from crosswords. == Algorithm == As described by Ginsberg, Dr.Fill works by converting a crossword to a weighted constraint satisfaction problem and then attempting to maximize the probability that the fill is correct. Probabilities for individual words or phrases in the puzzle are computed using relatively simple statistical techniques based on features such as previous appearances of the clue, number of Google hits for the fill, and so on. In doing this, Dr.Fill is attempting to solve a problem similar to that tackled by the Jeopardy!-playing program Watson; Dr.Fill runs on a laptop instead of a supercomputer and Ginsberg remarks that Watson is far more effective than Dr.Fill at solving this portion of the problem. Instead of computational horsepower, Dr.Fill relies on the constraints provided by crossing words to refine its answers. A variety of techniques from artificial intelligence are applied to attempt to find the most likely fill. These include a small amount of lookahead, limited discrepancy search, and postprocessing. Ginsberg remarks that postprocessing was chosen over branch and bound because the two techniques are mutually incompatible and postprocessing was found to be more effective in this domain. AI-assisted software development is the use of artificial intelligence (AI) to augment software development. It uses large language models (LLMs), AI agents and other AI technologies to assist software developers. It helps in a range of tasks of the software development life cycle, from code generation to debugging, editing, testing, UI design, understanding the code, and documentation. Agentic coding denotes the use of AI agents for software development. == Technologies == === Source code generation === Large language models trained or fine-tuned on source-code corpora can generate source code from natural-language descriptions, comments, or docstrings. Research on code-generation systems often evaluates generated programs by functional correctness, such as whether the output passes automated test cases, rather than by syntax alone. Such tools can be features or extensions of integrated development environments (IDEs). === Intelligent code completion === AI agents using pre-trained and fine-tuned LLMs can predict and suggest code completions based on context. According to Husein, Aburajouh & Catal in a 2025 literature review in Computer Standards & Interfaces, "LLMs significantly enhance code completion performance across several programming languages and contexts, and their capability to predict relevant code snippets based on context and partial input boosts developer productivity substantially." === Testing, debugging, code review and analysis === AI is used to automatically generate test cases, identify potential bugs and security vulnerabilities, and suggest fixes. AI can also be used to perform static code analysis and suggest potential performance improvements. == Limitations == Both ownership of and responsibility for AI-generated code is disputed. According to a report from the German Federal Office for Information Security, the use of AI coding assistants without careful oversight from experienced developers can introduce both minor and major security vulnerabilities, and any potential gain in productivity should be weighed against the cost of additional quality control and security measures. According to Deloitte, outputs from AI-assisted software development must be validated through a combination of automated testing, static analysis tools and human review, creating a governance layer to improve quality and accountability. == Vibe coding == David Krueger is an American machine learning professor and advocate for the reduction of risks related to artificial intelligence. Krueger is an assistant professor in Robust, Reasoning, and Responsible AI at the University of Montreal and a Core Academic Member at Mila. == Early life and education == Krueger obtained a B.A. in mathematics from Reed College, and completed his MSc and Ph.D. in Computer Science at the University of Montreal. He trained in deep learning under Yoshua Bengio, Roland Memisevic, and Aaron Courville from 2013 to 2021. Krueger was also an intern on Google DeepMind's AI Safety team in 2018. == Career == Krueger researches deep learning, AI alignment, and AI safety. His work is focused on reducing the risk of human extinction resulting from out-of-control AI systems. Krueger was an assistant professor at the University of Cambridge from 2021 to 2024, before taking a faculty position at the University of Montreal in 2024. In 2023, he was a founding research director at the UK AI Security Institute. That same year, Krueger initiated the Statement on AI Risk, which argues that AI could cause human extinction and was signed by Anthropic's Dario Amodei, OpenAI's Sam Altman, AI expert Geoffrey Hinton, and other leaders. In April 2026, Krueger discussed the risks of advanced AI at a Capitol Hill event hosted by Senator Bernie Sanders. === Evitable === In 2025, Krueger founded Evitable, a nonprofit organization that advocates for an AI moratorium. == Views == Krueger argues that AI will lead to a "gradual disempowerment" of workers, likening AI chips to nuclear bombs. He also says the military use of AI "poses an existential risk to humanity."Vicarious (company)
Oracle Database
ITU-WHO Focus Group on Artificial Intelligence for Health
Dr.Fill
AI-assisted software development
David Krueger (professor)