Sikidy is a form of algebraic geomancy practiced by Malagasy peoples in Madagascar. It involves algorithmic operations performed on random data generated from tree seeds, which are ritually arranged in a tableau called a toetry and divinely interpreted after being mathematically operated on. Columns of seeds, designated "slaves" or "princes" belonging to respective "lands" for each, interact symbolically to express vintana ('fate') in the interpretation of the diviner. The diviner also prescribes solutions to problems and ways to avoid fated misfortune, often involving a sacrifice. The centuries-old practice derives from Islamic influence brought to the island by medieval Arab traders. The sikidy is consulted for a range of divinatory questions pertaining to fate and the future, including identifying sources of and rectifying misfortune, reading the fate of newborns, and planning annual migrations. The mathematics of sikidy involves Boolean algebra, symbolic logic and parity. == History == The practice is several centuries old, and is influenced by Arab geomantic traditions of Arab Muslim traders on the island. Most writers link the origins of sikidy to the "sea-going trade involving the southwest coast of India, the Persian Gulf, and the east coast of Africa in the 9th or 10th century C.E." Stephen Ellis and Solofo Randrianja describe sikidy as "probably one of the oldest components of Malagasy culture", writing that it most likely the product of an indigenous divinatory art later influenced by Islamic practice. Umar H. D. Danfulani writes that the integration of Arabic divination into indigenous divination is "clearly demonstrated" in Madagascar, where the Arabic astrological system was adapted to the indigenous agricultural system and meshed with Malagasy lunar months by "adapting indigenous months, volana, to the astrological months, vintana". Danfulani also describes the concepts in sikidy of "houses" (lands) and "kings in their houses" as retained from medieval Arabic astrology. Chemillier et al. say the practice's spread across Madagascar likely originated with the southeastern Antemoro people, among whom Arab influence was the strongest. Though the etymology of sikidy is unknown, it has been posited that the word derives from the Arabic sichr ('incantation' or 'charm'). Sikidy was of central importance to pre-Christian Malagasy religion, with one practitioner quoted in 1892 as calling sikidy "the Bible of our ancestors". A missionary report from 1616 describes one form of sikidy using tamarind seeds, and another using fingered markings in the sand. The early colonial French governor of Madagascar Étienne de Flacourt documented sikidy in the mid-17th century: Matatane country in southeastern Madagascar [...] where the Antemoro [...] live was a center of astrological study as early as the fourteenth century [...]. This area was also the site of early Arab settlements, although strict Islamic observances were lost centuries ago [...]. Historical evidence shows that Antemoro diviners, bearers of the astrological system, infiltrated nearly all the ancient kingdoms of Madagascar beginning in the sixteenth century. [...] Today, although many persons claim to be ombiasy [diviners], only the Antemoro diviners are considered true professionals. The area is still a famous place of learning where specialists go for training and then return to their home communities with a certain body of knowledge. Now we can better understand the degree of similarity of divination forms found throughout Madagascar. For centuries Matitanana has remained a training center for diviners who have migrated widely, usually attaining important positions in their home communities and with various royal families. Comparison of contemporary rites with centuries-old texts show that sikidy has been remarkably unchanged throughout its history. The "infiltration" of Malagasy kingdoms by Antemoro diviners, and Matitanana's role as a place for astrological and divinatory learning, help to explain the relatively uniform practicing of sikidy across Madagascar. Chemallier et al. write that the mathematical construction of the arrangement of seeds is procedurally consistent across all of Madagascar, with variations in practice between groups and regions being limited to more minor aspects, such as the alignment of figures according to cardinal directions. One exception is the simplified Merina sikidy joria. === Origin myths === Mythic tradition relating to the origin of sikidy "links [the practice] both to the return by walking on water of Arab ancestors who had intermarried with Malagasy but then left, and to the names of the days of the week" and holds that the art was supernaturally communicated to the ancestors, with Zanahary (the supreme deity of Malagasy religion) giving it to Ranakandriana, who then gave it to a line of diviners (Ranakandriana to Ramanitralanana to Rabibi-andrano to Andriambavi-maitso (who was a woman) to Andriam-bavi-nosy), the last of whom terminated the monopoly by giving it to the people, declaring: "Behold, I give you the sikidy, of which you may inquire what offerings you should present in order to obtain blessings; and what expiation you should make so as to avert evils, when any are ill or under apprehension of some future calamity". A mythic anecdote of Ranakandriana says that two men observed him one day playing in the sand. In fact he was practicing a form of sikidy worked in sand called sikidy alanana. The two men seized him, and Ranakandriana promised that he would teach them something if they released him. They agreed, and Ranakandriana taught them in depth how to work the sikidy. The two men then went to their chief and told him that they could tell him "the past and the future—what was good and what was bad—what increased and what diminished." The chief asked them to tell him how he could obtain plenty of cattle. The two men worked their sikidy and told the chief to kill all of his bulls, and that "great numbers would come to him" on the following Friday. The chieftain, doubting, asked what would happen if their prediction didn't come true, and the two men promised they would pay with their lives. The chief agreed and killed his bulls. On Thursday, thinking he'd been duped, he prematurely killed the first man of the two who'd told him about the divinatory art. On Friday, however, "vast herds" came amidst heavy rain, actually filling an immense plain in their crowd. The chieftain lamented the mpisikidy's wrongful execution and ordered for him a pompous funeral. The chieftain took the second man as his close adviser and friend, and trusted the sikidy forever afterwards. The British missionary William Ellis recorded in 1839 two idiomatic expressions used in Madagascar that come from this story: "Tsy mahandry andro Zoma" (lit. 'He cannot wait 'til Friday') is said of someone extremely impatient, and heavy rainshowers falling in rapid succession are called "sese omby" (lit. 'a crowding together of cattle'). == Rites and arrangement of seeds == The divination is performed by a practitioner called an mpisikidy, ny màsina (lit. 'sacred one'), ombiasy, or ambiàsa (derived from the Arabic anbia, meaning 'prophet') who guides the client through the process and interprets the results in the context of the client's inquiries and desires. As part of an mpisikidy's formal initiation into the art, which includes a long period of apprenticeship, the initiate (called a mianatsy) must gather 124 and 200 fàno (Entada sp.) or kily (tamarind) tree seeds for his subsequent ritual use in sikidy. Raymond Decary writes that, at least among the Sakalava, a man must be 40 years old before learning and practicing sikidy, or he risks death. Before beginning to study, a student practitioner must make incisions at the tips of his index finger, his middle finger, and his tongue, and put within the incisions a paste containing red pepper and crushed wasp. This paste impregnates the fingers that will move the seeds of the sikidy and the tongue that will speak their revelations with the power to decipher the sikidy. Once this is done, he leaves at dawn to search for a fano (Entada chrysostachys) tree. Upon finding it, he throws his spear at its branches, shaking the tree and causing its large seed pods to fall. During this act, some initiates say: "When you were on the steep peak and in the dense forest, on you the crabs climbed, from you the crocodiles made their bed, with their paws the birds trod on you. Whether you are suspended in the trees or buried, you are never dried up nor rotten." In his study (written in 1941 and revised in 1948), Decary reported that the salary paid by a mianatsy to his master is "not very high": up to five francs, plus a red rooster's feather. The mpisikidy ritually arranges his seeds into a sixteen-column table consisting of four columns of randomly-generated data (representing fate) and eight columns of data derived from logical ope
Site Security Handbook
The Site Security Handbook, RFC 2196, is a guide on setting computer security policies and procedures for sites that have systems on the Internet (however, the information provided should also be useful to sites not yet connected to the Internet). The guide lists issues and factors that a site must consider when setting their own policies. It makes a number of recommendations and provides discussions of relevant areas. This guide is only a framework for setting security policies and procedures. In order to have an effective set of policies and procedures, a site will have to make many decisions, gain agreement, and then communicate and implement these policies. The guide is a product of the IETF SSH working group, and was published in 1997, obsoleting the earlier RFC 1244 from 1991.
Semantic analysis (knowledge representation)
Semantic analysis is a method for eliciting and representing knowledge about organisations. Initially the problem must be defined by domain experts and passed to the project analyst(s). The next step is the generation of candidate affordances. This step will generate a list of semantic units that may be included in the schema. The candidate grouping follows where some of the semantic units that will appear in the schema are placed in simple groups. Finally the groups will be integrated together into an ontology chart. Semantic analysis always starts from the problem definition which if not clear, require the analyst to employ relevant literature, interviews with the stakeholders and other techniques towards collecting supplementary information. All assumptions made must be genuine and not limiting the system.
Greg Brockman
Gregory Brockman (born November 29, 1987) is an American entrepreneur and software engineer. He is co-founder and president of OpenAI. He began his career at Stripe in 2010, upon leaving MIT, and became CTO in 2013. He left Stripe in 2015 to co-found OpenAI, where he also served as CTO. == Early life == Brockman was born in Thompson, North Dakota, and attended Red River High School, where he excelled in mathematics, chemistry, and computer science. He won a silver medal in the 2006 International Chemistry Olympiad and became the first finalist from North Dakota to participate in the Intel science talent search since 1973. In 2003, 2005, and 2007, he attended Canada/USA Mathcamp, a summer program for mathematically talented high-school students. In 2008, Brockman enrolled at Harvard University but left a year later, briefly enrolling at the Massachusetts Institute of Technology. == Career == In 2010, he dropped out of MIT to join Stripe, a company founded by Patrick Collison, his MIT classmate, and John Collison. In 2013, he became Stripe's first CTO, while the company grew from 5 to 205 employees. Brockman left Stripe in May 2015. === OpenAI === Brockman met with Sam Altman and Elon Musk, and led the recruiting of the OpenAI founding team. Many of its members, including Ilya Sutskever, were top AI research talent that left high paying jobs for the opportunity at OpenAI. He co-founded OpenAI in December 2015 alongside Altman, Sutskever and others. The company initially operated from Brockman's living room. He led various projects at OpenAI, including OpenAI Gym and OpenAI Five, a Dota 2 bot. On February 14, 2019, OpenAI announced that they had developed a new large language model called GPT-2, but kept it private due to their concern for its potential misuse. They released the model to a limited group of beta testers in May 2019. On March 14, 2023, in a live video demo, Brockman unveiled GPT-4, the fourth iteration in the GPT series. On November 17, 2023, alongside the firing of Sam Altman, Brockman was told he had been removed from the board. Sutskever supplied the board with a document of alleged bullying by Brockman. Mira Murati said Brockman's relationship with Altman made it impossible for her to do her job, and Altman had already "fielded many requests from OpenAI employees to rein in Brockman". Brockman was to report to Murati, but on November 17, he announced that he had quit the company. On November 20, 2023, Microsoft CEO Satya Nadella announced that Brockman and Altman would join Microsoft to lead a new advanced AI research team. The following day, after a deal was reached to reinstate Altman as CEO, Brockman returned to OpenAI. Brockman took a sabbatical from August to November 2024. === Elon Musk lawsuit === Jury selection for OpenAI cofounder Elon Musk's lawsuit against OpenAI and its current executives, including Brockman, began on April 27, 2026. On April 28, 2026, trial testimony was by now underway, with Elon Musk beginning his testimony against Altman and OpenAI. On April 30, 2026 Musk would enter his third day of testimony. == Personal life == In November 2019 after a year of dating, Brockman married Anna at OpenAI's offices on a workday. Ilya Sutskever officiated. == Political activities == Brockman and his wife were the biggest donors to Donald Trump's Super PAC, MAGA Inc., in 2025 with each of them donating US$12.5 million. Brockman and his wife also donated $50 million to Leading the Future, a super PAC dedicated to AI deregulation that he helped found with Andreessen Horowitz co-founders Marc Andreessen and Ben Horowitz. OpenAI publicly expressed openness to increased regulatory oversight and has a policy against donating to such Super PACs.
HTK (software)
HTK (Hidden Markov Model Toolkit) is a proprietary software toolkit for handling HMMs. It is mainly intended for speech recognition, but has been used in many other pattern recognition applications that employ HMMs, including speech synthesis, character recognition and DNA sequencing. Originally developed at the Machine Intelligence Laboratory (formerly known as the Speech Vision and Robotics Group) of the Cambridge University Engineering Department (CUED), HTK is now being widely used among researchers who are working on HMMs.
List of color palettes
The following is a list that contains color palettes for notable computer graphics, terminals and video game consoles. Only a simulated image using a palette and its name are given. Main articles are linked from the name of each palette, test charts, sample colours, simulated images, and further technical details (including references). During older eras of computing, manufacturers developed many different display systems often in a competitive, non-collaborative basis (with a few exceptions in the VESA consortium), creating many proprietary, non-standard different instances of display hardware. Often, as with early personal and home computers, a given machine employed its unique display subsystem, also with its unique color palette. Furthermore, software developers had made use of the color abilities of distinct display systems in many different ways. The result is that there is no single common standard nomenclature or classification taxonomy which can encompass every computer color palette. In order to organize the material, color palettes have been grouped following certain criteria. First, generic monochrome and full RGB repertories common to various computer display systems are listed. Then, usual color repertories used for display systems that employ indexed color techniques. And finally, specific manufacturers' color palettes implemented in many representative early personal computers and video game consoles of various brands. The list for personal computer palettes is split into two categories: 8-bit and 16-bit machines. This is not intended as a true strict categorization of such machines, because mixed architectures also exist (16-bit processors with an 8-bit data bus or 32-bit processors with a 16-bit data bus, among others). The distinction is based more on broad 8-bit and 16-bit computer ages or generations (around 1975–1985 and 1985–1995, respectively) and their associated state of the art in color display capabilities. The following is the common color test chart and sample image used to render each palette in this list: See further details in the summary paragraph of the corresponding article. == List of monochrome and RGB palettes == In this article, the term monochrome palette means a set of intensities for a monochrome display, and the term RGB palette is defined as the complete set of combinations a given RGB display can offer by mixing all the possible intensities of the red, green, and blue primaries available in its hardware. These are generic complete repertories of colors to produce black and white and RGB color pictures by the display hardware, not necessarily the total number of such colors that can be simultaneously displayed in a given text or graphic mode of any machine. RGB is the most common method to produce colors for displays; so these complete RGB color repertories have every possible combination of R-G-B triplets within any given maximum number of levels per component. For specific hardware and different methods to produce colors than RGB, see the List of computer hardware palettes and the List of video game consoles sections. For various software arrangements and sorts of colors, including other possible full RGB arrangements within 8-bit depth displays, see the List of software palettes section. === Monochrome palettes === These palettes only have shades of gray. === Dichrome palettes === Each permuted pair of red, green, and blue (16-bit color palette, with 65,536 colors). For example, "additive red green" has zero blue and "subtractive red green" has full blue. === Regular RGB palettes === These full RGB palettes employ the same number of bits to store the relative intensity for the red, green and blue components of every image's pixel color. Thus, they have the same number of levels per channel and the total number of possible colors is always the cube of a power of two. It should be understood that 'when developed' many of these formats were directly related to the size of some host computers 'natural word length' in bytes—the amount of memory in bits held by a single memory address such that the CPU can grab or put it in one operation. === Non-regular RGB palettes === These are also RGB palettes, in the sense defined above (except for 4-bit RGBI, which has an intensity bit that affects all channels at once), but either they do not have the same number of levels for each primary channel, or the numbers are not powers of two, so are not represented as separate bit fields. All of these have been used in popular personal computers. == List of software palettes == Systems that use a 4-bit or 8-bit pixel depth can display up to 16 or 256 colors simultaneously. Many personal computers in the later 1980s and early 1990s displayed at most 256 different colors, freely selected by software (either by the user or by a program) from their wider hardware's color palette. Usual selections of colors in limited subsets (generally 16 or 256) of the full palette includes some RGB level arrangements commonly used with the 8 bpp palettes as master palettes or universal palettes (i.e., palettes for multipurpose uses). These are some representative software palettes, but any selection can be made in such types of systems. === System specific palettes === These are selections of colors officially employed as system palettes in some popular operating systems for personal computers that feature 8-bit displays. === RGB arrangements === These are selections of colors based on evenly ordered RGB levels, mainly used as master palettes to display any kind of image within the limitations of the 8-bit pixel depth. === Other common uses of software palettes === == List of computer hardware palettes == In old personal computers and terminals that offered color displays, some color palettes were chosen algorithmically to provide the most diverse set of colors for a given palette size, and others were chosen to assure the availability of certain colors. In many early home computers, especially when the palette choices were determined at the hardware level by resistor combinations, the palette was determined by the manufacturer. Many early models output composite video colors. When seen on TV devices, the perception of the colors may not correspond with the value levels for the color values employed (most noticeable with NTSC TV color system). For current RGB display systems for PCs (Super VGA, etc.), see the 16-bit RGB and 24-bit RGB for High Color (thousands) and True Color (millions of colors) modes. For video game consoles, see the List of video game consoles section. For every model, their main different graphical color modes are listed based exclusively in the way they handle colors on screen, not all their different screen modes. The list is organized roughly historically by video hardware, not by branch. They are listed according to the original model of each system, which means that extended versions, clones, and compatibles also support the original palette. === Terminals and 8-bit machines === === 16-bit machines === === Video game console palettes === Color palettes of some of the most popular video game consoles. The criteria are the same as those of the List of computer hardware palettes section.
Trustworthy AI
Trustworthy AI refers to artificial intelligence systems that are designed to have transparent reasoning, are explainable (XAI), accountable, robust, fair and honest, respectful of data privacy, and steerable or alignable with human goals. == Terminology == Recent work in AI ethics distinguishes trustworthiness and trustability as two different conditions relevant to trustworthy AI. Trustworthiness is concerned with whether an AI system or the institutions deploying it merit trust by being reliable, fair, and accountable. Trustability, on the other hand, is the prior question of whether a given entity is even the kind of thing to which interpersonal trust can coherently apply as opposed to mere instrumental reliance. Some philosophers argue that current AI systems are best understood as tools that are not genuine targets of interpersonal trust. They argue that trust should be directed toward the human and institutional arrangements that govern the systems' design, deployment, and oversight. This stance supports interpreting "trustworthy AI" as trustworthy governance and use of AI rather than trust in the artifacts themselves. Transparency in AI involves making the processes and decisions of such systems understandable to users and stakeholders. Accountability ensures that there are protocols for addressing adverse outcomes or biases that may arise, with designated responsibilities for oversight and remediation. Robustness and security aim to ensure that AI systems perform reliably under various conditions and are safeguarded against malicious attacks. Harmlessness can be achieved by refusal training: training the models to avoid problematic requests, and by adding filters to detect and prevent discussion on biased, unethical, or dangerous outputs. There is research on how to train AI so that it aligns with human goals. == Techniques and ITU standardization == Trustworthy AI creation is a goal of AI governance and policymaking. To achieve transparency and data privacy, several privacy-enhancing technologies (PETs) can be used. These include: Homomorphic encryption for computing with encrypted data without ever decrypting it. Federated learning and secure multi-party computation (MPC) for distributing the model training without sharing information between the learning centers and computing servers. Differential privacy for exposing statistical data while guaranteeing that no private information is exposed. Zero-knowledge proof - providing proven validity for statements without disclosing any extra information. A work programme for achieving Trustworthy AI was set up by the International Telecommunication Union, an agency of the United Nations, initiated under its AI for Good programme. Its origin lies with the ITU-WHO Focus Group on Artificial Intelligence for Health, where a strong need for both privacy and analytics created demand for a standard in these technologies. In 2020, AI for Good moved online, and the TrustworthyAI seminar series was established to initiate discussions on these topics. This eventually led to standardization activities. === Multi-party computation === Secure multi-party computation (MPC) is being standardized under "Question 5" (the incubator) of ITU-T Study Group 17. === Homomorphic encryption === Homomorphic encryption allows for computing on encrypted data, where the outcomes or result is still encrypted and unknown to those performing the computation, but can be deciphered by the original encryptor. It is often developed with the goal of enabling use in jurisdictions different from the data creation (under, for instance, GDPR). ITU has been collaborating since the early stage of the HomomorphicEncryption.org standardization meetings, which has developed a standard on homomorphic encryption. The fifth homomorphic encryption meeting was hosted at ITU HQ in Geneva. === Federated learning === Zero-sum masks as used by federated learning for privacy preservation are used extensively in the multimedia standards of ITU-T Study Group 16 (VCEG) such as JPEG, MP3, H.264, and H.265 (commonly known as MPEG). === Zero-knowledge proof === Previous pre-standardization work on the topic of zero-knowledge proof has been conducted in the ITU-T Focus Group on Digital Ledger Technologies. === Differential privacy === The application of differential privacy in the preservation of privacy was examined at several of the "Day 0" machine learning workshops at AI for Good Global Summits. == Mozilla "Rebel Alliance" == In January 2026, the Mozilla Foundation and its subsidiaries announced a strategic shift to deploy their entire $1.4 billion reserve into building what foundation president Mark Surman termed a "rebel alliance" for trustworthy AI. Framed by Surman as a mission-driven alternative to the market dominance of OpenAI and Anthropic, the initiative seeks to establish an open-source AI stack by 2028. The alliance includes several startups funded via Mozilla Ventures, specifically focusing on decentralized governance and transparency: Trail: A firm developing AI compliance frameworks for regulated industries. Transformer Lab: A developer of open-source tools for AI model management. Oumi: A platform for training and deploying open-source models. The "rebel alliance" terminology is a historical reference to Mozilla's efforts in 1998 to challenge Microsoft's browser monopoly. While the $1.4 billion in funding is significant, it has been contrasted with the tens of billions in capital raised by proprietary competitors like OpenAI.