AI Avatar Based On Photo

AI Avatar Based On Photo — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

LCD crosstalk

LCD crosstalk is a visual defect in an LCD screen which occurs because of interference between adjacent pixels. Owing to the way rows and columns in the display are addressed, and charge is pushed around, the data on one part of the display has the potential to influence what is displayed elsewhere. This is generally known as crosstalk, and in matrix displays typically occurs in the horizontal and vertical directions. Crosstalk used to be a serious problem in the old passive-matrix (STN) displays, but is rarely discernable in modern active-matrix (TFT) displays. A fortunate side effect of inversion (see above) is that, for most display material, what little crosstalk there is largely cancelled out. For most practical purposes, the level of crosstalk in modern LCDs is negligible. Certain patterns, particularly those involving fine dots, can interact with the inversion and reveal visible crosstalk. If you try moving a small Window in front of the inversion pattern (above) which makes your screen flicker the most, you may well see crosstalk in the surrounding pattern. Different patterns are required to reveal crosstalk on different displays (depending on their inversion scheme).
Read more →
Semantic network

A semantic network, or frame network is a knowledge base that represents semantic relations between concepts in a network. This is often used as a form of knowledge representation. It is a directed or undirected graph consisting of vertices, which represent concepts, and edges, which represent semantic relations between concepts, mapping or connecting semantic fields. A semantic network may be instantiated as, for example, a graph database or a concept map. Typical standardized semantic networks are expressed as semantic triples. Semantic networks are used in natural language processing applications such as semantic parsing and word-sense disambiguation. Semantic networks can also be used as a method to analyze large texts and identify the main themes and topics (e.g., of social media posts), to reveal biases (e.g., in news coverage), or even to map an entire research field. == History == Examples of the use of semantic networks in logic, directed acyclic graphs as a mnemonic tool, dates back centuries. The earliest documented use being the Greek philosopher Porphyry's commentary on Aristotle's categories in the third century AD. In computing history, "Semantic Nets" for the propositional calculus were first implemented for computers by Richard H. Richens of the Cambridge Language Research Unit in 1956 as an "interlingua" for machine translation of natural languages. Although the importance of this work and the CLRU was only belatedly realized. Semantic networks were also independently implemented by Robert F. Simmons and Sheldon Klein, using the first order predicate calculus as a base, after being inspired by a demonstration of Victor Yngve. The "line of research was originated by the first President of the Association [Association for Computational Linguistics], Victor Yngve, who in 1960 had published descriptions of algorithms for using a phrase structure grammar to generate syntactically well-formed nonsense sentences. Sheldon Klein and I about 1962-1964 were fascinated by the technique and generalized it to a method for controlling the sense of what was generated by respecting the semantic dependencies of words as they occurred in text." Other researchers, most notably M. Ross Quillian and others at System Development Corporation helped contribute to their work in the early 1960s as part of the SYNTHEX project. It's from these publications at SDC that most modern derivatives of the term "semantic network" cite as their background. Later prominent works were done by Allan M. Collins and Quillian (e.g., Collins and Quillian; Collins and Loftus Quillian). Still later in 2006, Hermann Helbig fully described MultiNet. In the late 1980s, two Netherlands universities, Groningen and Twente, jointly began a project called Knowledge Graphs, which are semantic networks but with the added constraint that edges are restricted to be from a limited set of possible relations, to facilitate algebras on the graph. In the subsequent decades, the distinction between semantic networks and knowledge graphs was blurred. In 2012, Google gave their knowledge graph the name Knowledge Graph. The Semantic Link Network was systematically studied as a social semantics networking method. Its basic model consists of semantic nodes, semantic links between nodes, and a semantic space that defines the semantics of nodes and links and reasoning rules on semantic links. The systematic theory and model was published in 2004. This research direction can trace to the definition of inheritance rules for efficient model retrieval in 1998 and the Active Document Framework ADF. Since 2003, research has developed toward social semantic networking. This work is a systematic innovation at the age of the World Wide Web and global social networking rather than an application or simple extension of the Semantic Net (Network). Its purpose and scope are different from that of the Semantic Net (or network). The rules for reasoning and evolution and automatic discovery of implicit links play an important role in the Semantic Link Network. Recently it has been developed to support Cyber-Physical-Social Intelligence. It was used for creating a general summarization method. The self-organised Semantic Link Network was integrated with a multi-dimensional category space to form a semantic space to support advanced applications with multi-dimensional abstractions and self-organised semantic links It has been verified that Semantic Link Network play an important role in understanding and representation through text summarisation applications. Semantic Link Network has been extended from cyberspace to cyber-physical-social space. Competition relation and symbiosis relation as well as their roles in evolving society were studied in the emerging topic: Cyber-Physical-Social Intelligence More specialized forms of semantic networks has been created for specific use. For example, in 2008, Fawsy Bendeck's PhD thesis formalized the Semantic Similarity Network (SSN) that contains specialized relationships and propagation algorithms to simplify the semantic similarity representation and calculations. == Basics of semantic networks == A semantic network is used when one has knowledge that is best understood as a set of concepts that are related to one another. Most semantic networks are cognitively based. They also consist of arcs and nodes which can be organized into a taxonomic hierarchy. Semantic networks contributed ideas of spreading activation, inheritance, and nodes as proto-objects. == Examples == === In Lisp === The following code shows an example of a semantic network in the Lisp programming language using an association list. To extract all the information about the "canary" type, one would use the assoc function with a key of "canary". === WordNet === An example of a semantic network is WordNet, a lexical database of English. It groups English words into sets of synonyms called synsets, provides short, general definitions, and records the various semantic relations between these synonym sets. Some of the most common semantic relations defined are meronymy (A is a meronym of B if A is part of B), holonymy (B is a holonym of A if B contains A), hyponymy (or troponymy) (A is subordinate of B; A is kind of B), hypernymy (A is superordinate of B), synonymy (A denotes the same as B) and antonymy (A denotes the opposite of B). WordNet properties have been studied from a network theory perspective and compared to other semantic networks created from Roget's Thesaurus and word association tasks. From this perspective the three of them are a small world structure. === Other examples === It is also possible to represent logical descriptions using semantic networks such as the existential graphs of Charles Sanders Peirce or the related conceptual graphs of John F. Sowa. These have expressive power equal to or exceeding standard first-order predicate logic. Unlike WordNet or other lexical or browsing networks, semantic networks using these representations can be used for reliable automated logical deduction. Some automated reasoners exploit the graph-theoretic features of the networks during processing. Other examples of semantic networks are Gellish models. Gellish English with its Gellish English dictionary, is a formal language that is defined as a network of relations between concepts and names of concepts. Gellish English is a formal subset of natural English, just as Gellish Dutch is a formal subset of Dutch, whereas multiple languages share the same concepts. Other Gellish networks consist of knowledge models and information models that are expressed in the Gellish language. A Gellish network is a network of (binary) relations between things. Each relation in the network is an expression of a fact that is classified by a relation type. Each relation type itself is a concept that is defined in the Gellish language dictionary. Each related thing is either a concept or an individual thing that is classified by a concept. The definitions of concepts are created in the form of definition models (definition networks) that together form a Gellish Dictionary. A Gellish network can be documented in a Gellish database and is computer interpretable. SciCrunch is a collaboratively edited knowledge base for scientific resources. It provides unambiguous identifiers (Research Resource IDentifiers or RRIDs) for software, lab tools etc. and it also provides options to create links between RRIDs and from communities. Another example of semantic networks, based on category theory, is ologs. Here each type is an object, representing a set of things, and each arrow is a morphism, representing a function. Commutative diagrams also are prescribed to constrain the semantics. In the social sciences people sometimes use the term semantic network to refer to co-occurrence networks. == Software tools == There are also elaborate types of semantic networks connected with corresponding sets of software tools used for
Read more →
COTSBot

COTSBot is a small autonomous underwater vehicle (AUV) 4.5 feet (1.4 m) long, which is designed by Queensland University of Technology (QUT) to kill the very destructive crown-of-thorns starfish (Acanthaster planci) in the Great Barrier Reef off the north-east coast of Australia. It identifies its target using an image-analyzing neural net to analyze what an onboard camera sees, and then lethally injects the starfish with a bile salt solution using a needle on the end of a long underslung foldable arm. COTSBot uses GPS to navigate. The first version was created in the early 2000s with an accuracy rate of about 65%. After training COTSBot with machine learning, its accuracy rate rose to 99% by 2019. COTSBot is capable of killing 200 crown-of-thorns starfish with its two liters capacity of poison. COTSBot is capable of performing about 20 runs per day, but multiple COTSBots will be necessary to significantly impact the crown of thorns starfish populations. A smaller version of COTSBot called "RangerBot" is also being developed by QUT.
Read more →
Reason maintenance

Reason maintenance is a knowledge representation approach to efficient handling of inferred information that is explicitly stored. Reason maintenance distinguishes between base facts, which can be defeated, and derived facts. As such it differs from belief revision which, in its basic form, assumes that all facts are equally important. Reason maintenance was originally developed as a technique for implementing problem solvers. It encompasses a variety of techniques that share a common architecture: two components—a reasoner and a reason maintenance system—communicate with each other via an interface. The reasoner uses the reason maintenance system to record its inferences and justifications of ("reasons" for) the inferences. The reasoner also informs the reason maintenance system which are the currently valid base facts (assumptions). The reason maintenance system uses the information to compute the truth value of the stored derived facts and to restore consistency if an inconsistency is derived. == Truth maintenance system == A truth maintenance system, or TMS, is a knowledge representation method for representing both beliefs and their dependencies and an algorithm called the "truth maintenance algorithm" that manipulates and maintains the dependencies. The name truth maintenance is due to the ability of these systems to restore consistency. A truth maintenance system maintains consistency between old believed knowledge and current believed knowledge in the knowledge base (KB) through revision. If the current believed statements contradict the knowledge in the KB, then the KB is updated with the new knowledge. It may happen that the same data will again be believed, and the previous knowledge will be required in the KB. If the previous data are not present, but may be required for new inference. But if the previous knowledge was in the KB, then no retracing of the same knowledge is needed. The use of TMS avoids such retracing; it keeps track of the contradictory data with the help of a dependency record. This record reflects the retractions and additions which makes the inference engine (IE) aware of its current belief set. == Algorithm == Each statement having at least one valid justification is made a part of the current belief set. When a contradiction is found, the statement(s) responsible for the contradiction are identified and the records are appropriately updated. This process is called dependency-directed backtracking. The TMS algorithm maintains the records in the form of a dependency network. Each node in the network is an entry in the KB (a premise, antecedent, or inference rule etc.) Each arc of the network represent the inference steps through which the node was derived. A premise is a fundamental belief which is assumed to be true. They do not need justifications. The set of premises are the basis from which justifications for all other nodes will be derived. == Justification == There are two types of justification for a node. They are: Support list [SL] Conditional proof (CP) == Examples == Many kinds of truth maintenance systems exist. Two major types are single-context and multi-context truth maintenance. In single context systems, consistency is maintained among all facts in memory (KB) and relates to the notion of consistency found in classical logic. Multi-context systems support paraconsistency by allowing consistency to be relevant to a subset of facts in memory, a context, according to the history of logical inference. This is achieved by tagging each fact or deduction with its logical history. Multi-agent truth maintenance systems perform truth maintenance across multiple memories, often located on different machines. de Kleer's assumption-based truth maintenance system (ATMS, 1986) was utilized in systems based upon KEE on the Lisp Machine. The first multi-agent TMS was created by Mason and Johnson. It was a multi-context system. Bridgeland and Huhns created the first single-context multi-agent system.
Read more →
Software agent

In computer science, a software agent is a computer program that acts for a user or another program in a relationship of agency. The term agent is derived from the Latin agere (to do): an agreement to act on one's behalf. Such "action on behalf of" implies the authority to decide which, if any, action is appropriate. Some agents are colloquially known as bots, from robot. They may be embodied, as when execution is paired with a robot body, or as software such as a chatbot executing on a computer, such as a mobile device, e.g. Siri. Software agents may be autonomous or work together with other agents or people. Software agents interacting with people (e.g. chatbots, human-robot interaction environments) may possess human-like qualities such as natural language understanding and speech, personality or embody humanoid form (see Asimo). Related and derived concepts include intelligent agents (in particular exhibiting some aspects of artificial intelligence, such as reasoning), autonomous agents (capable of modifying the methods of achieving their objectives), distributed agents (being executed on physically distinct computers), multi-agent systems (distributed agents that work together to achieve an objective that could not be accomplished by a single agent acting alone), and mobile agents (agents that can relocate their execution onto different processors). == Concepts == The basic attributes of an autonomous software agent are that agents: are not strictly invoked for a task, but activate themselves, may reside in wait status on a host, perceiving context, may get to run status on a host upon starting conditions, do not require interaction of user, may invoke other tasks including communication. The concept of an agent provides a method of describing a complex software entity that is capable of acting with a certain degree of autonomy in order to accomplish tasks on behalf of its host. But unlike objects, which are defined in terms of methods and attributes, an agent is defined in terms of its behavior. Various authors have proposed different definitions of agents, these commonly include concepts such as: persistence: code is not executed on demand but runs continuously and decides for itself when it should perform some activity; autonomy: agents have capabilities of task selection, prioritization, goal-directed behavior, decision-making without human intervention; social ability: agents are able to engage other components through some sort of communication and coordination, they may collaborate on a task; reactivity: agents perceive the context in which they operate and react to it appropriately. === Distinguishing agents from programs === All agents are programs, but not all programs are agents. Contrasting the term with related concepts may help clarify its meaning. Franklin & Graesser (1997) discuss four key notions that distinguish agents from arbitrary programs: reaction to the environment, autonomy, goal-orientation and persistence. === Intuitive distinguishing agents from objects === Agents are more autonomous than objects. Agents have flexible behavior: reactive, proactive, social. Agents have at least one thread of control but may have more. === Distinguishing agents from expert systems === Expert systems are not coupled to their environment. Expert systems are not designed for reactive, proactive behavior. Expert systems do not consider social ability. === Distinguishing intelligent software agents from intelligent agents in AI === Intelligent agents (also known as rational agents) are not just computer programs: they may also be machines, human beings, communities of human beings (such as firms) or anything that is capable of goal-directed behavior. == Impact of software agents == Software agents may offer various benefits to their end users by automating complex or repetitive tasks. However, there are organizational and cultural impacts of this technology that need to be considered prior to implementing software agents. === Organizational impact === === Work contentment and job satisfaction impact === People like to perform easy tasks providing the sensation of success unless the repetition of the simple tasking is affecting the overall output. In general implementing software agents to perform administrative requirements provides a substantial increase in work contentment, as administering their own work does never please the worker. The effort freed up serves for a higher degree of engagement in the substantial tasks of individual work. Hence, software agents may provide the basics to implement self-controlled work, relieved from hierarchical controls and interference. Such conditions may be secured by application of software agents for required formal support. === Cultural impact === The cultural effects of the implementation of software agents include trust affliction, skills erosion, privacy attrition and social detachment. Some users may not feel entirely comfortable fully delegating important tasks to software applications. Those who start relying solely on intelligent agents may lose important skills, for example, relating to information literacy. In order to act on a user's behalf, a software agent needs to have a complete understanding of a user's profile, including his/her personal preferences. This, in turn, may lead to unpredictable privacy issues. When users start relying on their software agents more, especially for communication activities, they may lose contact with other human users and look at the world with the eyes of their agents. These consequences are what agent researchers and users must consider when dealing with intelligent agent technologies. === History === The concept of an agent can be traced back to Hewitt's Actor Model (Hewitt, 1977) - "A self-contained, interactive and concurrently-executing object, possessing internal state and communication capability." To be more academic, software agent systems are a direct evolution of Multi-Agent Systems (MAS). MAS evolved from Distributed Artificial Intelligence (DAI), Distributed Problem Solving (DPS) and Parallel AI (PAI), thus inheriting all characteristics (good and bad) from DAI and AI. John Sculley's 1987 "Knowledge Navigator" video portrayed an image of a relationship between end-users and agents. Being an ideal first, this field experienced a series of unsuccessful top-down implementations, instead of a piece-by-piece, bottom-up approach. The range of agent types is now (from 1990) broad: WWW, search engines, etc. == Examples of intelligent software agents == === Buyer agents (shopping bots) === Buyer agents travel around a network (e.g. the internet) retrieving information about goods and services. These agents, also known as 'shopping bots', work very efficiently for commodity products such as CDs, books, electronic components, and other one-size-fits-all products. Buyer agents are typically optimized to allow for digital payment services used in e-commerce and traditional businesses. === User agents (personal agents) === User agents, or personal agents, are intelligent agents that take action on your behalf. In this category belong those intelligent agents that already perform, or will shortly perform, the following tasks: Check your e-mail, sort it according to the user's order of preference, and alert you when important emails arrive. Play computer games as your opponent or patrol game areas for you. Assemble customized news reports for you. There are several versions of these, including CNN. Find information for you on the subject of your choice. Fill out forms on the Web automatically for you, storing your information for future reference Scan Web pages looking for and highlighting text that constitutes the "important" part of the information there Discuss topics with you ranging from your deepest fears to sports Facilitate with online job search duties by scanning known job boards and sending the resume to opportunities who meet the desired criteria Profile synchronization across heterogeneous social networks === Monitoring-and-surveillance (predictive) agents === Monitoring and surveillance agents are used to observe and report on equipment, usually computer systems. The agents may keep track of company inventory levels, observe competitors' prices and relay them back to the company, watch stock manipulation by insider trading and rumors, etc. For example, NASA's Jet Propulsion Laboratory has an agent that monitors inventory, planning, schedules equipment orders to keep costs down, and manages food storage facilities. These agents usually monitor complex computer networks that can keep track of the configuration of each computer connected to the network. A special case of monitoring-and-surveillance agents are organizations of agents used to automate decision-making process during tactical operations. The agents monitor the status of assets (ammunition, weapons available, platforms for transport, etc.) and receive goals from hi
Read more →
Juergen Pirner

Juergen Pirner (born 1956) is the German creator of Jabberwock, a chatterbot that won the 2003 Loebner prize. Pirner created Jabberwock modelling the Jabberwocky from Lewis Carroll's poem of the same name. Initially, Jabberwock would just give rude or fantasy-related answers; but over the years, Pirner has programmed better responses into it. As of 2007 he has taught it 2.7 million responses. Pirner lives in Hamburg, Germany.
Read more →
Ebert test

The Ebert test gauges whether a computer-based synthesized voice can tell a joke with sufficient skill to cause people to laugh. It was proposed by film critic Roger Ebert at the 2011 TED conference as a challenge to software developers to have a computerized voice master the inflections, delivery, timing, and intonations of human speech. The test is similar to the Turing test proposed by Alan Turing in 1950 as a way to gauge a computer's ability to exhibit intelligent behavior by generating performance indistinguishable from a human being. If the computer can successfully tell a joke, and do the timing and delivery as well as Henny Youngman, then that's the voice I want. Ebert lost his voice in 2006 after undergoing surgery to treat thyroid cancer. He employed a Scottish company called CereProc, which custom-tailors text-to-speech software for voiceless customers who record their voices at length before losing them, and mined tapes and DVD commentaries featuring Ebert to create a voice that sounded more like his own voice. He first publicly used the voice they devised for him in his March 2, 2010, appearance on The Oprah Winfrey Show. The audience of Ebert's 2011 TED talk about joke delivery by synthesized voices erupted with laughter when a synthesized voice delivered the following joke: "A guy goes into a psychiatrist. The psychiatrist says, 'You’re crazy.' The guy says, 'I want a second opinion.' The psychiatrist says, 'All right, you’re ugly, too.'"
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 →
Structural similarity index measure

The structural similarity index measure (SSIM) is a method for predicting the perceived quality of digital television and cinematic pictures, as well as other kinds of digital images and videos. It is also used for measuring the similarity between two images. The SSIM index is a full reference metric; in other words, the measurement or prediction of image quality is based on an initial uncompressed or distortion-free image as reference. SSIM is a perception-based model that considers image degradation as perceived change in structural information, while also incorporating important perceptual phenomena, including both luminance masking and contrast masking terms. This distinguishes from other techniques such as mean squared error (MSE) or peak signal-to-noise ratio (PSNR) that instead estimate absolute errors. Structural information is the idea that the pixels have strong inter-dependencies especially when they are spatially close. These dependencies carry important information about the structure of the objects in the visual scene. Luminance masking is a phenomenon whereby image distortions (in this context) tend to be less visible in bright regions, while contrast masking is a phenomenon whereby distortions become less visible where there is significant activity or "texture" in the image. == History == The predecessor of SSIM was called Universal Quality Index (UQI), or Wang–Bovik index, which was developed by Zhou Wang and Alan Bovik in 2001. This evolved, through their collaboration with Hamid Sheikh and Eero Simoncelli, into the current version of SSIM, which was published in April 2004 in the IEEE Transactions on Image Processing. In addition to defining the SSIM quality index, the paper provides a general context for developing and evaluating perceptual quality measures, including connections to human visual neurobiology and perception, and direct validation of the index against human subject ratings. The basic model was developed in the Laboratory for Image and Video Engineering (LIVE) at The University of Texas at Austin and further developed jointly with the Laboratory for Computational Vision (LCV) at New York University. Further variants of the model have been developed in the Image and Visual Computing Laboratory at University of Waterloo and have been commercially marketed. SSIM subsequently found strong adoption in the image processing community and in the television and social media industries. The 2004 SSIM paper has been cited over 50,000 times according to Google Scholar, making it one of the highest cited papers in the image processing and video engineering fields. It was recognized with the IEEE Signal Processing Society Best Paper Award for 2009. It also received the IEEE Signal Processing Society Sustained Impact Award for 2016, indicative of a paper having an unusually high impact for at least 10 years following its publication. Because of its high adoption by the television industry, the authors of the original SSIM paper were each accorded a Primetime Engineering Emmy Award in 2015 by the Television Academy. == Algorithm == The SSIM index is calculated between two windows of pixel values x {\displaystyle x} and y {\displaystyle y} of common size, from corresponding locations in two images to be compared. These SSIM values can be aggregated across the full images by averaging or other variations. === Special-case formula === In one simple special case, further explained in the next section, the SSIM measure between x {\displaystyle x} and y {\displaystyle y} is: SSIM ( x , y ) = ( 2 μ x μ y + c 1 ) ( 2 σ x y + c 2 ) ( μ x 2 + μ y 2 + c 1 ) ( σ x 2 + σ y 2 + c 2 ) {\displaystyle {\hbox{SSIM}}(x,y)={\frac {(2\mu _{x}\mu _{y}+c_{1})(2\sigma _{xy}+c_{2})}{(\mu _{x}^{2}+\mu _{y}^{2}+c_{1})(\sigma _{x}^{2}+\sigma _{y}^{2}+c_{2})}}} with: μ x {\displaystyle \mu _{x}} the pixel sample mean of x {\displaystyle x} ; μ y {\displaystyle \mu _{y}} the pixel sample mean of y {\displaystyle y} ; σ x 2 {\displaystyle \sigma _{x}^{2}} the sample variance of x {\displaystyle x} ; σ y 2 {\displaystyle \sigma _{y}^{2}} the sample variance of y {\displaystyle y} ; σ x y {\displaystyle \sigma _{xy}} the sample covariance of x {\displaystyle x} and y {\displaystyle y} ; c 1 = ( k 1 L ) 2 {\displaystyle c_{1}=(k_{1}L)^{2}} , c 2 = ( k 2 L ) 2 {\displaystyle c_{2}=(k_{2}L)^{2}} two variables to stabilize the division with weak denominator; L {\displaystyle L} the dynamic range of the pixel-values (typically this is 2 # b i t s p e r p i x e l − 1 {\displaystyle 2^{\#bits\ per\ pixel}-1} ); k 1 = 0.01 {\displaystyle k_{1}=0.01} and k 2 = 0.03 {\displaystyle k_{2}=0.03} by default. === General formula and components === The SSIM formula is based on three comparison measurements between the samples of x {\displaystyle x} and y {\displaystyle y} : luminance ( l {\displaystyle l} ), contrast ( c {\displaystyle c} ), and structure ( s {\displaystyle s} ). The individual comparison functions are: l ( x , y ) = 2 μ x μ y + c 1 μ x 2 + μ y 2 + c 1 {\displaystyle l(x,y)={\frac {2\mu _{x}\mu _{y}+c_{1}}{\mu _{x}^{2}+\mu _{y}^{2}+c_{1}}}} c ( x , y ) = 2 σ x σ y + c 2 σ x 2 + σ y 2 + c 2 {\displaystyle c(x,y)={\frac {2\sigma _{x}\sigma _{y}+c_{2}}{\sigma _{x}^{2}+\sigma _{y}^{2}+c_{2}}}} s ( x , y ) = σ x y + c 3 σ x σ y + c 3 {\displaystyle s(x,y)={\frac {\sigma _{xy}+c_{3}}{\sigma _{x}\sigma _{y}+c_{3}}}} The SSIM for each block is then a weighted combination of those comparative measures: SSIM ( x , y ) = l ( x , y ) α ⋅ c ( x , y ) β ⋅ s ( x , y ) γ {\displaystyle {\text{SSIM}}(x,y)=l(x,y)^{\alpha }\cdot c(x,y)^{\beta }\cdot s(x,y)^{\gamma }} Choosing the third denominator stabilizing constant as: c 3 = c 2 / 2 {\displaystyle c_{3}=c_{2}/2} leads to a simplification when combining the c and s components with equal exponents ( β = γ {\displaystyle \beta =\gamma } ), as the numerator of c is then twice the denominator of s, leading to a cancellation leaving just a 2. Setting the weights (exponents) α , β , γ {\displaystyle \alpha ,\beta ,\gamma } to 1, the formula can then be reduced to the special case shown above. === Mathematical properties === SSIM satisfies the identity of indiscernibles, and symmetry properties, but not the triangle inequality or non-negativity, and thus is not a distance function. However, under certain conditions, SSIM may be converted to a normalized root MSE measure, which is a distance function. The square of such a function is not convex, but is locally convex and quasiconvex, making SSIM a feasible target for optimization. === Application of the formula === In order to evaluate the image quality, this formula is usually applied only on luma, although it may also be applied on color (e.g., RGB) values or chromatic (e.g. YCbCr) values. The resultant SSIM index is a decimal value between -1 and 1, where 1 indicates perfect similarity, 0 indicates no similarity, and -1 indicates perfect anti-correlation. For an image, it is typically calculated using a sliding Gaussian window of size 11×11 or a block window of size 8×8. The window can be displaced pixel-by-pixel on the image to create an SSIM quality map of the image. In the case of video quality assessment, the authors propose to use only a subgroup of the possible windows to reduce the complexity of the calculation. === Variants === ==== Multi-scale SSIM ==== A more advanced form of SSIM, called Multiscale SSIM (MS-SSIM) is conducted over multiple scales through a process of multiple stages of sub-sampling, reminiscent of multiscale processing in the early vision system. It has been shown to perform equally well or better than SSIM on different subjective image and video databases. ==== Multi-component SSIM ==== Three-component SSIM (3-SSIM) is a form of SSIM that takes into account the fact that the human eye can see differences more precisely on textured or edge regions than on smooth regions. The resulting metric is calculated as a weighted average of SSIM for three categories of regions: edges, textures, and smooth regions. The proposed weighting is 0.5 for edges, 0.25 for the textured and smooth regions. The authors mention that a 1/0/0 weighting (ignoring anything but edge distortions) leads to results that are closer to subjective ratings. This suggests that edge regions play a dominant role in image quality perception. The authors of 3-SSIM have also extended the model into four-component SSIM (4-SSIM). The edge types are further subdivided into preserved and changed edges by their distortion status. The proposed weighting is 0.25 for all four components. ==== Structural dissimilarity ==== Structural dissimilarity (DSSIM) may be derived from SSIM, though it does not constitute a distance function as the triangle inequality is not necessarily satisfied. DSSIM ( x , y ) = 1 − SSIM ( x , y ) 2 {\displaystyle {\hbox{DSSIM}}(x,y)={\frac {1-{\hbox{SSIM}}(x,y)}{2}}} ==== Video quality metrics and temporal variants ==== It is worth noting that the original vers
Read more →
Logico-linguistic modeling

Logico-linguistic modeling is a method for building knowledge-based systems with a learning capability using conceptual models from soft systems methodology, modal predicate logic, and logic programming languages such as Prolog. == Overview == Logico-linguistic modeling is a six-stage method developed primarily for building knowledge-based systems (KBS), but it also has application in manual decision support systems and information source analysis. Logico-linguistic models have a superficial similarity to John F. Sowa's conceptual graphs; both use bubble style diagrams, both are concerned with concepts, both can be expressed in logic and both can be used in artificial intelligence. However, logico-linguistic models are very different in both logical form and in their method of construction. Logico-linguistic modeling was developed in order to solve theoretical problems found in the soft systems method for information system design. The main thrust of the research into has been to show how soft systems methodology (SSM), a method of systems analysis, can be extended into artificial intelligence. == Background == SSM employs three modeling devices i.e. rich pictures, root definitions, and conceptual models of human activity systems. The root definitions and conceptual models are built by stakeholders themselves in an iterative debate organized by a facilitator. The strengths of this method lie, firstly, in its flexibility, the fact that it can address any problem situation, and, secondly, in the fact that the solution belongs to the people in the organization and is not imposed by an outside analyst. Information requirements analysis (IRA) took the basic SSM method a stage further and showed how the conceptual models could be developed into a detailed information system design. IRA calls for the addition of two modeling devices: "Information Categories", which show the required information inputs and outputs from the activities identified in an expanded conceptual model; and the "Maltese Cross", a matrix which shows the inputs and outputs from the information categories and shows where new information processing procedures are required. A completed Maltese Cross is sufficient for the detailed design of a transaction processing system. The initial impetus to the development of logico-linguistic modeling was a concern with the theoretical problem of how an information system can have a connection to the physical world. This is a problem in both IRA and more established methods (such as SSADM) because none base their information system design on models of the physical world. IRA designs are based on a notional conceptual model and SSADM is based on models of the movement of documents. The solution to these problems provided a formula that was not limited to the design of transaction processing systems but could be used for the design of KBS with learning capability. == The six stages of logico-linguistic modeling == The logico-linguistic modeling method comprises six stages. === 1. Systems analysis === In the first stage logico-linguistic modeling uses SSM for systems analysis. This stage seeks to structure the problem in the client organization by identifying stakeholders, modelling organizational objectives and discussing possible solutions. At this stage it not assumed that a KBS will be a solution and logico-linguistic modeling often produces solutions that do not require a computerized KBS. Expert systems tend to capture the expertise, of individuals in different organizations, on the same topic. By contrast a KBS, produced by logico-linguistic modeling, seeks to capture the expertise of individuals in the same organization on different topics. The emphasis is on the elicitation of organizational or group knowledge rather than individual experts. In logico-linguistic modeling the stakeholders become the experts. The end point of this stage is an SSM style conceptual models such as figure 1. === 2. Language creation === According to the theory behind logico-linguistic modeling the SSM conceptual model building process is a Wittgensteinian language-game in which the stakeholders build a language to describe the problem situation. The logico-linguistic model expresses this language as a set of definitions, see figure 2. === 3. Knowledge elicitation === After the model of the language has been built putative knowledge about the real world can be added by the stakeholders. Traditional SSM conceptual models contain only one logical connective (a necessary condition). In order to represent causal sequences, "sufficient conditions" and "necessary and sufficient conditions" are also required. In logico-linguistic modeling this deficiency is remedied by two addition types of connective. The outcome of stage three is an empirical model, see figure 3. === 4. Knowledge representation === Modal predicate logic (a combination of modal logic and predicate logic) is used as the formal method of knowledge representation. The connectives from the language model are logically true (indicated by the "L" modal operator) and connective added at the knowledge elicitation stage are possibility true (indicated by the "M" modal operator). Before proceeding to stage 5, the models are expressed in logical formulae. === 5. Computer code === Formulae in predicate logic translate easily into the Prolog artificial intelligence language. The modality is expressed by two different types of Prolog rules. Rules taken from the language creation stage of model building process are treated as incorrigible. While rules from the knowledge elicitation stage are marked as hypothetical rules. The system is not confined to decision support but has a built in learning capability. === 6. Verification === A knowledge based system built using this method verifies itself. Verification takes place when the KBS is used by the clients. It is an ongoing process that continues throughout the life of the system. If the stakeholder beliefs about the real world are mistaken this will be brought out by the addition of Prolog facts that conflict with the hypothetical rules. It operates in accordance to the classic principle of falsifiability found in the philosophy of science == Applications == === Knowledge-based computer systems === Logico-linguistic modeling has been used to produce fully operational computerized knowledge based systems, such as one for the management of diabetes patients in a hospital out-patients department. === Manual decision support === In other projects the need to move into Prolog was considered unnecessary because the printed logico-linguistic models provided an easy-to-use guide to decision making. For example, a system for mortgage loan approval === Information source analysis === In some cases a KBS could not be built because the organization did not have all the knowledge needed to support all their activities. In these cases logico-linguistic modeling showed shortcomings in the supply of information and where more was needed. For example, a planning department in a telecoms company == Criticism == While logico-linguistic modeling overcomes the problems found in SSM's transition from conceptual model to computer code, it does so at the expense of increased stakeholder constructed model complexity. The benefits of this complexity are questionable and this modeling method may be much harder to use than other methods. This contention has been exemplified by subsequent research. An attempt by researchers to model buying decisions across twelve companies using logico-linguistic modeling required simplification of the models and removal of the modal elements.
Read more →
Production Rule Representation

The Production Rule Representation (PRR) is a proposed standard of the Object Management Group (OMG) that aims to define a vendor-neutral model for representing production rules within the Unified Modeling Language (UML), specifically for use in forward-chaining rule engines. == History == The OMG set up a Business Rules Working Group in 2002 as the first standards body to recognize the importance of the "Business Rules Approach". It issued 2 main RFPs in 2003 – a standard for modeling production rules (PRR), and a standard for modeling business rules as business documentation (BSBR, now SBVR). PRR was mostly defined by and for vendors of Business Rule Engines (BREs) (sometimes termed Business Rules Engine(s), like in Wikipedia). Contributors have included all the major BRE vendors, members of RuleML, and leading UML vendors. == Evolution == The PRR RFP originally suggested that PRR use a combination of UML OCL and Action Semantics for rule conditions and actions. However, expecting modellers to learn 2 relatively obscure UML languages in order to define a production rule proved unpalatable. Therefore, PRR OCL was defined that included OCL extensions for simple rule actions (as well as external functions). PRR OCL is currently considered "non-normative" i.e. is not part of the PRR standard per se. PRR beta applies just to a PRR Core that excludes an explicit expression language. The PRR RFP envisaged covering both forward and backward chaining rule engines. However, the lack of vendor support for / interest in backward chaining caused this to be revise to forward chaining and "sequential" semantics. The latter is simply the scripting mode provided by many BPM tools, where rules are listed and executed sequentially as if programmed. This provides PRR with better compatibility with typical BPM scripting engines (and acknowledges the fact that most BREs today support a "sequential" mode of operation, improving performance in some circumstances). == Status == PRR is currently at version 1.0.
Read more →
TRAIGA

TRAIGA, or the Texas Responsible Artificial Intelligence Governance Act, is a state law regulating the development and deployment of artificial intelligence (AI) systems in Texas. Sponsored by Representative Giovanni Capriglione, the Act establishes a framework governing certain uses of AI, outlines prohibited uses, and creates obligations on state government entities, among other provisions. TRAIGA was signed into law in 2025 and took effect on January 1, 2026. The law applies to AI developers and deployers that conduct business in Texas or whose systems are used by Texas residents. It prohibits the intentional development or deployment of AI systems to incite harm, violate constitutional rights, engage in unlawful discrimination, and produce child sexual abuse material or unlawful deepfakes. TRAIGA also establishes the Texas Artificial Intelligence Council and creates a regulatory sandbox program. The Texas Attorney General is charged with enforcement. It has received attention as one of the first comprehensive AI-related laws enacted by a U.S. state. Legal analysts have compared it to the European Union (EU) Artificial Intelligence Act and the Colorado AI Act, noting its intent-based discrimination standard and narrower scope relative to those frameworks. == Background == In June 2023, Texas Governor Greg Abbott signed House Bill 2060, which created an Artificial Intelligence Advisory Council within the Texas Department of Information Resources. The Council was tasked with monitoring the use of AI systems across state government. Its membership included representatives from law enforcement, academia, and the legal profession. After submitting a report to state policymakers, the Council was disbanded in December 2024. Separately, the Texas House Select Committee on Artificial Intelligence and Emerging Technologies was created in 2023 to examine the political and social implications of artificial intelligence. Among its recommendations was the creation of a regulatory sandbox to allow for controlled testing of AI systems. This recommendation informed the regulatory sandbox provision included in TRAIGA. == History == In December 2024, Representative Capriglione introduced House Bill 1709, the Texas Responsible Artificial Intelligence Governance Act. The bill sought to create a statewide framework for artificial intelligence, including transparency requirements for companies deploying AI systems, restrictions on certain uses of AI, and the creation of a regulatory sandbox. Modeled in part on the EU Artificial Intelligence Act and the Colorado AI Act, House Bill 1709 focused on "high-risk" AI systems and included provisions addressing private sector liability. House Bill 1709 did not advance during the legislative session. Industry stakeholders raised concerns that several provisions were overly burdensome. The bill informed the development of a revised proposal, House Bill 149, also titled the Texas Responsible Artificial Intelligence Governance Act. The revised version removed requirements for private companies to notify consumers when they interact with AI systems and to conduct impact assessments, among other provisions. In April 2025, an amended version of House Bill 149 passed the Texas House of Representatives and was referred to the Senate Committee on Business and Commerce. The bill later received approval from both chambers, where the House voted on amendments adopted by the Senate. On May 31, 2025, the state legislature passed House Bill 149, one of several AI-related bills considered during the legislative session. Governor Abbott signed TRAIGA into law on June 22, 2025. During the legislative process, a proposed federal moratorium on state-level AI regulation initially raised questions about the enforceability of state AI laws, including TRAIGA. At the time of signing, Governor Abbott stated that Texas would ensure compliance with applicable federal requirements. In July 2025, the United States Senate voted to remove the proposed moratorium from federal legislation. The Act took effect on January 1, 2026. == Provisions == === Definitions and scope === TRAIGA applies to AI developers and deployers that advertise or conduct business in Texas, develop products used by Texas residents, or develop or deploy AI systems within the state. The Act also applies to Texas state and local government entities. The Act defines a developer as a person who develops an AI system and a deployer as one who deploys an AI system in Texas. Consumers are defined as Texas residents. The Act defines an artificial intelligence system as a machine-based system that "infers from the inputs the system receives how to generate outputs, including content, decisions, predictions, or recommendations, that can influence physical or virtual environments." === Government use === The Act requires government agencies to provide consumers with plain language notices before interacting with AI systems. It also prohibits government agencies from using artificial intelligence systems to assign social scores to consumers. It also restricts the use of AI systems to identify individuals using biometric data without the individual’s consent. === Prohibitions === The Act prohibits the development or deployment of artificial intelligence systems intended to cause harm, self-harm, or criminal activity. It also prohibits the development or deployment of AI systems designed to violate constitutional rights or unlawfully discriminate based on protected classes. In addition, the Act prohibits the development or deployment of AI systems that are intended to produce or distribute child sexual abuse material or unlawful deepfakes. === Enforcement === Enforcement authority under the Act rests with the Texas Attorney General. The Act does not create a private right of action. The Act requires the Texas Attorney General to create an online complaint system where consumers may submit allegations of potential violations. The Attorney General can investigate complaints received through this system and may request information relevant to the operation of an AI system, including information about training data. Before initiating an enforcement action, the Attorney General must provide a written notice to the alleged violator, who is then provided with a 60-day period to cure the alleged violation. === Penalties === If a violation is not cured, the Act authorizes civil penalties. Penalties range from $10,000 to $12,000 per curable violation and from $80,000 to $200,000 per non-curable violation. The Act also authorizes additional penalties of $2,000 to $40,000 for each day the violation continues. If the Attorney General determines that a person certified or licensed by a state agency has violated the Act and recommends enforcement, the relevant agency may impose additional administrative sanctions, including license suspension or further monetary penalties. === Safe harbor === The Act provides an affirmative defense for AI developers and deployers who identify potential violations through internal testing or auditing or who demonstrate compliance with National Institute of Standards and Technology (NIST)'s Artificial Intelligence Risk Management Framework or a comparable risk management framework. The Act also affords protection to developers and deployers when a third party uses their AI systems in a way that violates the Act. === Texas Artificial Intelligence Council === The Act creates the Texas Artificial Intelligence Council to assist the state legislatures in evaluating artificial intelligence policy and oversight. The Council is charged with developing recommendations for state agencies regarding the use of AI systems and with overseeing the regulatory sandbox. TRAIGA gives the Council the ability to organize AI-related training for state entities and issue reports concerning artificial intelligence. The Council does not have binding rulemaking authority. The Council consists of seven members appointed by the governor, the lieutenant governor, and the speaker of the Texas House of Representatives. === Regulatory sandbox === The Act directs the Texas Department of Information Resources to create a regulatory sandbox program that allows participants to test AI systems under state supervision in a modified regulatory setting. To join the program, companies must submit applications that describe their AI systems and intended use. Approved participants may operate within the sandbox for up to 36 months. During that period, the Attorney General is restricted from initiating enforcement actions for certain categories of violations. == Reception == === Support === During legislative testimony, the Texas Public Policy Foundation stated that TRAIGA would benefit Texas businesses by reducing legal ambiguity and creating clearer compliance standards. Representatives of business groups also expressed support, stating that the Act would not impose overly burdensome regulations. The consum
Read more →
Digital Michelangelo Project

The Digital Michelangelo Project was a pioneering initiative undertaken during the 1998–1999 academic year to digitize the sculptures and architecture of Michelangelo using advanced laser scanning technology. The project was led by a team of 30 faculty, staff, and students from Stanford University and the University of Washington, with the aim of creating high-resolution 3D models of Michelangelo's works for scholarly, educational, and preservation purposes. == Objectives == The primary goals of the Digital Michelangelo Project were: To apply recent advancements in laser rangefinder technology for digitizing large cultural artifacts. To create detailed digital archives of Michelangelo's sculptures and architectural spaces for future study and analysis. To explore potential educational and curatorial applications for 3D scanned data. === Artworks digitized === The project involved scanning several iconic works by Michelangelo, including: David The Unfinished Slaves (Atlas, Awakening, Bearded, and Youthful) St. Matthew The allegorical statues from the Medici tombs (Night, Day, Dawn, and Dusk) The architectural interiors of the Tribuna del David at the Galleria dell'Accademia and the New Sacristy in the Medici Chapels. == Technology and methodology == === 3D scanning === The project's primary scanner was a laser triangulation rangefinder mounted on a motorized gantry, custom-built by Cyberware Inc. The scanner used a laser sheet to project onto an object, capturing its shape through triangulation. Multiple scans were taken from various angles and combined into a single, detailed 3D mesh. The resolution achieved was fine enough to capture even Michelangelo's chisel marks, with triangles approximately 0.25 mm on each side. In addition to shape data, color data was captured using a spotlight and a secondary camera, enabling the creation of textured 3D models. === Data processing === The project developed a software suite for processing the scanned data. This included: Aligning and merging multiple scans into a seamless 3D model. Filling holes in the geometry caused by inaccessible areas. Correcting color data for lighting inconsistencies and shadowing. Non-photorealistic rendering techniques were also applied, highlighting surface features such as Michelangelo’s chisel marks for enhanced visualization. == Logistical challenges == The scale and complexity of the project presented several challenges: Data size: The dataset for David alone comprised 2 billion polygons and 7,000 color images, occupying 60 GB of storage. Artifact safety: Ensuring the safety of the statues during scanning required extensive crew training, foam-encased equipment, and collision-prevention mechanisms. == Applications and impact == The digitized models have numerous potential applications: Art history: Allowing precise measurements and geometric analysis, such as determining chisel types or evaluating structural balance. Education: Providing new ways to study art, including interactive viewing from unconventional angles and with custom lighting. Museum curation: Enhancing visitor experiences through interactive kiosks and virtual models. The project demonstrated the potential for 3D technology to preserve and disseminate cultural heritage. == Data distribution == The project's models are available through Stanford University for scholarly purposes, under strict licensing due to Italian intellectual property laws. === ScanView === To provide public access to the 3D models while respecting usage restrictions, the project developed ScanView, a client/server rendering system. ScanView allows users to view and interact with high-resolution 3D models without downloading the data. The client component consists of a freely available viewer program and simplified 3D models. Users can navigate these models locally, adjusting position, orientation, lighting, and surface appearance. When a user finalizes a view, the client queries a remote server for a high-resolution rendering of the model, which is sent back to overwrite the simplified version on the user’s screen. A typical query-response cycle takes 1–2 seconds, depending on network conditions. To protect the models from unauthorized reconstruction, the system employs several security measures, including: Encrypting queries Perturbing viewpoint and lighting parameters Adding noise and warping rendered images Compressing images before transmission ScanView operates on Windows-based PCs and provides access to selected models, including David and St. Matthew, as well as other artifacts such as fragments of the Forma Urbis Romae and items from the Stanford 3D Scanning Repository. == Sponsors == The Digital Michelangelo Project was supported by Stanford University, Interval Research Corporation, and the Paul G. Allen Foundation for the Arts.
Read more →
SAS Viya

SAS Viya is an artificial intelligence, analytics and data management platform developed by SAS Institute. == History == SAS Viya was released in 2016. The software was containerized with the release of Viya 4 in 2020. Viya has become one of SAS' most widely used platforms during the AI boom, as artificial intelligence becomes more widely used in business and computing. == Technical overview == The platform is cloud-native, and is executed on SAS's Cloud Analytics Services (CAS) engine. It is compatible with open source software, allowing users to build models using open sources tool such as R, Python and Jupyter. It integrates with major large language models like GPT-4 and Gemini Pro. The platform uses econometrics to create predictive models for forecasting scenarios based on complex data. It also has features for detecting algorithmic bias, auditing decisions and monitoring models. It is implemented through a low-code, no-code platform. The software is available on Amazon AWS Marketplace, Google Cloud, Red Hat OpenShift, and on Microsoft Azure Marketplace under a pay-as-you-use model. == Software == SAS Viya has released software as a service (SaaS) modules for creating AI content. These include Viya Workbench, Viya App Factory, Viya Copilot, and SAS Data Maker. The company also develops industry specific models, used by companies including Georgia-Pacific. == Applications == === Banking === The software is also widely used in business, especially in areas such as predictive modelling and fraud detection. === Insurance === SAS Viya is used in insurance for tasks such as actuarial analytics and modelling, as well as regulatory reporting. === Healthcare and life sciences === In 2023, the company introduced SAS Health, a common health data model built on the SAS Viya platform. AstraZeneca has partnered with SAS to use SAS Viya and SAS Life Science Analytics Framework in its delivery and approval processes. In 2024, SAS partnered with the University of Cambridge's Maxwell Center to use SAS Viya for healthcare research and development. === Public sector === SAS Viya is used in partnership with national and local governments to provide services and detect tax fraud. === Education === SAS Viya is used in research and education, particularly studies related to business intelligence, cybersecurity and data management. SAS Institute has partnered with educational institutions such as Appalachian State University, Clemson University, University of Arkansas, Stockholm University, and Marian University, to provide access to and training for using SAS Viya.
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 →