AI Art Museum Los Angeles

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  List of security assessment tools

  This is a list of available software and hardware tools that are designed for or are particularly suited to various kinds of security assessment and security testing. == Operating systems and tool suites == Several operating systems and tool suites provide bundles of tools useful for various types of security assessment. === Operating system distributions === Kali Linux (formerly BackTrack), a penetration-test-focused Linux distribution based on Debian Pentoo, a penetration-test-focused Linux distribution based on Gentoo ParrotOS, a Linux distro focused on penetration testing, forensics, and online anonymity. == Tools ==

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  Lisp machine

  Lisp machines are general-purpose computers designed to efficiently run Lisp as their main software and programming language, usually via hardware support. They are an example of a high-level language computer architecture. In a sense, they were the first commercial single-user workstations. Despite being modest in number (perhaps 7,000 units total as of 1988) Lisp machines commercially pioneered some now-commonplace technologies, including networking innovations such as Chaosnet, and effective garbage collection. Several firms built and sold Lisp machines in the 1980s: Symbolics (3600, 3640, XL1200, MacIvory, and other models), Lisp Machines Incorporated (LMI Lambda), Texas Instruments (Explorer, MicroExplorer), and Xerox (Interlisp-D workstations). The operating systems were written in Lisp Machine Lisp, Interlisp (Xerox), and later partly in Common Lisp. == History == === Historical context === Artificial intelligence (AI) computer programs of the 1960s and 1970s intrinsically required what was then considered a huge amount of computer power, as measured in processor time and memory space. The power requirements of AI research were exacerbated by the Lisp symbolic programming language, when commercial hardware was designed and optimized for assembly- and Fortran-like programming languages. At first, the cost of such computer hardware meant that it had to be shared among many users. As integrated circuit technology shrank the size and cost of computers in the 1960s and early 1970s, and the memory needs of AI programs began to exceed the address space of the most common research computer, the Digital Equipment Corporation (DEC) PDP-10, researchers considered a new approach: a computer designed specifically to develop and run large artificial intelligence programs, and tailored to the semantics of the Lisp language. To provide consistent performance for interactive programs, these machines would often not be shared, but would be dedicated to a single user at a time. === Initial development === In 1973, Richard Greenblatt and Thomas Knight, programmers at Massachusetts Institute of Technology (MIT) Artificial Intelligence Laboratory (AI Lab), began what would become the MIT Lisp Machine Project when they first began building a computer hardwired to run certain basic Lisp operations, rather than run them in software, in a 24-bit tagged architecture. The machine also did incremental (or Arena) garbage collection. More specifically, since Lisp variables are typed at runtime rather than compile time, a simple addition of two variables could take five times as long on conventional hardware, due to test and branch instructions. Lisp Machines ran the tests in parallel with the more conventional single instruction additions. If the simultaneous tests failed, then the result was discarded and recomputed; this meant in many cases a speed increase by several factors. This simultaneous checking approach was used as well in testing the bounds of arrays when referenced, and other memory management necessities (not merely garbage collection or arrays). Type checking was further improved and automated when the conventional byte word of 32 bits was lengthened to 36 bits for Symbolics 3600-model Lisp machines and eventually to 40 bits or more (usually, the excess bits not accounted for by the following were used for error-correcting codes). The first group of extra bits were used to hold type data, making the machine a tagged architecture, and the remaining bits were used to implement compressed data representation (CDR) coding (wherein the usual linked list elements are compressed to occupy roughly half the space), aiding garbage collection by reportedly an order of magnitude. A further improvement was two microcode instructions which specifically supported Lisp functions, reducing the cost of calling a function to as little as 20 clock cycles, in some Symbolics implementations. The first machine was called the CONS machine (named after the list construction operator cons in Lisp). Often it was affectionately referred to as the Knight machine, perhaps since Knight wrote his master's thesis on the subject; it was extremely well received. It was subsequently improved into a version called CADR (a pun; in Lisp, the cadr function, which returns the second item of a list, is pronounced /ˈkeɪ.dəɹ/ or /ˈkɑ.dəɹ/, as some pronounce the word "cadre") which was based on essentially the same architecture. About 25 of what were essentially prototype CADRs were sold within and without MIT for ~$50,000; it quickly became the favorite machine for hacking – many of the most favored software tools were quickly ported to it (e.g. Emacs was ported from ITS in 1975). It was so well received at an AI conference held at MIT in 1978 that Defense Advanced Research Projects Agency (DARPA) began funding its development. === Commercializing MIT Lisp machine technology === In 1979, Russell Noftsker, being convinced that Lisp machines had a bright commercial future due to the strength of the Lisp language and the enabling factor of hardware acceleration, proposed to Greenblatt that they commercialize the technology. In a counter-intuitive move for an AI Lab hacker, Greenblatt acquiesced, hoping perhaps that he could recreate the informal and productive atmosphere of the Lab in a real business. These ideas and goals were considerably different from those of Noftsker. The two negotiated at length, but neither would compromise. As the proposed firm could succeed only with the full and undivided assistance of the AI Lab hackers as a group, Noftsker and Greenblatt decided that the fate of the enterprise was up to them, and so the choice should be left to the hackers. The ensuing discussions of the choice divided the lab into two factions. In February 1979, matters came to a head. The hackers sided with Noftsker, believing that a commercial venture-fund-backed firm had a better chance of surviving and commercializing Lisp machines than Greenblatt's proposed self-sustaining start-up. Greenblatt lost the battle. It was at this juncture that Symbolics, Noftsker's enterprise, slowly came together. While Noftsker was paying his staff a salary, he had no building or any equipment for the hackers to work on. He bargained with Patrick Winston that, in exchange for allowing Symbolics' staff to keep working out of MIT, Symbolics would let MIT use internally and freely all the software Symbolics developed. A consultant from CDC, who was trying to put together a natural language computer application with a group of West-coast programmers, came to Greenblatt, seeking a Lisp machine for his group to work with, about eight months after the disastrous conference with Noftsker. Greenblatt had decided to start his own rival Lisp machine firm, but he had done nothing. The consultant, Alexander Jacobson, decided that the only way Greenblatt was going to start the firm and build the Lisp machines that Jacobson desperately needed was if Jacobson pushed and otherwise helped Greenblatt launch the firm. Jacobson pulled together business plans, a board, a partner for Greenblatt (one F. Stephen Wyle). The newfound firm was named LISP Machine, Inc. (LMI), and was funded by CDC orders, via Jacobson. Around this time Symbolics (Noftsker's firm) began operating. It had been hindered by Noftsker's promise to give Greenblatt a year's head start, and by severe delays in procuring venture capital. Symbolics still had the major advantage that while 3 or 4 of the AI Lab hackers had gone to work for Greenblatt, 14 other hackers had signed onto Symbolics. Two AI Lab people were not hired by either: Richard Stallman and Marvin Minsky. Stallman, however, blamed Symbolics for the decline of the hacker community that had centered around the AI lab. For two years, from 1982 to the end of 1983, Stallman worked by himself to clone the output of the Symbolics programmers, with the aim of preventing them from gaining a monopoly on the lab's computers. Regardless, after a series of internal battles, Symbolics did get off the ground in 1980/1981, selling the CADR as the LM-2, while Lisp Machines, Inc. sold it as the LMI-CADR. Symbolics did not intend to produce many LM-2s, since the 3600 family of Lisp machines was supposed to ship quickly, but the 3600s were repeatedly delayed, and Symbolics ended up producing ~100 LM-2s, each of which sold for $70,000. Both firms developed second-generation products based on the CADR: the Symbolics 3600 and the LMI-LAMBDA (of which LMI managed to sell ~200). The 3600, which shipped a year late, expanded on the CADR by widening the machine word to 36-bits, expanding the address space to 28-bits, and adding hardware to accelerate certain common functions that were implemented in microcode on the CADR. The LMI-LAMBDA, which came out a year after the 3600, in 1983, was compatible with the CADR (it could run CADR microcode), but hardware differences existed. Texas Instruments (TI) joined the fray whe

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  CoDi

  CoDi is a cellular automaton (CA) model for spiking neural networks (SNNs). CoDi is an acronym for Collect and Distribute, referring to the signals and spikes in a neural network. CoDi uses a von Neumann neighborhood modified for a three-dimensional space; each cell looks at the states of its six orthogonal neighbors and its own state. In a growth phase a neural network is grown in the CA-space based on an underlying chromosome. There are four types of cells: neuron body, axon, dendrite and blank. The growth phase is followed by a signaling- or processing-phase. Signals are distributed from the neuron bodies via their axon tree and collected from connection dendrites. These two basic interactions cover every case, and they can be expressed simply, using a small number of rules. == Cell interaction during signaling == The neuron body cells collect neural signals from the surrounding dendritic cells and apply an internally defined function to the collected data. In the CoDi model the neurons sum the incoming signal values and fire after a threshold is reached. This behavior of the neuron bodies can be modified easily to suit a given problem. The output of the neuron bodies is passed on to its surrounding axon cells. Axonal cells distribute data originating from the neuron body. Dendritic cells collect data and eventually pass it to the neuron body. These two types of cell-to-cell interaction cover all kinds of cell encounters. Every cell has a gate, which is interpreted differently depending on the type of the cell. A neuron cell uses this gate to store its orientation, i.e. the direction in which the axon is pointing. In an axon cell, the gate points to the neighbor from which the neural signals are received. An axon cell accepts input only from this neighbor, but makes its own output available to all its neighbors. In this way axon cells distribute information. The source of information is always a neuron cell. Dendritic cells collect information by accepting information from any neighbor. They give their output, (e.g. a Boolean OR operation on the binary inputs) only to the neighbor specified by their own gate. In this way, dendritic cells collect and sum neural signals, until the final sum of collected neural signals reaches the neuron cell. Each axonal and dendritic cell belongs to exactly one neuron cell. This configuration of the CA-space is guaranteed by the preceding growth phase. == Synapses == The CoDi model does not use explicit synapses, because dendrite cells that are in contact with an axonal trail (i.e. have an axon cell as neighbor) collect the neural signals directly from the axonal trail. This results from the behavior of axon cells, which distribute to every neighbor, and from the behavior of the dendrite cells, which collect from any neighbor. The strength of a neuron-neuron connection (a synapse) is represented by the number of their neighboring axon and dendrite cells. The exact structure of the network and the position of the axon-dendrite neighbor pairs determine the time delay and strength (weight) of a neuron-neuron connection. This principle infers that a single neuron-neuron connection can consist of several synapse with different time delays with independent weights. == Genetic encoding and growth of the network == The chromosome is initially distributed throughout the CA-space, so that every cell in the CA-space contains one instruction of the chromosome, i.e. one growth instruction, so that the chromosome belongs to the network as a whole. The distributed chromosome technique of the CoDi model makes maximum use of the available CA-space and enables the growth of any type of network connectivity. The local connection of the grown circuitry to its chromosome, allows local learning to be combined with the evolution of grown neural networks. Growth signals are passed to the direct neighbors of the neuron cell according to its chromosome information. The blank neighbors, which receive a neural growth signal, turn into either an axon cell or a dendrite cell. The growth signals include information containing the cell type of the cell that is to be grown from the signal. To decide in which directions axonal or dendritic trails should grow, the grown cells consult their chromosome information which encodes the growth instructions. These growth instructions can have an absolute or a relative directional encoding. An absolute encoding masks the six neighbors (i.e. directions) of a 3D cell with six bits. After a cell is grown, it accepts growth signals only from the direction from which it received its first signal. This reception direction information is stored in the gate position of each cell's state. == Implementation as a partitioned CA == The states of our CAs have two parts, which are treated in different ways. The first part of the cell-state contains the cell's type and activity level and the second part serves as an interface to the cell's neighborhood by containing the input signals from the neighbors. Characteristic of our CA is that only part of the state of a cell is passed to its neighbors, namely the signal and then only to those neighbors specified in the fixed part of the cell state. This CA is called partitioned, because the state is partitioned into two parts, the first being fixed and the second is variable for each cell. The advantage of this partitioning-technique is that the amount of information that defines the new state of a CA cell is kept to a minimum, due to its avoidance of redundant information exchange. == Implementation in hardware == Since CAs are only locally connected, they are ideal for implementation on purely parallel hardware. When designing the CoDi CA-based neural networks model, the objective was to implement them directly in hardware (FPGAs). Therefore, the CA was kept as simple as possible, by having a small number of bits to specify the state, keeping the CA rules few in number, and having few cellular neighbors. The CoDi model was implemented in the FPGA based CAM-Brain Machine (CBM) by Korkin. == History == CoDi was introduced by Gers et al. in 1998. A specialized parallel machine based on FPGA Hardware (CAM) to run the CoDi model on a large scale was developed by Korkin et al. De Garis conducted a series of experiments on the CAM-machine evaluating the CoDi model. The original model, where learning is based on evolutionary algorithms, has been augmented with a local learning rule via feedback from dendritic spikes by Schwarzer.

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  Capsule neural network

  A capsule neural network (CapsNet) is a machine learning system that is a type of artificial neural network (ANN) that can be used to better model hierarchical relationships. The approach is an attempt to more closely mimic biological neural organization. The idea is to add structures called "capsules" to a convolutional neural network (CNN), and to reuse output from several of those capsules to form more stable (with respect to various perturbations) representations for higher capsules. The output is a vector consisting of the probability of an observation, and a pose for that observation. This vector is similar to what is done for example when doing classification with localization in CNNs. Among other benefits, capsnets address the "Picasso problem" in image recognition: images that have all the right parts but that are not in the correct spatial relationship (e.g., in a "face", the positions of the mouth and one eye are switched). For image recognition, capsnets exploit the fact that while viewpoint changes have nonlinear effects at the pixel level, they have linear effects at the part/object level. This can be compared to inverting the rendering of an object of multiple parts. == History == In 2000, Geoffrey Hinton et al. described an imaging system that combined segmentation and recognition into a single inference process using parse trees. So-called credibility networks described the joint distribution over the latent variables and over the possible parse trees. That system proved useful on the MNIST handwritten digit database. A dynamic routing mechanism for capsule networks was introduced by Hinton and his team in 2017. The approach was claimed to reduce error rates on MNIST and to reduce training set sizes. Results were claimed to be considerably better than a CNN on highly overlapped digits. In Hinton's original idea one minicolumn would represent and detect one multidimensional entity. == Transformations == An invariant is an object property that does not change as a result of some transformation. For example, the area of a circle does not change if the circle is shifted to the left. Informally, an equivariant is a property that changes predictably under transformation. For example, the center of a circle moves by the same amount as the circle when shifted. A nonequivariant is a property whose value does not change predictably under a transformation. For example, transforming a circle into an ellipse means that its perimeter can no longer be computed as π times the diameter. In computer vision, the class of an object is expected to be an invariant over many transformations. I.e., a cat is still a cat if it is shifted, turned upside down or shrunken in size. However, many other properties are instead equivariant. The volume of a cat changes when it is scaled. Equivariant properties such as a spatial relationship are captured in a pose, data that describes an object's translation, rotation, scale and reflection. Translation is a change in location in one or more dimensions. Rotation is a change in orientation. Scale is a change in size. Reflection is a mirror image. Unsupervised capsnets learn a global linear manifold between an object and its pose as a matrix of weights. In other words, capsnets can identify an object independent of its pose, rather than having to learn to recognize the object while including its spatial relationships as part of the object. In capsnets, the pose can incorporate properties other than spatial relationships, e.g., color (cats can be of various colors). Multiplying the object by the manifold poses the object (for an object, in space). == Pooling == Capsnets reject the pooling layer strategy of conventional CNNs that reduces the amount of detail to be processed at the next higher layer. Pooling allows a degree of translational invariance (it can recognize the same object in a somewhat different location) and allows a larger number of feature types to be represented. Capsnet proponents argue that pooling: violates biological shape perception in that it has no intrinsic coordinate frame; provides invariance (discarding positional information) instead of equivariance (disentangling that information); ignores the linear manifold that underlies many variations among images; routes statically instead of communicating a potential "find" to the feature that can appreciate it; damages nearby feature detectors, by deleting the information they rely upon. == Capsules == A capsule is a set of neurons that individually activate for various properties of a type of object, such as position, size and hue. Formally, a capsule is a set of neurons that collectively produce an activity vector with one element for each neuron to hold that neuron's instantiation value (e.g., hue). Graphics programs use instantiation value to draw an object. Capsnets attempt to derive these from their input. The probability of the entity's presence in a specific input is the vector's length, while the vector's orientation quantifies the capsule's properties. Artificial neurons traditionally output a scalar, real-valued activation that loosely represents the probability of an observation. Capsnets replace scalar-output feature detectors with vector-output capsules and max-pooling with routing-by-agreement. Because capsules are independent, when multiple capsules agree, the probability of correct detection is much higher. A minimal cluster of two capsules considering a six-dimensional entity would agree within 10% by chance only once in a million trials. As the number of dimensions increase, the likelihood of a chance agreement across a larger cluster with higher dimensions decreases exponentially. Capsules in higher layers take outputs from capsules at lower layers, and accept those whose outputs cluster. A cluster causes the higher capsule to output a high probability of observation that an entity is present and also output a high-dimensional (20-50+) pose. Higher-level capsules ignore outliers, concentrating on clusters. This is similar to the Hough transform, the RHT and RANSAC from classic digital image processing. == Routing by agreement == The outputs from one capsule (child) are routed to capsules in the next layer (parent) according to the child's ability to predict the parents' outputs. Over the course of a few iterations, each parents' outputs may converge with the predictions of some children and diverge from those of others, meaning that that parent is present or absent from the scene. For each possible parent, each child computes a prediction vector by multiplying its output by a weight matrix (trained by backpropagation). Next the output of the parent is computed as the scalar product of a prediction with a coefficient representing the probability that this child belongs to that parent. A child whose predictions are relatively close to the resulting output successively increases the coefficient between that parent and child and decreases it for parents that it matches less well. This increases the contribution that that child makes to that parent, thus increasing the scalar product of the capsule's prediction with the parent's output. After a few iterations, the coefficients strongly connect a parent to its most likely children, indicating that the presence of the children imply the presence of the parent in the scene. The more children whose predictions are close to a parent's output, the more quickly the coefficients grow, driving convergence. The pose of the parent (reflected in its output) progressively becomes compatible with that of its children. The coefficients' initial logits are the log prior probabilities that a child belongs to a parent. The priors can be trained discriminatively along with the weights. The priors depend on the location and type of the child and parent capsules, but not on the current input. At each iteration, the coefficients are adjusted via a "routing" softmax so that they continue to sum to 1 (to express the probability that a given capsule is the parent of a given child.) Softmax amplifies larger values and diminishes smaller values beyond their proportion of the total. Similarly, the probability that a feature is present in the input is exaggerated by a nonlinear "squashing" function that reduces values (smaller ones drastically and larger ones such that they are less than 1). This dynamic routing mechanism provides the necessary deprecation of alternatives ("explaining away") that is needed for segmenting overlapped objects. This learned routing of signals has no clear biological equivalent. Some operations can be found in cortical layers, but they do not seem to relate this technique. === Math/code === The pose vector u i {\textstyle \mathbf {u} _{i}} is rotated and translated by a matrix W i j {\textstyle \mathbf {W} _{ij}} into a vector u ^ j | i {\textstyle \mathbf {\hat {u}} _{j|i}} that predicts the output of the parent capsule. u ^ j | i = W i j u i {\displaystyle \mathbf {

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  Avizo (software)

  Avizo (pronounce: 'a-VEE-zo') is a general-purpose commercial software application for scientific and industrial data visualization and analysis. Avizo is developed by Thermo Fisher Scientific and was originally designed and developed by the Visualization and Data Analysis Group at Zuse Institute Berlin (ZIB) under the name Amira. Avizo was commercially released in November 2007. For the history of its development, see the Wikipedia article about Amira. == Overview == Avizo is a software application which enables users to perform interactive visualization and computation on 3D data sets. The Avizo interface is modelled on the visual programming. Users manipulate data and module components, organized in an interactive graph representation (called Pool), or in a Tree view. Data and modules can be interactively connected together, and controlled with several parameters, creating a visual processing network whose output is displayed in a 3D viewer. With this interface, complex data can be interactively explored and analyzed by applying a controlled sequence of computation and display processes resulting in a meaningful visual representation and associated derived data. == Application areas == Avizo has been designed to support different types of applications and workflows from 2D and 3D image data processing to simulations. It is a versatile and customizable visualization tool used in many fields: Scientific visualization Materials Research Tomography, Microscopy, etc. Nondestructive testing, Industrial Inspection, and Visual Inspection Computer-aided Engineering and simulation data post-processing Porous medium analysis Civil Engineering Seismic Exploration, Reservoir Engineering, Microseismic Monitoring, Borehole Imaging Geology, Digital Rock Physics (DRP), Earth Sciences Archaeology Food technology and agricultural science Physics, Chemistry Climatology, Oceanography, Environmental Studies Astrophysics == Features == Data import: 2D and 3D image stack and volume data: from microscopes (electron, optical), X-ray tomography (CT, micro-/nano-CT, synchrotron), neutron tomography and other acquisition devices (MRI, radiography, GPR) Geometric models (such as point sets, line sets, surfaces, grids) Numerical simulation data (such as Computational fluid dynamics or Finite element analysis data) Molecular data Time series and animations Seismic data Well logs 4D Multivariate Climate Models 2D/3D data visualization: Volume rendering Digital Volume Correlation Visualization of sections, through various slicing and clipping methods Isosurface rendering Polygonal meshes Scalar fields, Vector fields, Tensor representations, Flow visualization (Illuminated Streamlines, Stream Ribbons) Image processing: 2D/3D Alignment of image slices, Image registration Image filtering Mathematical Morphology (erode, dilate, open, close, tophat) Watershed Transform, Distance Transform Image segmentation 3D models reconstruction: Polygonal surface generation from segmented objects Generation of tetrahedral grids Surface reconstruction from point clouds Skeletonization (reconstruction of dendritic, porous or fracture network) Surface model simplification Quantification and analysis: Measurements and statistics Analysis spreadsheet and charting Material properties computation, based on 3D images: Absolute permeability Thermal conductivity Molecular diffusivity Electrical resistivity/formation factor 3D image-based meshing for CFD and FEA: From 3D imaging modalities (CT, micro-CT, MRI, etc.) Surface and volume meshes generation Export to FEA and CFD solvers for simulation Post-processing for simulation analysis Presentation, automation: MovieMaker, Multiscreen, Video wall, collaboration, and VR support TCL Scripting, C++ extension API Avizo is based on Open Inventor 3D graphics toolkits (FEI Visualization Sciences Group).

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  Noam Shazeer

  Noam Shazeer (born 1975 or 1976) is an American computer scientist and entrepreneur known for his contributions to the field of artificial intelligence and deep learning, particularly in the development of transformer models and natural language processing. He lives in Palo Alto, California. == Career == Noam Shazeer joined Google in 2000. One of his first major achievements was improving the spelling corrector of Google's search engine. In 2017, Shazeer was one of the lead authors of the seminal paper "Attention Is All You Need", which introduced the transformer architecture. At Google, Shazeer and his colleague Daniel de Freitas built a chatbot named Meena. Following the refusal of Google to release the chatbot to the public, Shazeer and Freitas left the company in 2021 to found Character.AI. In September 2023, Time Magazine chose Shazeer as one of the 100 most influential people in the AI world. In August 2024, it was reported that Shazeer would be returning to Google to co-lead the Gemini AI project. Shazeer was appointed as technical lead on Gemini, along with Jeff Dean and Oriol Vinyals. It was part of a $2.7 billion deal for Google to license Character's technology. Since he owns 30-40% of the company, it is estimated he netted $750 million-$1 billion. In 2026, he was elected a member of the National Academy of Engineering. == Views == Shazeer said about artificial general intelligence that he doesn't "particularly care about AGI in the sense of wanting something that can do absolutely everything a person can do”. When asked in 2023 if he is afraid that AGI will destroy the world, he said: "No. Not yet. [...] We’re going to work on it as the technology improves". When asked why do large language models work he answered: "My best guess is divine benevolence [...] Nobody really understands what’s going on. This is a very experimental science [...] It’s more like alchemy or whatever chemistry was in the Middle Ages.” Shazeer has stated, "I do not believe that humans have an attribute called gender... I do not believe that G-d puts people in the wrong bodies. I do not believe that it is okay to sterilize children." == Personal life == Shazeer is an orthodox Jew. His grandparents escaped the Holocaust into the Soviet Union and later lived some time in Israel before emigrating to the USA. His father, Dov Shazeer, was a math teacher who became an engineer and his mother was a homemaker. His sister was ordained as a rabbi by Hebrew College. Shazeer was born in Philadelphia, attended grade school at Cohen Hillel Academy in Marblehead, Massachusetts, and attended Swampscott High School in Swampscott, Massachusetts. He won a gold medal with perfect score at International Mathematical Olympiad 1994 as a member of the USA team. He went on to study math and computer science at Duke University in Durham, North Carolina from 1994 to 1998. At Duke he was a recipient of the Angier B. Duke Memorial Scholarship, and, as part of the Duke math team, won prizes in several math tournaments. He started studying in a graduate program in Berkeley but did not finish it. He is a father of three and is married to Yael Shacham Shazeer

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  VP-Expert

  VP-Expert is an artificial intelligence development tool that gained popularity in the late 1980s and early 1990s. Published by Paperback Software, VP-Expert was designed to facilitate the creation of rule-based expert systems, primarily for applications in business and industry. It was the best-selling expert-system software for microcomputers in the late 1980s. == History == VP-Expert was created by Brian Sawyer and published by Paperback Software in 1987. VP-Expert was widely adopted during the late 1980s. By April 1989, InfoWorld described it as "the best-selling expert-system software for personal computers." In June 1991, ownership of VP-Expert transferred from Paperback Software to WordTech Systems, Inc. following Paperback Software’s liquidation after a legal dispute with Lotus Development Corporation regarding its VP-Planner spreadsheet. VP-Expert continued to receive positive reviews with InfoWorld stating in 1992 "for automatically creating simple expert systems and being able to edit them into more sophisticated applications, hardly a better product exists than VP-Expert". == Features == VP-Expert used an inference engine based on backward chaining to reach conclusions through English-like if/then rules. It operated through a text interface and included an explanation facility that showed the reasoning steps used to justify its conclusions. == Applications == VP-Expert found applications across various domains. In environmental analysis, researchers used VP-Expert to develop a knowledge-based system for analyzing the impact of particulate matter air pollution on human health. In engineering design, VP-Expert was utilized in the creation of a prototype expert system to assist in fishway design. In aviation management, the tool was employed to develop an expert system aimed at maximizing airport capacity while adhering to noise-mitigation plans. == Limitations == While VP-Expert offered certain advantages, it also had limitations. Its rule-based approach could become challenging to manage for large and complex knowledge bases, and the process of eliciting and encoding knowledge from experts could be time-consuming and difficult.

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  Vivification

  Vivification is an operation on a description logic knowledge base to improve performance of a semantic reasoner. Vivification replaces a disjunction of concepts C 1 ⊔ C 2 … ⊔ C n {\displaystyle C_{1}\sqcup C_{2}\ldots \sqcup C_{n}} by the least common subsumer of the concepts C 1 , C 2 , … C n {\displaystyle C_{1},C_{2},\ldots C_{n}} . The goal of this operation is to improve the performance of the reasoner by replacing a complex set of concepts with a single concept which subsumes the original concepts. For example, consider the example given in (Cohen 92): Suppose we have the concept PIANIST(Jill) ∨ ORGANIST(Jill) {\displaystyle {\textrm {PIANIST(Jill)}}\vee {\textrm {ORGANIST(Jill)}}} . This concept can be vivified into a simpler concept KEYBOARD-PLAYER(Jill) {\displaystyle {\textrm {KEYBOARD-PLAYER(Jill)}}} . This summarization leads to an approximation that may not be exactly equivalent to the original. == An approximation == Knowledge base vivification is not necessarily exact. If the reasoner is operating under the open world assumption we may get surprising results. In the previous example, if we replace the disjunction with the vivified concept, we will arrive at a surprising results. First, we find that the reasoner will no longer classify Jill as either a pianist or an organist. Even though ORGANIST {\displaystyle {\textrm {ORGANIST}}} and PIANIST {\displaystyle {\textrm {PIANIST}}} are the only two sub-classes, under the OWA we can no longer classify Jill as playing one or the other. The reason is that there may be another keyboard instrument (e.g. a harpsichord) that Jill plays but which does not have a specific subclass.

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  TeaOnHer

  TeaOnHer is a male-oriented dating surveillance mobile app that allows men to anonymously rate and comment on women they are dating. It was set up in response to the existence of Tea, a female-oriented dating app that allowed women to rate and comment on men. In 2025, Cosmopolitian magazine described it as America's second most popular mobile app, with it being the second most popular app in the lifestyle section of Apple's App Store. The TeaOnHer app has fewer features than the rival Tea app, focusing instead on anonymous commenting. It is listed as having been developed by a company called Newville Media Corporation. TechCrunch reported in 2025 that TeaOnHer had leaked credentials of some of its users.

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  Ontology components

  Contemporary ontologies share many structural similarities, regardless of the ontology language in which they are expressed. Most ontologies describe individuals (instances), classes (concepts), attributes, and relations. == List == Common components of ontologies include: Individuals instances or objects (the basic or "ground level" objects; the tokens). Classes sets, collections, concepts, types of objects, or kinds of things. Attributes aspects, properties, features, characteristics, or parameters that individuals (and classes and relations) can have. Relations ways in which classes and individuals can be related to one another. Relations can carry attributes that specify the relation further. Function terms complex structures formed from certain relations that can be used in place of an individual term in a statement. Restrictions formally stated descriptions of what must be true in order for some assertion to be accepted as input. Rules statements in the form of an if-then (antecedent-consequent) sentence that describe the logical inferences that can be drawn from an assertion in a particular form. Axioms assertions (including rules) in a logical form that together comprise the overall theory that the ontology describes in its domain of application. This definition differs from that of "axioms" in generative grammar and formal logic. In these disciplines, axioms include only statements asserted as a priori knowledge. As used here, "axioms" also include the theory derived from axiomatic statements. Events the changing of attributes or relations. Actions types of events. Ontologies are commonly encoded using ontology languages. == Individuals == Individuals (instances) are the basic, "ground level" components of an ontology. The individuals in an ontology may include concrete objects such as people, animals, tables, automobiles, molecules, and planets, as well as abstract individuals such as numbers and words (although there are differences of opinion as to whether numbers and words are classes or individuals). Strictly speaking, an ontology need not include any individuals, but one of the general purposes of an ontology is to provide a means of classifying individuals, even if those individuals are not explicitly part of the ontology. In formal extensional ontologies, only the utterances of words and numbers are considered individuals – the numbers and names themselves are classes. In a 4D ontology, an individual is identified by its spatio-temporal extent. Examples of formal extensional ontologies are BORO, ISO 15926 and the model in development by the IDEAS Group. == Classes == == Attributes == Objects in an ontology can be described by relating them to other things, typically aspects or parts. These related things are often called attributes, although they may be independent things. Each attribute can be a class or an individual. The kind of object and the kind of attribute determine the kind of relation between them. A relation between an object and an attribute express a fact that is specific to the object to which it is related. For example, the Ford Explorer object has attributes such as: ⟨has as name⟩ Ford Explorer ⟨as by definition as part⟩ 6-speed transmission ⟨as by definition as part⟩ door (with as minimum and maximum cardinality: 4) ⟨as by definition as part one of⟩ {4.0L engine, 4.6L engine} The value of an attribute can be a complex data type; in this example, the related engine can only be one of a list of subtypes of engines, not just a single thing. Ontologies are only true ontologies if concepts are related to other concepts (the concepts do have attributes). If that is not the case, then you would have either a taxonomy (if hyponym relationships exist between concepts) or a controlled vocabulary. These are useful, but are not considered true ontologies. == Relations == Relations (also known as relationships) between objects in an ontology specify how objects are related to other objects. Typically a relation is of a particular type (or class) that specifies in what sense the object is related to the other object in the ontology. For example, in the ontology that contains the concept Ford Explorer and the concept Ford Bronco might be related by a relation of type ⟨is defined as a successor of⟩. The full expression of that fact then becomes: Ford Explorer is defined as a successor of : Ford Bronco This tells us that the Explorer is the model that replaced the Bronco. This example also illustrates that the relation has a direction of expression. The inverse expression expresses the same fact, but with a reverse phrase in natural language. Much of the power of ontologies comes from the ability to describe relations. Together, the set of relations describes the semantics of the domain: that is, its various semantic relations, such as synonymy, hyponymy and hypernymy, coordinate relation, and others. The set of used relation types (classes of relations) and their subsumption hierarchy describe the expression power of the language in which the ontology is expressed. An important type of relation is the subsumption relation (is-a-superclass-of, the converse of is-a, is-a-subtype-of or is-a-subclass-of). This defines which objects are classified by which class. For example, we have already seen that the class Ford Explorer is-a-subclass-of 4-Wheel Drive Car, which in turn is-a-subclass-of Car. The addition of the is-a-subclass-of relationships creates a taxonomy; a tree-like structure (or, more generally, a partially ordered set) that clearly depicts how objects relate to one another. In such a structure, each object is the 'child' of a 'parent class' (Some languages restrict the is-a-subclass-of relationship to one parent for all nodes, but many do not). Another common type of relations is the mereology relation, written as part-of, that represents how objects combine to form composite objects. For example, if we extended our example ontology to include concepts like Steering Wheel, we would say that a "Steering Wheel is-by-definition-a-part-of-a Ford Explorer" since a steering wheel is always one of the components of a Ford Explorer. If we introduce meronymy relationships to our ontology, the hierarchy that emerges is no longer able to be held in a simple tree-like structure since now members can appear under more than one parent or branch. Instead this new structure that emerges is known as a directed acyclic graph. As well as the standard is-a-subclass-of and is-by-definition-a-part-of-a relations, ontologies often include additional types of relations that further refine the semantics they model. Ontologies might distinguish between different categories of relation types. For example: relation types for relations between classes relation types for relations between individuals relation types for relations between an individual and a class relation types for relations between a single object and a collection relation types for relations between collections Relation types are sometimes domain-specific and are then used to store specific kinds of facts or to answer particular types of questions. If the definitions of the relation types are included in an ontology, then the ontology defines its own ontology definition language. An example of an ontology that defines its own relation types and distinguishes between various categories of relation types is the Gellish ontology. For example, in the domain of automobiles, we might need a made-in type relationship which tells us where each car is built. So the Ford Explorer is made-in Louisville. The ontology may also know that Louisville is-located-in Kentucky and Kentucky is-classified-as-a state and is-a-part-of the U.S. Software using this ontology could now answer a question like "which cars are made in the U.S.?"

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  Emospark

  EmoSpark is an artificial intelligence console created in London, United Kingdom by Patrick Levy-Rosenthal. The device uses facial recognition and language analysis to evaluate human emotion and convey responsive content according to the emotion. The console measures 90 mm x 90 mm x 90 mm and is cube shaped. It operates on an "Emotional Processing Unit", an emotion chip developed by Emoshape Inc. that enables the system to create emotional profile graphs of its surroundings. The emotional processing unit is a patent pending technology that is said to create synthesised emotional responses in machines. EmoSpark was funded through an Indiegogo campaign which aimed to raise $200,000. == Product overview == EmoSpark was created by French inventor Patrick Levy-Rosenthal, as an emotionally intelligent artificial life unit for the home that can interact with people. It is powered by Android and can communicate with users through typed input from a computer, tablet, smartphone or TV as well as through spoken commands. The EmoSpark's features are categorized into two types: functional and emotional. EmoSpark is said to have the ability to perform practical software-based tasks. Through the smartphone interface, it is able to gauge a person’s emotions and is reported to have a conversational library of over 2 million sentences. The face-tracking technology identifies users likes and dislikes to categorize their emotional responses to stimuli such as videos and music. The device has an emotional spectrum that is composed of eight emotions which are surprise, sadness, joy, trust, fear, disgust, anger and anticipation. EmoSpark monitors a person's facial expressions and emotions through images from an external camera, which are then processed through an emotion text analysis and content analysis. The New Scientist reported that EmoSpark had the ability to work on the best way to cheer up its users, emotionally. === Connectivity === EmoSpark is able to connect to Facebook and YouTube to present users with content designed to improve their mood, or to Wikipedia for collaborative knowledge that can be shared when users ask questions of it. Through Android OS, EmoSpark is able to be customized with Google Play store apps. The cube is expected to develop its own personality based on the communications it has had with the people using it. == EmoShape == The Emotion Chip (EPU) used in the cube is created by the US company Emoshape Inc, founded by Levy-Rosenthal. EmoShape Ltd (UK) was the company that developed EmoSpark cube. Patrick Levy-Rosenthal also received the IST Prize in 2005 from the European Council for Applied Science, Technology and Engineering.

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  Tabnine

  Tabnine is a code completion tool which uses generative artificial intelligence to assist users by autocompleting code. It was created in 2018 by Jacob Jackson, a student at the University of Waterloo. It is now developed by Tabnine, a software company founded under the name Codota by Dror Weiss and Eran Yahav in Tel Aviv, Israel, in 2013, and renamed to Tabnine in 2021. Initially established under the name Codota, the company underwent a rebranding in May 2021 following the release of the company’s first large language model based AI coding assistant, adopting the name Tabnine. == History == Tabnine was established as Codota in 2013 by Dror Weiss and Eran Yahav in Tel Aviv, Israel. Tabnine, initially founded under the name Codota, was created to develop tools based on over a decade of academic research at the Technion. Codota, the predecessor of Tabnine, secured $2 million in seed investment in June 2017. Following this, in June 2018, the company introduced the first AI-based code completion for Java IDE. In 2019, Codota acquired a product called Tabnine, which used the newly available large-language model technology to provide generative AI for software code across a broader range of programming languages across five IDEs. Codota replaced its earlier approach to code generation with this new approach to generative AI. The company secured a Series A round of funding in April 2020, raising $12 million. On May 26, 2021, Codota changed its name to Tabnine and underwent a corresponding rebranding. By April 2022, Tabnine reached over one million users. In June of the same year, Tabnine launched models that could predict full lines and snippets of code. The same year it raised $15.5 mln in a funding round led co-led by Qualcomm Ventures. In June 2023, Tabnine introduced an AI-powered chat agent, enabling developers to use natural language to generate code, to explain code, to generate tests and documentation, and to propose fixes to code. In November 2023, Tabnine closed a Series B round of funding, raising $25 million to scale the company’s operations. == Operations == Tabnine's headquarters is located in Tel Aviv, Israel, with an additional corporate entity in the United States. As of November 2023, Tabnine generative AI for software development is used by a million developers. It has 10 million installations across VS Code and JetBrains. Since its founding, Dror Weiss has served as CEO, with Eran Yahav as CTO.

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  Deep Learning Indaba

  The Deep Learning Indaba is an annual conference and educational event that aims to strengthen machine learning and artificial intelligence (AI) capacity across Africa. Launched in 2017, it brings together students, researchers, industry practitioners, and policymakers from across the African continent. == History == The Deep Learning Indaba began in 2017 at the University of the Witwatersrand with over 300 participants from 23 African countries, offering tutorials in advanced AI topics and featuring notable speakers like Nando de Freitas. In 2018, it expanded to 650 delegates at Stellenbosch University, introducing parallel sessions to encourage collaboration. The 2019 edition in Nairobi, Kenya, reflected further growth, with increasing sponsorship and support from major tech companies like Google and Microsoft. === Deep Learning IndabaX ===

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  Historical Thesaurus of English

  The Historical Thesaurus of English (HTE) is the largest thesaurus in the world. It is called a historical thesaurus as it arranges the whole vocabulary of English, from the earliest written records in Old English to the present, according to the first documented occurrence of a word in the entire history of the English language. The HTE was conceived and begun in 1965 by the English Language & Linguistics department of the University of Glasgow, who have ever since continued to compile the thesaurus. From the 1980s onwards the project was moved from paper-based records to a computer database. Today, the HTE is available to the public online, but a print version, the Historical Thesaurus of the Oxford English Dictionary (HTOED), was published in 2009. == Main project: The Historical Thesaurus of English (HTE) == The Historical Thesaurus of English (HTE) is a complete database of all the words in the Oxford English Dictionary and other dictionaries (including Old English), arranged by semantic field and date. In this way, the HTE arranges the whole vocabulary of English, from the earliest written records in Old English to the present, alongside dates of use. It is the first historical thesaurus to be compiled for any of the world's languages and contains 800,000 meanings for 600,000 words, within 230,000 categories. As the HTE website states, "in addition to providing hitherto unavailable information for linguistic and textual scholars, the Historical Thesaurus online is a rich resource for students of social and cultural history, showing how concepts developed through the words that refer to them." === Structure === The work is divided into three main sections: the External World, the Mind, and Society. These are broken down into successively narrower domains. The text eventually discriminates more than 236,000 categories. The second order categories are: === History === The ambitious project was announced at a 1965 meeting of the Philological Society by its originator, Michael Samuels. Work on the HTE started in the same year. In 2017, the University of Glasgow was awarded the Queen's Anniversary Prize for Higher Education for the HTE. A second edition of the online HTE is currently in progress and is expected to be launched in late 2020. Work is released on the freely-available HTE website when available. == Print edition: Historical Thesaurus of the Oxford English Dictionary (HTOED) == On 22 October 2009, after 44 years of work, version 1.0 of the HTE was published by Oxford University Press in a two-volume slipcased set as the Historical Thesaurus of the Oxford English Dictionary (HTOED). The two hardcover volumes together total nearly 4,500 pages.

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  OntoWiki

  OntoWiki was a free and open-source semantic wiki application, meant to serve as an ontology editor and a knowledge acquisition system. It is a web-based application written in PHP and using either a MySQL database or a Virtuoso triple store. OntoWiki is form-based rather than syntax-based, and thus tries to hide as much of the complexity of knowledge representation formalisms from users as possible. OntoWiki is mainly being developed by the Agile Knowledge Engineering and Semantic Web (AKSW) research group at the University of Leipzig, a group also known for the DBpedia project among others, in collaboration with volunteers around the world. In 2009 the AKSW research group got a budget of €425,000 from the Federal Ministry of Education and Research of Germany for the development of the OntoWiki. In 2010 OntoWiki became part of the technology stack supporting the LOD2 (linked open data) project. Leipzig University is one of the consortium members of the project, which is funded by a €6.5m EU grant. The development ended in 2016 due to the lack of capacity migrating from PHP 5 to 7 including the required Zend Framework from version 1 to 2.

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