Raseef22 (Arabic: رصيف22) is a liberal Arabic media network founded in 2013 based in Beirut, Lebanon. It publishes content in Arabic and English from different Arab states and describes itself as an independent media platform. International Media Support mentions Raseef22 along with HuffPost Arabic and Al Jazeera as one of the biggest Pan-Arab online platforms. == Name == The Arabic word raseef (رَصِيف) means platform or pavement, and the number 22 refers to the number of states in the Arab League. == History == Kareem Sakka co-founded Raseef22 in the aftermath of the Arab Spring, which he cites as a source of inspiration. In an article in The Washington Post, he wrote that Raseef22 was created as a "digital space for those eager to know what was going on around them." Raseef22 was one of the 500 websites censored in Egypt in late 2017 after it published an article on Egyptian security agencies' vies to influence the media. After the site was blocked in Egypt, it was targeted in a cyber attack that took it offline in locations around the world. Jamal Khashoggi wrote for Raseef22 regularly. One of his notable articles was "Notes on the Freedom of the Arabs from Oslo, Norway," published June 5, 2018. The site was blocked in Saudi Arabia December 2018 when the Saudi Ministry of Communications and Information Technology ordered its censorship due to its "unprecedented response to the assassination of Jamal Khashoggi in Istanbul." This decision might have also been related to Raseef22's coverage of Saudi-Israeli relations and interviews with activists later imprisoned or placed under house arrest coverage In 2019 the Association of LGBT Journalists (AJL) in Paris gave Raseef22 a golden foreign press award for its six-month series of articles on gender and sexuality issues. == Readership == According to its publisher in 2019, the news agency counted 12 million readers annually from 22 Arab nations. Of the readership, he wrote that it "believes in the talent and promise of the Arab mind and sees the ugliness of tyranny, patriarchy, misogyny and the futility of proxy rulers and wars." Al-Quds Al-Arabi described Raseef22 as "oriented to the youth."
Kinematic chain
In mechanical engineering, a kinematic chain is an assembly of rigid bodies connected by joints to provide constrained motion that is the mathematical model for a mechanical system. As the word chain suggests, the rigid bodies, or links, are constrained by their connections to other links. An example is the simple open chain formed by links connected in series, like the usual chain, which is the kinematic model for a typical robot manipulator. Mathematical models of the connections, or joints, between two links are termed kinematic pairs. Kinematic pairs model the hinged and sliding joints fundamental to robotics, often called lower pairs and the surface contact joints critical to cams and gearing, called higher pairs. These joints are generally modeled as holonomic constraints. A kinematic diagram is a schematic of the mechanical system that shows the kinematic chain. The modern use of kinematic chains includes analysis of Linkages (mechanical), compliance that arises from flexure joints in precision mechanisms, link compliance in compliant mechanisms and micro-electro-mechanical systems, and cable compliance in cable robotic and tensegrity systems. == Mobility formula == The degrees of freedom, or mobility, of a kinematic chain is the number of parameters that define the configuration of the chain. A system of n rigid bodies moving in space has 6n degrees of freedom measured relative to a fixed frame. This frame is included in the count of bodies, so that mobility does not depend on link that forms the fixed frame. This means the degree-of-freedom of this system is M = 6(N − 1), where N = n + 1 is the number of moving bodies plus the fixed body. Joints that connect bodies impose constraints. Specifically, hinges and sliders each impose five constraints and therefore remove five degrees of freedom. It is convenient to define the number of constraints c that a joint imposes in terms of the joint's freedom f, where c = 6 − f. In the case of a hinge or slider, which are one-degree-of-freedom joints, have f = 1 and therefore c = 6 − 1 = 5. The result in general where d {\displaystyle d} is the degrees of freedom for the mobility of a kinematic chain formed from n moving links and j joints each with freedom fi, i = 1, 2, …, j, is given by M = d n − ∑ i = 1 j ( d − f i ) = d ( N − 1 − j ) + ∑ i = 1 j f i {\displaystyle M=dn-\sum _{i=1}^{j}(d-f_{i})=d(N-1-j)+\sum _{i=1}^{j}f_{i}} Where N is the total number of links and includes the fixed link. Spacial linkages used d = 6 {\displaystyle d=6} and planar linkages use d = 3 {\displaystyle d=3} . This result is known as the Chebychev–Grübler–Kutzbach criterion. == Analysis of kinematic chains == The constraint equations of a kinematic chain couple the range of movement allowed at each joint to the dimensions of the links in the chain, and form algebraic equations that are solved to determine the configuration of the chain associated with specific values of input parameters, called degrees of freedom. The constraint equations for a kinematic chain are obtained using rigid transformations [Z] to characterize the relative movement allowed at each joint and separate rigid transformations [X] to define the dimensions of each link. In the case of a serial open chain, the result is a sequence of rigid transformations alternating joint and link transformations from the base of the chain to its end link, which is equated to the specified position for the end link. A chain of n links connected in series has the kinematic equations, [ T ] = [ Z 1 ] [ X 1 ] [ Z 2 ] [ X 2 ] ⋯ [ X n − 1 ] [ Z n ] , {\displaystyle [T]=[Z_{1}][X_{1}][Z_{2}][X_{2}]\cdots [X_{n-1}][Z_{n}],\!} where [T] is the transformation locating the end-link—notice that the chain includes a "zeroth" link consisting of the ground frame to which it is attached. These equations are called the forward kinematics equations of the serial chain. Kinematic chains of a wide range of complexity are analyzed by equating the kinematics equations of serial chains that form loops within the kinematic chain. These equations are often called loop equations. The complexity (in terms of calculating the forward and inverse kinematics) of the chain is determined by the following factors: Its topology: a serial chain, a parallel manipulator, a tree structure, or a graph. Its geometrical form: how are neighbouring joints spatially connected to each other? Explanation Two or more rigid bodies in space are collectively called a rigid body system. We can hinder the motion of these independent rigid bodies with kinematic constraints. Kinematic constraints are constraints between rigid bodies that result in the decrease of the degrees of freedom of rigid body system. == Synthesis of kinematic chains == The constraint equations of a kinematic chain can be used in reverse to determine the dimensions of the links from a specification of the desired movement of the system. This is termed kinematic synthesis. Perhaps the most developed formulation of kinematic synthesis is for four-bar linkages, which is known as Burmester theory. Ferdinand Freudenstein is often called the father of modern kinematics for his contributions to the kinematic synthesis of linkages beginning in the 1950s. His use of the newly developed computer to solve Freudenstein's equation became the prototype of computer-aided design systems. This work has been generalized to the synthesis of spherical and spatial mechanisms.
Dataism
Dataism is a term that has been used to describe the mindset or philosophy created by the emerging significance of big data. It was first used by David Brooks in The New York Times in 2013. The term has been expanded to describe what historian Yuval Noah Harari, in his book Homo Deus: A Brief History of Tomorrow from 2015, calls an emerging ideology or even a new form of religion, in which "information flow" is the "supreme value". In art, the term was used by Albert-Laszlo Barabasi to refer to an artist movement that uses data as its primary source of inspiration. == History == "If you asked me to describe the rising philosophy of the day, I'd say it is Data-ism", wrote David Brooks in The New York Times in February 2013. Brooks argued that in a world of increasing complexity, relying on data could reduce cognitive biases and "illuminate patterns of behavior we haven't yet noticed". In 2015, Steve Lohr's book Data-ism looked at how Big Data is transforming society, using the term to describe the Big Data revolution. In his 2016 book Homo Deus: A Brief History of Tomorrow, Yuval Noah Harari argues that all competing political or social structures can be seen as data processing systems: "Dataism declares that the universe consists of data flows, and the value of any phenomenon or entity is determined by its contribution to data processing" and "we may interpret the entire human species as a single data processing system, with individual humans serving as its chips." According to Harari, a Dataist should want to "maximise dataflow by connecting to more and more media". Harari predicts that the logical conclusion of this process is that, eventually, humans will give algorithms the authority to make the most important decisions in their lives, such as whom to marry and which career to pursue. Harari argues that Aaron Swartz could be called the "first martyr" of Dataism. In 2022, Albert-László Barabási coined the term "Dataism" to define an artistic movement that positions data as the central means of understanding nature, society, technology, and human essence. This movement underscores the necessity for art to integrate with data to stay relevant in contemporary society. Dataism responds to the intricacy and interconnectedness of modern social, economic, and technological realms, which exceed individual understanding. Advocating for the use of methodologies from various fields like science, business, and politics in art, Dataism sees this fusion as essential for art to retain its significance and influence. == Criticism == Commenting on Harari's characterisation of Dataism, security analyst Daniel Miessler believes that Dataism does not present the challenge to the ideology of liberal humanism that Harari claims, because humans will simultaneously be able to believe in their own importance and that of data. Harari himself raises some criticisms, such as the problem of consciousness, which Dataism is unlikely to illuminate. Humans may also find out that organisms are not algorithms, he suggests. Dataism implies that all data is public, even personal data, to make the system work as a whole, which is a factor that's already showing resistance today. Other analysts, such as Terry Ortleib, have looked at the extent to which Dataism poses a dystopian threat to humanity. The Facebook–Cambridge Analytica data scandal showed how political leaders manipulated Facebook's users' data to build specific psychological profiles that went on to manipulate the network. A team of data analysts reproduced the AI technology developed by Cambridge Analytica around Facebook's data and was able to define the following rules: 10 likes enables a machine to know a person like a coworker, 70 likes like a friend would, 150 likes like a parent would, 300 likes like a lover would, and beyond it may be possible to know a people better than they know themselves.
Quantum Artificial Intelligence Lab
The Quantum Artificial Intelligence Lab (also called the Quantum AI Lab or QuAIL) is a joint initiative of NASA, Universities Space Research Association, and Google (specifically, Google Research) whose goal is to pioneer research on how quantum computing might help with machine learning and other difficult computer science problems. The lab is hosted at NASA's Ames Research Center. == History == The Quantum AI Lab was announced by Google Research in a blog post on May 16, 2013. At the time of launch, the Lab was using the most advanced commercially available quantum computer, D-Wave Two from D-Wave Systems. On October 10, 2013, Google released a short film describing the current state of the Quantum AI Lab. On October 18, 2013, Google announced that it had incorporated quantum physics into Minecraft. In January 2014, Google reported results comparing the performance of the D-Wave Two in the lab with that of classical computers. The results were ambiguous and provoked heated discussion on the Internet. On 2 September 2014, it was announced that the Google Quantum AI Lab, in partnership with UC Santa Barbara, would be launching an initiative to create quantum information processors based on superconducting electronics. On the 23rd of October 2019, the Quantum AI Lab announced in a paper that it had achieved quantum supremacy with their Sycamore processor. The claim of quantum supremacy achievement has since been debated, with a far more accurate simulation on a classical computer being possible in 2.5 days as a conservative estimate. == Present == On December 9, 2024, Google introduced the Willow processor, describing it as a "state-of-the-art quantum chip". Google claims that this new chip takes just five minutes to solve a problem that takes traditional supercomputers ten septillion years. However, experts say Willow is, for now, a largely experimental device.
Lernmatrix
Lernmatrix (German for "learning matrix") is a special type of artificial neural network (ANN) architecture, similar to associative memory, invented around 1960 by Karl Steinbuch, a pioneer in computer science and ANNs. This model for learning systems could establish complex associations between certain sets of characteristics (e.g., letters of an alphabet) and their meanings. == Function == The Lernmatrix generally consists of n "characteristic lines" and m "meaning lines," where each characteristic line is connected to each meaning line, similar to how neurons in the brain are connected by synapses. (This can be realized in various ways – according to Steinbuch, this could be done by hardware or software). To train a Lernmatrix, values are specified on the corresponding characteristic and meaning lines (binary or real); then the connections between all pairs of characteristic and meaning lines are strengthened by the Hebb rule. A trained Lernmatrix, when given a specific input on the characteristic lines, activates the corresponding meaning lines. In modern language, it is a linear projection module. By appropriately interconnecting several Lernmatrices, a switching system can be built that, after completing certain training phases, is ultimately able to automatically determine the most probable associated meaning for an input sequence of features.
Chasys Photo
Chasys Photo (previously called Chasys Draw Artist, then Chasys Draw IES) is a suite of applications including a layer-based raster graphics editor with adjustment layers, linked layers, timeline and frame-based animation, icon editing, image stacking and comprehensive plug-in support (Chasys Draw IES Artist), a fast multi-threaded image file converter (Chasys Draw IES Converter) and a fast image viewer (Chasys Draw IES Viewer), with RAW image support in all components. It supports the native file formats of several competitors including Adobe Photoshop, Affinity Photo, Corel Photo-Paint, GIMP, Krita, Paint.NET and PaintShop Pro, and the whole suite is designed to make effective use of multi-core processors, touch-screens and pen-input devices. The software is developed by John Paul Chacha in Nairobi, Kenya. Chasys Draw IES is currently released as freeware, and is available for computers running Microsoft Windows operating systems. It is available in three distributions: the standard distro, a portable version and a Microsoft Store version. The suite is coded in a blend of C, C++ and assembly language. It runs on x86 processors and supports the MMX, SSE, SSE2, S-SSE3, and SSE4.1 instruction sets. == History == Chasys Draw is a project that was started in November 2001 by John Paul Chacha, mostly as a hobby than anything else. The original Chasys Draw was a rather simple bitmap editor done in Visual Basic, a lot like MS Paint save for its ability to do gradients. This application underwent many changes, eventually leading up to Chasys Draw 5. This was the first version to have its own native format, referred to simply as CD5. Major updates to the graphics code in May 2002 resulted in Chasys Draw DTFx (Direct Tool eFfects). The new graphics code being referred to here was actually a miniature bitmap abstraction engine that allowed for fast per-pixel operations and direct image buffer access (much as the DIB engine does for GDI). The engine was named JpDRAW. This version was also done in VB, but was much faster than all the previous versions. The new graphics code allowed for more tools to be implemented than was ever possible before. Later on in 2002, the developer decided to completely abandon VB as a programming platform and moved all the code to C/C++. The move to C/C++ allowed the development of a full-fledged graphics engine which was named JpDRAW2. Chasys was renamed to Chasys Draw Artist, and the CD5 image format was also updated to reflect the new features. By coincidence, the module that implemented the file format was the fifth module to be added, so the format was called Chasys Draw module 5, retaining the .cd5 file extension. First public release In April 2004, Chasys Draw Artist was released to the public via the internet for the first time (version 1.27). The release was done via betanews). In 2005, Chasys Draw underwent major user interface changes as well as internal changes. By December of that year, the project had reached version 1.63. This was the first version to introduce advanced features such as anti-aliasing. It was also the first version with full support for alpha channels. The CD5 image format was also upgraded to version 2, adding advanced compression, full alpha channels, encryption and metadata. Version 1.63 was the first version to win an IEEE (Kenya chapter) award in ICT. The "chazy-glass" interface, from which the all later versions' user interfaces borrowed, was introduced in version 1.80. Chasys Draw Artist adopted photo editing features in version 2.01. Comprehensive tutorials were added and many features were re-designed to make them easier to use. Multi-threading was introduced to accelerate some tasks, such as the improved auto-save engine. Utilities such as a converter and browser were added. Version 2.43 of Chasys Draw Artist was quietly released to the public in late 2007 without any announcements. It featured many fixes to the formal version 2.42, as well as many new features. The quiet release was due to a decision to re-build Chasys Draw Artist from scratch, while still continuing support for the old architecture. An experimental version 2.45 was released only to beta-testers for the purpose of testing new technologies that would be included in the new architecture and was officially withdrawn in May 2008. During the time when the versions 2.43~2.45 were being released, work was underway to create a new layer-based Chasys Draw, which was released as Chasys Draw IES (Image Editing Suite), with the initial version number 2.50. A new multi-layer tag-based image format was created to support layering and blending modes; this was named CD5 v3. The next version introduced animation and multi-resolution support as editing modes, and the next one brought in an unlimited undo engine, new plug-ins and several internal fixes. Further development led to the introduction of super-resolution and image stacking, support for video and video capture, Anti-aliasing, metadata save and restore, a "Pen and Path" tool, physical measurement specification, and a video sequence composer engine. The user interface was enhanced with adaptive scrolling and the auto-save engine was optimized. Some memory management was added for machines with low RAM. By version 2.60, Chasys Draw IES was capable of loading Photoshop's PSD files, as well as load and save JPEG 2000. This version also had shell integration with thumbnails and application-level support for multi-monitor display setups. Metadata was extended to support save, restore and scaling for text formatting and path data. There was also a new palette with exchangeable swatches, loadable from all kinds of palette files. A slicing tool for web and user interface design was also included. A C++ code module output for inline image generation was added, as was a constrained recolor brush. The concept of a "fully anti-aliased work-flow" was introduced in version 2.62, in which all drawing and selection tools were anti-aliased by default. Support for Photoshop plug-ins using Adobe's 8bf format was added in version 2.66, allowing users to utilize thousands of free plug-ins available online. Equivalents for the Pantone palettes (PMS 100 to 814-2x) were added, and the "Just-in-Time" memory compressor significantly reduced the editor's memory requirements. First freeware release Chasys Draw IES went freeware on 6 June 2009. With the coming of the freeware IES, two blending modes (Hue and Chroma) were added. Textures were improved to allow multiple layer-based textures. The TextArt G3 engine was enhanced with LINK metadata, and alpha shift was improved. IES 2.72 added the Luma Wand tool, fixed PNG and TIFF transparency issues, and fixed Smart-Paste transparency. IES 2.74 introduced alpha protection, and 2.75 followed with a new adjustments engine that faced out many effects implemented by the effects engine. The adjustments engine was designed to appeal to experienced image editors. IES 2.76 introduced a new transform engine and the Resizer for IES plug-in supporting multi-core and 18 scaling methods, including customizable windowed Sinc interpolation. IES 2.77 added Greyscale with Tint adjustment, separated the Lock and Click-Thru layer properties, extended the Cloning Brush with three options (this, below and composite) and also extended the Color Picker with multiple point sampling. IES 3.01 brought a new look and many breakthrough tools to the suite. It was geared toward touch and was fully compatible with Windows 7. The toolbox was reorganized, with some tools being grouped and new ones added. Some message boxes were replaced with a new popup system, and the working of the workspace was changed to use a back-blitter, which enabled the addition of new blending modes, Screen and Mask. The printing interface was modified and given accurate proofing. Alpha Function Adjustment was added and a new Anti-Quantization Engine included for all adjustments to remove the need for 16 bits per channel editing. An internal clipboard was created to cater for copying images that are too large for the Windows clipboard, and translucency full-page gradients added. Some new tutorials were added and keyboard shortcuts made configurable. IES 3.05 brought the power of custom full-page gradients to the suite, supporting .ggr, .grd and .gra gradients. New gradient styles were included, as was support for Adobe color tables (.act), palette previewing, point color editing and a highly improved TextArt engine. Digital lightroom IES 3.11 was introduced on 14 December 2009. It was done on a new development base and added a new application, raw-Input. This was a RAW image format processor based on dcraw. This application allowed the use of Chasys Draw IES in processing digital negatives, which are popular with professional photographers. Chasys Draw IES 3.24 was released with a re-designed user interface, powered by a higher performance graphics core and better memory management. A history palette w
Alec Radford
Alec Radford is an American artificial intelligence researcher. == Biography == Radford grew up in Texas. He graduated from Cistercian Preparatory School in 2011, where he became an Eagle Scout, and dropped out of Olin College in August 2014, where he and fellow students Slater Victoroff, Diana Yuan, and Madison May had formed the startup Indico in their dorm room. In 2015, the quartet were joined by Luke Metz and the firm and the Facebook AI research lab in New York used generative adversarial networks to create realistic low pixel images. A demonstration of Indico's technology was used without proper attribution in an April 2016 demonstration by Nvidia chief executive Jensen Huang. Radford joined OpenAI around 2016, where he worked on natural-language processing. The following year, Radford trained a neural network on Amazon reviews. The model was fairly basic, with layers which allowed for human understanding. Upon exploring it, he saw that it had a special neuron linked to the sentiment of the reviews, which it had created on its own. This was a drastic improvement from previous neural networks that had analysed sentiment, because they had to be told to do so and specially trained on data that was explicitly labeled according to sentiment. This development made OpenAI chief scientist Ilya Sutskever consider that a future model, using more diverse language data, could map far more structures of meaning, eventually becoming a "learned core module" for superintelligence. In 2018, Radford was the lead author on OpenAI's seminal research paper on generative pre-trained transformers, which form the foundation of ChatGPT. At OpenAI, he worked on early GPT models, Whisper, a speech recognition model, and the image generator DALL-E. He left OpenAI in December 2024 to pursue independent research. Around March 2025, Radford joined Thinking Machines Lab as an advisor. He joined along with Bob McGrew who was previously the chief research officer of OpenAI. In April 2026, Radford, Nick Levine, and David Duvenaud released Talkie, an AI model trained on books, newspapers, scientific journals, patents, and case law published before December 31, 1930. When asked about the state of the world in 2026, it stated that one billion people would live in Europe, that London and New York would be connected by steamships that transit between the two in ten days, and "winter will be passed in Paris, and the summer in London."