AI Face Generator From Photo Free

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Tribute (website)

Tribute is an American video-sharing website headquartered in Brooklyn. Created in 2014 by Andrew Horn and Rory Petty, the platform lets customers create video montages (called "tributes") for occasions including weddings, birthdays, anniversaries, get well soon, and memorials. Tribute.co allows users to record video messages, request submissions from friends and family, insert photos, add music, and send the resulting video tribute montage to a recipient. == Overview == Tribute's collaborative technology starts with inviting people to contribute via email, SMS or social media. Participants receive a prompt to record a short video via their phone, computer or tablet. The site's video editing software allows users to drag and drop the clips in their desired order without prior video editing experience. == History == When Andrew Horn turned twenty-seven, his girlfriend, Miki Agrawal surprised him with a video montage containing clips of his family and closest friends explaining why they loved him. This resulted in Andrew's idea to create Tribute–a "living eulogy" video-compilation service that he co-founded with software engineer Rory Petty. Founded in 2014, Tribute's activity accelerated in 2020 due to the COVID-19 pandemic, and it had sent over 5 million videos as of December 2021. While social distance restrictions were in effect, the site provided a way for people to connect while in-person celebrations were put on hold. For each video sold, Tribute makes one available to hospitals for free and has partnered with Cleveland Clinic Cancer Center in Ohio, Lurie Children's Hospital in Illinois and CarePoint Health in New Jersey.
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MIT Computer Science and Artificial Intelligence Laboratory

Computer Science and Artificial Intelligence Laboratory (CSAIL) is a research institute at the Massachusetts Institute of Technology (MIT) formed by the 2003 merger of the Laboratory for Computer Science (LCS) and the Artificial Intelligence Laboratory (AI Lab). Housed within the Ray and Maria Stata Center, CSAIL is the largest on-campus laboratory as measured by research scope and membership. It is part of the Schwarzman College of Computing but is also overseen by the MIT Vice President of Research. == Research activities == CSAIL's research activities are organized around a number of semi-autonomous research groups, each of which is headed by one or more professors or research scientists. These groups are divided up into seven general areas of research: Artificial intelligence Computational biology Graphics and vision Language and learning Theory of computation Robotics Systems (includes computer architecture, databases, distributed systems, networks and networked systems, operating systems, programming methodology, and software engineering, among others) == History == Computing Research at MIT began with Vannevar Bush's research into a differential analyzer and Claude Shannon's electronic Boolean algebra in the 1930s, the wartime MIT Radiation Laboratory, the post-war Project Whirlwind and the Research Laboratory of Electronics (RLE), and MIT Lincoln Laboratory's SAGE in the early 1950s. At MIT, research in the field of artificial intelligence began in the late 1950s. === Project MAC === On July 1, 1963, Project MAC (the Project on Mathematics and Computation, later backronymed to Multiple Access Computer, Machine Aided Cognitions, or Man and Computer) was launched with a $2 million grant from the Defense Advanced Research Projects Agency (DARPA). Project MAC's original director was Robert Fano of MIT's Research Laboratory of Electronics (RLE). Fano decided to call MAC a "project" rather than a "laboratory" for reasons of internal MIT politics – if MAC had been called a laboratory, then it would have been more difficult to raid other MIT departments for research staff. The program manager responsible for the DARPA grant was J. C. R. Licklider, who had previously been at MIT conducting research in RLE, and would later succeed Fano as director of Project MAC. Project MAC would become famous for groundbreaking research in operating systems, artificial intelligence, and the theory of computation. Its contemporaries included Project Genie at Berkeley, the Stanford Artificial Intelligence Laboratory, and (somewhat later) University of Southern California's (USC's) Information Sciences Institute. An "AI Group" including Marvin Minsky (the director), John McCarthy (inventor of Lisp), and a talented community of computer programmers were incorporated into Project MAC. They were interested principally in the problems of vision, mechanical motion and manipulation, and language, which they view as the keys to more intelligent machines. In the 1960s and 1970s the AI Group developed a time-sharing operating system called Incompatible Timesharing System (ITS) which ran on PDP-6 and later PDP-10 computers. The early Project MAC community included Fano, Minsky, Licklider, Fernando J. Corbató, and a community of computer programmers and enthusiasts among others who drew their inspiration from former colleague John McCarthy. These founders envisioned the creation of a computer utility whose computational power would be as reliable as an electric utility. To this end, Corbató brought the first computer time-sharing system, Compatible Time-Sharing System (CTSS), with him from the MIT Computation Center, using the DARPA funding to purchase an IBM 7094 for research use. One of the early focuses of Project MAC would be the development of a successor to CTSS, Multics, which was to be the first high availability computer system, developed as a part of an industry consortium including General Electric and Bell Laboratories. In 1966, Scientific American featured Project MAC in the September thematic issue devoted to computer science, that was later published in book form. At the time, the system was described as having approximately 100 TTY terminals, mostly on campus but with a few in private homes. Only 30 users could be logged in at the same time. The project enlisted students in various classes to use the terminals simultaneously in problem solving, simulations, and multi-terminal communications as tests for the multi-access computing software being developed. === AI Lab and LCS === In the late 1960s, Minsky's artificial intelligence group was seeking more space, and was unable to get satisfaction from project director Licklider. Minsky found that although Project MAC as a single entity could not get the additional space he wanted, he could split off to form his own laboratory and then be entitled to more office space. As a result, the MIT AI Lab was formed in 1970, and many of Minsky's AI colleagues left Project MAC to join him in the new laboratory, while most of the remaining members went on to form the Laboratory for Computer Science. Talented programmers such as Richard Stallman, who used TECO to develop EMACS, flourished in the AI Lab during this time. Those researchers who did not join the smaller AI Lab formed the Laboratory for Computer Science and continued their research into operating systems, programming languages, distributed systems, and the theory of computation. Two professors, Hal Abelson and Gerald Jay Sussman, chose to remain neutral—their group was referred to variously as Switzerland and Project MAC for the next 30 years. Among much else, the AI Lab led to the invention of Lisp machines and their attempted commercialization by two companies in the 1980s: Symbolics and Lisp Machines Inc. === CSAIL === On the fortieth anniversary of Project MAC's establishment, July 1, 2003, LCS was merged with the AI Lab to form the MIT Computer Science and Artificial Intelligence Laboratory, or CSAIL. This merger created the largest laboratory (over 600 personnel) on the MIT campus. In 2018, CSAIL launched a five-year collaboration program with IFlytek, a company sanctioned the following year for allegedly using its technology for surveillance and human rights abuses in Xinjiang. In October 2019, MIT announced that it would review its partnerships with sanctioned firms such as iFlyTek and SenseTime. In April 2020, the agreement with iFlyTek was terminated. CSAIL moved from the School of Engineering to the newly formed Schwarzman College of Computing by February 2020. == Offices == From 1963 to 2004, Project MAC, LCS, the AI Lab, and CSAIL had their offices at 545 Technology Square, taking over more and more floors of the building over the years. In 2004, CSAIL moved to the new Ray and Maria Stata Center, which was built specifically to house it and other departments. == Outreach activities == The IMARA (from Swahili word for "power") group sponsors a variety of outreach programs that bridge the global digital divide. Its aim is to find and implement long-term, sustainable solutions which will increase the availability of educational technology and resources to domestic and international communities. These projects are run under the aegis of CSAIL and staffed by MIT volunteers who give training, install and donate computer setups in greater Boston, Massachusetts, Kenya, Native American Indian tribal reservations in the American Southwest such as the Navajo Nation, the Middle East, and Fiji Islands. The CommuniTech project strives to empower under-served communities through sustainable technology and education and does this through the MIT Used Computer Factory (UCF), providing refurbished computers to under-served families, and through the Families Accessing Computer Technology (FACT) classes, it trains those families to become familiar and comfortable with computer technology. == Notable researchers == (Including members and alumni of CSAIL's predecessor laboratories) MacArthur Fellows Tim Berners-Lee, Erik Demaine, Dina Katabi, Daniela L. Rus, Regina Barzilay, Peter Shor, Richard Stallman, and Joshua Tenenbaum Turing Award recipients Leonard M. Adleman, Fernando J. Corbató, Shafi Goldwasser, Butler W. Lampson, John McCarthy, Silvio Micali, Marvin Minsky, Ronald L. Rivest, Adi Shamir, Barbara Liskov, and Michael Stonebraker IJCAI Computers and Thought Award recipients Terry Winograd, Patrick Winston, David Marr, Gerald Jay Sussman, Rodney Brooks Rolf Nevanlinna Prize recipients Madhu Sudan, Peter Shor, Constantinos Daskalakis Gödel Prize recipients Shafi Goldwasser (two-time recipient), Silvio Micali, Maurice Herlihy, Charles Rackoff, Johan Håstad, Peter Shor, and Madhu Sudan Grace Murray Hopper Award recipients Robert Metcalfe, Shafi Goldwasser, Guy L. Steele, Jr., Richard Stallman, and W. Daniel Hillis Textbook authors Harold Abelson and Gerald Jay Sussman, Richard Stallman, Thomas H. Cormen, Charles E. Leiserson, Patrick Winston, Ronald L.
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StepFun

Shanghai Jieyue Xingchen Intelligent Technology Co., Ltd, known as StepFun, is an artificial intelligence (AI) company based in Shanghai, China. It has been dubbed one of China's "AI Tiger" companies by investors. == Background == StepFun was founded in April 2023 by former Microsoft employees. Investors include Tencent, Qiming Venture Partners and Shanghai State-owned Capital Investment. In July 2025 at the World Artificial Intelligence Conference, StepFun announced the "Model-Chip Ecosystem Innovation Alliance" which consisted of Chinese developers of large language models (LLMs) and AI chip manufacturers. This included companies such as Huawei, Biren Technology, Moore Threads and Enflame. Another second alliance named the "Shanghai General Chamber of Commerce AI Committee" was also established that included StepFun, SenseTime, MiniMax, MetaX and Iluvatar CoreX. On 25 February 2026, it was reported that StepFun was seeking an initial public offering on the Hong Kong Stock Exchange. StepFun focuses on multimodal models which are designed to understand multiple types of input data such as text, video and audio. == Products == In July 2024 at the World Artificial Intelligence Conference, StepFun officially launched Step-2, a trillion-parameter LLM, along with the Step-1.5V multimodal model and the Step-1X image generation model. In February 2025, StepFun and Geely jointly announced the open-sourcing of two multimodal large models to global developers. They were Step-Video-T2V and Step-Audio. In July 2025, StepFun released Step 3. The Model-Chip Ecosystem Innovation Alliance aimed to optimize Step 3 for domestic chips. In April 2025, Step-R1-V-Mini was released. It is a multimodal reasoning model designed for visual interpretation and image understanding. In February 2026, Step-3.5-Flash, a mixture-of-experts model with 196 billion parameters and 11 billion active parameters was released under the free and open-source Apache 2.0 license. It supports tool use and a 256k token context window. == Models ==
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Minion (solver)

Minion is a solver for satisfaction problems. Unlike constraint programming toolkits, which expect users to write programs in a traditional programming language like C++, Java or Prolog, Minion takes a text file which specifies the problem, and solves using only this. This makes using Minion much simpler, at the cost of much less customization. Minion has been shown to be faster than major commercial constraint solvers including CPLEX (formerly IBM ILOG). == Overview == Minion was introduced in 2006 by researchers at the University of St Andrews as a “fast, scalable” solver for large and hard CSP instances. The project provides a compact input language and a low-overhead C++ implementation aimed at throughput and memory efficiency. == Design and features == Minion implements a range of variable and constraint types commonly used in CSP modelling, plus search heuristics and optimisation support. The solver architecture prioritises cache-friendly data structures and specialised propagators. Notably, the developers adapted watched literal techniques from SAT solving to speed up constraint propagation for, among others, Boolean sums, the element global constraint, and table constraints. The modelling approach relies on a plain-text format (parsed by Minion) rather than embedding models into a host programming language. This reduces overhead and supports rapid “model-and-run” experimentation for large benchmark sets. == Performance == In the original evaluation on standard benchmarks, the authors reported that Minion often ran between one and two orders of magnitude faster than state-of-the-art toolkits of the time (including ILOG Solver and Gecode) on large, hard instances, with smaller gains—or slowdowns—on easier problems. Subsequent research has used Minion as a baseline solver in empirical studies and test generation tasks, reflecting its adoption within parts of the constraint programming community. == Applications == Minion has been applied in academic work on combinatorial search, scheduling and test generation, and is available to other environments via wrappers (for example, from the R language).
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Coghead

Coghead was a web application company based out of Redwood City, California. The company offered a web-based service for building and hosting custom online database applications. Applications were built around custom data collections and were typically designed to facilitate management of, and collaboration on, business data. Examples of Coghead's "gallery" applications include project management, simple Customer relationship management, bug tracking and extreme programming. Coghead's service was available through a limited-access beta program before "going live" for free trial accounts in April, 2007. Coghead launched a paid subscription plans in June, 2007. On February 19, 2009, Coghead announced that its intellectual property assets (its 'service') had been acquired by SAP AG (NYSE:SAP). == Product == Coghead's product was a fully hosted environment for building, accessing, and maintaining applications and the associated business data. Like other so-called "Web 2.0" companies, Coghead built its product around the idea of "software as a service". The product was intended to allow users to design a range of applications from scratch using only a drag and drop, WYSIWYG user interface, with very limited scripting or coding (if any) required. Coghead also offered its paid subscribers the ability to develop and publish "Coglets," web forms that allowed site visitors to view data in, or submit data into, the host's Coghead database. On February 19, 2009, Coghead announced that SAP AG had acquired the Coghead service through an asset purchase. The SAP asset purchase closed in the 1st Quarter 2009. Immediately upon closing the asset purchase, the public-facing service was taken off-line by SAP as they prepared to integrate the Coghead code with other SAP assets. This forced many of Coghead's customers to find alternative solutions.
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Social History and Industrial Classification

Social History and Industrial Classification (SHIC) is a classification system used by many British museums for social history and industrial collections. It was first published in 1983. == Purpose == SHIC classifies materials (books, objects, recordings etc.) by their interaction with the people who used them. For example, a carpenter's hammer is classified with other tools of the carpenter, and not with a blacksmith's hammer. In contrast other classification systems, for example the Dewey Decimal Classification, might class all hammers together and close to the classification for other percussive tools. The specialist subject network, Social History Curator's Group (SHCG), obtained funding in 2012 to develop an on-line version, now on their website http://www.shcg.org.uk/ == Scheme == Materials are classified under four major category numbers: Community life Domestic and family life Personal life Working life Further classification within a category is by the use of further numbers after the decimal point. It is permissible to assign more than one classification in cases where the object had more than one use.
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Freddy II

Freddy (1969–1971) and Freddy II (1973–1976) were experimental robots built in the Department of Machine Intelligence and Perception (later Department of Artificial Intelligence, now part of the School of Informatics at the University of Edinburgh). == Technology == Technical innovations involving Freddy were at the forefront of the 70s robotics field. Freddy was one of the earliest robots to integrate vision, manipulation and intelligent systems as well as having versatility in the system and ease in retraining and reprogramming for new tasks. The idea of moving the table instead of the arm simplified the construction. Freddy also used a method of recognising the parts visually by using graph matching on the detected features. The system used an innovative collection of high level procedures for programming the arm movements which could be reused for each new task. == Lighthill controversy == In the mid 1970s there was controversy about the utility of pursuing a general purpose robotics programme in both the USA and the UK. A BBC TV programme in 1973, referred to as the "Lighthill Debate", pitched James Lighthill, who had written a critical report for the science and engineering research funding agencies in the UK, against Donald Michie from the University of Edinburgh and John McCarthy from Stanford University. The Edinburgh Freddy II and Stanford/SRI Shakey robots were used to illustrate the state-of-the-art at the time in intelligent robotics systems. == Freddy I and II == Freddy Mark I (1969–1971) was an experimental prototype, with 3 degrees-of-freedom created by a rotating platform driven by a pair of independent wheels. The other main components were a video camera and bump sensors connected to a computer. The computer moved the platform so that the camera could see and then recognise the objects. Freddy II (1973–1976) was a 5 degrees of freedom manipulator with a large vertical 'hand' that could move up and down, rotate about the vertical axis and rotate objects held in its gripper around one horizontal axis. Two remaining translational degrees of freedom were generated by a work surface that moved beneath the gripper. The gripper was a two finger pinch gripper. A video camera was added as well as later a light stripe generator. The Freddy and Freddy II projects were initiated and overseen by Donald Michie. The mechanical hardware and analogue electronics were designed and built by Stephen Salter (who also pioneered renewable energy from waves (see Salter's Duck)), and the digital electronics and computer interfacing were designed by Harry Barrow and Gregan Crawford. The software was developed by a team led by Rod Burstall, Robin Popplestone and Harry Barrow which used the POP-2 programming language, one of the world's first functional programming languages. The computing hardware was an Elliot 4130 computer with 384KB (128K 24-bit words) RAM and a hard disk linked to a small Honeywell H316 computer with 16KB of RAM which directly performed sensing and control. Freddy was a versatile system which could be trained and reprogrammed to perform a new task in a day or two. The tasks included putting rings on pegs and assembling simple model toys consisting of wooden blocks of different shapes, a boat with a mast and a car with axles and wheels. Information about part locations was obtained using the video camera, and then matched to previously stored models of the parts. It was soon realised in the Freddy project that the 'move here, do this, move there' style of robot behavior programming (actuator or joint level programming) is tedious and also did not allow for the robot to cope with variations in part position, part shape and sensor noise. Consequently, the RAPT robot programming language was developed by Pat Ambler and Robin Popplestone, in which robot behavior was specified at the object level. This meant that robot goals were specified in terms of desired position relationships between the robot, objects and the scene, leaving the details of how to achieve the goals to the underlying software system. Although developed in the 1970s RAPT is still considerably more advanced than most commercial robot programming languages. The team of people who contributed to the project were leaders in the field at the time and included Pat Ambler, Harry Barrow, Ilona Bellos, Chris Brown, Rod Burstall, Gregan Crawford, Jim Howe, Donald Michie, Robin Popplestone, Stephen Salter, Austin Tate and Ken Turner. Also of interest in the project was the use of a structured-light 3D scanner to obtain the 3D shape and position of the parts being manipulated. The Freddy II robot is currently on display at the Royal Museum in Edinburgh, Scotland, with a segment of the assembly video shown in a continuous loop.
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Reification (computer science)

In computer science, reification is the process by which an abstract idea about a program is turned into an explicit data model or other object created in a programming language. A computable/addressable object—a resource—is created in a system as a proxy for a non computable/addressable object. By means of reification, something that was previously implicit, unexpressed, and possibly inexpressible is explicitly formulated and made available to conceptual (logical or computational) manipulation. Informally, reification is often referred to as "making something a first-class citizen" within the scope of a particular system. Some aspect of a system can be reified at language design time, which is related to reflection in programming languages. It can be applied as a stepwise refinement at system design time. Reification is one of the most frequently used techniques of conceptual analysis and knowledge representation. == Reflective programming languages == In the context of programming languages, reification is the process by which a user program or any aspect of a programming language that was implicit in the translated program and the run-time system, are expressed in the language itself. This process makes it available to the program, which can inspect all these aspects as ordinary data. In reflective languages, reification data is causally connected to the related reified aspect such that a modification to one of them affects the other. Therefore, the reification data is always a faithful representation of the related reified aspect . Reification data is often said to be made a first class object. Reification, at least partially, has been experienced in many languages to date: in early Lisp dialects and in current Prolog dialects, programs have been treated as data, although the causal connection has often been left to the responsibility of the programmer. In Smalltalk-80, the compiler from the source text to bytecode has been part of the run-time system since the very first implementations of the language. The C programming language reifies the low-level detail of memory addresses.Many programming language designs encapsulate the details of memory allocation in the compiler and the run-time system. In the design of the C programming language, the memory address is reified and is available for direct manipulation by other language constructs. For example, the following code may be used when implementing a memory-mapped device driver. The buffer pointer is a proxy for the memory address 0xB8000000. Functional programming languages based on lambda-calculus reify the concept of a procedure abstraction and procedure application in the form of the Lambda expression. The Scheme programming language reifies continuations (approximately, the call stack). In C#, reification is used to make parametric polymorphism implemented in the form of generics as a first-class feature of the language. In the Java programming language, there exist "reifiable types" that are "completely available at run time" (i.e. their information is not erased during compilation). REBOL reifies code as data and vice versa. Many languages, such as Lisp, JavaScript, and Curl, provide an eval or evaluate procedure that effectively reifies the language interpreter. Smalltalk and Actor languages permit the reification of blocks and messages, which are equivalent of lambda expressions in Lisp, and thisContext in Smalltalk, which is a reification of the current executing block. Homoiconic languages reify the syntax of the language as data that is understood by the language itself. This allows the user to write programs whose inputs and outputs are code (see macros, eval). Common representations of code include S-expressions (e.g. Clojure, Lisp), and abstract syntax trees (e.g. Rust). == Data reification vs. data refinement == Data reification (stepwise refinement) involves finding a more concrete representation of the abstract data types used in a formal specification. Data reification is the terminology of the Vienna Development Method (VDM) that most other people would call data refinement. An example is taking a step towards an implementation by replacing a data representation without a counterpart in the intended implementation language, such as sets, by one that does have a counterpart (such as maps with fixed domains that can be implemented by arrays), or at least one that is closer to having a counterpart, such as sequences. The VDM community prefers the word "reification" over "refinement", as the process has more to do with concretising an idea than with refining it. For similar usages, see Reification (linguistics). == In conceptual modeling == Reification is widely used in conceptual modeling. Reifying a relationship means viewing it as an entity. The purpose of reifying a relationship is to make it explicit, when additional information needs to be added to it. Consider the relationship type IsMemberOf(member:Person, Committee). An instance of IsMemberOf is a relationship that represents the fact that a person is a member of a committee. The figure below shows an example population of IsMemberOf relationship in tabular form. Person P1 is a member of committees C1 and C2. Person P2 is a member of committee C1 only. The same fact, however, could also be viewed as an entity. Viewing a relationship as an entity, one can say that the entity reifies the relationship. This is called reification of a relationship. Like any other entity, it must be an instance of an entity type. In the present example, the entity type has been named Membership. For each instance of IsMemberOf, there is one and only one instance of Membership, and vice versa. Now, it becomes possible to add more information to the original relationship. As an example, we can express the fact that "person p1 was nominated to be the member of committee c1 by person p2". Reified relationship Membership can be used as the source of a new relationship IsNominatedBy(Membership, Person). For related usages see Reification (knowledge representation). == In Unified Modeling Language (UML) == UML provides an association class construct for defining reified relationship types. The association class is a single model element that is both a kind of association and a kind of class. The association and the entity type that reifies are both the same model element. Note that attributes cannot be reified. == On Semantic Web == === RDF and OWL === In Semantic Web languages, such as Resource Description Framework (RDF) and Web Ontology Language (OWL), a statement is a binary relation. It is used to link two individuals or an individual and a value. Applications sometimes need to describe other RDF statements, for instance, to record information like when statements were made, or who made them, which is sometimes called "provenance" information. As an example, we may want to represent properties of a relation, such as our certainty about it, severity or strength of a relation, relevance of a relation, and so on. The example from the conceptual modeling section describes a particular person with URIref person:p1, who is a member of the committee:c1. The RDF triple from that description is Consider to store two further facts: (i) to record who nominated this particular person to this committee (a statement about the membership itself), and (ii) to record who added the fact to the database (a statement about the statement). The first case is a case of classical reification like above in UML: reify the membership and store its attributes and roles etc.: Additionally, RDF provides a built-in vocabulary intended for describing RDF statements. A description of a statement using this vocabulary is called a reification of the statement. The RDF reification vocabulary consists of the type rdf:Statement, and the properties rdf:subject, rdf:predicate, and rdf:object. Using the reification vocabulary, a reification of the statement about the person's membership would be given by assigning the statement a URIref such as committee:membership12345 so that describing statements can be written as follows: These statements say that the resource identified by the URIref committee:membership12345Stat is an RDF statement, that the subject of the statement refers to the resource identified by person:p1, the predicate of the statement refers to the resource identified by committee:isMemberOf, and the object of the statement refers to the resource committee:c1. Assuming that the original statement is actually identified by committee:membership12345, it should be clear by comparing the original statement with the reification that the reification actually does describe it. The conventional use of the RDF reification vocabulary always involves describing a statement using four statements in this pattern. Therefore, they are sometimes referred to as the "reification quad". Using reification according to this convention, we could record the fact that pe
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Color layout descriptor

In digital image and video processing, a color layout descriptor (CLD) is designed to capture the spatial distribution of color in an image. The feature extraction process consists of two parts: grid based representative color selection and discrete cosine transform with quantization. Color is the most basic quality of the visual contents, therefore it is possible to use colors to describe and represent an image. The MPEG-7 standard has tested the most efficient procedure to describe the color and has selected those that have provided more satisfactory results. This standard proposes different methods to obtain these descriptors, and one tool defined to describe the color is the CLD, that permits describing the color relation between sequences or group of images. The CLD captures the spatial layout of the representative colors on a grid superimposed on a region or image. Representation is based on coefficients of the discrete cosine transform (DCT). This is a very compact descriptor being highly efficient in fast browsing and search applications. It can be applied to still images as well as to video segments. == Definition == The CLD is a very compact and resolution-invariant representation of color for high-speed image retrieval and it has been designed to efficiently represent the spatial distribution of colors. This feature can be used for a wide variety of similarity-based retrieval, content filtering and visualization. It is especially useful for spatial structure-based retrieval applications. This descriptor is obtained by applying the DCT transformation on a 2-D array of local representative colors in Y or Cb or Cr color space. The functionalities of the CLD are basically the matching: – Image-to-image matching – Video clip-to-video clip matching Remark that the CLD is one of the most precise and fast color descriptor. == Extraction == The extraction process of this color descriptor consists of four stages: Image partitioning Representative color selection DCT transformation Zigzag scanning The standard MPEG-7 recommends using the YCbCr color space for the CLD. === Image partitioning === In the image partitioning stage, the input picture (on RGB color space) is divided into 64 blocks to guarantee the invariance to resolution or scale. The inputs and outputs of this step are summarized in the following table: === Representative color selection === After the image partitioning stage, a single representative color is selected from each block. Any method to select the representative color can be applied, but the standard recommends the use of the average of the pixel colors in a block as the corresponding representative color, since it is simpler and the description accuracy is sufficient in general. The selection results in a tiny image icon of size 8x8. The next figure shows this process. Note that in the image of the figure, the resolution of the original image has been maintained only in order to facilitate its representation. The inputs and outputs of this stage are summarized in the next table: Once the tiny image icon is obtained, the color space conversion between RGB and YCbCr is applied. === DCT transformation === In the fourth stage, the luminance (Y) and the blue and red chrominance (Cb and Cr) are transformed by 8x8 DCT, so three sets of 64 DCT coefficients are obtained. To calculate the DCT in a 2D array, the formulas below are used. B p q = α p α q ∑ m = 0 M − 1 ∑ n = 0 N − 1 A m n cos ⁡ π ( 2 m + 1 ) p 2 M cos ⁡ π ( 2 n + 1 ) q 2 N , 0 ≤ p ≤ M − 1 , 0 ≤ q ≤ N − 1 {\displaystyle B_{pq}=\alpha _{p}\alpha _{q}\sum _{m=0}^{M-1}\sum _{n=0}^{N-1}A_{mn}\cos {\frac {\pi (2m+1)p}{2M}}\cos {\frac {\pi (2n+1)q}{2N}},\qquad 0\leq p\leq M-1,\;0\leq q\leq N-1} α p = { 1 M , p = 0 2 M , 1 ≤ p ≤ M − 1 α q = { 1 N , q = 0 2 N , 1 ≤ q ≤ N − 1 {\displaystyle \alpha _{p}={\begin{cases}{\frac {1}{\sqrt {M}}},&p=0\\{\sqrt {\frac {2}{M}}},&1\leq p\leq M-1\end{cases}}\qquad \alpha _{q}={\begin{cases}{\frac {1}{\sqrt {N}}},&q=0\\{\sqrt {\frac {2}{N}}},&1\leq q\leq N-1\end{cases}}} The inputs and outputs of this stage are summarized in the next table: === Zigzag scanning === A zigzag scanning is performed with these three sets of 64 DCT coefficients, following the schema presented in the figure. The purpose of the zigzag scan is to group the low frequency coefficients of the 8x8 matrix into a vector. The inputs and outputs of this stage are summarized in the next table: Finally, these three set of matrices correspond to the CLD of the input image. == Matching == The matching process helps to evaluate if two elements are equal comparing both elements and calculating the distance between them. In the case of color descriptors the matching process helps to evaluate if two images are similar. Its procedure is the following: – Given an image as an input, the application attempts to find an image with a similar descriptor in a data base of images. If we consider two CLDs: {DY, DCb, DCr} { DY‟, DCb‟, DCr‟ }, The distance between the two descriptors can be computed as: D = ∑ i w y i ( D Y i − D Y i ′ ) 2 + ∑ i w b i ( D C b i − D C b i ′ ) 2 + ∑ i w r i ( D C r i − D C r i ′ ) 2 {\displaystyle D={\sqrt {\sum _{i}w_{yi}(DY_{i}-DY_{i}')^{2}}}+{\sqrt {\sum _{i}w_{bi}(DCb_{i}-DCb_{i}')^{2}}}+{\sqrt {\sum _{i}w_{ri}(DCr_{i}-DCr_{i}')^{2}}}} The subscript i represents the zigzag-scanning order of the coefficients. Furthermore, notice that is possible to weight the coefficients (w) in order to adjust the performance of the matching process. These weights let us give to some components of the descriptor more importance than others. Observing the formula, it can be extracted that: – 2 images are the same if the distance is 0 – 2 images are similar if the distance is near to 0 Therefore, this matching process will let to identify images with similar color descriptors. Since the complexity of the similarity matching process shown above is low, high-speed image matching can be achieved.
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Shakey the robot

Shakey the Robot was the first general-purpose mobile robot able to reason about its own actions. While other robots would have to be instructed on each individual step of completing a larger task, Shakey could analyze commands and break them down into basic chunks by itself. Due to its nature, the project combined research in robotics, computer vision, and natural language processing. Because of this, it was the first project that melded logical reasoning and physical action. Shakey was developed at the Artificial Intelligence Center of Stanford Research Institute (now called SRI International). Some of the most notable results of the project include the A search algorithm, the Hough transform, and the visibility graph method. == History == Shakey was developed from approximately 1966 through 1972 with Charles Rosen, Nils Nilsson and Peter Hart as project managers. Other major contributors included Alfred Brain, Sven Wahlstrom, Bertram Raphael, Richard Duda, Richard Fikes, Thomas Garvey, Helen Chan Wolf and Michael Wilber. The project was funded by the Defense Advanced Research Projects Agency (DARPA) based on a SRI proposal submitted in April 1964 for research in "Intelligent Automata", later "Intelligent Automata to Reconnaissance". It was originally designed to have two retractable arms. Now retired from active duty, Shakey is currently on view in a glass display case at the Computer History Museum in Mountain View, California. The project inspired numerous other robotics projects, most notably the Centibots. == Software == The robot's programming was primarily done in LISP. The Stanford Research Institute Problem Solver (STRIPS) planner it used was conceived as the main planning component for the software it utilized. As the first robot that was a logical, goal-based agent, Shakey experienced a limited world. A version of Shakey's world could contain a number of rooms connected by corridors, with doors and light switches available for the robot to interact with. Shakey had a short list of available actions within its planner. These actions involved traveling from one location to another, turning the light switches on and off, opening and closing the doors, climbing up and down from rigid objects, and pushing movable objects around. The STRIPS automated planner could devise a plan to enact all the available actions, even though Shakey himself did not have the capability to execute all the actions within the plan personally. An example mission for Shakey might be something like, an operator types the command "push the block off the platform" at a computer console. Shakey looks around, identifies a platform with a block on it, and locates a ramp in order to reach the platform. Shakey then pushes the ramp over to the platform, rolls up the ramp onto the platform, and pushes the block off the platform. == Hardware == Physically, the robot was particularly tall, and had an antenna for a radio link, sonar range finders, a television camera, on-board processors, and collision detection sensors ("bump detectors"). The robot's tall stature and tendency to shake resulted in its name: We worked for a month trying to find a good name for it, ranging from Greek names to whatnot, and then one of us said, 'Hey, it shakes like hell and moves around, let’s just call it Shakey.' == Research results == The development of Shakey provided far-reaching impact on the fields of robotics and artificial intelligence, as well as computer science in general. Some of the more notable results include the development of the A search algorithm, which is widely used in pathfinding and graph traversal, the process of plotting an efficiently traversable path between points; the Hough transform, which is a feature extraction technique used in image analysis, computer vision, and digital image processing; and the visibility graph method for finding Euclidean shortest paths among obstacles in the plane. == Media and awards == In 1969 the SRI published "SHAKEY: Experimentation in Robot Learning and Planning", a 24-minute video. The project then received media attention. This included an article in the New York Times on April 10, 1969. In 1970, Life referred to Shakey as the "first electronic person"; and in November 1970 National Geographic Magazine covered Shakey and the future of computers. The Association for the Advancement of Artificial Intelligence's AI Video Competition's awards are named "Shakeys" because of the significant impact of the 1969 video. Shakey was inducted into Carnegie Mellon University's Robot Hall of Fame in 2004 alongside such notables as ASIMO and C-3PO. Shakey has been honored with an IEEE Milestone in Electrical Engineering and Computing. Shakey was showcased in the BBC's Towards Tomorrow: Robot (1967) documentary.
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Pinakes

The Pinakes (Ancient Greek: Πίνακες 'tables', plural of πίναξ pinax) is a lost bibliographic work composed by Callimachus (310/305–240 BCE) that is popularly considered to be the first library catalog in the West; its contents were based upon the holdings of the Library of Alexandria during Callimachus's tenure there during the third century BCE. == History == The Library of Alexandria had been founded by Ptolemy I Soter about 306 BCE. The first recorded librarian was Zenodotus of Ephesus. During Zenodotus' tenure, Callimachus, who was never the head librarian, compiled many catalogues/lists, each called Pinakes. His most famous one listed authors and their works; thus he became the first known bibliographer and the scholar who organized the library by authors and subjects about 245 BCE. His work was 120 volumes long. Apollonius of Rhodes was the successor to Zenodotus. Eratosthenes of Cyrene succeeded Apollonius in 235 BCE and compiled his tetagmenos epi teis megaleis bibliothekeis, the 'scheme of the great bookshelves'. In 195 BCE Aristophanes of Byzantium, Eratosthenes' successor, was the librarian and updated the Pinakes, although it is also possible that his work was not a supplement of Callimachus' Pinakes themselves, but an independent polemic against, or commentary upon, their contents. == Description == The collection at the Library of Alexandria contained nearly 500,000 papyrus scrolls, which were grouped together by subject matter and stored in bins. Each bin carried a label with painted tablets hung above the stored papyri. Pinakes was named after these tablets and are a set of index lists. The bins gave bibliographical information for every roll. A typical entry started with a title and also provided the author's name, birthplace, father's name, any teachers trained under, and educational background. It contained a brief biography of the author and a list of the author's publications. The entry had the first line of the work, a summary of its contents, the name of the author, and information about the origin of the roll, as well as any doubts about the genuineness of the ascription. Callimachus' system divided works into six genres of poetry and five sections of prose: rhetoric, law, epic, tragedy, comedy, lyric poetry, history, medicine, mathematics, natural science, and miscellanies. Each category was alphabetized by author. Callimachus composed two other works that were referred as pinakes and were probably somewhat similar in format to the Pinakes (of which they "may or may not be subsections"), but were concerned with individual topics. These are listed by the Suda as: A Chronological Pinax and Description of Didaskaloi from the Beginning and Pinax of the Vocabulary and Treatises of Democritus. == Later bibliographic pinakes == The term pinax was used for bibliographic catalogs beyond Callimachus. For example, Ptolemy-el-Garib's catalog of Aristotle's writings comes to us with the title Pinax (catalog) of Aristotle's writings. == Legacy == The Pinakes proved indispensable to librarians for centuries, and they became a model for organizing knowledge throughout the Mediterranean. Their later influence can be traced to medieval times, even to the Arabic counterpart of the tenth century: Ibn al-Nadim's Al-Fihrist ("Index"). Local variations for cataloging and library classification continued through the late 19th century, when Anthony Panizzi and Melvil Dewey paved the way for more shared and standardized approaches.
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Semantic similarity network

A semantic similarity network (SSN) is a special form of semantic network. designed to represent concepts and their semantic similarity. Its main contribution is reducing the complexity of calculating semantic distances. Bendeck (2004, 2008) introduced the concept of semantic similarity networks (SSN) as the specialization of a semantic network to measure semantic similarity from ontological representations. Implementations include genetic information handling. The concept is formally defined (Bendeck 2008) as a directed graph, with concepts represented as nodes and semantic similarity relations as edges. The relationships are grouped into relation types. The concepts and relations contain attribute values to evaluate the semantic similarity between concepts. The semantic similarity relationships of the SSN represent several of the general relationship types of the standard Semantic network, reducing the complexity of the (normally, very large) network for calculations of semantics. SSNs define relation types as templates (and taxonomy of relations) for semantic similarity attributes that are common to relations of the same type. SSN representation allows propagation algorithms to faster calculate semantic similarities, including stop conditions within a specified threshold. This reduces the computation time and power required for calculation. A more recent publications on Semantic Matching and Semantic Similarity Networks could be found in (Bendeck 2019). Specific Semantic Similarity Network application on healthcare was presented at the Healthcare information exchange Format (FHIR European Conference) 2019. The latest evolution in Artificial Intelligence (like ChatGPT, based on Large language model), relay strongly on evolutionary computation, the next level will be to include semantic unification (like in the Semantic Networks and this Semantic similarity network) to extend the current models with more powerful understanding tools.
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Kai's Power Tools

Kai's Power Tools (KPT) are a set of API plugins created by the German computer scientist Kai Krause in 1992 that were designed for use with Adobe Photoshop and Corel Photo-Paint. Kai's Power Tools were sold to Corel in 2000 when MetaCreations was closed. There are various versions of Kai's Power Tools. KPT 3, 5, 6, and X sets are compilations of different filters. The program interface features a reward-based function in which a bonus function is revealed as the user moves towards more complex aspects of the tool. == Filters == The KPT Convolver is a mathematics based filter; the level of precision and varying effects can be achieved by using numerical values of colour, tint, hue, saturation, contrast, brightness, luminosity, and posterize. The KPT Projector takes the current image or selection and offers a number of interactive perspective warp effects. To a large extent, with its draggable distortion handles and its moving, scaling and rotating options, this simply duplicates Adobe Photoshop's Free Transform capabilities. What is completely different is the ability to rotate the bitmap image in 3D space and to tile the results if desired. It can also animate the distortions by dragging keyframes from the preview window into an animation palette. KPT 6 will then preview the animation and output it to various sizes in avi or mov format. This animation capability is even more useful with the KPT Turbulence filter. This is another distortion filter, but one that treats the image as if it was completely liquid. The preview panel shows the animation in real time. The KPT Goo filter is used to produce a single frame freeform liquid distortion. This filter is available both with KPT 6 and the standalone version. It works by effectively turning a bitmap image into a liquid that can be interactively smeared, smudged, twirled, and pinched with the range of tools on offer. The obvious use is to distort photographic portraits into caricatures. KPT Materializer can create advanced surface textures based on bump maps that define troughs and peaks. It can use any external image for the basis of the bump map or alternatively the user can pick out the hue, saturation, luminance or red, green, or blue channel of the current image. It can then offset, scale and rotate the texture map, control its lighting, and even blend in a reflection map. The filter can be used for anything from providing an oil-painting feel to an entire image, to giving the illusion of depth to a selection. Also producing the impression of depth is the KPT Gel filter which uses various paint tools to synthesize photo-realistic 3D materials such as metals, liquids, or plastics. Gel painting is very different from traditional 2D painting as the brush strokes pool together when they touch and refract the underlying image. It can also manipulate 3D paint—once it has been added—by twirling, pinching, and carving it. The opposite is true of the Equalizer filter, which is used for applying variations on sharpening effects. The filter has three modes. The first mode, Equalizer, looks and works rather like the graphic equalizer on a stereo system, enabling adjustment of the level of pixel contrast within nine bands of different visual frequencies. The second mode, Contrast Sharpen, allows for increasing the contrast between light and dark areas in an image. The third mode, Bounded Sharpen, can sharpen an image without causing oversharpening, which can lead to halo effects. This feature is particularly useful when pulling out the detail in an image softened by resizing. KPT SceneBuilder is used for producing photorealistic 3D scenes by importing and rendering 3DS files. The main image window offers three tabs for editing in 2D and 3D mode and for setting up the object's final texture. Many users regard this filter as being the most impressive because it acts as a standalone 3D rendering tool and provides control over everything from transparency, reflection, refraction, bump mapping through to multiple light sources, and so on but without the ability to create or edit objects. The final filter, KPT SkyEffects, also has its roots in Metacreations' experience with 3D programs such as Bryce and RayDream. This filter is designed to simulate the interaction between the light from the sun or moon with no less than six atmospheric layers of haze, fog and cloud. The filter is typical of the KPT 6 collection as a whole: at times the interface is inspired and offers the ability to create beautiful reddening sunsets simply by interactively dragging the sun toward the horizon, producing realistic sunsets and moonscapes. == Other effects == Kai's Power Tools 6 features a lens flare effect for precisely managing the type of glow, halo, streaks, and reflection. The addition of a library of preset effects helps to overcome this by allowing the user to choose a standard effect and then interactively position the flare in the image preview. KPT 6 provides a new engine in the form of the KPT Reaction, which takes a reaction seed and turns it into a seamlessly tiling pattern based on a reaction diffusion process. It offers random noise, regular dots or reticulated voronoi patterns or a bitmap image itself as the seed. Corel has no plans for any updates.
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Reification (computer science)

In computer science, reification is the process by which an abstract idea about a program is turned into an explicit data model or other object created in a programming language. A computable/addressable object—a resource—is created in a system as a proxy for a non computable/addressable object. By means of reification, something that was previously implicit, unexpressed, and possibly inexpressible is explicitly formulated and made available to conceptual (logical or computational) manipulation. Informally, reification is often referred to as "making something a first-class citizen" within the scope of a particular system. Some aspect of a system can be reified at language design time, which is related to reflection in programming languages. It can be applied as a stepwise refinement at system design time. Reification is one of the most frequently used techniques of conceptual analysis and knowledge representation. == Reflective programming languages == In the context of programming languages, reification is the process by which a user program or any aspect of a programming language that was implicit in the translated program and the run-time system, are expressed in the language itself. This process makes it available to the program, which can inspect all these aspects as ordinary data. In reflective languages, reification data is causally connected to the related reified aspect such that a modification to one of them affects the other. Therefore, the reification data is always a faithful representation of the related reified aspect . Reification data is often said to be made a first class object. Reification, at least partially, has been experienced in many languages to date: in early Lisp dialects and in current Prolog dialects, programs have been treated as data, although the causal connection has often been left to the responsibility of the programmer. In Smalltalk-80, the compiler from the source text to bytecode has been part of the run-time system since the very first implementations of the language. The C programming language reifies the low-level detail of memory addresses.Many programming language designs encapsulate the details of memory allocation in the compiler and the run-time system. In the design of the C programming language, the memory address is reified and is available for direct manipulation by other language constructs. For example, the following code may be used when implementing a memory-mapped device driver. The buffer pointer is a proxy for the memory address 0xB8000000. Functional programming languages based on lambda-calculus reify the concept of a procedure abstraction and procedure application in the form of the Lambda expression. The Scheme programming language reifies continuations (approximately, the call stack). In C#, reification is used to make parametric polymorphism implemented in the form of generics as a first-class feature of the language. In the Java programming language, there exist "reifiable types" that are "completely available at run time" (i.e. their information is not erased during compilation). REBOL reifies code as data and vice versa. Many languages, such as Lisp, JavaScript, and Curl, provide an eval or evaluate procedure that effectively reifies the language interpreter. Smalltalk and Actor languages permit the reification of blocks and messages, which are equivalent of lambda expressions in Lisp, and thisContext in Smalltalk, which is a reification of the current executing block. Homoiconic languages reify the syntax of the language as data that is understood by the language itself. This allows the user to write programs whose inputs and outputs are code (see macros, eval). Common representations of code include S-expressions (e.g. Clojure, Lisp), and abstract syntax trees (e.g. Rust). == Data reification vs. data refinement == Data reification (stepwise refinement) involves finding a more concrete representation of the abstract data types used in a formal specification. Data reification is the terminology of the Vienna Development Method (VDM) that most other people would call data refinement. An example is taking a step towards an implementation by replacing a data representation without a counterpart in the intended implementation language, such as sets, by one that does have a counterpart (such as maps with fixed domains that can be implemented by arrays), or at least one that is closer to having a counterpart, such as sequences. The VDM community prefers the word "reification" over "refinement", as the process has more to do with concretising an idea than with refining it. For similar usages, see Reification (linguistics). == In conceptual modeling == Reification is widely used in conceptual modeling. Reifying a relationship means viewing it as an entity. The purpose of reifying a relationship is to make it explicit, when additional information needs to be added to it. Consider the relationship type IsMemberOf(member:Person, Committee). An instance of IsMemberOf is a relationship that represents the fact that a person is a member of a committee. The figure below shows an example population of IsMemberOf relationship in tabular form. Person P1 is a member of committees C1 and C2. Person P2 is a member of committee C1 only. The same fact, however, could also be viewed as an entity. Viewing a relationship as an entity, one can say that the entity reifies the relationship. This is called reification of a relationship. Like any other entity, it must be an instance of an entity type. In the present example, the entity type has been named Membership. For each instance of IsMemberOf, there is one and only one instance of Membership, and vice versa. Now, it becomes possible to add more information to the original relationship. As an example, we can express the fact that "person p1 was nominated to be the member of committee c1 by person p2". Reified relationship Membership can be used as the source of a new relationship IsNominatedBy(Membership, Person). For related usages see Reification (knowledge representation). == In Unified Modeling Language (UML) == UML provides an association class construct for defining reified relationship types. The association class is a single model element that is both a kind of association and a kind of class. The association and the entity type that reifies are both the same model element. Note that attributes cannot be reified. == On Semantic Web == === RDF and OWL === In Semantic Web languages, such as Resource Description Framework (RDF) and Web Ontology Language (OWL), a statement is a binary relation. It is used to link two individuals or an individual and a value. Applications sometimes need to describe other RDF statements, for instance, to record information like when statements were made, or who made them, which is sometimes called "provenance" information. As an example, we may want to represent properties of a relation, such as our certainty about it, severity or strength of a relation, relevance of a relation, and so on. The example from the conceptual modeling section describes a particular person with URIref person:p1, who is a member of the committee:c1. The RDF triple from that description is Consider to store two further facts: (i) to record who nominated this particular person to this committee (a statement about the membership itself), and (ii) to record who added the fact to the database (a statement about the statement). The first case is a case of classical reification like above in UML: reify the membership and store its attributes and roles etc.: Additionally, RDF provides a built-in vocabulary intended for describing RDF statements. A description of a statement using this vocabulary is called a reification of the statement. The RDF reification vocabulary consists of the type rdf:Statement, and the properties rdf:subject, rdf:predicate, and rdf:object. Using the reification vocabulary, a reification of the statement about the person's membership would be given by assigning the statement a URIref such as committee:membership12345 so that describing statements can be written as follows: These statements say that the resource identified by the URIref committee:membership12345Stat is an RDF statement, that the subject of the statement refers to the resource identified by person:p1, the predicate of the statement refers to the resource identified by committee:isMemberOf, and the object of the statement refers to the resource committee:c1. Assuming that the original statement is actually identified by committee:membership12345, it should be clear by comparing the original statement with the reification that the reification actually does describe it. The conventional use of the RDF reification vocabulary always involves describing a statement using four statements in this pattern. Therefore, they are sometimes referred to as the "reification quad". Using reification according to this convention, we could record the fact that pe
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Six Little Dragons

Six Little Dragons (Chinese: 杭州六小龙), or Six Little Dragons of Hangzhou, are an informal grouping of the tech startups Game Science, DeepSeek, Unitree Robotics, DEEP Robotics, BrainCo and Manycore Tech. All six were established in Hangzhou, They are active in artificial intelligence, robotics, gaming, and brain-computer interface technology. Hangzhou is referred to as the China’s “e-commerce capital” (电商之都). The nickname "Six Little Dragons" originated from the Chinese internet. == Background == === Chinese government investments (2002 — 2010s) === From 2002 to 2007, under Xi Jinping's leadership as party secretary of Zhejiang, provincial spending on technology research grew over four times to 28 billion RMB. The province launched "Digital Zhejiang" (数字浙江) to advance modernization and the "Eight Eight Strategy" (八八战略), focusing on eight advantages and actions to boost industrial development, including specialized industries. In 2010, Hangzhou's government started "Project Eagle" (雏鹰计划) to aid science and technology startups. The project works with incubators and accelerators to find promising tech companies and offers public funding and other help, especially for startups by graduates and returning students. Unitree received support in the initial phase, along with government subsidies from Binjiang District. === AI-startups and further investments (2025 — present) === In January 2025, the Chinese government created the "Hangzhou AI Industry Chain High-Quality Development Action Plan" which focuses on computing power, LLM technologies, and AI applications. The plan was made to certify over 2,000 new high-tech enterprises, initiate over 300 major tech projects, and invest more than 300 billion RMB (US$40 billion) annually. The Chinese government also renewed "Project Eagle" and to allocate 15% of industrial policy funds for future industries. Hangzhou aimed to become a center for tech startups, highlighting the "six little dragons of Hangzhou," a nickname popularized in early 2025. This group includes DeepSeek, Game Science, Unitree Robotics, Manycore Tech, BrainCo, and DEEP Robotics, companies in gaming, robotics, and software development. Earlier in 2025, DeepSeek, one of the six dragons, launched an AI system at a much lower cost than those from Silicon Valley. Since then, DeepSeek and Alibaba have produced top-performing open source AI models. Game Science launched the successful video game Black Myth: Wukong in 2024, while Unitree gained attention for their dancing robots in the 2025 annual spring gala broadcast by Chinese state media. The group was acknowledged by Chinese authorities in Hangzhou in a New Years message for local businesses in January 2025. Hangzhou’s universities were given credit for the development of Chinese technological industry. Zhejiang University alumni founded three of the "Six Little Dragons". By September 2024, the university produced 102 executives in Chinese AI start-ups, ranking third among China's top institutions. On February 20, 2025, Alibaba's Eddie Wu stated that the company would focus on artificial generative intelligence and plans significant investment in AI. The company also sought to boost foreign investment to China's "Six Little Dragons" following Alibaba's founder Jack Ma attended General Secretary of the Chinese Communist Party Xi Jinping's business symposium with corporate leaders and entrepreneurs that same month. == Challenges == China's net foreign direct investment (FDI) fell by US$168 billion in 2024, marking the largest capital flight since 1990. Foreign investment peaked at US$344 billion in 2021 but has since declined according to the State Administration of Foreign Exchange. In 2024, foreign investors put in only US$4.5 billion while Chinese firms invested US$173 billion abroad. According to interviews conducted by The New York Times, some start-up company founders believe that Chinese government's support for Hangzhou's technological sector has deterred foreign investors. Tensions with the United States led many international companies to adopt a China Plus One strategy, while Chinese firms build factories overseas to avoid potential Trump tariffs. China also faced US restrictions on its access of advanced chips, forcing Chinese tech companies to stockpile Nvidia chips while Chinese producers like Huawei and Semiconductor Manufacturing International Corporation (SMIC) were competing to produce their own.
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