FactorDaily is an Indian digital media publication founded in 2016 by Pankaj Mishra and Jayadevan PK. Mishra was formerly an Editor at TechCrunch and the Economic Times. The digital publication was launched with an intent to produce stories on the impact of technology on life in India. == History == FactorDaily began publishing in May 2016, with daily reported stories on technology, culture and life in India. Prior to its launch, the company had raised $1 million in seed funding from Accel India, Blume Ventures, Girish Mathrubootham of Freshdesk, Vijay Shekhar Sharma of PayTm, and Jay Vijayan of Tekion. Josey Puliyenthuruthel John, formerly Managing Editor at Business Today and National Corporate Editor at Mint, later joined the company as a Consulting Editor. In January 2017, FactorDaily launched its first Podcast called The Outliers. The inaugural episode featured a conversation with Manish Sharma of Printo on his journey starting up. == Awards == The FactorDaily team won the Bengaluru Editors Lab 2017, a journalism hackathon organised by the Global Editors Network (GEN). The story titled "India has 3,800 psychiatrists for 1.2bn people. Can tech step in to manage mental health?" won the first prize in the online category of the fifth Schizophrenia Research Foundation’s (SCARF) ‘Media for Mental Health’ awards. The story titled 'The dark hand of tech that stokes sex trafficking in India', won the Stop Slavery media Awards by the Thomson Reuters Foundation for the year 2020.
Language Server Protocol
The Language Server Protocol (LSP) is an open, JSON-RPC-based protocol for use between source-code editors or integrated development environments (IDEs) and servers that provide "language intelligence tools": programming language-specific features like code completion, syntax highlighting and marking of warnings and errors, as well as refactoring routines. The goal of the protocol is to allow programming language support to be implemented and distributed independently of any given editor or IDE. In the early 2020s, LSP quickly became a "norm" for language intelligence tools providers. == History == LSP was originally developed for Microsoft Visual Studio Code and is now an open standard. On June 27, 2016, Microsoft announced a collaboration with Red Hat and Codenvy to standardize the protocol's specification. Its specification is hosted and developed on GitHub. == Background == Modern IDEs provide programmers with sophisticated features like code completion, refactoring, navigating to a symbol's definition, syntax highlighting, and error and warning markers. For example, in a text-based programming language, a programmer might want to rename a method read. The programmer could either manually edit the respective source code files and change the appropriate occurrences of the old method name into the new name, or instead use an IDE's refactoring capabilities to make all the necessary changes automatically. To be able to support this style of refactoring, an IDE needs a sophisticated understanding of the programming language that the program's source is written in. A programming tool without such an understanding—for example, one that performs a naive search-and-replace instead—could introduce errors. When renaming a read method, for example, the tool should not replace the partial match in a variable that might be called readyState, nor should it replace the portion of a code comment containing the word "already". Neither should renaming a local variable read, for example, end up altering identically-named variables in other scopes. Conventional compilers or interpreters for a specific programming language are typically unable to provide these language services, because they are written with the goal of either transforming the source code into object code or immediately executing the code. Additionally, language services must be able to handle source code that is not well-formed, e.g. because the programmer is in the middle of editing and has not yet finished typing a statement, procedure, or other construct. Additionally, small changes to a source code file which are done during typing usually change the semantics of the program. In order to provide instant feedback to the user, the editing tool must be able to very quickly evaluate the syntactical and semantical consequences of a specific modification. Compilers and interpreters therefore provide a poor candidate for producing the information needed for an editing tool to consume. Prior to the design and implementation of the Language Server Protocol for the development of Visual Studio Code, most language services were generally tied to a given IDE or other editor. In the absence of the Language Server Protocol, language services are typically implemented by using a tool-specific extension API. Providing the same language service to another editing tool requires effort to adapt the existing code so that the service may target the second editor's extension interfaces. The Language Server Protocol allows for decoupling language services from the editor so that the services may be contained within a general-purpose language server. Any editor can inherit sophisticated support for many different languages by making use of existing language servers. Similarly, a programmer involved with the development of a new programming language can make services for that language available to existing editing tools. Making use of language servers via the Language Server Protocol thus also reduces the burden on vendors of editing tools, because vendors do not need to develop language services of their own for the languages the vendor intends to support, as long as the language servers have already been implemented. The Language Server Protocol also enables the distribution and development of servers contributed by an interested third party, such as end users, without additional involvement by either the vendor of the compiler for the programming language in use or the vendor of the editor to which the language support is being added. LSP is not restricted to programming languages. It can be used for any kind of text-based language, like specifications or domain-specific languages (DSL). == Technical overview == When a user edits one or more source code files using a language server protocol-enabled tool, the tool acts as a client that consumes the language services provided by a language server. The tool may be a text editor or IDE and the language services could be refactoring, code completion, etc. The client informs the server about what the user is doing, e.g., opening a file or inserting a character at a specific text position. The client can also request the server to perform a language service, e.g. to format a specified range in the text document. The server answers a client's request with an appropriate response. For example, the formatting request is answered either by a response that transfers the formatted text to the client or by an error response containing details about the error. The Language Server Protocol defines the messages to be exchanged between client and language server. They are JSON-RPC preceded by headers similar to HTTP. Messages may originate from the server or client. The protocol does not make any provisions about how requests, responses and notifications are transferred between client and server. For example, client and server could be components within the same process exchanging JSON strings via method calls. They could also be different processes on the same or on different machines communicating via network sockets. == Registry == There are lists of LSP-compatible implementations, maintained by the community-driven Langserver.org or Microsoft.
AI Bug Finders: Free vs Paid (2026)
Curious about the best AI bug finder? An AI bug finder is software that uses machine learning to help you get more done — it combines speed, accuracy, and an interface that just works. Hands-on testing shows real-world results vary, so a short free trial is the smartest way to decide. Whether you are a beginner or a pro, the right AI bug finder slots into your workflow and pays for itself fast. Read on for hands-on impressions, pricing tiers, and the standout features that matter.
Markov chain geostatistics
Markov chain geostatistics uses Markov chain spatial models, simulation algorithms and associated spatial correlation measures (e.g., transiogram) based on the Markov chain random field theory, which extends a single Markov chain into a multi-dimensional random field for geostatistical modeling. A Markov chain random field is still a single spatial Markov chain. The spatial Markov chain moves or jumps in a space and decides its state at any unobserved location through interactions with its nearest known neighbors in different directions. The data interaction process can be well explained as a local sequential Bayesian updating process within a neighborhood. Because single-step transition probability matrices are difficult to estimate from sparse sample data and are impractical in representing the complex spatial heterogeneity of states, the transiogram, which is defined as a transition probability function over the distance lag, is proposed as the accompanying spatial measure of Markov chain random fields.
András Kornai
András Kornai (born 1957 in Budapest) is a mathematical linguist. == Education == Kornai is the son of economist János Kornai. He earned two PhDs with the first being in mathematics in 1983 from Eötvös Loránd University in Budapest, where his advisor was Miklós Ajtai. His second was in linguistics in 1991 from Stanford University, where his advisor was Paul Kiparsky. == Career == He is a professor in the Department of Algebra at the Budapest Institute of Technology, where he works on an open source Hungarian morphological analyzer. He was Chief Scientist at MetaCarta, where he worked on information extraction before the company was acquired by Nokia. Prior to MetaCarta, he was Chief Scientist at Northern Light. He is on the board of the journal Grammars and YourAmigo PLC. His research interests include all mathematical aspects of natural language processing, speech recognition, and OCR. As area editor he was responsible for the Mathematical Linguistics area of the Oxford International Encyclopedia of Linguistics, and his joint work with Geoffrey Pullum, "The X-bar Theory of Phrase Structure", formally reconstructed that then-popular linguistic theory. == Awards and honors == 2009: ACM Distinguished Member == Monographs == Semantics. Springer Nature, 2020. ISBN 978-3-319-65644-1 Mathematical Linguistics. Springer Verlag, in the series Advanced Information and Knowledge Processing, November 2007. ISBN 978-1-84628-985-9 Hardbound, approximately 300 pages. See description. Formal Phonology. In the series Outstanding Dissertations in Linguistics, Garland Publishing, 1994, ISBN 0-8153-1730-1, hardbound, 240 pages Contents, Preface, Introduction (20 pages) On Hungarian Morphology. In the series Linguistica, Hungarian Academy of Sciences, 1994, ISBN 963-8461-73-X, paperbound, 174 pages Contents, Preface, Introduction (10 pages) == Books edited == Oxford International Encyclopedia of Linguistics (Mathematical Linguistics Area Editor under Editor in Chief William Frawley). 4 volumes, Oxford University Press, 2003, ISBN 978-0-19-513977-8. Proceedings of the HLT-NAACL Workshop on the Analysis of Geographic References. Jointly with Beth Sundheim. Association for Computational Linguistics, 2003, ISBN 1-932432-04-3 (WS9), paperbound, vi+81 pages. See related material. Extended Finite State Models of Language (editor). In the series Studies in Natural Language Processing, Cambridge University Press, 1999, ISBN 0-521-63198-X, hardbound, x+278 pages Contents, Introduction (7 pages). == Selected papers == Digital Language Death. PLoS ONE 8(10): e77056, 2012. [1] Hunmorph: open source word analysis (Jointly with V. Tron, Gy. Gyepesi, P. Halacsy, L. Nemeth, and D. Varga). In Proc. ACL 2005 Software Workshop 77-85 [2] Leveraging the open source ispell codebase for minority language analysis (Jointly with P. Halacsy, L. Nemeth, A. Rung, I. Szakadat, and V. Tron). In J. Carson-Berndsen (ed): Proc. SALTMIL 2004 56-59 [3] Explicit Finitism, International Journal of Theoretical Physics 2003/2 301-307 [4] Mathematical Linguistics (Jointly with G.K. Pullum) In W. Frawley (ed): Oxford International Encyclopedia of Linguistics, Oxford University Press 2003, v3 17-20 [5] Optical Character Recognition, In W. Frawley (ed): Oxford International Encyclopedia of Linguistics, Oxford University Press 2003, v3 33-34 [6] How many words are there? Glottometrics 2002/4 61-86 [7] Zipf's law outside the middle range Proc. Sixth Meeting on Mathematics of Language University of Central Florida, 1999 347-356 [8] A Robust, Language-Independent OCR System. (Jointly with Z. Lu, I. Bazzi, J. Makhoul, P. Natarajan, and R. Schwartz) In: Robert J. Mericsko (ed): Proc. 27th AIPR Workshop: Advances in Computer-Assisted Recognition SPIE Proceedings 3584 1999 [9] Quantitative Comparison of Languages. Grammars 1998/2 155-165 [10] The generative power of feature geometry. Annals of Mathematics and Artificial Intelligence 8 1993 37-46 [11] The X-bar Theory of Phrase Structure. (Jointly with G.K. Pullum) Language 66 1990 24-50 [12]
Automated storage and retrieval system
An automated storage and retrieval system (ASRS or AS/RS) consists of a variety of computer-controlled systems for automatically placing and retrieving loads from defined storage locations. Automated storage and retrieval systems (AS/RS) are typically used in applications where: There is a very high volume of loads being moved into and out of storage Storage density is important because of space constraints No value is added in this process (no processing, only storage and transport) Accuracy is critical because of potential expensive damages to the load An AS/RS can be used with standard loads as well as nonstandard loads, meaning that each standard load can fit in a uniformly-sized volume; for example, the film canisters in the image of the Defense Visual Information Center are each stored as part of the contents of the uniformly sized metal boxes, which are shown in the image. Standard loads simplify the handling of a request of an item. In addition, audits of the accuracy of the inventory of contents can be restricted to the contents of an individual metal box, rather than undergoing a top-to-bottom search of the entire facility, for a single item. They can also be used in self storage places. == Overview == AS/RS systems are designed for automated storage and retrieval of parts and items in manufacturing, distribution, retail, wholesale and institutions. They first originated in the 1960s, initially focusing on heavy pallet loads but with the evolution of the technology the handled loads have become smaller. The systems operate under computerized control, maintaining an inventory of stored items. Retrieval of items is accomplished by specifying the item type and quantity to be retrieved. The computer determines where in the storage area the item can be retrieved from and schedules the retrieval. It directs the proper automated storage and retrieval machine (SRM) to the location where the item is stored and directs the machine to deposit the item at a location where it is to be picked up. A system of conveyors and or automated guided vehicles is sometimes part of the AS/RS system. These take loads into and out of the storage area and move them to the manufacturing floor or loading docks. To store items, the pallet or tray is placed at an input station for the system, the information for inventory is entered into a computer terminal and the AS/RS system moves the load to the storage area, determines a suitable location for the item, and stores the load. As items are stored into or retrieved from the racks, the computer updates its inventory accordingly. The benefits of an AS/RS system include reduced labor for transporting items into and out of inventory, reduced inventory levels, more accurate tracking of inventory, and space savings. Items are often stored more densely than in systems where items are stored and retrieved manually. Within the storage, items can be placed on trays or hang from bars, which are attached to chains/drives in order to move up and down. The equipment required for an AS/RS include a storage & retrieval machine (SRM) that is used for rapid storage and retrieval of material. SRMs are used to move loads vertically or horizontally, and can also move laterally to place objects in the correct storage location. The trend towards Just In Time production often requires sub-pallet level availability of production inputs, and AS/RS is a much faster way of organizing the storage of smaller items next to production lines. The Material Handling Institute of America (MHIA), the non-profit trade association for the material handling world, and its members have categorised AS/RS into two primary segments: Fixed Aisle and Carousels/Vertical Lift Modules (VLMs). Both sets of technologies provide automated storage and retrieval for parts and items, but use different technologies. Each technology has its unique set of benefits and disadvantages. Fixed Aisle systems are characteristically larger systems whereas carousels and Vertical Lift Modules are used individually or grouped, but in small to medium-sized applications. A fixed-aisle AS/R machine (stacker crane) is one of two main designs: single-masted or double masted. Most are supported on a track and ceiling guided at the top by guide rails or channels to ensure accurate vertical alignment, although some are suspended from the ceiling. The 'shuttles' that make up the system travel between fixed storage shelves to deposit or retrieve a requested load (ranging from a single book in a library system to a several ton pallet of goods in a warehouse system). The entire unit moves horizontally within an aisle, while the shuttles are able to elevate up to the necessary height to reach the load, and can extend and retract to store or retrieve loads that are several positions deep in the shelving. A semi-automated system can be achieved by utilizing only specialized shuttles within an existing rack system. Another AS/RS technology is known as shuttle technology. In this technology the horizontal movement is made by independent shuttles each operating on one level of the rack while a lift at a fixed position within the rack is responsible for the vertical movement. By using two separate machines for these two axes the shuttle technology is able to provide higher throughput rates than stacker cranes. Storage and Retrieval Machines pick up or drop off loads to the rest of the supporting transportation system at specific stations, where inbound and outbound loads are precisely positioned for proper handling. In addition, there are several types of Automated Storage & Retrieval Systems (AS/RS) devices called Unit-load AS/RS, Mini-load AS/RS, Mid-Load AS/RS, Vertical Lift Modules (VLMs), Horizontal Carousels and Vertical Carousels. These systems are used either as stand-alone units or in integrated workstations called pods or systems. These units are usually integrated with various types of pick to light systems and use either a microprocessor controller for basic usage or inventory management software. These systems are ideal for increasing space utilization up to 90%, productivity levels by 90%, accuracy to 99.9%+ levels and throughput up to 750 lines per hour/per operator or more depending on the configuration of the system. == Horizontal carousels == Robotic Inserter/Extractor devices can be used for horizontal carousels. The robotic device is positioned in the front or rear of up to three horizontal carousels tiered high. The robot grabs the tote required in the order and often replenishes at the same time to speed up throughput. The tote(s) are then delivered to a conveyor, which routes it to a work station for picking or replenishing. Up to eight transactions per minute per unit can be done. Totes or containers up to 36" x 36" x 36" can be used in a system. On a simplistic level, horizontal carousels are also often used as "rotating shelving". With simple "fetch" command, items are brought to the operator and otherwise wasted space is eliminated. AS/RS Applications: Most applications of AS/RS technology have been associated with warehousing and distribution operations. An AS/RS can also be used to store raw materials and work in process in manufacturing. Three application areas can be distinguished for AS/RS: (1) Unit load storage and handling, (2) Order picking, and (3) Work in process storage. Unit load storage and retrieval applications are represented by unit load AS/RS and deep-lane storage systems. These kinds of applications are commonly found in warehousing for finishing goods in a distribution center, rarely in manufacturing. Deep-lane systems are used in the food industry. As described above, order picking involves retrieving materials in less than full unit load quantities. Minilpass, man-on board, and items retrieval systems are used for this second application area. Work in process storage is a more recent application of automated storage technology. While it is desirable to minimize the amount of work in process, WIP is unavoidable and must be effectively managed. Automated storage systems, either automated storage/retrieval systems or carousel systems, represent an efficient way to store materials between processing steps, particularly in batch and job shop production. In high production, work in process is often carried between operations by conveyor system, which this serve both storage and transport functions. === Inventory Category-specific AS/RS === Each inventory category—raw materials, work-in-process, and finished goods—requires its own specialized Automated Storage and Retrieval System (AS/RS). Particularly for work-in-process (WIP) inventories, due to variations in manufacturing processes, the AS/RS systems are significantly different in design and function, tailored specifically to match unique handling, storage, and retrieval requirements === Installed applications === Installed applications of this technology can be wide-ranging. In some librarie
Pedro Domingos
Pedro Domingos (born 1965) is a Professor Emeritus of computer science and engineering at the University of Washington. He is a researcher in machine learning known for Markov logic network enabling uncertain inference. == Education == Domingos received an undergraduate degree and Master of Science degree from Instituto Superior Técnico (IST). He moved to the University of California, Irvine, where he received a Master of Science degree followed by his PhD. == Research and career == After spending two years as an assistant professor at IST, he joined the University of Washington as an assistant professor of Computer Science and Engineering in 1999 and became a full professor in 2012. He started a machine learning research group at the hedge fund D. E. Shaw & Co. in 2018, but left in 2019. He co-founded the International Machine Learning Society. As of 2018, he was on the editorial board of Machine Learning journal. === Publications === Pedro Domingos, The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World, New York, Basic Books, 2015, ISBN 978-0-465-06570-7. Pedro Domingos, "Our Digital Doubles: AI will serve our species, not control it", Scientific American, vol. 319, no. 3 (September 2018), pp. 88–93. "AIs are like autistic savants and will remain so for the foreseeable future.... AIs lack common sense and can easily make errors that a human never would... They are also liable to take our instructions too literally, giving us precisely what we asked for instead of what we actually wanted." (p. 93.) Pedro Domingos, 2040: A Silicon Valley Satire, BookBaby, 2024, ISBN 979-8-350-96334-2. === Awards and honors === 2014: ACM SIGKDD Innovation Award. for his foundational research in data stream analysis, cost-sensitive classification, adversarial learning, and Markov logic networks, as well as applications in viral marketing and information integration. 2010: Elected an Association for the Advancement of Artificial Intelligence (AAAI) Fellow. For significant contributions to the field of machine learning and to the unification of first-order logic and probability. 2003: Sloan Fellowship 1992–1997: Fulbright Scholarship