The Artificial Inventor Project (AIP) is a global legal initiative headed by Professor Ryan Abbott dedicated to pursuing intellectual property (IP) rights for inventions and creative works generated autonomously by artificial intelligence (AI) systems without traditional human inventorship or authorship. The project coordinates a series of pro bono test cases worldwide, aiming to prompt law reform and public debate on how IP law should accommodate non-human creators. == History == In 2019, AIP filed patent applications in multiple jurisdictions, including the United States, United Kingdom, European Patent Office, Australia, Switzerland, and South Africa, naming the AI system DABUS (Device for the Autonomous Bootstrapping of Unified Sentience), created by Stephen Thaler, as the inventor. The aim was to challenge legal norms that require inventors to be natural persons and highlight pressing policy questions about AI-generated innovation and IP regimes. == Legal proceedings by jurisdiction == === Australia === In July 2021, a Federal Court of Australia judge (Beach J) ruled that AI can be considered an inventor under the Patents Act 1990, ordering IP Australia to reinstate the relevant patent. However, the full court then overturned this ruling on appeal and denied further review. === European Patent Office === The EPO Board of Appeal determined in 2022 that only a human inventor may be named, rendering DABUS‑based applications unacceptable. === South Africa === In 2021, a patent was granted listing DABUS as the inventor. As South Africa’s procedural system does not involve substantive inventorship review, the grant proceeded on formal grounds alone. === Switzerland === On 26 June 2025, the Swiss Federal Administrative Court ruled that artificial intelligence systems such as DABUS cannot be listed as inventors on patent applications. The court upheld the existing practice of the Swiss Federal Institute of Intellectual Property (IPI), affirming that only natural persons may be recognized as inventors under Swiss patent law. === United Kingdom === In December 2023, the UK Supreme Court unanimously held that AI systems cannot be legally recognized as inventors, affirming that "an inventor must be a person" under current British law. === United States === In Thaler v. Hirshfeld (2021), a U.S. federal court agreed with the USPTO that inventors must be natural persons, rejecting the DABUS application and setting a precedent consistent with existing statute and administrative policy. == Criticism and impact == The project has fueled substantial discourse. Critics caution that allowing AI inventorship may complicate notions of accountability and ownership. Proponents argue that legal recognition must evolve to avoid disincentivizing innovation produced by AI and to maintain honesty about the true source of invention.
Two-phase locking
In databases and transaction processing, two-phase locking (2PL) is a pessimistic concurrency control method that guarantees conflict-serializability. It is also the name of the resulting set of database transaction schedules (histories). The protocol uses locks, applied by a transaction to data, which may block (interpreted as signals to stop) other transactions from accessing the same data during the transaction's life. By the 2PL protocol, locks are applied and removed in two phases: Expanding phase: locks are acquired and no locks are released. Shrinking phase: locks are released and no locks are acquired. Two types of locks are used by the basic protocol: Shared and Exclusive locks. Refinements of the basic protocol may use more lock types. Using locks that block processes, 2PL, S2PL, and SS2PL may be subject to deadlocks that result from the mutual blocking of two or more transactions. == Read and write locks == Locks are used to guarantee serializability. A transaction is holding a lock on an object if that transaction has acquired a lock on that object which has not yet been released. For 2PL, the only used data-access locks are read-locks (shared locks) and write-locks (exclusive locks). Below are the rules for read-locks and write-locks: A transaction is allowed to read an object if and only if it is holding a read-lock or write-lock on that object. A transaction is allowed to write an object if and only if it is holding a write-lock on that object. A schedule (i.e., a set of transactions) is allowed to hold multiple locks on the same object simultaneously if and only if none of those locks are write-locks. If a disallowed lock attempts on being held simultaneously, it will be blocked. == Variants == Note that all conflict serializable schedules are also view serializable (but not vice-versa). === Two-phase locking === According to the two-phase locking protocol, each transaction handles its locks in two distinct, consecutive phases during the transaction's execution: Expanding phase (aka Growing phase): locks are acquired and no locks are released (the number of locks can only increase). Shrinking phase (aka Contracting phase): locks are released and no locks are acquired. The two phase locking rules can be summarized as: each transaction must never acquire a lock after it has released a lock. The serializability property is guaranteed for a schedule with transactions that obey this rule. Typically, without explicit knowledge in a transaction on end of phase 1, the rule is safely determined only when a transaction has completed processing and requested commit. In this case, all the locks can be released at once (phase 2). === Conservative two-phase locking === Conservative two-phase locking (C2PL) differs from 2PL in that transactions obtain all the locks they need before the actual execution begins. This is to ensure that a transaction that already holds some locks will not block waiting for other locks. C2PL prevents deadlocks. In cases of heavy lock contention, C2PL reduces the time locks are held on average, relative to 2PL and Strict 2PL, because transactions that hold locks are never blocked. In light lock contention, C2PL holds more locks than is necessary, because it is difficult to predict which locks will be needed in the future, thus leading to higher overhead. A C2PL transaction will not obtain any locks if it cannot obtain all the locks it needs in its initial request. Furthermore, each transaction needs to declare its read and write set (the data items that will be read/written), which is not always possible. Because of these limitations, C2PL is not used very frequently. === Strict two-phase locking === To comply with the strict two-phase locking (S2PL) protocol, a transaction needs to comply with 2PL, and release its write (exclusive) locks only after the transaction has ended (i.e., either committed or aborted). On the other hand, read (shared) locks are released regularly during the shrinking phase. Unlike 2PL, S2PL provides strictness (a special case of cascade-less recoverability). This protocol is not appropriate in B-trees because it causes Bottleneck (while B-trees always starts searching from the parent root). === Strong strict two-phase locking === or Rigorousness, or Rigorous scheduling, or Rigorous two-phase locking To comply with strong strict two-phase locking (SS2PL), a transaction's read and write locks are released only after that transaction has ended (i.e., either committed or aborted). A transaction obeying SS2PL has only a phase 1 and lacks a phase 2 until the transaction has completed. Every SS2PL schedule is also an S2PL schedule, but not vice versa.
CarPlay
CarPlay is an Apple standard that enables a car radio or automotive head unit to be a display and controller for an iOS device. It is available on iPhone 5 and later models running iOS 7.1 or later. More than 800 car and motorcycle models support CarPlay, according to Apple. Vehicle owners can add support by installing certain aftermarket vehicle audio products. Most CarPlay systems connect to iOS through USB, some are wireless, and wireless support can be added through aftermarket dongles. CarPlay Ultra, a more integrated version of CarPlay, was first announced on Aston Martin DBX707 in May 2025. == Software == Apple's CarPlay-enabled apps include: Phone Apple Music Apple Maps Calendar Messages Audiobooks (part of Apple Books) Podcasts Settings News Developers must obtain permission from Apple to develop CarPlay-enabled apps. Such apps fall into five categories: Audio: primarily provide audio content, such as music or podcasts. Examples: Amazon Music, Audible, Google Play Music, iHeartRadio, QQ Music, Spotify, and Overcast. Navigation: turn-by-turn guidance, including searching for points of interests and navigating to a destination. Examples: AutoNavi, Baidu Maps, Google Maps, ChargeFinder and Waze. Automaker-made apps allow a user to control vehicle-specific features such as climate controls, gas levels, or radio via CarPlay. Messaging/Voice over IP (VoIP): listen to new messages and reply using dictation in an audio-only interface. Messaging apps on CarPlay integrate with third-party Siri support (known as SiriKit), while VoIP apps integrate with the iOS calling interface using CallKit. Examples: Telegram, WhatsApp, and Zoom. Food-ordering and parking-services apps. To discourage distracted driving, Siri is used extensively, providing voice turn-by-turn navigation guidance and voice-input for text messages. Newscast-style weather and stock results are announced instead of displayed. Requests that bring up visual information may be blocked when the car is in gear, and most native CarPlay apps deliver audio content with minimal interaction. CarPlay-enabled apps installed on the device appear on the CarPlay home screen unless disabled by the user. The inclusion or exclusion and order of app appearance can be changed on a per-vehicle basis. == Hardware == Most of the CarPlay software runs on the connected iPhone. The CarPlay interface provides audio output and a visual display to the vehicle's infotainment system, while adapting to the vehicle's available control methods, including touch screens, rotary dials, physical buttons, steering-wheel controls, and hands-free microphones. Aftermarket head units may support CarPlay or Android Auto, and many support both platforms. === Wired CarPlay === In a wired CarPlay configuration, the iPhone connects to the vehicle or head unit via a USB cable. The USB connection supplies power to the iPhone and provides a stable data link for audio, video, and control input. Wired CarPlay is supported by a wide range of factory-installed infotainment systems and aftermarket head units. Some third-party devices marketed as wireless CarPlay adapters operate by emulating a wired CarPlay connection to the vehicle. These devices plug into the vehicle's USB port and present themselves as a wired CarPlay interface, while separately establishing a wireless connection to the iPhone. Such devices still require the vehicle or head unit to support standard (wired) CarPlay. === Wireless CarPlay === Wireless CarPlay allows the iPhone to connect to a compatible vehicle or head unit without a physical cable. During the initial pairing process, the iPhone exchanges network credentials with the CarPlay receiver over Bluetooth. Once paired, CarPlay data is transmitted over a two-way Wi-Fi connection between the phone and the vehicle. Wireless CarPlay support depends on both the vehicle or head unit hardware and the iPhone model, and is generally limited to newer factory systems and select aftermarket receivers. == History == === Predecessor === In 2008, one year after the release of the iPhone, Mercedes vehicles were first to sell an audio system incorporating both the iPod and iPhone, equipped with 30-pin iOS input jacks. The new 2008 Harman Kardon NTG 2.5 featured full audio streaming, syncing, charging and control integrated into the steering wheel controls, instrument panel, and head unit. Apple was working with Mercedes to develop iOS compatible audio systems into their cars first only a year after iPhone launch. With an Apple Lightning-to-30-pin adapter, iPhones/iPods remain backwards-compatible with the Harman Kardon 2.5 and later models. This is the earliest audio system specifically engineered for iPod/iPhone integration, which predated CarPlay and every other manufacturer incorporating iOS into vehicles. The concept of CarPlay was based on the iOS 4 feature called "iPod Out" which was produced through several years of joint development by Apple and the BMW Group's Technology Office USA. iPod Out enabled vehicles with the necessary infrastructure to "host" the analog video and audio from a supporting iOS device while receiving inputs, such as button presses and knob rotations, from a car's infotainment system, to drive the "hosted" user interface in the vehicle's built-in display. It was announced at WWDC 2010 and first shipped in BMW Group vehicles in early 2011. The BMW and Mini option was called "PlugIn" and paved the way for the first cross-OEM platforms, introducing the concept of requiring a car-specific interface for apps (as opposed to MirrorLink's simple and insufficient mirroring of what was shown on the smartphone's screen). === Development === CarPlay's codename was Stark. Apple's Eddy Cue announced it as iOS in the Car at WWDC 2013. In January 2014, it was reported that Apple's hardware-oriented corporate culture had led to release delays. iOS in the Car was then rebranded and launched as CarPlay with significant design changes at the Geneva Motor Show in March 2014 with Ferrari, Kia, Mercedes-Benz, and Volvo among the first car manufacturers. At WWDC 2022, Apple announced plans to release an all-new version of CarPlay, informally dubbed CarPlay 2. The new version was said to be able to control vehicle functions, access vehicle stats, and take over multiple vehicle screens. Officials said they planned to release it in late 2024 and that manufacturers that are planning to adopt the new CarPlay include: Audi, Acura, Ford, Honda, Infiniti, Jaguar, Land Rover, Lincoln, Mercedes-Benz, Nissan, Polestar, Porsche, Renault, and Volvo. In January 2025, amidst delays, Apple removed the planned released date from its website. On May 15, 2025, Apple announced that next-generation CarPlay, now called CarPlay Ultra, would be included with all new vehicles from Aston Martin. Existing vehicles will also be receiving CarPlay Ultra through a future software update. It is only available in the US and Canada. == Timeline == June 2013: Apple introduced iOS in the Car; an early version of CarPlay that was never publicly released, at WWDC 2013. June 2013: BMW officials announced their cars would not support iOS in the Car; they later changed their minds. November 2013: Siri Eyes Free mode was offered as a dealer-installed accessory in the US to some Honda Accord and Acura RDX & ILX models. In December, Honda offered additional integration, featuring new HondaLink services, on some US and Canada models of the Civic and the Fit. March 2014: Apple introduced CarPlay, which was renamed from iOS in the Car with significant design changes, at the 2014 Geneva Motor Show with automakers Ferrari, Mercedes-Benz and Volvo. September 2014: A Ferrari FF was the first car with a full version of CarPlay. November 2014: Hyundai announced the Sonata sedan would be their first model with available CarPlay by the end of the first quarter of 2015. January 2015: Volkswagen announced CarPlay support would be coming later in 2015 and would be either standard or available on the majority of their 2016 model year lineup. May 2015: General Motors announced CarPlay would be available starting with 14 different 2016 model year Chevrolet vehicles. July 2015: Honda announced CarPlay would be available in their vehicles starting with the 2016 Honda Accord. December 2015: Volvo implemented CarPlay in the 2016 Volvo XC90 as their first vehicle with CarPlay support. December 2015: Mercedes-Benz confirmed that CarPlay would be available starting with select 2016 model year vehicles. January 2016: Apple released a list detailing the car models which support CarPlay. January 2016: Ford announced CarPlay would be available on all 2017 Ford/Lincoln model year vehicles equipped with the Sync 3 infotainment system. January 2016: FCA (now a part of Stellantis) announced CarPlay would be available on their UConnect infotainment system starting with select 2016 model year vehicles. March 2016: Subaru announced the beginning of CarPlay and Android Auto support, st
Marine Carpuat
Marine Carpuat is a computer scientist who works on machine translation and natural language processing. She is known for her research connecting cross-lingual semantics with machine translation. She has been recognized with a NSF Career Award in 2018, a Google Research award in 2016, and Amazon Faculty Awards in 2016 and 2018. == Education == Marine Carpuat obtained her MPhil and PhD from Hong Kong University of Science and Technology in 2008 under the supervision of Dekai Wu. Her PhD thesis was on the topic of machine translation, and demonstrated the first results showing that explicit modeling of lexical semantics could improve the accuracy of a machine translation system. == Career == After completing her education, Carpuat worked at the National Research Council Canada as a researcher. In 2015, she joined University of Maryland as an assistant professor in Computer Science where she is a member of the CLIP lab. Carpuat works in the area of natural language processing with a focus on machine translation and cross-lingual semantics. She has published over 100 peer-reviewed research papers. Her work is published in the proceedings of computer science conferences, including the Annual Meeting of the Association for Computational Linguistics and Empirical Methods in Natural Language Processing. == Selected honors and distinctions == 2016 Google Research Award 2016, 2018 Amazon Research Awards 2018 NSF Career Award
Best AI Paragraph Rewriters in 2026
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Pixel binning
Pixel binning, also known as binning, is a process image sensors of digital cameras use to combine adjacent pixels throughout an image, by summing or averaging their values, during or after readout. It improves low-light performance while still allowing for highly detailed photographs in good light. Charge from adjacent pixels in CCD or charge-coupled device image sensors and some other image sensors can be combined during readout, increasing the line rate or frame rate. In the context of image processing, binning is the procedure of combining clusters of adjacent pixels, throughout an image, into single pixels. For example, in 2×2 binning, an array of 4 pixels becomes a single larger pixel, reducing the number of pixels to 1/4 and halving the image resolution in each dimension. The result can be the sum, average, median, minimum, or maximum value of the cluster. Some systems use more advanced algorithms such as considering the values of nearby pixels, edge detection, self-claimed "AI", etc. to increase the perceived visual quality of the final downsized image. This aggregation, although associated with loss of information, reduces the amount of data to be processed, facilitating analysis. The binned image has lower resolution, but the relative noise level in each pixel is generally reduced. == History == Normally, an increase in megapixel count on a constant image sensor size would lead to a sacrifice of the surface size of the individual pixels, which would result in each pixel being able to catch less light in the same time, thus leading to a darker and/or noisier image in low light (given the same exposure time). In the past, camera manufacturers had to compromise between low-light performance and the amount of detail in good light, by dropping the megapixel count like HTC did in 2013 with their four-megapixel "UltraPixel" camera. However, this results in less detailed images in daylight where enough light is available. With pixel binning, the camera has "the best of both worlds", meaning both the benefit of high detail in good light and the benefit of high brightness in low light. In low light, the surfaces of four or more pixels can act as one large pixel that catches far more light. For example, some smartphones such as the Samsung Galaxy A15 are able to capture photographs with up to fifty megapixels in daylight. However, in low light, the individual pixels would be too small to capture the light needed for a bright image with the short exposure time available for handheld shooting. Therefore, with pixel binning activated, the 50-megapixel image sensor acts as a 12.5-megapixel image sensor, a quarter of its original resolution, with an accordingly larger surface area per pixel.
Jaime Carbonell
Jaime Guillermo Carbonell (July 29, 1953 – February 28, 2020) was a computer scientist who made seminal contributions to the development of natural language processing tools and technologies. His research in machine translation resulted in the development of several state-of-the-art language translation and artificial intelligence systems. He earned his B.S. degrees in Physics and in Mathematics from MIT in 1975 and did his Ph.D. under Dr. Roger Schank at Yale University in 1979. He joined Carnegie Mellon University as an assistant professor of computer science in 1979 and moved to Pittsburgh. He was affiliated with the Language Technologies Institute, Computer Science Department, Machine Learning Department, and Computational Biology Department at Carnegie Mellon. His interests spanned several areas of artificial intelligence, language technologies and machine learning. In particular, his research focused on areas such as text mining (extraction, categorization, novelty detection) and in new theoretical frameworks such as a unified utility-based theory bridging information retrieval, summarization, free-text question-answering and related tasks. He also worked on machine translation, both high-accuracy knowledge-based MT and machine learning for corpus-based MT (such as generalized example-based MT). == Career == Carbonell was the Allen Newell Professor of Computer Science and head of the Language Technologies Institute at Carnegie Mellon University. He joined Carnegie Mellon in 1979, and became a key faculty member in the artificial intelligence area. He was appointed full professor in 1987, Newell Chair in 1995, and University Professor in 2012. He completed his undergraduate studies at MIT. He received dual degrees in Mathematics and Physics. He received his Ph.D. in computer science from Yale University in 1979. At the time of his appointment, Carbonell was the youngest chaired professor in the School of Computer Science at CMU. His research spanned several areas of computer science, mostly in artificial intelligence, including: machine learning, data and text mining, natural language processing, very-large-scale knowledge bases, translingual information retrieval and automated summarization. He wrote more than 300 technical papers and gave over 500 invited or refereed-paper presentations (colloquia, seminars, panels, conferences, keynotes, etc.). He died following a long illness on February 28, 2020. Mona Talat Diab became the director of CMU's Language Technologies Institute in 2023. == Research == Carbonell created MMR (maximal marginal relevance) technology for text summarization and informational novelty detection in search engines, invention of transformational analogy, a generalized method for case-based reasoning (CBR) to re-use, modify and compose past successful plans for increasingly complex problems and knowledge-based interlingual machine translation. He was instrumental in setting up the Computational Biolinguistics Program, a joint venture between Carnegie Mellon and the University of Pittsburgh, which combines Language Technologies and Machine Learning to model and predict genomic, proteomic and glycomic 3D structures. Carbonell also did work in machine learning. He organized the first four machine learning conferences, starting with CMU in 1981. The Language Technologies Institute (LTI), founded and directed by Carbonell, achieved top honors in multiple areas. These areas include machine translation, search engines (including founding of Lycos by Michael Mauldin, one of Carbonell’s PhD students), speech synthesis, and education. LTI remains the original, largest and best-known institute for language technologies, with over $12M in annual funding and 200 researchers (faculty, staff, PhD students, MS students, visiting scholars etc.). Carbonell made major technical contributions in several fields, including (1) Creation of MMR (maximal marginal relevance) technology for text summarization and informational novelty detection in search engines,(2) Proactive machine learning for multi-source cost-sensitive active learning, (3) Linked conditional random fields for predicting tertiary and quaternary protein folds, (4) Symmetric optimal phrasal alignment method for trainable example-based and statistical machine translation, (5) Series- anomaly modeling for financial fraud detection and syndromic surveillance, (6) Knowledge-based interlingual machine translation, (7) Robust case-frame parsing, (8) Seeded version-space learning and (9) Invention of transformational and derivational analogy, generalized methods for case-based reasoning (CBR) to re-use, modify and compose past successful plans for increasingly complex problems. The teams led by Carbonell achieved top honors in many areas such as first scalable high-accuracy interlingual machine translation (1991), first speech-to-speech machine translation (1992), first large-scale spider and search engine (1994), and first trainable, large-scale protein-structure topology predictor (2005). Modern machine learning, co-founded by Carbonell, Michalski and Mitchell, is a fundamental enabling technology in search engines, data mining and social networking. Starting in 1980, he co-edited the first three books on ML, launched the ML conferences and was a co-founder and editor-in-chief of ML Journal. Carbonell’s innovations have led to several successful start-ups: Carnegie Group (AI expertsystems), Lycos (web search), Wisdom (financial optimization & ML), Carnegie Speech (spoken-language tutoring), Dynamix (data mining and pattern discovery), and Meaningful Machines (context-based machine translation). Carbonell was the founding director of The Language Technology Institute, the preeminent global institution in language studies, unparalleled in size and scope and has since been adopted/imitated in Germany (DFKI), Japan (Tokyo Univ.), and the US (Johns Hopkins). == Awards and honors == Okawa Prize, 2015 Best paper award, “Translingual Search” w/Yang, International Joint Conference on AI, 1997 Allen Newell endowed chair, Carnegie Mellon University, 1995 Elected fellow of AAAI, 1991 Computer Science teaching award, Carnegie Mellon University, 1987 Sperry Fellowship for excellence in AI research, 1986 Herbert Simon teaching award, 1986 "Recognition of Service" award from the ACM for the SIGART presidency, 1983–1985 Provided congressional testimony on machine translation, 1990 == Selected works == === Books === 1983. (with Ryszard S. Michalski & Tom M. Mitchell, Eds.) Machine learning: An artificial intelligence approach. Los Altos, CA: Morgan Kaufmann. 1986. (with Ryszard S. Michalski & Tom Mitchell, Eds.) Machine learning: An artificial intelligence approach. Vol. II. Los Altos, CA: Morgan-Kaufmann. 1986. (with Ryszard S. Michalski & Tom Mitchell, Eds.) Machine Learning: A Guide to Current Research. Kluwer Academic Publishers. == Contributions == “Protein Quaternary Fold Recognition Using Conditional Graphical Models” IJCAI 2007 (w/Liu et al.) “Context-Based Machine Translation” AMTA 2006 (w/Klein et al.) “SCRFs: A New Approach for Protein Fold Recognition,’’ Journal of Computational Biology, 13,2, 2006 (w/Liu et al) “MT for Resource-Poor Languages Using Elicitation-Based Learning” Machine Translation, 2004 ‘‘Learning Approaches for Detecting and Tracking News Events,’’ IEEE Trans I.S., 14, 4, 2000 (w/Yang)