YouNoodle, Inc. is a San Francisco-based company, with offices in Barcelona and Santiago, founded in 2010, building a platform for entrepreneurship competitions all over the world. YouNoodle matches entrepreneurs with competitions, accelerators, and startup programs, and provides a judging and voting SaaS platform to university, non-profit, government and enterprise clients organizing innovation challenges and competitions. Stanford's BASES, UC Berkeley LAUNCH, Start-Up Chile, Amazon Startup Challenge, and NASA are all running one or more competitions on YouNoodle's platform. == History and structure == YouNoodle was founded by Rebeca Hwang and Torsten Kolind in 2010. The company was spun off a project started by Bob Goodson (Quid) and Kirill Makharinsky (Enki) in 2007 with support from Peter Thiel (Founders Fund), Max Levchin (PayPal) and Charles Lho (Amicus Group), founding investor and Chairman of YouNoodle today. This project also spawned Quid (Goodson) and indirectly Ostrovok (Makharinsky). Although also named YouNoodle, this project/company was discontinued in 2010, when the three new entities started operations. The founders of the 2007-2010 entity were Goodson and Makharinsky, both former students of the University of Oxford. Goodson had studied medieval English literature before moving from Oxford to California when Levchin, the co-founder of PayPal, invited him to join a start-up there. Makharinsky's degree was in applied mathematics, and he was also encouraged to pursue opportunities in the United States by Levchin. Other significant employees included Hwang (co-founder of today's YouNoodle), a Stanford University doctoral student whose research is into social network theory. == Startup predictor == YouNoodle's now discontinued "Startup predictor", part of the 2007-2010 entity and developed by Makharinsky and Hwang, used mathematical models to predict the success of new businesses. The user fills in a questionnaire, which takes about half an hour to complete and concentrates on the business concept, finances, founders and advisers. Because the procedure was designed for new companies, questions on revenue and traffic are not included. The site then provided an estimate of what the company's value will be after three years and a score from 1 to 1000 representing its value as an investment. The service was free for the startups themselves, but YouNoodle intended to charge third parties for access to the results. The level of detail required by the questionnaire makes it difficult for people without inside knowledge of a company to provide the data for a prediction on their own. The company's founders have declined to explain the algorithm in detail, but state that it takes into account the entrepreneurs' experience, networks and mutual relations. Information provided by companies which use the site's networking features is used to improve the algorithm. As of August 2008, the algorithm was based on data from 3,000 startups. In the same month the company had four patents pending on the technology.
AI-complete
In the field of artificial intelligence (AI), tasks that are hypothesized to require artificial general intelligence to solve are informally known as AI-complete or AI-hard. Calling a problem AI-complete reflects the belief that it cannot be solved by a simple specific algorithm. Prior to 2013, problems supposed to be AI-complete included computer vision, natural language understanding, and dealing with unexpected circumstances while solving any real-world problem. AI-complete tasks were notably considered useful for distinguishing humans from automated agents, as CAPTCHAs aim to do. == History == The term was coined by Fanya Montalvo by analogy with NP-complete and NP-hard in complexity theory, which formally describes the most famous class of difficult problems. Early uses of the term are in Erik Mueller's 1987 PhD dissertation and in Eric Raymond's 1991 Jargon File. Expert systems, that were popular in the 1980s, were able to solve very simple and/or restricted versions of AI-complete problems, but never in their full generality. When AI researchers attempted to "scale up" their systems to handle more complicated, real-world situations, the programs tended to become excessively brittle without commonsense knowledge or a rudimentary understanding of the situation: they would fail as unexpected circumstances outside of its original problem context would begin to appear. When human beings are dealing with new situations in the world, they are helped by their awareness of the general context: they know what the things around them are, why they are there, what they are likely to do and so on. They can recognize unusual situations and adjust accordingly. Expert systems lacked this adaptability and were brittle when facing new situations. DeepMind published a work in May 2022 in which they trained a single model to do several things at the same time. The model, named Gato, can "play Atari, caption images, chat, stack blocks with a real robot arm and much more, deciding based on its context whether to output text, joint torques, button presses, or other tokens." Similarly, some tasks once considered to be AI-complete, like machine translation, are among the capabilities of large language models. == AI-complete problems == AI-complete problems have been hypothesized to include: AI peer review (composite natural language understanding, automated reasoning, automated theorem proving, formalized logic expert system) Bongard problems Computer vision (and subproblems such as object recognition) Natural language understanding (and subproblems such as text mining, machine translation, and word-sense disambiguation) Autonomous driving Dealing with unexpected circumstances while solving any real world problem, whether navigation, planning, or even the kind of reasoning done by expert systems. == Formalization == Computational complexity theory deals with the relative computational difficulty of computable functions. By definition, it does not cover problems whose solution is unknown or has not been characterized formally. Since many AI problems have no formalization yet, conventional complexity theory does not enable a formal definition of AI-completeness. == Research == Roman Yampolskiy suggests that a problem C {\displaystyle C} is AI-Complete if it has two properties: It is in the set of AI problems (Human Oracle-solvable). Any AI problem can be converted into C {\displaystyle C} by some polynomial time algorithm. On the other hand, a problem H {\displaystyle H} is AI-Hard if and only if there is an AI-Complete problem C {\displaystyle C} that is polynomial time Turing-reducible to H {\displaystyle H} . This also gives as a consequence the existence of AI-Easy problems, that are solvable in polynomial time by a deterministic Turing machine with an oracle for some problem. Yampolskiy has also hypothesized that the Turing Test is a defining feature of AI-completeness. Groppe and Jain classify problems which require artificial general intelligence to reach human-level machine performance as AI-complete, while only restricted versions of AI-complete problems can be solved by the current AI systems. For Šekrst, getting a polynomial solution to AI-complete problems would not necessarily be equal to solving the issue of artificial general intelligence, while emphasizing the lack of computational complexity research being the limiting factor towards achieving artificial general intelligence. For Kwee-Bintoro and Velez, solving AI-complete problems would have strong repercussions on society.
Record sealing
Record sealing is the process of making public records inaccessible to the public. In many cases, a person with a sealed record gains the legal right to deny or not acknowledge anything to do with the arrest and the legal proceedings from the case itself. Records are commonly sealed in a number of situations: Sealed birth records (typically after adoption or determination of paternity) Juvenile criminal records may be sealed Other types of cases involving juveniles may be sealed, anonymized, or pseudonymized ("impounded"); e.g., child sex offense or custody cases Cases using witness protection information may be partly sealed Cases involving trade secrets Cases involving state secrets == Filing under seal in US court == Normally, records should not be filed under seal without a court permission. However, FRCP 5.2 requires that sensitive text – like Social Security number, Taxpayer Identification Number, birthday, bank accounts, and children’s names – should be redacted off the filings made with the court and accompanying exhibits. A person making a redacted filing can file an unredacted copy under seal, or the Court can choose to order later that an additional filing be made under seal without redaction. Alternately, the filing party may ask the court’s permission to file some exhibits completely under seal. When the document is filed "under seal", it should have a clear indication for the court clerk to file it separately – most often by stamping words "Filed Under Seal" on the bottom of each page. Person making filing should also provide instructions to the court clerk that the document needs to be filed "under seal". Courts often have specific requirements to these filings in their Local Rules. == Difference from expungement == Expungement, which is a physical destruction, namely a complete erasure of one's criminal records, and therefore usually carries a higher standard, differs from record sealing, which is only to restrict the public's access to records, so that only certain law enforcement agencies or courts, under special circumstances, will have access to them. A record seal will greatly improve the chance of employment, as employers will not have access to damning records. There are occasions, like expungement, where one can truthfully state under oath that they have never been convicted before. Most of the time, a record seal has more relaxed requirements than an expungement. If an expungement is not allowed with a case, then sealing a record may be the best bet. Different states have different terms for what constitutes sealing of a record. == Cybersecurity incidents involving sealed records == Several cybersecurity incidents have demonstrated that sealed court documents are not always secure in practice, with vulnerabilities and data breaches exposing sensitive information. In January 2021, following the SolarWinds cyber attack, the U.S. Bankruptcy Court United States District Court for the District of Nevada announced that its Case Management/Electronic Case Files CM/ECF system had been potentially compromised. The judiciary stated that additional safeguards were being implemented to protect filings, and that the review of the incident and its impact was ongoing. Reports noted that the breach raised concerns about exposure of highly sensitive and sealed documents submitted through the CM/ECF system. In 2023, security researcher Jason Parker, following a tip from an activist, identified flaws in online court systems that exposed sealed records including confidential testimony and medical records through publicly accessible portals. In 2024, a cyber intrusion targeting attorneys in a civil case involving Representative Matt Gaetz led to the unauthorized access and leak of sealed depositions and related records. The breach exposed confidential testimony and financial records, some of which were later reported by news outlets, raising concerns about the security of electronically stored legal materials and the handling of sealed filings. In 2025, multiple reports confirmed that the federal judiciary's CM/ECF and PACER (law) filing system was compromised, exposing sealed indictments, confidential informant information, and other sensitive filings. Some courts temporarily reverted to paper-based filing to mitigate the risks of further disclosure. The FBI later confirmed that the breach had exposed sealed records, and investigators suspected foreign state actors were involved. == GAO publications referencing sealed records == Closed Criminal Plea and Sentencing Proceedings (1983) – Reviewed Department of Justice policies on closing plea and sentencing hearings. GAO noted that sealed transcripts should be unsealed once the reasons for closure no longer applied. Information on Plea Agreements and Settlements in Defense Procurement Fraud Cases (1992) – Examined outcomes of procurement fraud prosecutions. GAO observed that in some instances the results were sealed from public access. Military Recruiting: More Needs to Be Done to Better Screen Applicants and Detect Fraud (1999) – Investigated fraudulent enlistments in the armed forces. The report highlighted that sealed juvenile records often prevented recruiters from discovering prior offenses. Social Security Numbers: Governments Could Do More to Reduce Display in Public Records (2004) – Analyzed risks associated with SSN availability in state and local records. GAO pointed out that some categories of records, such as adoption proceedings, were sealed and less likely to expose identifiers. Social Security Numbers: Stronger Safeguards Needed to Protect Privacy (2005 testimony) – Testimony before Congress reiterating concerns over SSN exposure in public records, while noting that sealed categories (e.g., adoption) were exceptions. U.S. Supreme Court: Policies and Perspectives on Video and Audio Coverage of Appellate Court Proceedings (2016) – Surveyed appellate court policies on courtroom media coverage. The report acknowledged distinctions between public filings, confidential submissions, and sealed materials. Evictions: National Data Are Limited and Challenging to Collect (2024) – Examined nationwide eviction data. GAO reported that in some states eviction records may be sealed or expunged, limiting researchers' ability to compile datasets. DOD Fraud Risk Management: Enhanced Data and Collaboration Could Improve Efforts (2024) – Reviewed Department of Defense fraud-risk management. GAO noted that some adjudicative records in its dataset were sealed, restricting completeness of oversight data.
ParkMobile
ParkMobile is a mobile and web app providing parking payments in North America. Headquartered in Atlanta, Georgia, users can pay for on-street and off-street parking via app on their smartphone, web browser, or through calling a phone number. ParkMobile also offers parking reservations at stadiums or venues for concerts and sporting events, and in metro area garages. == History == ParkMobile was founded in the United States in 2008 by Albert Bogaard after originally starting in the Netherlands. The initial product served only zone (on-demand) parkers and payment for the parking spot was made via a phone call through an IVR system. In 2009, the ParkMobile app was released and the product launched in its first city, Grand Rapids, Michigan. Parking payments have since been accepted through a user's account by connecting a credit card. ParkMobile deployed in Washington, D.C., in 2011. As of 2023, ParkMobile now has over 50 million users. Parking reservations were introduced in 2017, allowing users to reserve parking in advance. In 2018, the company recapitalized with BMW as the shareholder. ParkMobile was then acquired by a joint venture with BMW and Daimler. Under this joint venture, ParkMobile parking payment functionality was available and integrated with BMW's navigation system in many of its 2018 models. EasyPark Group, the Swedish-based parking solutions company, acquired ParkMobile in 2021 and is the current owner rebranded as Arrive. In 2022, ParkMobile launched in the City of Boston with a city-wide parking app, ParkBoston, powered by ParkMobile. == Operations == === Products === ParkMobile's product offerings include zone (on-demand) parking payments, parking reservations, and a self-service reporting engine. Zone parking is the company's most widely used service. Users can use the app on their smartphone to pay parking fees. In 2017, ParkMobile began offering parking reservations. The service is provided in addition to on-demand parking options at stadiums and venues, as well as metro area parking garages. After launching the reservations feature, ParkMobile became the first mobile parking app provider in North America to have a consolidated app with both on-demand and reservations parking in one. ParkMobile 360, the company's self-service management and reporting platform for operators, launched in 2018. It is a web-based application for parking operators to manage parking inventory, adjust rates, create special parking events, and track analytics. In 2020, ParkMobile began offering an option to pay for parking with Google through integrating the ParkMobile experience with Google Maps In 2021, ParkMobile launched its web application, allowing users to complete their parking transactions directly from the mobile website without having to download the app or have an account. ParkMobile integrates with parking gate equipment so customers can use their app to pay for parking and scan to enter and exit the garage. === Locations === ParkMobile has over 50 million users across the United States, Canada, and Puerto Rico. The app is available in over 550 cities in the U.S. and over 150 colleges and universities. == Controversies == === Predatory towing and excessive ticketing === Since all paid parking sessions from a single supplier are able to be viewed together, the ease of viewing and enforcing parking violations has caused controversy. Parking Enforcement Services in Birmingham, Alabama, has been the subject complaints by users of the ParkMobile app who had paid for a parking session and still had their vehicle towed. Customers often use old or expired license plates and forget to update to the correct number, or mistype when entering their information into the ParkMobile app. The complaints are that the towing companies offer no lenience for these mistakes. They return to their car as the session expires, and find their car has been towed. Additionally, other municipality across the country have received complaints about excessive parking ticket issuing when inputting their information incorrectly in the ParkMobile app. In Stone Harbor, New Jersey, parking ticket violations increased by over 1,600% from the previous year since launching with the ParkMobile app. Police officers refute complaints of being "too strict" on writing tickets by admitting the ParkMobile system allows officers to "more seamlessly enforce" the city's parking laws. === Data security breach === In March 2021, ParkMobile suffered a cybersecurity incident "linked to a vulnerability in a third-party software," potentially exposing users' email addresses, phone numbers, and license plate numbers. ParkMobile responded by launching an investigation and notifying law enforcement authorities and affected municipalities. The investigation concluded "no sensitive data or Payment Card Information was affected" but ParkMobile confirmed that basic account information, such as license plate numbers and possibly email addresses or phone numbers, was accessed.
International Medical Education Directory
The International Medical Education Directory (IMED) was a public database of worldwide medical schools. The IMED was published as a joint collaboration of the Educational Commission for Foreign Medical Graduates (ECFMG) and the Foundation for Advancement of International Medical Education and Research (FAIMER). The information available in IMED was derived from data collected by the Educational Commission for Foreign Medical Graduates (ECFMG) throughout its history of evaluating the medical education credentials of international medical graduates. Using these data as a starting point, Foundation for Advancement of International Medical Education and Research (FAIMER) began developing IMED in 2001 and made it publicly available in April 2002. In April 2014, IMED was merged with the Avicenna Directory to create the World Directory of Medical Schools. The World Directory is now the definitive list of medical schools in the world, as IMED and Avicenna were discontinued in 2015.
Application enablement
Application enablement is an approach which brings telecommunications network providers and developers together to combine their network and web abilities in creating and delivering high demand advanced services and new intelligent applications. Network providers, in addition to bandwidth, provide abilities such as billing, location, presence, and security, which have allowed them to establish long-term relationships with end-users. By offering these select abilities as application programming interfaces (APIs), providers give developers access to a set of tools to create (mashup) new applications and services to run on provider networks. Unifying the strengths of providers and developers facilitates the creation of mash-up applications, and in turn, a better end user quality of experience (QoE) for improved profit margins. Apple's iOS with App Store, and Google's Android with Android Market exemplify this approach. Both have introduced mobile platforms that are supported by a comprehensive ecosystem in order to perpetuate innovation in product design, content and service offerings, and overall consumer behavior. By the end of April 2010, downloadable applications numbered over 200,000 for iPhone and over 50,000 for Android. == Background == Historically, telecommunication providers primarily based their business models on network performance, emphasizing connectivity, availability, and quality of service (QoS) as key sources of revenue and customer value. With the increasing demand for bandwidth-intensive data and video applications, maintaining service continuity has required substantial infrastructure investments. To address rising operational costs and declining average revenue per user (ARPU), providers have increasingly adopted customer-oriented strategies and diversified business models to expand their roles within the telecommunications value chain. Application enablement supports providers in making this transition by providing an environment, or ecosystem, where providers and developers can collaborate to build, test, manage, and distribute applications across networks including television, broadband, Internet, and mobile. This cooperative effort produces mutually beneficial results for all parties, opening up new revenue streams while enhancing value and rate of return (ROI). The following are some examples of key network abilities which function as application enablers in the telecommunications market: Billing systems Security for private transactions Network-based storage of digital content End-to-end bandwidth for high-quality transmissions Scoring abilities to identify end-user preferences and behaviors Subscriber data to customize the end-user experience Context information, such as location and presence, to localize services. == New business models == As network providers work toward effective collaboration with application and content developers, several new business models are emerging to help facilitate the business relationships: === Vendor-led === A type of business model driven by telecommunications vendors, who assist network providers in building relationships with application and content developers to lower the cost and complexity of managing third parties. Examples of this model include: Forum Nokia IBM Technology Partner Ecosystem Ng Connect Huawei Intouch program === Operator-led === Characterized by network providers who want to maintain a high degree of flexibility and control over applications created for their end-consumers, this model lets them create and manage their own developer program, development platform, and application store. Under this arrangement, independent developers provide their own branding, marketing communications, pricing and customer care. Network providers pursuing this model will often seek to partner with a large number of third parties using standardized on-boarding processes. Examples of this model include: o2 Litmus Orange Partner Joint Innovation Lab === Aggregator === Network providers who choose not to create/manage their own developer relationships will partner with one or multiple aggregators, to administer a portion of or their entire application strategy. Examples of this model include: Ovi Operator Partnership Blackberry Operator Partnership Cellmania Buongiorno === Mass wholesale === Select network providers also participate in wholesale models that exist primarily for applications (BT's Ribbit- an Internet Protocol (IP) based calling and messaging platform) and devices (Verizon's Open Device initiative). This business-to-business approach reduces a large portion of the potential costs of third party application enablement (marketing, acquisition and support). Examples of this model include: BT's Ribbit Verizon Wireless ODI AT&T Synaptic Hosting === The enterprise customer === Some network providers are focusing on enabling applications in the enterprise space. In this model, the network provider establishes a platform for their large enterprise customers who want to blend custom software with enhanced abilities, and will provide standardized processes around mobilizing enterprise applications, and exposing core back-office abilities to allow for dynamic customer interaction. Examples of this model include: Vodafone Applications Service Verizon Private Network Sprint Solution Launchpad === Trusted partner === In this model, the network provider builds one-on-one relationships with trusted third-party developers by exposing customized network abilities, bringing a greater variety of brands to the network provider's portfolio. Network providers using this model tend to only have a few partners (in contrast to the operator led model). Under this scenario, network providers benefit from a pre-established customer base and the developer's marketing resources. Examples of this model include: 3/Skype Partnership (UK) Virgin Media and BBC iPlayer == Network operator developer resources == Operator led model o2 Litmus Orange Partner Joint Innovations Lab Aggregator model Ovi Operator Partnership Cellmania Buongiorno Mass wholesale model BT Ribbit Verizon Wireless ODI AT&T Synaptic Hosting Enterprise customer model Vodafone Applications Service Verizon Private Network Sprint Solution Launchpad == Rerencesfe ==
Key–value database
A key-value database, or key-value store, is a data storage paradigm designed for storing, retrieving, and managing associative arrays, a data structure more commonly known today as a dictionary. Dictionaries contain a collection of objects, or records, which in turn have many different fields within them. These records are stored and retrieved using a key that uniquely identifies the record, and is used to find the data within the database. Key-value databases differ from the better known relational databases (RDB). RDBs pre-define the data structure in the database as a series of tables containing fields with well-defined data types. Exposing the data types to the database program allows it to apply various optimizations. In contrast, key-value systems treat the value as opaque to the database itself, and typically support only simple operations such as storing, retrieving, updating, and deleting a value by its key. This offers considerable flexibility and makes such systems well suited to low-latency, high-throughput workloads dominated by direct key lookups, but less suitable for applications that require complex queries or explicit relationships among records. A lack of standardization, limited transaction support, and relatively simple query interfaces long restricted many key-value systems to specialized uses, but the rapid move to cloud computing after 2010 helped drive renewed interest in them as part of the broader NoSQL movement. Some graph databases, such as ArangoDB, are also key–value databases internally, adding the concept of relationships (pointers) between records as a first-class data type. == Types and examples == Key–value systems span a wide consistency spectrum, from eventually consistent designs to strongly consistent or serializable ones, and some allow the consistency level to be configured as part of the trade-off against latency and availability. Renewed interest in key–value and other NoSQL systems was driven in part by the demands of big data, distributed, and cloud applications. Their scalability and availability made them attractive for cloud data management, although limited transaction support, low-level query interfaces, and the lack of standardization remained obstacles to wider adoption. Some maintain data in memory (RAM), while others employ solid-state drives or rotating disks. Some key–value systems add additional structure to their keys. For example, Oracle NoSQL Database organizes records using composite keys with "major" and "minor" components, an arrangement that Oracle compares to a directory-path structure in a file system. More generally, however, key–value stores are defined by their use of unique keys associated with opaque values and by their emphasis on simple key-based operations. Unix included dbm (database manager), a minimal database library written by Ken Thompson for managing associative arrays with a single key and hash-based access. Later implementations and related libraries included sdbm, GNU dbm (gdbm), and Berkeley DB. A more recent example is RocksDB, a persistent key–value storage engine developed at Facebook and designed for large-scale applications. Other examples include in-memory systems such as Memcached and Redis, and persistent systems such as Berkeley DB, Riak, and Voldemort.