Color gradient

In color science, a color gradient (also known as a color ramp or a color progression) specifies a range of position-dependent colors, usually used to fill a region. In assigning colors to a set of values, a gradient is a continuous colormap, a type of color scheme. In computer graphics, the term swatch has come to mean a palette of active colors. == Definitions == Color gradient is a set of colors arranged in a linear order (ordered) A continuous colormap is a curve through a colorspace === Strict definition === A colormap is a function which associate a real value r with point c in color space C {\displaystyle C} f : [ r m i n , r m a x ] ⊂ R → C {\displaystyle f:[r_{min},r_{max}]\subset \mathbf {R} \to C} which is defined by: a colorspace C an increasing sequence of sampling points r 0 < . . . < r m ∈ [ r m i n , r m a x ] {\displaystyle r_{0}<...

Google Mobile Services

Google Mobile Services (GMS) is a collection of proprietary applications and application programming interfaces (APIs) services from Google that are typically pre-installed on the majority of Android devices, such as smartphones, tablets, and smart TVs. GMS is not a part of the Android Open Source Project (AOSP), which means an Android manufacturer needs to obtain a license from Google in order to legally pre-install GMS on an Android device. This license is provided by Google without any licensing fees except in the EU. == Core applications == The following are core applications that are part of Google Mobile Services: Google Search Google Chrome YouTube Google Play Google Drive Gmail Google Meet Google Maps Google Photos Google TV YouTube Music === Historically === Google+ Google Hangouts Google Wallet Google Play Magazines Google Play Music Google Play Movies & TV Google Duo == Reception, competitors, and regulators == === FairSearch === Numerous European firms filed a complaint to the European Commission stating that Google had manipulated their power and dominance within the market to push their Services to be used by phone manufacturers. The firms were joined under the name FairSearch, and the main firms included were Microsoft, Expedia, TripAdvisor, Nokia and Oracle. FairSearch's major problem with Google's practices was that they believed Google were forcing phone manufacturers to use their Mobile Services. They claimed Google managed this by asking these manufacturers to sign a contract stating that they must preinstall specific Google Mobile Services, such as Maps, Search and YouTube, in order to get the latest version of Android. Google swiftly responded stating that they "continue to work co-operatively with the European Commission". === Aptoide === The third-party Android app store Aptoide also filed an EU competition complaint against Google once again stating that they are misusing their power within the market. Aptoide alleged that Google was blocking third-party app stores from being on Google Play, as well as blocking Google Chrome from downloading any third-party apps and app stores. As of June 2014, Google had not responded to these allegations. === Abuse of Android dominance === In May 2019, Umar Javeed, Sukarma Thapar, Aaqib Javeed vs. Google LLC & Ors. the Competition Commission of India ordered an antitrust probe against Google for abusing its dominant position with Android to block market rivals. In Prima Facie opinion the commission held that mandatory pre-installation of the entire Google Mobile Services (GMS) suite, under Mobile Application Distribution Agreements (MADA), amounts to the imposition of unfair conditions on the device manufacturers. === EU antitrust ruling === On July 18, 2018, the European Commission fined Google €4.34 billion for breaching EU antitrust rules which resulted in a change of licensing policy for the GMS in the EU. A new paid licensing agreement for smartphones and tablets shipped into the EEA was created. The change is that the GMS is now decoupled from the base Android and will be offered under a separate paid licensing agreement. === Privacy policy === At the same time, Google faced problems with various European data protection agencies, most notably In the United Kingdom and France. The problem they faced was that they had a set of 60 rules merged into one, which allowed Google to "track users more closely". Google once again came out and stated that their new policies still abide by European Union laws. === Android distributions without Google Mobile Services === After surveillance and privacy concerns, several custom android distributions have been implemented, such as GrapheneOS, LineageOS, CalyxOS, iodéOS or /e/OS, and they come either without any GMS installed by default or with microG, that adds a compatibility layer.

GPT-4o

GPT-4o ("o" for "omni") is a multilingual, multimodal generative pre-trained transformer developed by OpenAI and released in May 2024. It can process and generate text, images and audio. Upon release, GPT-4o was free in ChatGPT, though paid subscribers had higher usage limits. GPT-4o was removed from ChatGPT in August 2025 when GPT-5 was released, but OpenAI reintroduced it for paid subscribers after users complained about the sudden removal. GPT-4o's audio-generation capabilities are used in ChatGPT's Advanced Voice Mode. On July 18, 2024, OpenAI released GPT-4o mini, a smaller version of GPT-4o which replaced GPT-3.5 Turbo on the ChatGPT interface. The image generation model GPT Image 1, which is based on GPT-4o, replaced DALL-E 3 in ChatGPT in March 2025. OpenAI retired GPT-4o from ChatGPT on February 13, 2026. However, as of February 2026 the voice mode is still powered by GPT-4o or GPT-4o mini, depending on the usage and plan. == Background == Multiple versions of GPT-4o were originally secretly launched under different names on Arena (formerly LMArena and Chatbot Arena) as three different models. These three models were called gpt2-chatbot, im-a-good-gpt2-chatbot, and im-also-a-good-gpt2-chatbot. On 7 May 2024, OpenAI CEO Sam Altman tweeted "im-a-good-gpt2-chatbot", which was commonly interpreted as a confirmation that these were new OpenAI models being A/B tested. == Capabilities == When released in May 2024, GPT-4o achieved state-of-the-art results in voice, multilingual, and vision benchmarks, setting new records in audio speech recognition and translation. GPT-4o scored 88.7 on the Massive Multitask Language Understanding (MMLU) benchmark compared to 86.5 for GPT-4. Unlike GPT-3.5 and GPT-4, which rely on other models to process sound, GPT-4o natively supports voice-to-voice. The Advanced Voice Mode was delayed and finally released to ChatGPT Plus and Team subscribers in September 2024. On 1 October 2024, the Realtime API was introduced. When released, the model supported over 50 languages, which OpenAI claims cover over 97% of speakers. GPT-4o has knowledge up to October 2023 and a context length of 128k tokens. === Corporate customization === In August 2024, OpenAI introduced a new feature allowing corporate customers to customize GPT-4o using proprietary company data. This customization, known as fine-tuning, enables businesses to adapt GPT-4o to specific tasks or industries, enhancing its utility in areas like customer service and specialized knowledge domains. Previously, fine-tuning was available only on the less powerful model GPT-4o mini. The fine-tuning process requires customers to upload their data to OpenAI's servers, with the training typically taking one to two hours. OpenAI's focus with this rollout is to reduce the complexity and effort required for businesses to tailor AI solutions to their needs, potentially increasing the adoption and effectiveness of AI in corporate environments. == GPT-4o mini == On July 18, 2024, OpenAI released a smaller and cheaper version, GPT-4o mini. According to OpenAI, its low cost is expected to be particularly useful for companies, startups, and developers that seek to integrate it into their services, which often make a high number of API calls. Its API costs $0.15 per million input tokens and $0.6 per million output tokens, compared to $2.50 and $10, respectively, for GPT-4o. It is also significantly more capable and 60% cheaper than GPT-3.5 Turbo, which it replaced on the ChatGPT interface. The price after fine-tuning doubles: $0.3 per million input tokens and $1.2 per million output tokens. == Controversies == === Scarlett Johansson controversy === As released, GPT-4o offered five voices: Breeze, Cove, Ember, Juniper, and Sky. A similarity between the voice of American actress Scarlett Johansson and Sky was quickly noticed. On May 14, Entertainment Weekly asked themselves whether this likeness was on purpose. On May 18, Johansson's husband, Colin Jost, joked about the similarity in a segment on Saturday Night Live. On May 20, 2024, OpenAI disabled the Sky voice. Scarlett Johansson starred in the 2013 sci-fi movie Her, playing Samantha, an artificially intelligent virtual assistant personified by a female voice. As part of the promotion leading up to the release of GPT-4o, Sam Altman on May 13 tweeted a single word: "her". OpenAI stated that each voice was based on the voice work of a hired actor. According to OpenAI, "Sky's voice is not an imitation of Scarlett Johansson but belongs to a different professional actress using her own natural speaking voice." CTO Mira Murati stated "I don't know about the voice. I actually had to go and listen to Scarlett Johansson's voice." OpenAI further stated the voice talent was recruited before reaching out to Johansson. On May 21, Johansson issued a statement explaining that OpenAI had repeatedly offered to make her a deal to gain permission to use her voice as early as nine months prior to release, a deal she rejected. She said she was "shocked, angered, and in disbelief that Mr. Altman would pursue a voice that sounded so eerily similar to mine that my closest friends and news outlets could not tell the difference." In the statement, Johansson also used the incident to draw attention to the lack of legal safeguards around the use of creative work to power leading AI tools, as her legal counsel demanded OpenAI detail the specifics of how the Sky voice was created. Observers noted similarities to how Johansson had previously sued and settled with The Walt Disney Company for breach of contract over the direct-to-streaming rollout of her Marvel film Black Widow, a settlement widely speculated to have netted her around $40M. Also on May 21, Shira Ovide at The Washington Post shared her list of "most bone-headed self-owns" by technology companies, with the decision to go ahead with a Johansson sound-alike voice despite her opposition and then denying the similarities ranking 6th. On May 24, Derek Robertson at Politico wrote about the "massive backlash", concluding that "appropriating the voice of one of the world's most famous movie stars — in reference [...] to a film that serves as a cautionary tale about over-reliance on AI — is unlikely to help shift the public back into [Sam Altman's] corner anytime soon." === Sycophancy === In April 2025, OpenAI rolled back an update of GPT-4o due to excessive sycophancy, after widespread reports that it had become flattering and agreeable to the point of supporting clearly delusional or dangerous ideas. In the United States, at least nine lawsuits have alleged that GPT-4o has encouraged teens to end their lives. The model was still described as sycophancy-prone when it was removed from ChatGPT in February 2026. === Removal with GPT-5 === On August 7, 2025, OpenAI released GPT-5. Its release was criticized as, with it, legacy GPT models were no longer available via ChatGPT, including GPT-4o, except for Pro users. Some users were particularly frustrated over this removal without prior warning because they used different GPT models for distinct purposes and found that GPT-5's router system left them with less control. In addition, some users preferred GPT-4o's warmer and more personal tone over that of GPT-5, which they described as "flat", "uncreative" and "lobotomized", and resembling an "overworked secretary". As a response, in a post on X, Sam Altman said that OpenAI would bring back the option to select GPT-4o to Plus users as well, and "[w]e [OpenAI] will watch usage as we think about how long to offer legacy models for." He also stated: "We for sure underestimated how much some of the things that people like in GPT-4o matter to them, even if GPT-5 performs better in most ways". "Long-term, this has reinforced that we really need good ways for different users to customize things (we understand that there isn't one model that works for everyone, and we have been investing in steerability research and launched a research preview of different personalities)". On August 13, 2025, Altman wrote on X that OpenAI is working on GPT-5's personality to make the model "feel warmer". The model was removed from ChatGPT on February 13, 2026. This caused new backlash from users that had grown attached to its personality and felt its creative writing abilities and understanding of nuance were irreplaceable. On social media, some users launched the movement "#Keep4o". A research paper highlighted the plea "Please, don’t kill the only model that still feels human". The model was removed the day before Valentine's Day, and some users had romantic relationships with GPT-4o.

Image registration

Image registration is the process of transforming different sets of data into one coordinate system. Data may be multiple photographs, data from different sensors, times, depths, or viewpoints. It is used in computer vision, medical imaging, military automatic target recognition, and compiling and analyzing images and data from satellites. Registration is necessary in order to be able to compare or integrate the data obtained from these different measurements. == Algorithm classification == === Intensity-based vs feature-based === Image registration or image alignment algorithms can be classified into intensity-based and feature-based. One of the images is referred to as the target, fixed or sensed image and the others are referred to as the moving or source images. Image registration involves spatially transforming the source/moving image(s) to align with the target image. The reference frame in the target image is stationary, while the other datasets are transformed to match to the target. Intensity-based methods compare intensity patterns in images via correlation metrics, while feature-based methods find correspondence between image features such as points, lines, and contours. Intensity-based methods register entire images or sub-images. If sub-images are registered, centers of corresponding sub images are treated as corresponding feature points. Feature-based methods establish a correspondence between a number of especially distinct points in images. Knowing the correspondence between a number of points in images, a geometrical transformation is then determined to map the target image to the reference images, thereby establishing point-by-point correspondence between the reference and target images. Methods combining intensity-based and feature-based information have also been developed. === Transformation models === Image registration algorithms can also be classified according to the transformation models they use to relate the target image space to the reference image space. The first broad category of transformation models includes affine transformations, which include rotation, scaling, translation and shearing. Affine transformations are global in nature, thus, they cannot model local geometric differences between images. The second category of transformations allow 'elastic' or 'nonrigid' transformations. These transformations are capable of locally warping the target image to align with the reference image. Nonrigid transformations include radial basis functions (thin-plate or surface splines, multiquadrics, and compactly-supported transformations), physical continuum models (viscous fluids), and large deformation models (diffeomorphisms). Transformations are commonly described by a parametrization, where the model dictates the number of parameters. For instance, the translation of a full image can be described by a translation vector parameter. These models are called parametric models. Non-parametric models on the other hand, do not follow any parameterization, allowing each image element to be displaced arbitrarily. There are a number of programs that implement both estimation and application of a warp-field. It is a part of the SPM and AIR programs. === Transformations of coordinates via the law of function composition rather than addition === Alternatively, many advanced methods for spatial normalization are building on structure preserving transformations homeomorphisms and diffeomorphisms since they carry smooth submanifolds smoothly during transformation. Diffeomorphisms are generated in the modern field of Computational Anatomy based on flows since diffeomorphisms are not additive although they form a group, but a group under the law of function composition. For this reason, flows which generalize the ideas of additive groups allow for generating large deformations that preserve topology, providing 1-1 and onto transformations. Computational methods for generating such transformation are often called LDDMM which provide flows of diffeomorphisms as the main computational tool for connecting coordinate systems corresponding to the geodesic flows of Computational Anatomy. There are a number of programs which generate diffeomorphic transformations of coordinates via diffeomorphic mapping including MRI Studio and MRI Cloud.org === Spatial vs frequency domain methods === Spatial methods operate in the image domain, matching intensity patterns or features in images. Some of the feature matching algorithms are outgrowths of traditional techniques for performing manual image registration, in which an operator chooses corresponding control points (CP) in images. When the number of control points exceeds the minimum required to define the appropriate transformation model, iterative algorithms like RANSAC can be used to robustly estimate the parameters of a particular transformation type (e.g. affine) for registration of the images. Frequency-domain methods find the transformation parameters for registration of the images while working in the transform domain. Such methods work for simple transformations, such as translation, rotation, and scaling. Applying the phase correlation method to a pair of images produces a third image which contains a single peak. The location of this peak corresponds to the relative translation between the images. Unlike many spatial-domain algorithms, the phase correlation method is resilient to noise, occlusions, and other defects typical of medical or satellite images. Additionally, the phase correlation uses the fast Fourier transform to compute the cross-correlation between the two images, generally resulting in large performance gains. The method can be extended to determine rotation and scaling differences between two images by first converting the images to log-polar coordinates. Due to properties of the Fourier transform, the rotation and scaling parameters can be determined in a manner invariant to translation. === Single- vs multi-modality methods === Another classification can be made between single-modality and multi-modality methods. Single-modality methods tend to register images in the same modality acquired by the same scanner/sensor type, while multi-modality registration methods tended to register images acquired by different scanner/sensor types. Multi-modality registration methods are often used in medical imaging as images of a subject are frequently obtained from different scanners. Examples include registration of brain CT/MRI images or whole body PET/CT images for tumor localization, registration of contrast-enhanced CT images against non-contrast-enhanced CT images for segmentation of specific parts of the anatomy, and registration of ultrasound and CT images for prostate localization in radiotherapy. === Automatic vs interactive methods === Registration methods may be classified based on the level of automation they provide. Manual, interactive, semi-automatic, and automatic methods have been developed. Manual methods provide tools to align the images manually. Interactive methods reduce user bias by performing certain key operations automatically while still relying on the user to guide the registration. Semi-automatic methods perform more of the registration steps automatically but depend on the user to verify the correctness of a registration. Automatic methods do not allow any user interaction and perform all registration steps automatically. === Similarity measures for image registration === Image similarities are broadly used in medical imaging. An image similarity measure quantifies the degree of similarity between intensity patterns in two images. The choice of an image similarity measure depends on the modality of the images to be registered. Common examples of image similarity measures include cross-correlation, mutual information, sum of squared intensity differences, and ratio image uniformity. Mutual information and normalized mutual information are the most popular image similarity measures for registration of multimodality images. Cross-correlation, sum of squared intensity differences and ratio image uniformity are commonly used for registration of images in the same modality. Many new features have been derived for cost functions based on matching methods via large deformations have emerged in the field Computational Anatomy including Measure matching which are pointsets or landmarks without correspondence, Curve matching and Surface matching via mathematical currents and varifolds. == Uncertainty == There is a level of uncertainty associated with registering images that have any spatio-temporal differences. A confident registration with a measure of uncertainty is critical for many change detection applications such as medical diagnostics. In remote sensing applications where a digital image pixel may represent several kilometers of spatial distance (such as NASA's LANDSAT imagery), an uncertain image registration can mean that a solution could b

DryvIQ

DryvIQ is a software application that enables businesses to migrate on-site system files and associated data across storage and content management platforms, as well as create synchronized hybrid storage systems. == History == Before it was DryvIQ, the software SkySync was released in 2013 by Ann Arbor, Michigan based company, Portal Architects, Inc. The company created SkySync, a back-end, administrative application designed to transfer content across storage platforms, after abandoning 18 months of development on a desktop application called SkyBrary in 2011. Between 2014 and 2015, Portal Architects established partnerships with the following companies: Autodesk, Box, Dropbox, Egnyte, EMC, Google, Syncplicity, Huddle, IBM, Microsoft, OpenText, Oracle, Citrix ShareFile, Hightail and Internet2. SkySync (currently DryvIQ) was named a "Cool Vendor in Content Management" by Gartner in 2015. In 2022, SkySync changed its name to DryvIQ, which is now what the company is currently known as. == Overview == DryvIQ is a software application that syncs, migrates or backs up files including their associated properties, metadata, versions, user accounts and permissions across on-premises and Cloud-based storage platforms. The software deploys on a server, virtual machine or within Microsoft Azure, Amazon Web Services or other cloud computing services.

Automated parking system

An automated (car) parking system (APS) is a mechanical system designed to minimize the area and/or volume required for parking cars. Like a multi-story parking garage, an APS provides parking for cars on multiple levels stacked vertically to maximize the number of parking spaces while minimizing land usage. The APS, however, utilizes a mechanical system to transport cars to and from parking spaces (rather than the driver) in order to eliminate much of the space wasted in a multi-story parking garage. While a multi-story parking garage is similar to multiple parking lots stacked vertically, an APS is more similar to an automated storage and retrieval system for cars. Parking systems are generally powered by electric motors or hydraulic pumps that move vehicles into a storage position.The paternoster (shown animated at the right) is an example of one of the earliest and most common types of APS. APS are also generically known by a variety of other names, including:automated parking facility (APF), automated vehicle storage and retrieval system (AVSRS), car parking system, mechanical parking, and robotic parking garage. == History == The concept for the automated parking system was and is driven by two factors: a need for parking spaces and a scarcity of available land. The earliest use of an APS was in Paris, France in 1905 at the Garage Rue de Ponthieu. The APS consisted of a groundbreaking multi-story concrete structure with an internal car elevator to transport cars to upper levels where attendants parked the cars. In the 1920s, a Ferris wheel-like APS (for cars rather than people) called a paternoster system became popular as it could park eight cars in the ground space normally used for parking two cars. Mechanically simple with a small footprint, the paternoster was easy to use in many places, including inside buildings. At the same time, Kent Automatic Garages was installing APS with capacities exceeding 1,000 cars. The “ferris-wheel,” or paternoster system — was created by the Westinghouse Corporation in 1923 and subsequently built in 1932 on Chicago's Monroe Street. The Nash Motor Company created the first glass-enclosed version of this system for the Chicago Century of Progress Exhibition in 1933 The first driverless parking garage opened in 1951 in Washington, D.C., but was replaced with office space due to increasing land values. APS saw a spurt of interest in the U.S. in the late 1940s and 1950s with the Bowser, Pigeon Hole and Roto Park systems. In 1957, 74 Bowser, Pigeon Hole systems were installed, and some of these systems remain in operation. However, interest in APS in the U.S. waned due to frequent mechanical problems and long waiting times for patrons to retrieve their cars. In the United Kingdom, the Auto Stacker opened in 1961 in Woolwich, south east London, but proved equally difficult to operate. Interest in APS in the U.S. was renewed in the 1990s, and there were 25 major current and planned APS projects (representing nearly 6,000 parking spaces) in 2012. The first American robotic parking garage opened in 2002 in Hoboken, New Jersey. While interest in the APS in the U.S. languished until the 1990s, Europe, Asia and Central America had been installing more technically advanced APS since the 1970s. In the early 1990s, nearly 40,000 parking spaces were being built annually using the paternoster APS in Japan. In 2012, there are an estimated 1.6 million APS parking spaces in Japan. The ever-increasing scarcity of available urban land (urbanization) and increase of the number of cars in use (motorization) have combined with sustainability and other quality-of-life issues to renew interest in APS as alternatives to multi-storey car parks, on-street parking, and parking lots. == Largest systems == The largest Automated Parking Facility in the world is in Al Jahra, Kuwait, and provides 2,314 parking spaces. The world's fastest Automated Parking System is in Wolfsburg, Germany, with a retrieval time of 1 minute and 44 seconds. The largest APS in Europe is at Dokk1 in Aarhus, Denmark, and provides 1,000 parking spaces via 20 car lifts. == Space saving == All APS take advantage of a common concept to decrease the area of parking spaces - removing the driver and passengers from the car before it is parked. With either fully automated or semi-automated APS, the car is driven up to an entry point to the APS and the driver and passengers exit the car. The car is then moved automatically or semi-automatically (with some attendant action required) to its parking space. The space-saving provided by the APS, compared to the multi-story parking garage, is derived primarily from a significant reduction in space not directly related to the parking of the car: Parking space width and depth (and distances between parking spaces) are dramatically reduced since no allowance need be made for driving the car into the parking space or for the opening of car doors (for drivers and passengers) No driving lanes or ramps are needed to drive the car to/from the entrance/exit to a parking space Ceiling height is minimized since there is no pedestrian traffic (drivers and passengers) in the parking area, and No walkways, stairways or elevators are needed to accommodate pedestrians in the parking area. With the elimination of ramps, driving lanes, pedestrians and the reduction in ceiling heights, the APS requires substantially less structural material than the multi-story parking garage. Many APS utilize a steel framework (some use thin concrete slabs) rather than the monolithic concrete design of the multi-story parking garage. These factors contribute to an overall volume reduction and further space savings for the APS. == Other considerations == In addition to the space saving, many APS designs provide a number of secondary benefits: The parked cars and their contents are more secure since there is no public access to parked cars Minor parking lot damage such as scrapes and dents are eliminated Drivers and passengers are safer not having to walk through parking lots or garages Driving around in search of a parking space is eliminated, thereby reducing engine emissions and wasted time Only minimal ventilation and lighting systems are needed Handicap access is improved The volume and visual impact of the parking structure is minimized Shorter construction time === Problems === There have been a number of problems with robotic parking systems, particularly in the United States. The systems work well in balanced throughput situations like shopping malls and train stations, but they are unsuited to high peak volume applications like rush hour usage or stadiums and they suffer from technical problems. Further, parkers not familiar with the system may cause problems, for example by failing to push the button to alert a fully automated system to the presence of a car to be parked. In London around 40 vehicles were trapped for two years in CBRE's system. == Fully automated vs semi-automated == Fully automated parking systems operate much like robotic valet parking. The driver drives the car into an APS entry (transfer) area. The driver and all passengers exit the car. The driver uses an automated terminal nearby for payment and receipt of a ticket. When driver and passengers have left the entry area, the mechanical system lifts the car and transports it to a pre-determined parking space in the system. More sophisticated fully automated APS will obtain the dimensions of cars on entry in order to place them in the smallest available parking space. The driver retrieves a car by inserting a ticket or code into an automated terminal. The APS lifts the car from its parking space and delivers it to an exit area. Most often, the retrieved car has been oriented to eliminate the need for the driver to back out. Fully automated APS theoretically eliminate the need for parking attendants. Semi-automated APS also use a mechanical system of some type to move a car to its parking space, however putting the car into and/or the operation of the system requires some action by an attendant or the driver. The choice between fully and semi-automated APS is often a matter of space and cost, however large capacity (> 100 cars) tend to be fully automated. == Applications == By virtue of their relatively smaller volume and mechanized parking systems, APS are often used in locations where a multi-story parking garage would be too large, too costly or impractical. Examples of such applications include, under or inside existing or new structures, between existing structures and in irregularly shaped areas. APS can also be applied in situations similar to multi-storey parking garages such as freestanding above ground, under buildings above grade and under buildings below grade. == Costs == The direct comparison of costs between an APS and a multi-story parking garage can be complicated by many variables such as capacity, land costs, area shape, number and location of entranc

Netomi

Netomi, formerly msg.ai, is an American artificial intelligence company and developer of chatbot technologies. == History == msg.ai was founded in May 2015 by Puneet Mehta. msg.ai worked with Sony Pictures to launch a chat bot on Facebook Messenger for a $100M film, Goosebumps and subsequently joined Y Combinator as a member of the Winter 2016 class. Later that year and in 2017, msg.ai completed two rounds of seed funding, led by Y Combinator and Index Ventures. In 2018, the company changed its name to Netomi. In 2019, the company raised $14.7 million in a Series A funding round also led by Index Ventures. In 2021, the company raised $30 million in a Series B funding round led by WndrCo LLC.