AI Detector Winston

AI Detector Winston — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

Qstack

Qstack is a cloud management platform developed by GreenQloud, a cloud computing software company founded in Reykjavik, Iceland in February 2010. Qstack enables its users to manage multiple clouds and hybrid deployments through a single self-service portal. Qstack is in continuous development, incorporating developments within infrastructure, cloud, and application management solutions. The next release of Qstack is slated for June 2017. == History == In 2014 when Jonsi Stefansson joined as CEO, Greenqloud pivoted its operational focus to development of Qstack with beta launch in the fall of 2015, and began offering support, technical services and certifications for the software. == Features == Qstack is hypervisor agnostic (KVM, VMware, Hyper-V) and can manage private clouds in multiple locations as well as AWS, Azure, and EC2-compatible public clouds from its user interface. Qstack combines proprietary software with open-source components, and the company claims to harden them to meet the strict security standards often required by enterprise deployments. Qstack features VM templates for Windows, Linux, and other operating systems. It also features full SSH/RDP access to instances, virtual routers, firewalls, and load balancers built into the interface. == Reception == In a 2015 review, IDG columnist J. Peter Bruzzese praised Qstack’s user interface for its ease-of-use and clean look.
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Kruti

Kruti is a multilingual AI agent and chatbot developed by the Indian company Ola Krutrim. It is designed to perform real-world tasks for users, such as booking taxis and ordering food, by integrating directly with various online services. It is notable for its ability to understand and respond in multiple Indian languages. Developed by a team founded by Bhavish Aggarwal, Kruti functions as an "agentic" AI, meaning it can reason, plan, and execute multi-step tasks to fulfill a user's request. The backend technology combines several open-source large language models with Ola's proprietary Krutrim V2 model. The system was developed to work primarily on smartphones, addressing the Indian market's specific needs, including language diversity and potential bandwidth constraints. Kruti was officially released in June 2025, replacing an earlier chatbot from the company that was also named Krutrim. Initially supporting 13 languages, the company plans to expand its capabilities to 22 Indian languages. == Background == Kruti is an improved version of Ola's Krutrim chatbot, which was first launched in 2023 and was intended to be replaced by Kruti. It was officially released on 12 June 2025 as an upgrade to passive chatbots, with support for text and voice in 13 Indian languages. As an agentic AI, it can execute tasks with customization and reasoning, providing adaptive answers based on user preferences and past interactions. Kruti is optimized for smartphone usage and designed to accommodate bandwidth constraints and usage patterns in India. To ensure scalability and cost-effective performance, it combines various open-source large language models with Ola's own Krutrim V2, which has 12 billion parameters. Its speech recognition is built to identify regional Indian languages, dialects, and accents. Due to its integration with numerous apps and services, Kruti is context-aware and can proactively complete tasks. Initially connected only with Ola ecosystem services, Krutrim intends to expand and incorporate various Indian services into Kruti, with the goal of adding services from Blinkit, Swiggy, and Uber with respective voice command support. On 20 June 2025, Krutrim acquired the AI platform BharatSah‘AI’yak to increase its involvement in government, education, and agriculture projects. This acquisition will allow Kruti to assist in broadening the scope of BharatSah'AI'yak's work on India-centric, vernacular retrieval-augmented generation AI bots. == Development == Kruti is designed to perform tasks with minimal user input, accepting documents, images, and text, without requiring users to switch between applications. Its agentic framework breaks queries into sub-tasks executed by multiple agents working sequentially or concurrently, with reported accuracy exceeding 90%. Kruti connects to company databases and APIs via the Model Context Protocol and presents responses as summaries, tables, or narratives adapted to user behaviour. The system supports payments via credit/debit cards and UPI. The underlying stack, which includes foundation models and AI training and inference systems, is intended to support adaptation across sectors such as healthcare, education, and finance. Ola Cabs and the Open Network for Digital Commerce have begun integrating Kruti into their platforms pending broader reliability testing.
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Contextual AI

Contextual AI is an enterprise software company based in Mountain View, California. It develops a platform for building specialized Retrieval-Augmented Generation (RAG) agents for enterprise use. The company was founded in 2023 by Douwe Kiela and Amanpreet Singh, both former AI researchers at Facebook AI Research (FAIR) and Hugging Face. Douwe Kiela previously led the Meta research team that introduced the Retrieval-Augmented Generation (RAG) approach in 2020. Contextual AI focuses on enterprise generative AI applications using RAG 2.0 technology, with deployments primarily in the technology, banking, finance and media sectors. == History == In June 2023, Contextual AI announced it had raised $20 million in a seed funding round led by Bain Capital Ventures (BCV), with participation from Lightspeed Venture Partners, Greycroft, SV Angel, and several angel investors. In August 2024, the company raised $80 million in a Series A funding round led by Greycroft, with participation from previous investors including Bain Capital Ventures, Lightspeed, and Conviction Partners. The round also included new backers such as Bezos Expeditions, NVentures (Nvidia), HSBC Ventures, and Snowflake Ventures. == Features == Retrieval-Augmented Generation (RAG) is an artificial intelligence framework that integrates information retrieval with text generation to improve the performance of large language models (LLMs) on complex, knowledge-intensive tasks. It was introduced in 2020 by researchers at Meta AI, including Douwe Kiela, Patrick Lewis and others, in their paper Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. RAG enables language models to access and incorporate external information, such as proprietary databases or real-time web content, at query time, instead of relying solely on pre-trained, internal, static knowledge. This architecture addresses common limitations of standard LLMs, including hallucination, outdated information, and lack of attribution to source materials. RAG systems retrieve relevant context through a variety of techniques - including vector search, keyword search, text-to-SQL - and feeds this context into the language model to generate responses. The approach improves factual accuracy, supports domain-specific customization, enables citation of sources, and allows for more updated information without retraining the model itself. General Availability. In January 2025, Contextual AI announced the general availability of its enterprise platform for building specialized RAG agents. Early adopters included Qualcomm, which used the platform for their Customer Engineering team needs. Grounded Language Model. In March 2025, the company introduced a Grounded Language Model (GLM) for factual accuracy in enterprise AI applications. Reranker. In March 2025, Contextual AI released an instruction-following reranker that allows users to influence the ranking of retrieved documents through natural language instructions, such as prioritizing recent files, specific formats, or content from designated sources. == Applications == Contextual AI's platform has been adopted across a range of industries, including finance, technology, media and professional services. Clients include Fortune 500 companies such as Qualcomm and HSBC.
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Local ternary patterns

Local ternary patterns (LTP) are an extension of local binary patterns (LBP). Unlike LBP, it does not threshold the pixels into 0 and 1, rather it uses a threshold constant to threshold pixels into three values. Considering k as the threshold constant, c as the value of the center pixel, a neighboring pixel p, the result of threshold is: { 1 , if p > c + k 0 , if p > c − k and p < c + k − 1 if p < c − k {\displaystyle {\begin{cases}1,&{\text{if }}p>c+k\\0,&{\text{if }}p>c-k{\text{ and }}p Read more →
TikTok

TikTok is a social media and short-form online video platform. It hosts user-submitted videos, which range in duration from three seconds to 60 minutes. It can be accessed through a mobile app or through its website. Since its launch, TikTok has become one of the world's most popular social media platforms, using recommendation algorithms to connect content creators and influencers with new audiences. In April 2020, TikTok surpassed two billion mobile downloads worldwide. The popularity of TikTok has allowed viral trends in food, fashion, and music to take off and increase the platform's cultural impact worldwide. TikTok has come under scrutiny due to data privacy violations, mental health concerns, misinformation, offensive content, addictive algorithm, its role during the Gaza war, and, following its 2026 divestiture in the U.S., alleged censorship of criticism of Donald Trump and discussions of Jeffrey Epstein. While TikTok remains accessible to users in most countries, a minority of countries (including India and Afghanistan) have implemented full or partial bans. Many other countries limit TikTok's use on government-issued devices for security or privacy reasons. == Corporate structure == TikTok Ltd was incorporated in the Cayman Islands in the Caribbean and is based in both Singapore and Los Angeles. It owns entities which are based respectively in Australia (which also runs the New Zealand business), United Kingdom (also owns subsidiaries in the European Union), and Singapore (owns operations in Southeast Asia and India). A spin-off company, TikTok USDS Joint Venture LLC was formed on 22 January 2026 to handle TikTok and other ByteDance properties in the United States, Oracle Corporation, MGX Fund Management Limited, Silver Lake each holding a 15% stake, ByteDance holds a 19.9% stake and the remaining 35.1% is shared between Dell Technologies founder Michael Dell and Vastmere Strategic Investments. Its parent company, Beijing-based ByteDance, is owned by founders and Chinese investors, other global investors, and employees. One of ByteDance's main domestic subsidiaries is owned by Chinese state funds and entities through a 1% golden share. Employees have reported that multiple overlaps exist between TikTok and ByteDance in terms of personnel management and product development. TikTok says that since 2020, its US-based CEO is responsible for making important decisions, and has downplayed its China connection. == History == === Douyin === Douyin (Chinese: 抖音; pinyin: Dǒuyīn; lit. 'Shaking Sound') was launched on 20 September 2016, by ByteDance, originally under the name A.me, before changing its name to Douyin in December 2016. Douyin was developed in nearly 7 months and within a year had 100 million users, with more than one billion videos viewed every day. While TikTok and Douyin share a similar user interface, the platforms operate separately. Douyin includes an in-video search feature that can search by people's faces for more videos of them, along with other features such as buying, booking hotels, and making geo-tagged reviews. === TikTok === ByteDance planned on Douyin expanding overseas. The founder of ByteDance, Zhang Yiming, stated that "China is home to only one-fifth of Internet users globally. If we don't expand on a global scale, we are bound to lose to peers eyeing the four-fifths. So, going global is a must." ByteDance created TikTok as an overseas version of Douyin. TikTok was launched in the international market in September 2017. On 9 November 2017, ByteDance spent nearly $1 billion to purchase Musical.ly, a startup headquartered in Shanghai with an overseas office in Santa Monica, California. Musical.ly was a social media video platform that allowed users to create short lip-sync and comedy videos, initially released in August 2014. TikTok merged with Musical.ly on 2 August 2018 with existing accounts and data consolidated into one app, keeping the title TikTok. On 23 January 2018, the TikTok app ranked first among free application downloads on app stores in Thailand and other countries. TikTok has been downloaded more than 130 million times in the United States and has reached 2 billion downloads worldwide, according to data from mobile research firm Sensor Tower (those numbers exclude Android users in China). In the United States, Jimmy Fallon, Tony Hawk, and other celebrities began using the app in 2018. Other celebrities like Jennifer Lopez, Jessica Alba, Will Smith, and Justin Bieber joined TikTok. In January 2019, TikTok allowed creators to embed merchandise sale links into their videos. On 3 September 2019, TikTok and the US National Football League (NFL) announced a multi-year partnership. The agreement came just two days before the NFL's 100th season kick-off at Soldier Field in Chicago where TikTok hosted activities for fans in honor of the deal. The partnership entails the launch of an official NFL TikTok account, which is to bring about new marketing opportunities such as sponsored videos and hashtag challenges. In July 2020, TikTok, excluding Douyin, reported close to 800 million monthly active users worldwide after less than four years of existence. In May 2021, TikTok appointed Shou Zi Chew as their new CEO who assumed the position from interim CEO Vanessa Pappas, following the resignation of Kevin A. Mayer on 27 August 2020. In September 2021, TikTok reported that it had reached 1 billion users. In 2021, TikTok earned $4 billion in advertising revenue. In October 2022, TikTok was reported to be planning an expansion into the e-commerce market in the US, following the launch of TikTok Shop in the United Kingdom. The company posted job listings for staff for a series of order fulfillment centers in the US and was reportedly planning to start the new live shopping business before the end of the year. The Financial Times reported that TikTok will launch a video gaming channel, but the report was denied in a statement to Digiday, with TikTok instead aiming to be a social hub for the gaming community. According to data from app analytics group Sensor Tower, advertising on TikTok in the US grew by 11% in March 2023, with companies including Pepsi, DoorDash, Amazon, and Apple among the top spenders. According to estimates from research group Insider Intelligence, TikTok is projected to generate $14.15 billion in revenue in 2023, up from $9.89 billion in 2022. In March 2024, The Wall Street Journal reported that TikTok's growth in the US had stagnated. ==== Plans to sell TikTok's US operations ==== Since at least 2020, following calls to ban TikTok in the country, the Committee on Foreign Investment in the United States (CFIUS) has been investigating the company's 2017 merger with Musical.ly but has not finalized any of its negotiations with TikTok, such as the Project Texas proposal, waiting instead for Congress to act. In January 2025, Chinese officials began preliminary talks about potentially selling TikTok's US operations to Elon Musk if the app faced an impending ban due to national security concerns. While Beijing preferred TikTok remain under ByteDance's control, the sale could happen through a competitive process or with US government involvement. One possibility involved Musk's platform, X, taking over TikTok's US business. The move came ahead of a Supreme Court case that upheld the constitutionality of a law that would force a sale or ban of TikTok in the US by 19 January 2025, due to national security concerns regarding its ties to China. Other potential buyers included Project Liberty's "The People's Bid For TikTok" consortium of Frank McCourt with Kevin O'Leary, Steven Mnuchin, MrBeast and Bobby Kotick, the seriousness of these potential buyers was unclear. The day before the impending ban, California-based conversational search engine company Perplexity AI submitted a bid for a merger with TikTok US. On 14 September 2025, the Wall Street Journal reported the US and China have reached the "framework of a deal" for the US operations of TikTok to be sold to a consortium of investors in the US including close Trump ally Larry Ellison of Oracle. The deal was completed by 22 January 2026, with a consortium of investors—including Oracle, Silver Lake, MGX, and others including the personal investment entity for Michael Dell—owning more than 80% of the new venture. ByteDance retained 19.9% ownership. Under the deal, the app would remain the same, and the algorithm would be adjusted over time to favor American topics for those users. === Expansion in other markets === TikTok was downloaded over 104 million times on Apple's App Store during the first half of 2018, according to data provided to CNBC by Sensor Tower. After merging with musical.ly in August, downloads increased and TikTok subsequently became the most downloaded app in the US in October 2018, which musical.ly had done once before. In February 2019, TikTok, together with Douyin, hit one billion downloads globally, excluding Android
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Inception (deep learning architecture)

Inception is a family of convolutional neural network (CNN) for computer vision, introduced by researchers at Google in 2014 as GoogLeNet (later renamed Inception v1). The series was historically important as an early CNN that separates the stem (data ingest), body (data processing), and head (prediction), an architectural design that persists in all modern CNN. == Version history == === Inception v1 === In 2014, a team at Google developed the GoogLeNet architecture, an instance of which won the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). The name came from the LeNet of 1998, since both LeNet and GoogLeNet are CNNs. They also called it "Inception" after a "we need to go deeper" internet meme, a phrase from Inception (2010) the film. Because later, more versions were released, the original Inception architecture was renamed again as "Inception v1". The models and the code were released under Apache 2.0 license on GitHub. The Inception v1 architecture is a deep CNN composed of 22 layers. Most of these layers were "Inception modules". The original paper stated that Inception modules are a "logical culmination" of Network in Network and (Arora et al, 2014). Since Inception v1 is deep, it suffered from the vanishing gradient problem. The team solved it by using two "auxiliary classifiers", which are linear-softmax classifiers inserted at 1/3-deep and 2/3-deep within the network, and the loss function is a weighted sum of all three: L = 0.3 L a u x , 1 + 0.3 L a u x , 2 + L r e a l {\displaystyle L=0.3L_{aux,1}+0.3L_{aux,2}+L_{real}} These were removed after training was complete. This was later solved by the ResNet architecture. The architecture consists of three parts stacked on top of one another: The stem (data ingestion): The first few convolutional layers perform data preprocessing to downscale images to a smaller size. The body (data processing): The next many Inception modules perform the bulk of data processing. The head (prediction): The final fully-connected layer and softmax produces a probability distribution for image classification. This structure is used in most modern CNN architectures. === Inception v2 === Inception v2 was released in 2015, in a paper that is more famous for proposing batch normalization. It had 13.6 million parameters. It improves on Inception v1 by adding batch normalization, and removing dropout and local response normalization which they found became unnecessary when batch normalization is used. === Inception v3 === Inception v3 was released in 2016. It improves on Inception v2 by using factorized convolutions. As an example, a single 5×5 convolution can be factored into 3×3 stacked on top of another 3×3. Both has a receptive field of size 5×5. The 5×5 convolution kernel has 25 parameters, compared to just 18 in the factorized version. Thus, the 5×5 convolution is strictly more powerful than the factorized version. However, this power is not necessarily needed. Empirically, the research team found that factorized convolutions help. It also uses a form of dimension-reduction by concatenating the output from a convolutional layer and a pooling layer. As an example, a tensor of size 35 × 35 × 320 {\displaystyle 35\times 35\times 320} can be downscaled by a convolution with stride 2 to 17 × 17 × 320 {\displaystyle 17\times 17\times 320} , and by maxpooling with pool size 2 × 2 {\displaystyle 2\times 2} to 17 × 17 × 320 {\displaystyle 17\times 17\times 320} . These are then concatenated to 17 × 17 × 640 {\displaystyle 17\times 17\times 640} . Other than this, it also removed the lowest auxiliary classifier during training. They found that the auxiliary head worked as a form of regularization. They also proposed label-smoothing regularization in classification. For an image with label c {\displaystyle c} , instead of making the model to predict the probability distribution δ c = ( 0 , 0 , … , 0 , 1 ⏟ c -th entry , 0 , … , 0 ) {\displaystyle \delta _{c}=(0,0,\dots ,0,\underbrace {1} _{c{\text{-th entry}}},0,\dots ,0)} , they made the model predict the smoothed distribution ( 1 − ϵ ) δ c + ϵ / K {\displaystyle (1-\epsilon )\delta _{c}+\epsilon /K} where K {\displaystyle K} is the total number of classes. === Inception v4 === In 2017, the team released Inception v4, Inception ResNet v1, and Inception ResNet v2. Inception v4 is an incremental update with even more factorized convolutions, and other complications that were empirically found to improve benchmarks. Inception ResNet v1 and v2 are both modifications of Inception v4, where residual connections are added to each Inception module, inspired by the ResNet architecture. === Xception === Xception ("Extreme Inception") was published in 2017. It is a linear stack of depthwise separable convolution layers with residual connections. The design was proposed on the hypothesis that in a CNN, the cross-channels correlations and spatial correlations in the feature maps can be entirely decoupled. Training each network took 3 days on 60 K80 GPUs, or approximately 0.5 petaFLOP-days.
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Gallery software

Gallery software is software that helps the user publish or share photos, pictures, videos or other digital media. Most galleries are located on Web servers, where users are allowed to register and publish their pictures. Gallery software usually features automatic image resizing, allows digital media be categorized into sets, and allows comments. == Types == Early digital media publishing and sharing was done with imageboards. The boards are by topics, sometimes called "chan". Each discussion in a "chan" are started with a piece of digital media, and follow-up discussions can contain another piece too. Software works in this way: Futallaby, Danbooru. Traditionally, galleries are managed. An administrator maintains a set of or hierarchy of albums. The users can upload their digital media in one of the existing albums defined by an administrator, or create their own albums. The users with sufficient permission can re-categorise the digital media others uploaded. Often, the site's administrator can define which album the users are allowed to categorise their media into, or delete other user's content. Examples are open source galleries Coppermine, Gallery Project. There are decentralised gallery software that does not have an administrator for managing contents. Pinterest, Flickr and DeviantArt has been successful with this model. Open source gallery software MediaGoblin works in this way. Each user can create their own "collections", to categorise theirs or other users' media. However users cannot put media into other user's collections. Each user's category is separate. There is no centralised theme or hierarchy for the media.
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Conversational user interface

A conversational user interface (CUI) is a user interface for computers that emulates a conversation with a human. Historically, computers have relied on text-based user interfaces and graphical user interfaces (GUIs) (such as the user pressing a "back" button) to translate the user's desired action into commands the computer understands. While an effective mechanism of completing computing actions, there is a learning curve for the user associated with GUI. Instead, CUIs provide opportunity for the user to communicate with the computer in their natural language rather than in a syntax specific commands.
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CPT Corporation

CPT Corporation was founded in 1971 by Dean Scheff in Minneapolis, Minnesota, with co-founders James Wienhold and Richard Eichhorn. CPT first designed, manufactured, and marketed the CPT 4200, a dual-cassette-tape machine that controlled a modified IBM Selectric typewriter to support text editing and word processing. The CPT 4200 was followed in 1976 by the CPT VM (Visual Memory), a partial-page display-screen dual-cassette-tape unit, and shortly thereafter by the CPT 8000, a full-page display dual-diskette desktop microcomputer that drove stand-alone daisy wheel printers. Subsequent products included (1) variants on the 8000 series; (2) the CPT 6000 series, which had a lower capacity, smaller screen, and was less expensive; (3) the CPT 9000 series, which had a larger capacity and could run IBM personal computer software; (4) the CPT Phoenix series, which had a graphical capabilities; (5) CPT PT, a software-only reduced version that ran on IBM personal computers and clones; and (6) other related products. The CPT logo—originally three letters chosen to sound well together—began to be taken as an acronym for "cassette powered typewriting," and subsequently for "computer processed text," and numerous other variants. Major competition was IBM, Wang, Lanier, Xerox, and other word processing vendors. CPT Corporation was fifth in size among Minnesota-based top high-tech companies, after 3M, Honeywell, Control Data, and Medtronic. Corporate revenues grew to approximately a quarter-billion dollars per year in the mid-1980s, then declined with the proliferation of personal computers. CPT ultimately ceased major manufacturing late in the 20th century. == Selected products == === Cassette based === The CPT 4200 was a dual-cassette-tape unit with a small built-in keyboard that controlled a modified IBM Selectric typewriter. Keystrokes entered on the typewriter appeared on the paper as they were recorded on the output cassette, which formed a magnetic replica of the characters printed on the page. That output cassette could later be used as an input cassette, where it would be played back to the typewriter along with new keystrokes to accomplish text editing. The keyboard of the CPT 4200 had action keys for "skip", "read" and "stop", mode keys for "word", "line", "paragraph," and "page." Pressing "read" transferred a word, line, paragraph, or page (depending on which mode key had been selected) from the input tape to both the typewriter and the output tape. Line boundaries (aka printer margins) recorded on the input tape were ignored or retained depending on whether or not the "adjust" key had been selected. Alternatively, pressing "skip" moved past the corresponding amount of text on the input tape without sending it to the typewriter or to the output tape. The Selectric's keyboard was active for any new typing, which would appear on the paper and transferred to the output tape. Thus a document was edited by reading back those parts of the text to be retained and skipping those parts to be discarded, with new typing added from the Selectric's keyboard. Price: approx. $5000, 1980-era values. The CPT Communicator was an add-on to the CPT 4200 that allowed data to be transferred from one text-editing machine to another, or between a text-editing machine and a remote computer, via phone lines. Price: not available. === Microprocessor based === ==== CPT 8000 series ==== The CPT 8000 was the company's first microcomputer product, exhibited in spring of 1976. It was a self-contained desktop machine with two 8-inch floppy diskette drives, a movable keyboard, and a full-page vertically oriented CRT display simulating paper with black characters on a white background, for a wysiwyg view of text on paper. It was promoted as familiar and easy to use for those experienced with typewriters. A keyboard with a large set of extra keys made operating the 8000 quite easy even for people without any computer skills or background. IN, OUT, PRINT, OOPS OOPS was changed thinking it was insulting to the buyer to assume they would ever make an error. The CPT 8000 was designed to show a full page of text with a static line showing the margin and tab stops. An additional line would display status or error messages with a times square like display. The times square error and status messages were very well done, "The printer needs a new ribbon" rather than "ERROR 034892". The text page could both smooth pan and scroll by the hardware in the display board and nothing quite like it existed for a very long time. The 8000 ran its own multitasking hardware interrupt-driven operating system but it also ran CP/M quite well. So unlike other companies that sold Wordprocessor only systems, CPT had a system that could run any of the many popular CP/M applications. Using the CP/M OS users could develop Fortran, CBasic, Cobol and other language's programs. The 8000 used Intel's 8080 microprocessor. The display board was bleeding-edge, high-speed logic. The parts available at this time were pushed to their limits to provide the speed needed to display this much text. There were times that batches of parts from one manufacturer simply could not be clocked as fast as the 8000 display required. Memory was initially 64K, but larger boards of 128K were most common then later 256K were offered. The 8080 accessed this additional RAM by running a custom page flipping circuit. The 8000 was originally priced at $8000 and its daisy wheel printer an additional $8000. The model number having been confused with the price at its first appearance at the Hanover fair. An RS-232 serial communication option was available for the 8000 series that allowed the electronic transfer of documents. One very popular use of this was to access the Westlaw system. A tempest approved version of the 8000 was developed that was RF tight with nothing being emitted that could be monitored or spied on. === Storage Systems === ==== CPT WordPak ==== The CPT WordPak series was CPT's first external document storage system that enabled multiple 8000 series workstations to store documents in an electronic filing cabinet. Prior to WordPak, all documents were stored on removable 8-inch floppy diskettes. Sharing documents involved handing off the original disk, or copying the document to a second disk and 'sneaker-net-ing' (walking it over) to the second 8000. But this resulted in two copies of the document, one at each workstation. A circuit board with a proprietary cable connector was installed in the 8000/6000 family of "workstations" and connected to the WordPak by a multi-conductor cable. WordPak 1 consisted of a single Shugart Associates SA4000 14"-diameter hard disk with a capacity of 30 megabytes. WordPak 2 added a 2nd drive for a total of 60 megabytes. ==== CPT SRS 45 ==== The CPT SRS 45 was what would now be called a server (quite likely the first of its kind) but in practice was much more. It was maybe the worlds easiest networking shared resource system. It combined a ZIP drive for backup and hard disk(s) that would be shared simultaneously by up to eight CPT machines that had the PC AT bus. The primary person responsible for its development was Bill Davidson whose wife Cheryl was responsible for bringing up CP/M, MP/M and other Digital Research products running on the Phoenix. The brilliance of the system were the networking cards that plugged into the individual machines. These used the 55AA installable driver of the IBM BIOS to simply add the zip and hard disk drives to each computers drives list. So a system that started with floppy drives A and B and a C hard disk on the machine would have the SRS 45 drives added as drives D (E, F depending on the number of hard disk) and Z for the zip drive. Sharing (avoiding writing to the same file at the same time) was handled by simply assigning parts of the drives for individuals and other directories for shared use. No "driver" software was needed. You simply plugged in the networking card and your machine had additional drives that were internal to the SRS45. This approach was far ahead of its time and sadly never recognized for its brilliance. The SRS45 as were all CPT machines not just dedicated Word Processors. === Personal-computer based === ==== CPT PT software ==== CPT PT was a reduced a version of the software that ran under MS-DOS as an application on IBM PC compatible computers. The corporation intended it as a bridge to allow data to flow in and out of personal computer packages, as well as providing a personal-computer word processing application for those familiar with standalone CPT equipment or who preferred the CPT style of dual-window text editing. Price: approx. $200, 1980-era values. ==== CPT Genius Display ==== The Genius display was a stand-alone, vertically-oriented (portrait) configuration monochrome grey-scale CRT monitor unit and an IBM PC form factor display card to allow high-resolution, full-page text & graphics on IBM PC compatible computers.
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Augmented Analytics

Augmented Analytics is an approach of data analytics that employs the use of machine learning and natural language processing to automate analysis processes normally done by a specialist or data scientist. The term was introduced in 2017 by Rita Sallam, Cindi Howson, and Carlie Idoine in a Gartner research paper. Augmented analytics is based on business intelligence and analytics. In the graph extraction step, data from different sources are investigated. == Defining Augmented Analytics == Machine Learning – a systematic computing method that uses algorithms to sift through data to identify relationships, trends, and patterns. It is a process that allows algorithms to dynamically learn from data instead of having a set base of programmed rules. Natural language generation (NLG) – a software capability that takes unstructured data and translates it into plain-English, readable, language. Automating Insights – using machine learning algorithms to automate data analysis processes. Natural Language Query – enabling users to query data using business terms that are either typed onto a search box or spoken. == Data Democratization == Data Democratization is the democratizing data access in order to relieve data congestion and get rid of any sense of data "gatekeepers". This process must be implemented alongside a method for users to make sense of the data. This process is used in hopes of speeding up company decision making and uncovering opportunities hidden in data. There are three aspects to democratising data: Data Parameterisation and Characterisation. Data Decentralisation using an OS of blockchain and DLT technologies, as well as an independently governed secure data exchange to enable trust. Consent Market-driven Data Monetisation. When it comes to connecting assets, there are two features that will accelerate the adoption and usage of data democratisation: decentralized identity management and business data object monetization of data ownership. It enables multiple individuals and organizations to identify, authenticate, and authorize participants and organizations, enabling them to access services, data or systems across multiple networks, organizations, environments, and use cases. It empowers users and enables a personalized, self-service digital onboarding system so that users can self-authenticate without relying on a central administration function to process their information. Simultaneously, decentralized identity management ensures the user is authorized to perform actions subject to the system’s policies based on their attributes (role, department, organization, etc.) and/ or physical location. == Use cases == Agriculture – Farmers collect data on water use, soil temperature, moisture content and crop growth, augmented analytics can be used to make sense of this data and possibly identify insights that the user can then use to make business decisions. Smart Cities – Many cities across the United States, known as Smart Cities collect large amounts of data on a daily basis. Augmented analytics can be used to simplify this data in order to increase effectiveness in city management (transportation, natural disasters, etc.). Analytic Dashboards – Augmented analytics has the ability to take large data sets and create highly interactive and informative analytical dashboards that assist in many organizational decisions. Augmented Data Discovery – Using an augmented analytics process can assist organizations in automatically finding, visualizing and narrating potentially important data correlations and trends. Data Preparation – Augmented analytics platforms have the ability to take large amounts of data and organize and "clean" the data in order for it to be usable for future analyses. Business – Businesses collect large amounts of data, daily. Some examples of types of data collected in business operations include; sales data, consumer behavior data, distribution data. An augmented analytics platform provides access to analysis of this data, which could be used in making business decisions.
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Topological deep learning

Topological deep learning (TDL) is a research field that extends deep learning to handle complex, non-Euclidean data structures. Traditional deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), excel in processing data on regular grids and sequences. However, scientific and real-world data often exhibit more intricate data domains encountered in scientific computations, including point clouds, meshes, time series, scalar fields graphs, or general topological spaces like simplicial complexes and CW complexes. TDL addresses this by incorporating topological concepts to process data with higher-order relationships, such as interactions among multiple entities and complex hierarchies. This approach leverages structures like simplicial complexes and hypergraphs to capture global dependencies and qualitative spatial properties, offering a more nuanced representation of data. TDL also encompasses methods from computational and algebraic topology that permit studying properties of neural networks and their training process, such as their predictive performance or generalization properties. The mathematical foundations of TDL are algebraic topology, differential topology, and geometric topology. Therefore, TDL can be generalized for data on differentiable manifolds, knots, links, tangles, curves, etc. == History and motivation == Traditional techniques from deep learning often operate under the assumption that a dataset is residing in a highly-structured space (like images, where convolutional neural networks exhibit outstanding performance over alternative methods) or a Euclidean space. The prevalence of new types of data, in particular graphs, meshes, and molecules, resulted in the development of new techniques, culminating in the field of geometric deep learning, which originally proposed a signal-processing perspective for treating such data types. While originally confined to graphs, where connectivity is defined based on nodes and edges, follow-up work extended concepts to a larger variety of data types, including simplicial complexes and CW complexes, with recent work proposing a unified perspective of message-passing on general combinatorial complexes. An independent perspective on different types of data originated from topological data analysis, which proposed a new framework for describing structural information of data, i.e., their "shape," that is inherently aware of multiple scales in data, ranging from local information to global information. While at first restricted to smaller datasets, subsequent work developed new descriptors that efficiently summarized topological information of datasets to make them available for traditional machine-learning techniques, such as support vector machines or random forests. Such descriptors ranged from new techniques for feature engineering over new ways of providing suitable coordinates for topological descriptors, or the creation of more efficient dissimilarity measures. Contemporary research in this field is largely concerned with either integrating information about the underlying data topology into existing deep-learning models or obtaining novel ways of training on topological domains. == Learning on topological spaces == One of the core concepts in topological deep learning is considering the domain upon which this data is defined and supported. In case of Euclidean data, such as images, this domain is a grid, upon which the pixel value of the image is supported. In a more general setting this domain might be a topological domain. Studying and developing deep learning models that are supported ln topological domains constitute the essence of topological deep learning. Next, we introduce the most common topological domains that are encountered in a deep learning setting. These domains include, but not limited to, graphs, simplicial complexes, cell complexes, combinatorial complexes and hypergraphs. Given a finite set S of abstract entities, a neighborhood function N {\displaystyle {\mathcal {N}}} on S is an assignment that attach to every point x {\displaystyle x} in S a subset of S or a relation. Such a function can be induced by equipping S with an auxiliary structure. Edges provide one way of defining relations among the entities of S. More specifically, edges in a graph allow one to define the notion of neighborhood using, for instance, the one hop neighborhood notion. Edges however, limited in their modeling capacity as they can only be used to model binary relations among entities of S since every edge is connected typically to two entities. In many applications, it is desirable to permit relations that incorporate more than two entities. The idea of using relations that involve more than two entities is central to topological domains. Such higher-order relations allow for a broader range of neighborhood functions to be defined on S to capture multi-way interactions among entities of S. Next we review the main properties, advantages, and disadvantages of some commonly studied topological domains in the context of deep learning, including (abstract) simplicial complexes, regular cell complexes, hypergraphs, and combinatorial complexes. ==== Comparisons among topological domains ==== Each of the enumerated topological domains has its own characteristics, advantages, and limitations: Simplicial complexes Simplest form of higher-order domains. Extensions of graph-based models. Admit hierarchical structures, making them suitable for various applications. Hodge theory can be naturally defined on simplicial complexes. Require relations to be subsets of larger relations, imposing constraints on the structure. Cell Complexes Generalize simplicial complexes. Provide more flexibility in defining higher-order relations. Each cell in a cell complex is homeomorphic to an open ball, attached together via attaching maps. Boundary cells of each cell in a cell complex are also cells in the complex. Represented combinatorially via incidence matrices. Hypergraphs Allow arbitrary set-type relations among entities. Relations are not imposed by other relations, providing more flexibility. Do not explicitly encode the dimension of cells or relations. Useful when relations in the data do not adhere to constraints imposed by other models like simplicial and cell complexes. Combinatorial Complexes : Generalize and bridge the gaps between simplicial complexes, cell complexes, and hypergraphs. Allow for hierarchical structures and set-type relations. Combine features of other complexes while providing more flexibility in modeling relations. Can be represented combinatorially, similar to cell complexes. ==== Hierarchical structure and set-type relations ==== The properties of simplicial complexes, cell complexes, and hypergraphs give rise to two main features of relations on higher-order domains, namely hierarchies of relations and set-type relations. ===== Rank function ===== A rank function on a higher-order domain X is an order-preserving function rk: X → Z, where rk(x) attaches a non-negative integer value to each relation x in X, preserving set inclusion in X. Cell and simplicial complexes are common examples of higher-order domains equipped with rank functions and therefore with hierarchies of relations. ===== Set-type relations ===== Relations in a higher-order domain are called set-type relations if the existence of a relation is not implied by another relation in the domain. Hypergraphs constitute examples of higher-order domains equipped with set-type relations. Given the modeling limitations of simplicial complexes, cell complexes, and hypergraphs, we develop the combinatorial complex, a higher-order domain that features both hierarchies of relations and set-type relations. The learning tasks in TDL can be broadly classified into three categories: Cell classification: Predict targets for each cell in a complex. Examples include triangular mesh segmentation, where the task is to predict the class of each face or edge in a given mesh. Complex classification: Predict targets for an entire complex. For example, predict the class of each input mesh. Cell prediction: Predict properties of cell-cell interactions in a complex, and in some cases, predict whether a cell exists in the complex. An example is the prediction of linkages among entities in hyperedges of a hypergraph. In practice, to perform the aforementioned tasks, deep learning models designed for specific topological spaces must be constructed and implemented. These models, known as topological neural networks, are tailored to operate effectively within these spaces. === Topological neural networks === Central to TDL are topological neural networks (TNNs), specialized architectures designed to operate on data structured in topological domains. Unlike traditional neural networks tailored for grid-like structures, TNNs are adept at handling more intricate data representations, such as graphs
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Spleak

Spleak was an IM platform where users could publish and rate content. It existed in the form of six bots covering as many subject areas: CelebSpleak, SportSpleak, VoteSpleak, TVSpleak, GameSpleak, and StyleSpleak. == Overview == Users can add a "multi-Spleak" (which contains all of the different Spleak bots in one) or add the separate bots to their IM buddy lists on MSN and AIM. Users are also allowed access to Spleak online by using a CelebSpleak, SportSpleak, or VoteSpleak widget, or through the CelebSpleak and SportSpleak applications with Facebook. Spleak was an alternate reality game and is moving to its own company, Spleak Media Network. "Celebrate Spleak" was introduced throughout 2007, launched in 2008, and was forced to retire in 2009. == Key people == Spleak was co-founded by Morten Lund and Nicolaj Reffstrup. The company's chief executive officer is Morrie Eisenburg; Josh Scott is Vice President in Product and Tyler Wells is Vice President in Engineering.
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Data classification (data management)

Data classification is the process of organizing data into categories based on attributes like file type, content, or metadata. The data is then assigned class labels that describe a set of attributes for the corresponding data sets. The goal is to provide meaningful class attributes to former less structured information, enabling organizations to manage, protect, and govern their data more effectively. Data classification can be viewed as a multitude of labels that are used to define the type of data, especially on confidentiality and integrity issues. == Approaches == Classification techniques might be used for reports generated by ERP systems or where the data includes specific personal information that is identified. Many organizations also employ context-based classification that considers factors such as data source, user identity, and application context. == Regulatory frameworks == Data classification schemes are mandated or implied by numerous regulatory frameworks that require organizations to identify, categorize, and protect sensitive information according to its level of sensitivity. The Health Insurance Portability and Accountability Act (HIPAA) Security Rule requires covered entities to conduct an accurate and thorough assessment of potential risks and vulnerabilities to the confidentiality, integrity, and availability of protected health information under 45 CFR 164.308(a)(1)(ii)(A), which necessitates classification of data to distinguish protected health information from other organizational data."Security Standards: Administrative Safeguards". U.S. Department of Health and Human Services. Retrieved April 1, 2026. The December 2024 HIPAA Security Rule notice of proposed rulemaking (90 FR 898) would mandate comprehensive technology asset inventories and require mapping of how electronic protected health information moves through an organization, formalizing data classification as an explicit compliance obligation."HIPAA Security Rule To Strengthen the Cybersecurity of Electronic Protected Health Information". Federal Register. January 6, 2025. Retrieved April 1, 2026. NIST Special Publication 800-60 provides guidelines for mapping information types to security categories, establishing a structured methodology for federal agencies to classify data and apply appropriate security controls based on the potential impact of a security breach."NIST SP 800-60 Vol. 1 Rev. 1: Guide for Mapping Types of Information and Information Systems to Security Categories". National Institute of Standards and Technology. August 2008. Retrieved April 1, 2026.
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Cleverbot

Cleverbot is a chatterbot web application. It was created by British AI scientist Rollo Carpenter and launched in October 2008. It was preceded by Jabberwacky, a chatbot project that began in 1988 and went online in 1997. In its first decade, Cleverbot held several thousand conversations with Carpenter and his associates. Since launching on the web, the number of conversations held has exceeded 150 million. Besides the web application, Cleverbot is also available as an iOS, Android, and Windows Phone app. == Operation == Cleverbot's responses are not pre-programmed because it learns from human input: Humans type into the box below the Cleverbot logo and the system finds all keywords or an exact phrase matching the input. After searching through its saved conversations, it responds to the input by finding how a human responded to that input when it was asked, in part or in full, by Cleverbot. Cleverbot participated in a formal Turing test at the 2011 Techniche festival at the Indian Institute of Technology Guwahati on 3 September 2011. Out of the 1334 votes cast, Cleverbot was judged to be 59.3% human, compared to the rating of 63.3% human achieved by human participants. A score of 50.05% or higher is often considered to be a passing grade. The software running for the event had to handle just 1 or 2 simultaneous requests, whereas online Cleverbot is usually talking to around 10,000 to 50,000 people at once. == Developments == Cleverbot is constantly growing in data size at the rate of 4 to 7 million interactions per day. Updates to the software have been mostly behind the scenes. In 2014, Cleverbot was upgraded to use GPU serving techniques. Unlike Eliza, the program does not respond in a fixed way, instead choosing its responses heuristically using fuzzy logic, the whole of the conversation being compared to the millions that have taken place before. Cleverbot now uses over 279 million interactions, about 3-4% of the data it has already accumulated. The developers of Cleverbot are attempting to build a new version using machine learning techniques. An app that uses the Cleverscript engine to play a game of 20 Questions has been launched under the name Clevernator. Unlike other such games, the player asks the questions and it is the role of the AI to understand, and answer factually. An app that allows owners to create and talk to their own small Cleverbot-like AI has been launched, called Cleverme! for Apple products. == In popular culture == Cleverbot received media attention after being featured in the popular 2010 creepypasta ARG web serial Ben Drowned by Alexander D. Hall. In early 2017, a Twitch stream of two Google Home devices modified to talk to each other using Cleverbot garnered over 700,000 visitors and over 30,000 peak concurrent viewers.
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Google Books Ngram Viewer

The Google Books Ngram Viewer is an online search engine that charts the frequencies of any set of search strings using a yearly count of n-grams found in printed sources published between 1500 and 2022 in Google's text corpora in English, Chinese (simplified), French, German, Hebrew, Italian, Russian, or Spanish. There are also some specialized English corpora, such as American English, British English, and English Fiction. The program can search for a word or a phrase. The n-grams are matched with the text within the selected corpus, and if found in 40 or more books, are then displayed as a graph. The program supports searches for parts of speech and wildcards. It is routinely used in research. == History == The Ngram Viewer was created by Google software engineers Will Brockman and Jon Orwant , who teamed up with Harvard researchers Jean-Baptiste Michel and Erez Lieberman Aiden. The service was released on December 16, 2010. Before the release, it was difficult to quantify the rate of linguistic change because of the absence of a database that was designed for this purpose, said Steven Pinker, a well-known linguist who was one of the co-authors of the Science paper published on the same day. The Google Books Ngram Viewer was developed in the hope of opening a new window to quantitative research in the humanities field, and the database contained 500 billion words from 5.2 million books publicly available from the very beginning. The intended audience was scholarly, but the Google Books Ngram Viewer made it possible for anyone with a computer to see a graph that represents the diachronic change of the use of words and phrases with ease. Lieberman said in response to The New York Times that the developers aimed to provide even children with the ability to browse cultural trends throughout history. In the Science paper, Lieberman and his collaborators called the method of high-volume data analysis in digitized texts "culturomics". == Usage == Commas delimit user-entered search terms, where each comma-separated term is searched in the database as an n-gram (for example, "nursery school" is a 2-gram or bigram). The Ngram Viewer then returns a plotted line chart. Due to limitations on the size of the Ngram database, only matches found in at least 40 books are indexed. == Limitations == The data sets of the Ngram Viewer have been criticized for their reliance upon inaccurate optical character recognition (OCR) and for including large numbers of incorrectly dated and categorized texts. Because of these errors, and because they are uncontrolled for bias (such as the increasing amount of scientific literature, which causes other terms to appear to decline in popularity), care must be taken in using the corpora to study language or test theories. Furthermore, the data sets may not reflect general linguistic or cultural change and can only hint at such an effect because they do not involve any metadata like date published, author, length, or genre, to avoid any potential copyright infringements. Systemic errors like the confusion of s and f in pre-19th century texts (due to the use of ſ, the long s, which is similar in appearance to f) can cause systemic bias. Although the Google Books team claims that the results are reliable from 1800 onwards, poor OCR and insufficient data mean that frequencies given for languages such as Chinese may only be accurate from 1970 onward, with earlier parts of the corpus showing no results at all for common terms, and data for some years containing more than 50% noise. Guidelines for doing research with data from Google Ngram have been proposed that try to address some of the issues discussed above.
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