AI Assistant

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

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  Nanosemantics

  Nanosemantics Lab is a Russian IT company specializing in natural language processing (NLP), computer vision (CV), speech technologies (ASR/TTS) and creation of interactive dialog interfaces, particularly chatbots and virtual assistants, based on artificial intelligence (AI). The company uses neural network platforms, including its own-made platform PuzzleLib which works on Russian-made microprocessor architecture Elbrus and Russia-based Astra Linux operating system. The company was founded in 2005 by Igor Ashmanov and Natalya Kaspersky. == Profile == The company was one of the first on Russian market to develop dialog interfaces for different branches of businesses, as well as to support community of AI developers. The company's most demanded product, as for beginning of the 2020s, is the automated "online advisers", functioning as chat bots, made for helping customers with usage of commercial products. In 2009 the company released an online service called iii.ru, where visitors were able to create their own AI-based virtual personalities entitles "infs" (for free). A visitor was able to train its own "inf" and let them chat to other "live" visitors as well with other "infs". More than 2.3 million of "infs" were created and trained by visitors over several years. Nanosemantics Lab maintains its own linguistic programming language for AI development called Dialog Language (DL). Popular social networks and instant messaging services may be used as base platforms. Nanosemantics' AI bots support different types of businesses: banks and financial services, telecommunications, retail, travel and automobile industry, home appliances production, etc. Among its solutions, Nanosemantics lists projects for various companies and institutions, among them VTB, Beeline, MTS, Sberbank, Higher School of Economics, Webmoney, Gazpromneft, Rostelecom, Ford Motors, Ministry of Health of the Russian Federation and others. The company uses the term "inf" for naming its numerous types of chat bots. The term was coined by co-founder Igor Ashmanov, head of Ashmanov & Partners. A 2014 scholarly research at Higher School of Economics, called "Basics of Business Informatics", states that such "infs", when used at business, may lower load on employees, collect statistics useful for understanding market demand and also may increase customer loyalty by providing fast and informative answers due to usage of large databases. The same research describes Nanosemantics' project for Russian branch of Ford Motors company, when AI capabilities were used for promoting the car model Ford Kuga. The research pointed out that within 2 months since beginning, the promo-website conducted 47774 talks of visitors with the specialized "inf", which indicated several hundred thousand of questions and the longest chat lasted for 3 hours 10 minutes. One-year promo campaign showed that 28.6% of people who made pre-orders talked to an "inf". In 2016 Nanosemantics launched a SaaS platform aimed at creating customized virtual assistants by users. The company's flagship product is considered to be Dialog Operating System (DialogOS), a professional corporate platform for creating intellectual voice and textual bots. It has its own linguistic programming language for creation of flexible scenarios and ready-studied neural natural language processing modules that are able to understand human interlocutors. In 2021 the company presented technology called NLab Speech ASR which contains a set of neural-networking algorithms for processing audio signals and analysis of texts that were trained and calibrated using speech-based big data marked up manually. The technology allows speed of processing of data up to "6 real-time factor" and precision values in noisy audio data may exceed 82%. In March 2022 the technology was included in Russia's Joint Registry for Russian Programs for Computers and Databases. As well, another technology was included: NLab Speech TTS, which is text-to-speech system that produces synthesized speech from printed text. == Joint projects == Nanosemantics participates in Ashmanov & Partners' projects related to AI. Since 2014, it helps in development of hardware "personal assistant" called Lexy, a solution similar to Amazon Alexa and the analogues. In August 2019 it was announced that Nanosemantics is going to participate in creation of open operating system for creating automated voice assistants. The project was called SOVA (Smart Open Virtual Assistant) and received investment of 300 million roubles (~$4,6 million) from Russian state-maintained National Technological Initiative. The company maintains long-term partnerships with Skolkovo Innovation Center (resident of IT cluster), branch association "Neuronet" and Yandex. Together with USA-based startup Remedy Logic, Nanosemantics has developed a medical diagnostic system for finding, using AI, spinal pathologies in tomography images of human bodies. Among them: central, foraminal and lateral lumbar stenosis, hernias, arthrosis. The system offers options of treatment. Since August 2021 the company is the resident of Technology Valley of Moscow State University. Also in 2021, Nanosemantics became a member of Committee on Artificial Intelligence within the Russian Association of Software Developers "Native Soft". The company states as one of its missions support of initiatives aimed at preservation and development of the Russian language. In May 2021, together with Pushkin Institute, the company created a chat bot called Phil, that explains to Russian people meaning of different Russian neologisms, and offers synonyms for them. Bot's vocabulary contains more than 500 neologisms, as well the bot can give advice on jargonisms and other types of specific words. Also in 2021, Nanosemanics Lab has signed the first-ever Russian "Codex of ethics of artificial intelligence". It establishes guidelines for ethical behavior of businesses that implement AI-based solutions. === IT contests === The company regularly organizes All-Russian Turing Test competitions for IT developers. Some of these events are co-organized with Microsoft. During the competitions, judges randomly choose virtual interlocutor and have a short conversation with them. They have to determine if a human or a machine is talking to them. An interlocutor may be either a bot or its human creator or operator. The results are measured in per cent of judges that were successfully convinced by a machine that it was a human. In 2021 Nanosemantics took part in federal project "Artificial Intelligence" by National Technological Initiative. In December 2021 the company together with state enterprise "Resource Center of Universal Design and Rehabilitation Technologies" (RCUD-RT) held an all-Russian hackathon aimed at development of AI solutions for medicine. During 3 days, participants created several training programs for patients with speech disorders. In April 2022, another hackathon by Nanosemantics was held together with MIREA – Russian Technological University. Students were participating and trying to generate algorithms for voice deepfakes. 17 teams contested in creation of software that generated artificial voice of a certain person. == Recognition == Since its foundation, Nanosemantics Lab has received a number of recognitions and awards. Among them are several professional ROTOR awards for the website iii.ru (created in 2009). The website gives the general public the means to create and train virtual assistants, which can then be used on a website or integrated into social networks. In 2013, a virtual assistant called Dana, created for Beeline Kazakhstan, was awarded with professional prize "Crystal Headset" in nomination "the best applying of technology". In 2015, the RBTH international media service included Nanosemantics in its list of "Top 50 Startups" in Russia. In 2016, the company received Russian state-maintained award called Runet Prize in two nominations: "State and Society" and "Technology and Innovation". In 2021, in Velikiy Novgorod, Nanosemantics team has won a hackathon aimed at finding means of discovering corruption schemes in Russian laws. In February 2022 the company won another contest by National Technological Initiative, called "Prochtenie", aimed at creation of AI systems for checking schoolchildren's school essays. The Nanosemantics team was awarded 20 million rubles for "overcoming technological barrier" in contest dedicated to English language, and 12 million for 1st place in special nomination "Structure" in Russian-language essay contest.

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  Cryptochannel

  In telecommunications, a cryptochannel is a complete system of crypto-communications between two or more holders or parties. It includes: (a) the cryptographic aids prescribed; (b) the holders thereof; (c) the indicators or other means of identification; (d) the area or areas in which effective; (e) the special purpose, if any, for which provided; and (f) pertinent notes as to distribution, usage, etc. A cryptochannel is analogous to a radio circuit.

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  Pivot to video

  "Pivot to video" is a phrase referring to the trend, starting in 2015, of media publishing companies cutting staff resources for written content (generally published on their own web sites) in favor of short-form video content (often published on third-party platforms such as Facebook, Instagram, Twitter, YouTube, Snapchat, and TikTok). These moves were generally presented by publishers as a response to changes in social media traffic or to changes in the media consumption habits of younger audiences. However, many media commentators have argued that this shift was primarily motivated by advertising revenue, and that only advertisers, not consumers, prefer video over text. The pivot's contribution to job loss in the media industry has given the phrase "pivot to video" an association with decline, especially in a business context. Commentators have also noted a lack of transparency and accuracy in the viewership metrics reported by platforms such as Facebook, pointing out that abrupt shifts in platforms' proprietary algorithms can have devastating effects on publishers' viewership, traffic, and revenue. Following a scandal in which Facebook revealed it had artificially inflated numbers to its advertisers about how long viewers watched ads, many journalists and industry analysts concluded that the shift to video was based on such misleading or inaccurate metrics, which created a false impression that there was customer demand for additional video content. == History == Streaming media technology has been available since the early 1990s, though it was relatively low-fidelity and not widely available until the mid-2000s. In 2007, traditional media publishers including the New York Times, Washington Post and Time Inc. created new divisions to develop web videos, and Facebook launched its video platform. Twitter purchased micro-video service Vine in October 2012, began adding native video streaming in late 2014, and acquired video-streaming service Periscope in January 2015. An August 2014 profile on BuzzFeed noted the publisher's large investment into video production, and observed that "the future of BuzzFeed may not even be on BuzzFeed.com. One of the company’s nascent ideas, BuzzFeed Distributed, will be a team of 20 people producing content that lives entirely on other popular platforms, like Tumblr, Instagram or Snapchat." On 7 January 2015, Facebook issued a statement about "the shift to video," reporting that "since June 2014, Facebook has averaged more than 1 billion video views every day." Media critic John Herrman argued that "What the shift to Facebook video means is that Facebook is more interested in hosting the things media companies make than just spreading them, that it views links to outside pages as a problem to be solved, and that it sees Facebook-hosted video as an example of the solution." In February 2015, the digital video-journalism publisher NowThis announced that it would operate without a home page, producing content to be published directly on social media platforms. In April 2016, Mashable fired much of its editorial staff, attempting to pivot away from hard news coverage while "growing Mashable across every platform" and doubling down on branded content and video. By December 2017, following a sale to Ziff Davis, Mashable retreated from this focus on video; Bernard Gershon, president of GershonMedia, said that the announcement of many such "pivots" were actually aimed primarily at investors. By 2017, "advertiser interest in video [was] insatiable... Any CFO is going to say 'How can we get more video?'" according to an executive of the publishers' trade association Digital Content Next. Publishers such as Vanity Fair, the Washington Post, and Sports Illustrated began adapting their own articles into cheap video content, either dictated by a newsreader or animated as a slideshow with captions, which could be shared on social platforms or even played alongside the articles themselves. June 2017 saw numerous high-profile pivots to video. Vocativ laid off at least 20 staff, including its entire newsroom, explaining that "as the industry evolves, we are undertaking a strategic shift to focus exclusively on video content that will be distributed via social media and other platforms." Fox Sports eliminated its entire writing staff to focus on creating "premium video across all platforms." And MTV News announced a restructuring that would cut its writing team. Less than two years earlier, MTV News had hired Grantland co-founder Dan Fierman to lead a significant investment in "longform" political and cultural reporting, but Fierman left in April 2017, and in June MTV announced it was "shifting resources into short-form video content more in line with young people's media consumption habits." In July, Vice Media laid off at least 60 employees, including the editor-in-chief of Vice Sports, while expanding video production. August 2017 saw Mic cut ten writers and directed the remainder of the newsroom to generate videos for social platforms. CEO Chris Altchek said "When you think about how many hours people spend watching video versus reading, the audience has already spoken." The move was ultimately unsuccessful, and Mic laid off the majority of its staff a year later before being sold to Bustle Media Group for a fraction of its former value. In September 2017, the for-profit wiki-hosting company Fandom began adding commercially produced videos to its otherwise user-generated wiki subdomains, explicitly citing the need to "keep up with user and advertiser expectations" by "diversifying our content," claiming without substantiation that "consumer patterns are changing," necessitating the addition of "complementary video" to accommodate that supposed need. Objection to the content in these videos and its sharp contrast against the content of the wiki sites to which they were applied led to vocal user backlash, leading Fandom CCO Dorth Raphaely to offer the following non-committal response: "I agree that with these videos in particular we did not deliver the right type of content experience." Movie Pilot CEO Tobi Bauckhage explained his company's fall 2017 layoffs as part of moving "from a text-based publishing model to video... a reaction to the fact that Facebook has changed their algorithms in favor of video instead of referral traffic over the last 12 months and we were losing money in the publishing bit of our business." As part of the company's change in direction, the majority of its staff was laid off and its parent company was sold to Webedia. In November 2017, magazine publisher Condé Nast cut jobs, reduced the frequency of several magazines, and shut down the print edition of Teen Vogue, then invested significant new resources in video production, with a senior executive saying "In the next 24 months, I hope that video is half our business... It’s critical. It’s the macro trend of content consumption." In February 2018, Vox Media cut approximately 50 employees, primarily those assigned to "social video," as Vox CEO Jim Bankoff admitted that those efforts were not "viable audience or revenue growth drivers." In August 2020, Facebook Inc. (now Meta Platforms) pivoted Instagram to video in an effort to replicate the success of TikTok and appeal to a younger audience, introducing "reels" as a form of video and promoting them aggressively. Reels accounted more than half the 20 most-viewed posts on Facebook; however, most of these reels were anonymous aggregations of content from TikTok. Elon Musk declared in early 2024 that X (formerly Twitter) was now a "video-first platform", which has been described by critics as a "pivot to video". == As euphemism == In 2017, Journalist Brian Feldman said that "'Pivoting to video' has become a business strategy for digital publishers common enough in recent months to be a kind of cliché — a slick way to describe something else: layoffs." In response, writers use the phrase as gallows humor shorthand for death or cancellation, as in "how do i tell my bf i want our relationship to pivot to video" (SkyNews' Mollie Goodfellow) or "Horse broke its leg, so we had to take it out back and help it 'pivot to video'" (blogger Anil Dash). == Facebook metrics controversy == In September 2016, Facebook admitted that it had reported artificially inflated numbers to its advertisers about how long viewers watched ads leading to an overestimation of 60-80%. Plaintiffs in a later court case allege the discrepancy was as high as 150-900%. Facebook apologized in an official statement and in multiple staff appearances at New York Advertising Week. Two months later, Facebook disclosed additional discrepancies in audience metrics. In October 2018, a California federal court unsealed the text of a class action lawsuit filed by advertisers against Facebook, alleging that Facebook had known since 2015 that its viewership numbers were highly inflated, that internal records showed it "was far from an hon

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  Social media reach

  Social media reach is a media analytics metric that refers to the number of users who have come across a particular content on a particular social media platform. Social media platforms have their own individual ways of tracking, analyzing and reporting the traffic on each of the individual platforms. As these platforms are a main source of communication between companies and their target audiences, by conducting research, companies are able to utilize analytical information, such as the reach of their posts, to better understand the interactions between the users and their content. There are multiple underlying factors that will determine what shows up on a newsfeed or timeline. Algorithms, for example, are a type of factor that can alter the reach of a post due to the way the algorithm is coded, which can affect who sees a post and when. Other examples of factors that can impede the reach can include the time at which posts are made, as well as how frequent the posts are between one another. In comparison, an impression is the total number of circumstances where content has been shown on a social timeline, meanwhile, engagement looks at how people interact with the content that they see on a social platform such as like, share or retweet. == Reach on Facebook == Facebook has their own analytic platform which allows the user to see how other users are interacting with their posts, with the use of multiple metrics. This is not something the average user uses, but rather a tool that is used by pages or public figures. For example, Facebook pages that represent a business often look at the activity their posts have generated. There are three types of reach that can be looked at on the Facebook analytic platform. === Types of reach === ==== Organic Reach ==== This type of reach regards the number of distinct users that have seen a specific post on their feed. Organic reach, in other words is the number of people who have seen the post being analyzed on their Facebook newsfeed. Data gathered from this type of reach can give intel to those doing the analysis, such as the demographics of those who have seen the post. ==== Paid Reach ==== This type of reach regards the number of times that distinct users have come across sponsored posts, ads or content. In other words, paid reach is the number of times Facebook users have seen a post that has been paid for by a company. Data collected can give insight, to advertisers or marketers for example, on the activity based around the reach of their post. ==== Viral Reach ==== This type of reach regards the number of views by distinct users on posts that have been commented on or shared by their friends on Facebook. In other words, viral reach looks at the number of people who have seen a post after a friend of theirs commented or shared the original post, therefore it showed on their timeline. Viral reach can be looked at in terms of a collective number of times that the post has been on individual user's timelines. Data collected from viral reach can be used in multiple ways, for example, it can be used to analyze the type of content that gets shared or commented on and can be further used to compare to other posts. === Engaged users === This refers to the number of individual users who have clicked and interacted with a post on Facebook. == Reach on Twitter == Twitter gives access to any of their users to analytics of their tweets as well as their followers. Their dashboard is user friendly, which allows anyone to take a look at the analytics behind their Twitter account. This open access is useful for both the average user and companies as it can provide a quick glance or general outlook of who has seen their tweets. The way that Twitter works is slightly different than the way of Facebook in terms of the reach. On Twitter, especially for users with a higher profile, they are not only engaging with the people who follow them, but also with the followers of their own followers. The reach metric on Twitter looks at the quantity of Twitter users who have been engaged, but also the number of users that follow them as well. This metric is useful to see the if the tweets/content being shared on Twitter are contributing to the growth of audience on this platform. == Reach on Instagram == Instagram gives their users access to their reach, in the Instagram Insights section. Instagram insights can be used to learn more about an account's followers and performance. Reach indicates the total number of unique Instagram accounts that have seen your Instagram post or story. You can find this data by looking at each individual post insights. == Uses of reach == The reach can be a useful metric to analyze for marketers and advertisers. Social media is a platform that is used by marketers to directly target their intended audience with ease. These platforms not only allow marketers to get a better understanding of their audience, but also allow advertisers to insert their ads onto the timelines of specific users to later be able to conduct research to see the reach of their posts/content. The basic goal of marketers is to increase their reach as much as possible to impact bigger audiences of their dream customers and, in the end, make more sales. When doing organic social media marketing, using paid methods like ads or doing influencer marketing whether it is paid or free, it allows marketers to track the performance of their strategy and tweak it based on what works and what does not. == Analytics and reach == Social analytics looks at the data collected based on the interactions of users on social media platforms. A lot of information can be gathered which can provide intel based on user activities on social media. When looking into analytics in regard to social media, each company or group has a different goal in mind to engage their audience. At a glance, the three might seem as if they are very similar, however the differences between them are significant. There are many aspects that can be analyzed from the data gathered from social media platforms, depending on what is being observed, the correct metric would then be selected to further analyze. One example of the many metrics that can be used through social analytics is the reach. == Reach formula == To calculate social media reach one can use the following formula: R = I f ¯ {\displaystyle R={\frac {I}{\bar {f}}}} where R {\displaystyle R} — is social media reach, I {\displaystyle I} stands for the number of impressions, f ¯ {\displaystyle {\bar {f}}} is the average frequency of impressions per user. f ¯ {\displaystyle {\bar {f}}} represents the number of events when the ad is shown to a particular user. The average value should be calculated over the time period with stable settings of advertisement campaign. == Commenting For Better Reach == Commenting For Better Reach also known as "CFBR" is a widely used strategy for organically boosting post reach on social media platforms. Algorithms tend to favor posts with substantial likes and comments, granting them broader exposure compared to less engaging content. Primarily seen on LinkedIn, a platform geared toward professional networking and business connections, the use of CFBR signals active engagement aimed at enhancing post visibility. It is important to note that genuine and meaningful comments are key to effective engagement. Spammy or irrelevant comments not only detract from the conversation but may also limit a post's potential reach and impact.

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  Timeline of artificial intelligence risks in global finance

  The following article is a broad timeline of the course of events related to artificial intelligence risks in global finance. The AI boom has led to concerns including the existential risk from artificial intelligence, as the uptake on applications of artificial intelligence increases. By late 2025, global finance and artificial intelligence were "deeply intertwined". A June 2025 Menlo Ventures report raised concerns about the sustainability of future revenue and long-term profitability of AI, given the relatively low rate of consumer monetization. == 2017 == 30 NovemberThe New York Times said that new AI reports by McKinsey & Company, the National Bureau of Economic Research, and an AI Index created by university researchers, indicated an early AI boom. The Index built on a project—"The One Hundred Year Study on Artificial Intelligence" launched in 2014. == 2018 == 2018 was a year of incremental AI growth in finance. == 2022 == The release of ChatGPT by OpenAI became the catalyst for an artificial intelligence boom that continues to remake the global economy. According to a European Central Bank report, public interest in AI increased rapidly as evidenced with rising Google searches, AI jobs, models, patents, and innovations since late 2022. At that time Europe led the US in the size of its AI workforce. == 2023 == The regulatory body, the International Monetary Fund (IMF), published their report, "Generative Artificial Intelligence in Finance: Risk Considerations", drawing attention to oversight gaps and the need for regulations. The report explores the risks posed by using generative artificial intelligence (GenAI) systems in the financial sector including "broader risks to financial stability." == 2024 == January 12 In January 2024 Bloomberg's published its list of the "Magnificent Seven" Big Tech companies on the stock market based on their strength, size and market capitalization:Apple, Microsoft, Alphabet (Google), Amazon, Meta Platforms (Facebook), Nvidia, and Tesla. 21 June During the AI boom, Nvidia became the world's most valuable company, surpassing Microsoft, as its value increased to over US$4 trillion. In 2023 and 2024, the "Magnificent Seven" stocks were the primary drivers behind the increase in equity indexes, according to Reuters. == 2025 == === January === 23 January President Donald Trump's AI policy was announced calling for United States global leadership in artificial intelligence. The Economist noted that this politic shift in which the United States seeks "global dominance" in AI includes trimming regulations and assisting in expansion of infrastructure and increase in number of AI workers. Governments of Gulf nations were also investing trillions of dollars in AI. 27 January Against the backdrop of a tech war between China and the United States over AI dominance, within days of the launch of China's free DeepSeek App, it was the most downloaded app in the United States, rising to the first place in the Apple app store. President Trump responded immediately, saying this "sudden rise" should be a "wake-up" call to the United States, and called on US companies to be more competitive. === June === 26 June In their June 2025 report, Menlo Ventures estimated that only about 3% of consumers paid for artificial intelligence-related services, representing about $USD12 billion in annual spending. This is relatively low in contrast to the massive capital expenditure by AI infrastructure companies, which raises concerns about revenue sustainability and long-term profitability. === July === 23 July The Trump administration launched the US AI Action Plan, positioning the United States in a high-stakes technological race with China for global dominance in artificial intelligence, emphasizing that neither nation can afford to fall behind due to the exponential nature of AI advancement. The plan, a new government website and policy speech called for accelerated AI adoption across federal agencies, and a number of initiatives to make is easier for AI infrastructure expansion, and other measures to ensure American leadership in AI standards. Some leading experts warned that the administration failed to provide sufficient regulations and safeguards for AI safety. Concerns were raised about the negative impacts of cuts to research funding and tightened visa policies for scientists, potentially undermining public trust and America's ability to compete internationally. === September === 7 September The Economist cautioned that AI revenues are relatively modest compared to the high cost and investments in the creation of new data centers. Even Sam Altman, OpenAI CEO and one of the leading figures of the AI boom,, raised concerns about investors' outsized hopes for financial returns. At the same time, history has shown that new technologies, like railways and electricity, endured and spread after the initial hype faded. 12 September Economists warn that U.S. households' direct and indirect investments—mutual funds or retirement plans—in the stock market reached an unprecedented historically high level, now representing 45% of all financial assets, or about $USD51.2 trillion. Compared to the Dot-com bubble this represents a sharp increase in exposure. This makes U.S. households vulnerable to market downturns which in turn would result in decreasing consumer spending. U.S. household net worth rose to a record $176.3 trillion in the second quarter, an increase of $7.3 trillion since early 2025 and about $46 trillion higher than before the pandemic. Federal Reserve data attribute the surge primarily to gains in stock markets and housing values. However, the rise in wealth on paper coincided with increased household borrowing and growing government debt. 18 September Questions were being raised about how quickly the data centers, chips, servers, and GPUs assets of major AI companies will depreciate in value. Comparisons have been made to the Railway Mania in the aftermath of the stock market bubble where a valuable physical infrastructure remained standing, and the telecoms crash after the dot-com bubble which left fiber networks. 28 September There were warnings that record-high American stock ownership during the AI-fueled market boom is a red flag for systemic risk, as the current concentration in equities exceeds levels seen before the dot-com bubble burst in 2000, and could amplify the impact of any future stock market correction. === October === 3 October In 2025 alone, venture capitalists invested almost $USD200 billion in the artificial intelligence sector. 29 October Nvidia was the first company in the world to be valued at US$5 trillion, largely due to AI demand and strategic partnerships with leading technology and AI firms. Nvidia's increase in value was "meteoric". === November === 2 November Forbes reported that, since April, the 'Magnificent Seven' tech giants together contributed over 40% of the S&P 500's return, highlighting their outsized influence and the growing impact of AI on market valuations. CNN warned that while there is a current benefit to investors, with such a high concentration in the S&P 500, they are highly exposed to the fate of the Mag Seven. 2 November Globally there are 11,000 datacentres—huge campuses for AI infrastructure, including thousands of chips, GPUS, and servers. This represents a 500% increase over the last two decades. It is anticipated that $3USDtn more will be spent on increasing that number over the next two or three years. 5 November Concerns about the potential for a market bubble were raised as six of the AI-related Big Tech "Magnificent Seven"—that contribute to the AI boom—reported losing ground in the stock market. Global markets and artificial intelligence have become "deeply intertwined", according to a Reuters report. As of November 2025, more than 50% of the 20 largest S&P firms were deeply exposed to AI. In contrast, in 2000, the 20 S&P 500 firms represented 39% of its total value only 11 of these companies were exposed to the internet. If AI fails to deliver strong returns on their investments, these top S&P firms would be significantly impacted, according to the Economist. Analysts suggest that the AI market in 2025 may not behave like a traditional one, as investors are simultaneously aware of the risks and driven by the potential for outsized rewards. Leading AI labs may believe that the first company to achieve artificial general intelligence (AGI), when an AI system surpasses all human cognitive abilities and becomes capable of self-improvement—could dominate the future of technology and finance. While some have estimated that the potential value of such a breakthrough could be as high as $1.46 quadrillion, this figure is speculative and widely debated. 5 November Bloomberg described Nvidia's H100 Hopper-Blackwell AI chips as the "King of AI chips". Nvidia dominates the AI chip market with over 78% of the market share because of both speed and cost. According to B

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  Key (cryptography)

  A key in cryptography is a piece of information, usually a string of numbers or letters that are stored in a file, which, when processed through a cryptographic algorithm, can encode or decode cryptographic data. Based on the used method, the key can be different sizes and varieties, but in all cases, the strength of the encryption relies on the security of the key being maintained. A key's security strength is dependent on its algorithm, the size of the key, the generation of the key, and the process of key exchange. == Scope == The key is what is used to encrypt data from plaintext to ciphertext. There are different methods for utilizing keys and encryption. === Symmetric cryptography === Symmetric cryptography refers to the practice of the same key being used for both encryption and decryption. === Asymmetric cryptography === Asymmetric cryptography has separate keys for encrypting and decrypting. These keys are known as the public and private keys, respectively. == Purpose == Since the key protects the confidentiality and integrity of the system, it is important to be kept secret from unauthorized parties. With public key cryptography, only the private key must be kept secret, but with symmetric cryptography, it is important to maintain the confidentiality of the key. Kerckhoff's principle states that the entire security of the cryptographic system relies on the secrecy of the key. == Key sizes == Key size is the number of bits in the key defined by the algorithm. This size defines the upper bound of the cryptographic algorithm's security. The larger the key size, the longer it will take before the key is compromised by a brute force attack. Since perfect secrecy is not feasible for key algorithms, researches are now more focused on computational security. In the past, keys were required to be a minimum of 40 bits in length, however, as technology advanced, these keys were being broken quicker and quicker. As a response, restrictions on symmetric keys were enhanced to be greater in size. Currently, 2048 bit RSA is commonly used, which is sufficient for current systems. However, current RSA key sizes would all be cracked quickly with a powerful quantum computer. "The keys used in public key cryptography have some mathematical structure. For example, public keys used in the RSA system are the product of two prime numbers. Thus public key systems require longer key lengths than symmetric systems for an equivalent level of security. 3072 bits is the suggested key length for systems based on factoring and integer discrete logarithms which aim to have security equivalent to a 128 bit symmetric cipher." == Key generation == To prevent a key from being guessed, keys need to be generated randomly and contain sufficient entropy. The problem of how to safely generate random keys is difficult and has been addressed in many ways by various cryptographic systems. A key can directly be generated by using the output of a Random Bit Generator (RBG), a system that generates a sequence of unpredictable and unbiased bits. A RBG can be used to directly produce either a symmetric key or the random output for an asymmetric key pair generation. Alternatively, a key can also be indirectly created during a key-agreement transaction, from another key or from a password. Some operating systems include tools for "collecting" entropy from the timing of unpredictable operations such as disk drive head movements. For the production of small amounts of keying material, ordinary dice provide a good source of high-quality randomness. == Establishment scheme == The security of a key is dependent on how a key is exchanged between parties. Establishing a secured communication channel is necessary so that outsiders cannot obtain the key. A key establishment scheme (or key exchange) is used to transfer an encryption key among entities. Key agreement and key transport are the two types of a key exchange scheme that are used to be remotely exchanged between entities . In a key agreement scheme, a secret key, which is used between the sender and the receiver to encrypt and decrypt information, is set up to be sent indirectly. All parties exchange information (the shared secret) that permits each party to derive the secret key material. In a key transport scheme, encrypted keying material that is chosen by the sender is transported to the receiver. Either symmetric key or asymmetric key techniques can be used in both schemes. The Diffie–Hellman key exchange and Rivest-Shamir-Adleman (RSA) are the most two widely used key exchange algorithms. In 1976, Whitfield Diffie and Martin Hellman constructed the Diffie–Hellman algorithm, which was the first public key algorithm. The Diffie–Hellman key exchange protocol allows key exchange over an insecure channel by electronically generating a shared key between two parties. On the other hand, RSA is a form of the asymmetric key system which consists of three steps: key generation, encryption, and decryption. Key confirmation delivers an assurance between the key confirmation recipient and provider that the shared keying materials are correct and established. The National Institute of Standards and Technology recommends key confirmation to be integrated into a key establishment scheme to validate its implementations. == Management == Key management concerns the generation, establishment, storage, usage and replacement of cryptographic keys. A key management system (KMS) typically includes three steps of establishing, storing and using keys. The base of security for the generation, storage, distribution, use and destruction of keys depends on successful key management protocols. == Key vs password == A password is a memorized series of characters including letters, digits, and other special symbols that are used to verify identity. It is often produced by a human user or a password management software to protect personal and sensitive information or generate cryptographic keys. Passwords are often created to be memorized by users and may contain non-random information such as dictionary words. On the other hand, a key can help strengthen password protection by implementing a cryptographic algorithm which is difficult to guess or replace the password altogether. A key is generated based on random or pseudo-random data and can often be unreadable to humans. A password is less safe than a cryptographic key due to its low entropy, randomness, and human-readable properties. However, the password may be the only secret data that is accessible to the cryptographic algorithm for information security in some applications such as securing information in storage devices. Thus, a deterministic algorithm called a key derivation function (KDF) uses a password to generate the secure cryptographic keying material to compensate for the password's weakness. Various methods such as adding a salt or key stretching may be used in the generation.

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  Merit Network

  Merit Network, Inc., is a nonprofit member-governed organization providing high-performance computer networking and related services to educational, government, health care, and nonprofit organizations, primarily in Michigan. Created in 1966, Merit operates the longest running regional computer network in the United States. == Organization == Created in 1966 as the Michigan Educational Research Information Triad by Michigan State University (MSU), the University of Michigan (U-M), and Wayne State University (WSU), Merit was created to investigate resource sharing by connecting the mainframe computers at these three Michigan public research universities. Merit's initial three node packet-switched computer network was operational in October 1972 using custom hardware based on DEC PDP-11 minicomputers and software developed by the Merit staff and the staffs at the three universities. Over the next dozen years the initial network grew as new services such as dial-in terminal support, remote job submission, remote printing, and file transfer were added; as gateways to the national and international Tymnet, Telenet, and Datapac networks were established, as support for the X.25 and TCP/IP protocols was added; as additional computers such as WSU's MVS system and the UM's electrical engineering's VAX running UNIX were attached; and as new universities became Merit members. Merit's involvement in national networking activities started in the mid-1980s with connections to the national supercomputing centers and work on the 56 kbit/s National Science Foundation Network (NSFNET), the forerunner of today's Internet. From 1987 until April 1995, Merit re-engineered and managed the NSFNET backbone service. MichNet, Merit's regional network in Michigan was attached to NSFNET and in the early 1990s Merit began extending "the Internet" throughout Michigan, offering both direct connect and dial-in services, and upgrading the statewide network from 56 kbit/s to 1.5 Mbit/s, and on to 45, 155, 622 Mbit/s, and eventually 1 and 10 Gbit/s. In 2003 Merit began its transition to a facilities based network, using fiber optic facilities that it shares with its members, that it purchases or leases under long-term agreements, or that it builds. In addition to network connectivity services, Merit offers a number of related services within Michigan and beyond, including: Internet2 connectivity, VPN, Network monitoring, Voice over IP (VOIP), Cloud storage, E-mail, Domain Name, Network Time, VMware and Zimbra software licensing, Colocation, and professional development seminars, workshops, classes, conferences, and meetings. == History == === Creating the network: 1966 to 1973 === The Michigan Educational Research Information Triad (MERIT) was formed in the fall of 1966 by Michigan State University (MSU), University of Michigan (U-M), and Wayne State University (WSU). More often known as the Merit Computer Network or simply Merit, it was created to design and implement a computer network connecting the mainframe computers at the universities. In the fall of 1969, after funding for the initial development of the network had been secured, Bertram Herzog was named director for MERIT. Eric Aupperle was hired as senior engineer, and was charged with finding hardware to make the network operational. The National Science Foundation (NSF) and the State of Michigan provided the initial funding for the network. In June 1970, the Applied Dynamics Division of Reliance Electric in Saline, Michigan was contracted to build three Communication Computers or CCs. Each would consist of a Digital Equipment Corporation (DEC) PDP-11 computer, dataphone interfaces, and interfaces that would attach them directly to the mainframe computers. The cost was to be slightly less than the $300,000 ($2,487,100, adjusted for inflation) originally budgeted. Merit staff wrote the software that ran on the CCs, while staff at each of the universities wrote the mainframe software to interface to the CCs. The first completed connection linked the IBM S/360-67 mainframe computers running the Michigan Terminal System at WSU and U-M, and was publicly demonstrated on December 14, 1971. The MSU node was completed in October 1972, adding a CDC 6500 mainframe running Scope/Hustler. The network was officially dedicated on May 15, 1973. === Expanding the network: 1974 to 1985 === In 1974, Herzog returned to teaching in the University of Michigan's Industrial Engineering Department, and Aupperle was appointed as director. Use of the all uppercase name "MERIT" was abandoned in favor of the mixed case "Merit". The first network connections were host to host interactive connections which allowed person to remote computer or local computer to remote computer interactions. To this, terminal to host connections, batch connections (remote job submission, remote printing, batch file transfer), and interactive file copy were added. And, in addition to connecting to host computers over custom hardware interfaces, the ability to connect to hosts or other networks over groups of asynchronous ports and via X.25 were added. Merit interconnected with Telenet (later SprintNet) in 1976 to give Merit users dial-in access from locations around the United States. Dial-in access within the U.S. and internationally was further expanded via Merit's interconnections to Tymnet, ADP's Autonet, and later still the IBM Global Network as well as Merit's own expanding network of dial-in sites in Michigan, New York City, and Washington, D.C. In 1978, Western Michigan University (WMU) became the fourth member of Merit (prompting a name change, as the acronym Merit no longer made sense as the group was no longer a triad). To expand the network, the Merit staff developed new hardware interfaces for the Digital PDP-11 based on printed circuit technology. The new system became known as the Primary Communications Processor (PCP), with the earliest PCPs connecting a PDP-10 located at WMU and a DEC VAX running UNIX at U-M's Electrical Engineering department. A second hardware technology initiative in 1983 produced the smaller Secondary Communication Processors (SCP) based on DEC LSI-11 processors. The first SCP was installed at the Michigan Union in Ann Arbor, creating UMnet, which extended Merit's network connectivity deeply into the U-M campus. In 1983 Merit's PCP and SCP software was enhanced to support TCP/IP and Merit interconnected with the ARPANET. === National networking, NSFNET, and the Internet: 1986 to 1995 === In 1986 Merit engineered and operated leased lines and satellite links that allowed the University of Michigan to access the supercomputing facilities at Pittsburgh, San Diego, and NCAR. In 1987, Merit, IBM and MCI submitted a winning proposal to NSF to implement a new NSFNET backbone network. The new NSFNET backbone network service began July 1, 1988. It interconnected supercomputing centers around the country at 1.5 megabits per second (T1), 24 times faster than the 56 kilobits-per-second speed of the previous network. The NSFNET backbone grew to link scientists and educators on university campuses nationwide and connect them to their counterparts around the world. The NSFNET project caused substantial growth at Merit, nearly tripling the staff and leading to the establishment of a new 24-hour Network Operations Center at the U-M Computer Center. In September 1990 in anticipation of the NSFNET T3 upgrade and the approaching end of the 5-year NSFNET cooperative agreement, Merit, IBM, and MCI formed Advanced Network and Services (ANS), a new non-profit corporation with a more broadly based Board of Directors than the Michigan-based Merit Network. Under its cooperative agreement with NSF, Merit remained ultimately responsible for the operation of NSFNET, but subcontracted much of the engineering and operations work to ANS. In 1991 the NSFNET backbone service was expanded to additional sites and upgraded to a more robust 45 Mbit/s (T3) based network. The new T3 backbone was named ANSNet and provided the physical infrastructure used by Merit to deliver the NSFNET Backbone Service. On April 30, 1995, the NSFNET project came to an end, when the NSFNET backbone service was decommissioned and replaced by a new Internet architecture with commercial Internet service providers (ISPs) interconnected at Network Access Points provided by multiple providers across the country. === Bringing the Internet to Michigan: 1985 to 2001 === During the 1980s, Merit Network grew to serve eight member universities, with Oakland University joining in 1985 and Central Michigan University, Eastern Michigan University, and Michigan Technological University joining in 1987. In 1990, Merit's board of directors formally changed the organization's name to Merit Network, Inc., and created the name MichNet to refer to Merit's statewide network. The board also approved a staff proposal to allow organizations other than publicly supported universities, referred to as aff

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  Hyper-encryption

  Hyper-encryption is a form of encryption invented by Michael O. Rabin which uses a high-bandwidth source of public random bits, together with a secret key that is shared by only the sender and recipient(s) of the message. It uses the assumptions of Ueli Maurer's bounded-storage model as the basis of its secrecy. Although everyone can see the data, decryption by adversaries without the secret key is still not feasible, because of the space limitations of storing enough data to mount an attack against the system. Unlike almost all other cryptosystems except the one-time pad, hyper-encryption can be proved to be information-theoretically secure, provided the storage bound cannot be surpassed. Moreover, if the necessary public information cannot be stored at the time of transmission, the plaintext can be shown to be impossible to recover, regardless of the computational capacity available to an adversary in the future, even if they have access to the secret key at that future time. A highly energy-efficient implementation of a hyper-encryption chip was demonstrated by Krishna Palem et al. using the Probabilistic CMOS or PCMOS technology and was shown to be ~205 times more efficient in terms of Energy-Performance-Product.

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  BERT (language model)

  Bidirectional encoder representations from transformers (BERT) is a language model introduced in October 2018 by researchers at Google. It learns to represent text as a sequence of vectors using self-supervised learning. It uses the encoder-only transformer architecture. BERT dramatically improved the state of the art for large language models. As of 2020, BERT is a ubiquitous baseline in natural language processing (NLP) experiments. BERT is trained by masked token prediction and next sentence prediction. With this training, BERT learns contextual, latent representations of tokens in their context, similar to ELMo and GPT-2. It found applications for many natural language processing tasks, such as coreference resolution and polysemy resolution. It improved on ELMo and spawned the study of "BERTology", which attempts to interpret what is learned by BERT. BERT was originally implemented in the English language at two model sizes, BERTBASE (110 million parameters) and BERTLARGE (340 million parameters). Both were trained on the Toronto BookCorpus (800M words) and English Wikipedia (2,500M words). The weights were released on GitHub. On March 11, 2020, 24 smaller models were released, the smallest being BERTTINY with just 4 million parameters. == Architecture == BERT is an "encoder-only" transformer architecture. At a high level, BERT consists of 4 modules: Tokenizer: This module converts a piece of English text into a sequence of integers ("tokens"). Embedding: This module converts the sequence of tokens into an array of real-valued vectors representing the tokens. It represents the conversion of discrete token types into a lower-dimensional Euclidean space. Encoder: a stack of Transformer blocks with self-attention, but without causal masking. Task head: This module converts the final representation vectors into one-shot encoded tokens again by producing a predicted probability distribution over the token types. It can be viewed as a simple decoder, decoding the latent representation into token types, or as an "un-embedding layer". The task head is necessary for pre-training, but it is often unnecessary for so-called "downstream tasks," such as question answering or sentiment classification. Instead, one removes the task head and replaces it with a newly initialized module suited for the task, and finetune the new module. The latent vector representation of the model is directly fed into this new module, allowing for sample-efficient transfer learning. === Embedding === This section describes the embedding used by BERTBASE. The other one, BERTLARGE, is similar, just larger. The tokenizer of BERT is WordPiece, which is a sub-word strategy like byte-pair encoding. Its vocabulary size is 30,000, and any token not appearing in its vocabulary is replaced by [UNK] ("unknown"). The first layer is the embedding layer, which contains three components: token type embeddings, position embeddings, and segment type embeddings. Token type: The token type is a standard embedding layer, translating a one-hot vector into a dense vector based on its token type. Position: The position embeddings are based on a token's position in the sequence. BERT uses absolute position embeddings, where each position in a sequence is mapped to a real-valued vector. Each dimension of the vector consists of a sinusoidal function that takes the position in the sequence as input. Segment type: Using a vocabulary of just 0 or 1, this embedding layer produces a dense vector based on whether the token belongs to the first or second text segment in that input. In other words, type-1 tokens are all tokens that appear after the [SEP] special token. All prior tokens are type-0. The three embedding vectors are added together representing the initial token representation as a function of these three pieces of information. After embedding, the vector representation is normalized using a LayerNorm operation, outputting a 768-dimensional vector for each input token. After this, the representation vectors are passed forward through 12 Transformer encoder blocks, and are decoded back to 30,000-dimensional vocabulary space using a basic affine transformation layer. === Architectural family === The encoder stack of BERT has 2 free parameters: L {\displaystyle L} , the number of layers, and H {\displaystyle H} , the hidden size. There are always H / 64 {\displaystyle H/64} self-attention heads, and the feed-forward/filter size is always 4 H {\displaystyle 4H} . By varying these two numbers, one obtains an entire family of BERT models. For BERT: the feed-forward size and filter size are synonymous. Both of them denote the number of dimensions in the middle layer of the feed-forward network. the hidden size and embedding size are synonymous. Both of them denote the number of real numbers used to represent a token. The notation for encoder stack is written as L/H. For example, BERTBASE is written as 12L/768H, BERTLARGE as 24L/1024H, and BERTTINY as 2L/128H. == Training == === Pre-training === BERT was pre-trained simultaneously on two tasks: Masked language modeling (MLM): In this task, BERT ingests a sequence of words, where one word may be randomly changed ("masked"), and BERT tries to predict the original words that had been changed. For example, in the sentence "The cat sat on the [MASK]," BERT would need to predict "mat." This helps BERT learn bidirectional context, meaning it understands the relationships between words not just from left to right or right to left but from both directions at the same time. Next sentence prediction (NSP): In this task, BERT is trained to predict whether one sentence logically follows another. For example, given two sentences, "The cat sat on the mat" and "It was a sunny day", BERT has to decide if the second sentence is a valid continuation of the first one. This helps BERT understand relationships between sentences, which is important for tasks like question answering or document classification. ==== Masked language modeling ==== In masked language modeling, 15% of tokens would be randomly selected for masked-prediction task, and the training objective was to predict the masked token given its context. In more detail, the selected token is: replaced with a [MASK] token with probability 80%, replaced with a random word token with probability 10%, not replaced with probability 10%. The reason not all selected tokens are masked is to avoid the dataset shift problem. The dataset shift problem arises when the distribution of inputs seen during training differs significantly from the distribution encountered during inference. A trained BERT model might be applied to word representation (like Word2Vec), where it would be run over sentences not containing any [MASK] tokens. It is later found that more diverse training objectives are generally better. As an illustrative example, consider the sentence "my dog is cute". It would first be divided into tokens like "my1 dog2 is3 cute4". Then a random token in the sentence would be picked. Let it be the 4th one "cute4". Next, there would be three possibilities: with probability 80%, the chosen token is masked, resulting in "my1 dog2 is3 [MASK]4"; with probability 10%, the chosen token is replaced by a uniformly sampled random token, such as "happy", resulting in "my1 dog2 is3 happy4"; with probability 10%, nothing is done, resulting in "my1 dog2 is3 cute4". After processing the input text, the model's 4th output vector is passed to its decoder layer, which outputs a probability distribution over its 30,000-dimensional vocabulary space. ==== Next sentence prediction ==== Given two sentences, the model predicts if they appear sequentially in the training corpus, outputting either [IsNext] or [NotNext]. During training, the algorithm sometimes samples two sentences from a single continuous span in the training corpus, while at other times, it samples two sentences from two discontinuous spans. The first sentence starts with a special token, [CLS] (for "classify"). The two sentences are separated by another special token, [SEP] (for "separate"). After processing the two sentences, the final vector for the [CLS] token is passed to a linear layer for binary classification into [IsNext] and [NotNext]. For example: Given "[CLS] my dog is cute [SEP] he likes playing [SEP]", the model should predict [IsNext]. Given "[CLS] my dog is cute [SEP] how do magnets work [SEP]", the model should predict [NotNext]. === Fine-tuning === BERT is meant as a general pretrained model for various applications in natural language processing. That is, after pre-training, BERT can be fine-tuned with fewer resources on smaller datasets to optimize its performance on specific tasks such as natural language inference and text classification, and sequence-to-sequence-based language generation tasks such as question answering and conversational response generation. The original BERT paper published results demonstrating that a small amount of fine

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  Initialization vector

  In cryptography, an initialization vector (IV) or starting variable is an input to a cryptographic primitive being used to provide the initial state. The IV is typically required to be random or pseudorandom, but sometimes an IV only needs to be unpredictable or unique. Randomization is crucial for some encryption schemes to achieve semantic security, a property whereby repeated usage of the scheme under the same key does not allow an attacker to infer relationships between (potentially similar) segments of the encrypted message. For block ciphers, the use of an IV is described by the modes of operation. Some cryptographic primitives require the IV only to be non-repeating, and the required randomness is derived internally. In this case, the IV is commonly called a nonce (a number used only once), and the primitives (e.g. CBC) are considered stateful rather than randomized. This is because an IV need not be explicitly forwarded to a recipient but may be derived from a common state updated at both sender and receiver side. (In practice, a short nonce is still transmitted along with the message to consider message loss.) An example of stateful encryption schemes is the counter mode of operation, which has a sequence number for a nonce. The IV size depends on the cryptographic primitive used; for block ciphers it is generally the cipher's block-size. In encryption schemes, the unpredictable part of the IV has at best the same size as the key to compensate for time/memory/data tradeoff attacks. When the IV is chosen at random, the probability of collisions due to the birthday problem must be taken into account. Traditional stream ciphers such as RC4 do not support an explicit IV as input, and a custom solution for incorporating an IV into the cipher's key or internal state is needed. Some designs realized in practice are known to be insecure; the WEP protocol is a notable example, and is prone to related-IV attacks. == Motivation == A block cipher is one of the most basic primitives in cryptography, and frequently used for data encryption. However, by itself, it can only be used to encode a data block of a predefined size, called the block size. For example, a single invocation of the AES algorithm transforms a 128-bit plaintext block into a ciphertext block of 128 bits in size. The key, which is given as one input to the cipher, defines the mapping between plaintext and ciphertext. If data of arbitrary length is to be encrypted, a simple strategy is to split the data into blocks each matching the cipher's block size, and encrypt each block separately using the same key. This method is not secure as equal plaintext blocks get transformed into equal ciphertexts, and a third party observing the encrypted data may easily determine its content even when not knowing the encryption key. To hide patterns in encrypted data while avoiding the re-issuing of a new key after each block cipher invocation, a method is needed to randomize the input data. In 1980, the NIST published a national standard document designated Federal Information Processing Standard (FIPS) PUB 81, which specified four so-called block cipher modes of operation, each describing a different solution for encrypting a set of input blocks. The first mode implements the simple strategy described above, and was specified as the electronic codebook (ECB) mode. In contrast, each of the other modes describe a process where ciphertext from one block encryption step gets intermixed with the data from the next encryption step. To initiate this process, an additional input value is required to be mixed with the first block, and which is referred to as an initialization vector. For example, the cipher-block chaining (CBC) mode requires an unpredictable value, of size equal to the cipher's block size, as additional input. This unpredictable value is added to the first plaintext block before subsequent encryption. In turn, the ciphertext produced in the first encryption step is added to the second plaintext block, and so on. The ultimate goal for encryption schemes is to provide semantic security: by this property, it is practically impossible for an attacker to draw any knowledge from observed ciphertext. It can be shown that each of the three additional modes specified by the NIST are semantically secure under so-called chosen-plaintext attacks. == Properties == Properties of an IV depend on the cryptographic scheme used. A basic requirement is uniqueness, which means that no IV may be reused under the same key. For block ciphers, repeated IV values devolve the encryption scheme into electronic codebook mode: equal IV and equal plaintext result in equal ciphertext. In stream cipher encryption uniqueness is crucially important as plaintext may be trivially recovered otherwise. Example: Stream ciphers encrypt plaintext P to ciphertext C by deriving a key stream K from a given key and IV and computing C as C = P xor K. Assume that an attacker has observed two messages C1 and C2 both encrypted with the same key and IV. Then knowledge of either P1 or P2 reveals the other plaintext since C1 xor C2 = (P1 xor K) xor (P2 xor K) = P1 xor P2. Many schemes require the IV to be unpredictable by an adversary. This is effected by selecting the IV at random or pseudo-randomly. In such schemes, the chance of a duplicate IV is negligible, but the effect of the birthday problem must be considered. As for the uniqueness requirement, a predictable IV may allow recovery of (partial) plaintext. Example: Consider a scenario where a legitimate party called Alice encrypts messages using the cipher-block chaining mode. Consider further that there is an adversary called Eve that can observe these encryptions and is able to forward plaintext messages to Alice for encryption (in other words, Eve is capable of a chosen-plaintext attack). Now assume that Alice has sent a message consisting of an initialization vector IV1 and starting with a ciphertext block CAlice. Let further PAlice denote the first plaintext block of Alice's message, let E denote encryption, and let PEve be Eve's guess for the first plaintext block. Now, if Eve can determine the initialization vector IV2 of the next message she will be able to test her guess by forwarding a plaintext message to Alice starting with (IV2 xor IV1 xor PEve); if her guess was correct this plaintext block will get encrypted to CAlice by Alice. This is because of the following simple observation: CAlice = E(IV1 xor PAlice) = E(IV2 xor (IV2 xor IV1 xor PAlice)). Depending on whether the IV for a cryptographic scheme must be random or only unique the scheme is either called randomized or stateful. While randomized schemes always require the IV chosen by a sender to be forwarded to receivers, stateful schemes allow sender and receiver to share a common IV state, which is updated in a predefined way at both sides. == Block ciphers == Block cipher processing of data is usually described as a mode of operation. Modes are primarily defined for encryption as well as authentication, though newer designs exist that combine both security solutions in so-called authenticated encryption modes. While encryption and authenticated encryption modes usually take an IV matching the cipher's block size, authentication modes are commonly realized as deterministic algorithms, and the IV is set to zero or some other fixed value. == Stream ciphers == In stream ciphers, IVs are loaded into the keyed internal secret state of the cipher, after which a number of cipher rounds are executed prior to releasing the first bit of output. For performance reasons, designers of stream ciphers try to keep that number of rounds as small as possible, but because determining the minimal secure number of rounds for stream ciphers is not a trivial task, and considering other issues such as entropy loss, unique to each cipher construction, related-IVs and other IV-related attacks are a known security issue for stream ciphers, which makes IV loading in stream ciphers a serious concern and a subject of ongoing research. == WEP IV == The 802.11 encryption algorithm called WEP (short for Wired Equivalent Privacy) used a short, 24-bit IV, leading to reused IVs with the same key, which led to it being easily cracked. Packet injection allowed for WEP to be cracked in times as short as several seconds. This ultimately led to the deprecation of WEP. == SSL 2.0 IV == In cipher-block chaining mode (CBC mode), the IV need not be secret, but must be unpredictable (In particular, for any given plaintext, it must not be possible to predict the IV that will be associated to the plaintext in advance of the generation of the IV.) at encryption time. Additionally for the output feedback mode (OFB mode), the IV must be unique. In particular, the (previously) common practice of re-using the last ciphertext block of a message as the IV for the next message is insecure (for example, this method was used by SSL 2.0). If an attacker knows

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  PGP word list

  The PGP Word List ("Pretty Good Privacy word list", also called a biometric word list for reasons explained below) is a list of words for conveying data bytes in a clear unambiguous way via a voice channel. They are analogous in purpose to the NATO phonetic alphabet, except that a longer list of words is used, each word corresponding to one of the 256 distinct numeric byte values. == History and structure == The PGP Word List was designed in 1995 by Patrick Juola, a computational linguist, and Philip Zimmermann, creator of PGP. The words were carefully chosen for their phonetic distinctiveness, using genetic algorithms to select lists of words that had optimum separations in phoneme space. The candidate word lists were randomly drawn from Grady Ward's Moby Pronunciator list as raw material for the search, successively refined by the genetic algorithms. The automated search converged to an optimized solution in about 40 hours on a DEC Alpha, a particularly fast machine in that era. The Zimmermann–Juola list was originally designed to be used in PGPfone, a secure VoIP application, to allow the two parties to verbally compare a short authentication string to detect a man-in-the-middle attack (MiTM). It was called a biometric word list because the authentication depended on the two human users recognizing each other's distinct voices as they read and compared the words over the voice channel, binding the identity of the speaker with the words, which helped protect against the MiTM attack. The list can be used in many other situations where a biometric binding of identity is not needed, so calling it a biometric word list may be imprecise. Later, it was used in PGP to compare and verify PGP public key fingerprints over a voice channel. This is known in PGP applications as the "biometric" representation. When it was applied to PGP, the list of words was further refined, with contributions by Jon Callas. More recently, it has been used in Zfone and the ZRTP protocol, the successor to PGPfone. The list is actually composed of two lists, each containing 256 phonetically distinct words, in which each word represents a different byte value between 0 and 255. Two lists are used because reading aloud long random sequences of human words usually risks three kinds of errors: 1) transposition of two consecutive words, 2) duplicate words, or 3) omitted words. To detect all three kinds of errors, the two lists are used alternately for the even-offset bytes and the odd-offset bytes in the byte sequence. Each byte value is actually represented by two different words, depending on whether that byte appears at an odd or an even offset from the beginning of the byte sequence. The two lists are readily distinguished by the number of syllables; the odd list has words of three syllables, the even list has two. The two lists have a maximum word length of 11 and 9 letters, respectively. Using a two-list scheme was suggested by Zhahai Stewart. == Examples == Each byte in a bytestring is encoded as a single word. A sequence of bytes is rendered in network byte order, from left to right. For example, the leftmost (i.e. byte 0) is considered "even" and is encoded using the PGP Even Word table. The next byte to the right (i.e. byte 1) is considered "odd" and is encoded using the PGP Odd Word table. This process repeats until all bytes are encoded. Thus, "E582" produces "topmost Istanbul", whereas "82E5" produces "miser travesty". A PGP public key fingerprint that displayed in hexadecimal as E582 94F2 E9A2 2748 6E8B 061B 31CC 528F D7FA 3F19 would display in PGP Words (the "biometric" fingerprint) as topmost Istanbul Pluto vagabond treadmill Pacific brackish dictator goldfish Medusa afflict bravado chatter revolver Dupont midsummer stopwatch whimsical cowbell bottomless The order of bytes in a bytestring depends on endianness. == Other word lists for data == There are several other word lists for conveying data in a clear unambiguous way via a voice channel: the NATO phonetic alphabet maps individual letters and digits to individual words the S/KEY system maps 64 bit numbers to 6 short words of 1 to 4 characters each from a publicly accessible 2048-word dictionary. The same dictionary is used in RFC 1760 and RFC 2289. the Diceware system maps five base-6 random digits (almost 13 bits of entropy) to a word from a dictionary of 7,776 distinct words. the Electronic Frontier Foundation has published a set of improved word lists based on the same concept FIPS 181: Automated Password Generator converts random numbers into somewhat pronounceable "words". mnemonic encoding converts 32 bits of data into 3 words from a vocabulary of 1626 words. what3words encodes geographic coordinates in 3 dictionary words. the BIP39 standard permits encoding a cryptographic key of fixed size (128 or 256 bits, usually the unencrypted master key of a Cryptocurrency wallet) into a short sequence of readable words known as the seed phrase, for the purpose of storing the key offline. This is used in cryptocurrencies such as Bitcoin or Monero. Like the PGP word list, the Bytewords standard maps each possible byte to a word. There is only one list, rather than two. The words are uniformly four letters long and can be uniquely identified by their first and last letters

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  Data storage

  Data storage is the recording (storing) of information (data) in a storage medium. Handwriting, phonographic recording, magnetic tape, and optical discs are all examples of storage media. Biological molecules such as RNA and DNA are considered by some as data storage. Recording may be accomplished with virtually any form of energy. Electronic data storage requires electrical power to store and retrieve data. Data stored in a digital, machine-readable medium is called digital data. Computer data storage is one of the core functions of a general-purpose computer. Electronic documents can be stored in much less space than paper documents. Barcodes and magnetic ink character recognition (MICR) are two ways of recording machine-readable data on paper. == Recording media == A recording medium is physical material that holds information. Newly created information is distributed and can be stored in four storage media–print, film, magnetic, and optical–and seen or heard in four information flows–telephone, radio, TV, and the Internet as well as being observed directly. Digital information is stored on electronic media in many different recording formats. With electronic media, the data and the recording media are sometimes referred to as "software" despite the more common use of the word to describe computer software. With (traditional art) static media, art materials such as crayons may be considered both equipment and medium as the wax, charcoal or chalk material from the equipment becomes part of the surface of the medium. Some recording media may be temporary, either by design or by nature. Volatile organic compounds may be used to purposely make data expire over time or to reduce environmental impact. Data such as smoke signals or skywriting are temporary by nature. Depending on the volatility, a gas (e.g., atmosphere, smoke) or a liquid surface such as a lake would be considered a temporary recording medium, if it could be considered a recording medium at all. == Global capacity, digitization, and trends == A 2003 UC Berkeley report estimated that about five exabytes of new information were produced in 2002 and that 92% of this data was stored on magnetic media (primarily hard disk drives). This was about twice the data produced in 1999. The amount of data transmitted over telecommunications systems in 2002 was nearly 18 exabytes—three and a half times more than was recorded on non-volatile storage. Telephone calls constituted 98% of the telecommunicated information in 2002. The researchers' highest estimate for the growth rate of newly stored information (uncompressed) was more than 30% per year. In a more limited study, the International Data Corporation estimated that the total amount of digital data in 2007 was 281 exabytes and that the total amount of digital data produced exceeded the global storage capacity for the first time. A 2011 article in Science estimated that the year 2002 was the beginning of the digital age for information storage: an age in which more information is stored on digital storage devices than on analog storage devices. In 1986, approximately 1% of the world's capacity to store information was in digital format; this grew to 3% by 1993, to 25% by 2000, and to 94% by 2007. These figures correspond to less than three compressed exabytes in 1986, and 295 compressed exabytes in 2007. The quantity of digital storage doubled roughly every three to four years. It is estimated that around 120 zettabytes of data will be generated in 2023, an increase of 60x from 2010, and that it will increase to 181 zettabytes generated in 2025. == Mass storage ==

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  Resilience week

  Resilience week is an annual symposium established to enable cross-disciplinary and role based discussions to advance strategies and research that engenders resilience in critical infrastructure systems and communities. Damaging storms, cyber attack and the interconnection of critical infrastructure systems can lead to cascading events that not only affect local but also across regions. However, many of these interdependencies are not easily recognized and obscure and complicate the mitigation of risk. The purpose of the symposia series is hence to facilitate best practice in managing critical infrastructure risks, by bringing together businesses, government and researchers. == Background == Originally organized in 2008 as a focus on the new research area of resilient control systems, including the disciplinary areas of control system, cyber-security, cognitive psychology and any number of critical infrastructure domains. Resilience has long been recognized as an area that requires not only the contributions of multiple disciplines or multidisciplinary participation, but interdisciplinary interaction where there is a common language and familiarity of the contributors to what other disciplines (and roles) contribute. The resulting interactions developed by Resilience Week and associated activities are intended to culture this sharing environment as a safe zone for inclusion; more importantly, an environment that lends to developing the new science and practice. As the attributes of resilience are complex, the contributions and topics for the event have included both the disciplinary and the project considerations, in keynotes, panels and research presentations. Keynotes have included senior leadership in the Department of Energy, Department of Defense, Department of Homeland Security, the National Science Foundation, and other agencies in addition to National Academy and professional organization fellows and senior industry leaders. Project panels and research presentations include emergent topics in resilience to climate change, cyber attack, damaging storms and the energy assurance. Topics Areas of focus have included: Control Systems Cyber Systems Cognitive Systems Communications Systems Communities and Infrastructure Project Focus Areas have included: Dependencies and Interdependencies Cyber Resilience for Operating Technology Commercializing Research and Development Building Critical Infrastructure Resilience through Distributed Energy Resources Energy Equity and Community Resilience Proceedings are developed for each year of the event, documenting the diversity of the research and engagements within these topical areas. == Impacts for the future == Since its inception, the Resilience Week community has evolved from one that primarily included only university researchers to one that includes many government laboratories, universities and private industries in the US and internationally. This type of collaboration forms a feedback loop that informs the research with the current needs and hones best practices. The future of the event is to further advance discussions that advance investment, recognize priorities and expedite technologies and tools to proactively address our energy future, in light of the natural and manmade challenges, and rationalizing the complex relationships that exist in critical infrastructure.

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  ISO 15765-2

  ISO 15765-2, or ISO-TP (Transport Layer), is an international standard for sending data packets over a CAN bus. The protocol allows for the transport of messages that exceed the eight byte maximum payload of CAN frames. ISO-TP segments longer messages into multiple frames, adding metadata (CAN-TP Header) that allows the interpretation of individual frames and reassembly into a complete message packet by the recipient. It can carry up to 232-1 (4294967295) bytes of payload per message packet starting from the 2016 version. Prior versions were limited to a maximum payload size of 4095 bytes. In the OSI model, ISO-TP covers the layer 3 (network layer) and 4 (transport layer). The most common application for ISO-TP is the transfer of diagnostic messages with OBD-II equipped vehicles using KWP2000 and UDS, but is used broadly in other application-specific CAN implementations where one might need to send messages longer than what the CAN protocol physical layer allows (eight bytes for CAN, 64 bytes for CAN FD, and 2048 bytes for CAN-XL). ISO-TP can be operated with its own addressing as so-called Extended Addressing or without address using only the CAN ID (so-called Normal Addressing). Extended addressing uses the first data byte of each frame as an additional element of the address, reducing the application payload by one byte. For clarity the protocol description below is based on Normal Addressing with eight byte CAN frames. In total, six types of addressing are allowed by the ISO 15765-2 Protocol. ISO-TP prepends one or more metadata bytes to the payload data in the eight byte CAN frame, reducing the payload to seven or fewer bytes per frame. The metadata is called the Protocol Control Information, or PCI. The PCI is one, two or three bytes. The initial field is four bits indicating the frame type, and implicitly describing the PCI length. ISO 15765-2 is a part of ISO 15765 (headlined Road vehicles — Diagnostic communication over Controller Area Network (DoCAN)), which has the following parts: ISO 15765-1 Part 1: General information and use case definition ISO 15765-2 Part 2: Transport protocol and network layer services ISO 15765-3 Part 3: Implementation of unified diagnostic services (UDS on CAN) – replaced by ISO 14229-3 Road vehicles — Unified diagnostic services ISO 15765-4 Part 4: Requirements for emissions-related systems == List of protocol control information (PCI) field types == The ISO-TP defines four frame types: A message of seven bytes or less is sent in a single frame, with the initial byte containing the type (0) and payload length (1-7 bytes). With the 0 in the type field, this can also pass as a simpler protocol with a length-data format and is often misinterpreted as such. A message longer than 7 bytes requires segmenting the message packet over multiple frames. A segmented transfer starts with a First Frame. The PCI is two bytes in this case, with the first 4 bit field the type (type 1) and the following 12 bits the message length (excluding the type and length bytes). The recipient confirms the transfer with a flow control frame. The flow control frame has three PCI bytes specifying the interval between subsequent frames and how many consecutive frames may be sent (Block Size). For CAN FD, the ISO 15765-2 protocol has been extended for Single and First frame, to allow larger size values, but still backwards compatible with traditional ISO 15765. See CAN FD. The initial byte contains the type (type = 3) in the first four bits, and a flag in the next four bits indicating if the transfer is allowed (0 = Continue To Send, 1 = Wait, 2 = Overflow/abort). The next byte is the block size, the count of frames that may be sent before waiting for the next flow control frame. A value of zero allows the remaining frames to be sent without flow control or delay. The third byte is the minimum Separation Time (STmin), the minimum delay time between frames. STmin values up to 127 (0x7F) specify the minimum number of milliseconds to delay between frames, while values in the range 241 (0xF1) to 249 (0xF9) specify delays increasing from 100 to 900 microseconds. Note that the Separation Time is defined as the minimum time between the end of one frame to the beginning of the next. Robust implementations should be prepared to accept frames from a sender that misinterprets this as the frame repetition rate i.e. from start-of-frame to start-of-frame. Even careful implementations may fail to account for the minor effect of bit-stuffing in the physical layer. The sender transmits the rest of the message using Consecutive Frames. Each Consecutive Frame has a one byte PCI, with a four bit type (type = 2) followed by a 4-bit sequence number. The sequence number starts at 1 and increments with each frame sent (1, 2,..., F, 0, 1,...), with which lost or discarded frames can be detected. Each consecutive frame starts at 0, initially for the first set of data in the first frame will be considered as 0th data. So the first set of CF(Consecutive frames) start from 0x1. There afterwards when it reaches 0x2F, will be started from 0x20 (e.g. 0x21, 0x22, 0x23...0x2F, 0x20, 0x21...). The 12-bit length field (as indicated in the First Frame) allows up to 4095 bytes of user data in a segmented message, but in practice the typical application-specific limit is considerably lower because of receive buffer or hardware limitations. == Timing parameters == Timing parameters, such as P1 and P2 timers, have to be mentioned. == Standards == ISO 15765-2:2016 Road vehicles -- Diagnostic communication over Controller Area Network (DoCAN) -- Part 2: Transport protocol and network layer services

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  Simply Local

  Simply Local is a decentralized community social networking and neighborhood broadcasting service developed by Simply Local, based in New Delhi. The app is used as a tool by residents to bridge the information gap and know what is happening in the locality. Simply Local creates private geo-fenced networks for people living in an area and provides social and community related services within that network. The user doesn’t post to a single person but broadcasts to a chosen community. One of its primary purposes is also to connect citizens to their elected representatives. Each community is independent of the other and information shared remains telescoped to that particular community. The app has been designed to maintain privacy and security of users and provides decentralized social networking in the sense that it forms an owner-independent, micro community, which is not connected with the world outside. Simply Local is available on Android Play and iOS App Store. It is available in two languages - English and Hindi. Simply Local’s founder and CEO is Nikhil Bapna. == History == 2020 May: Included as a Top 5 Useful App by Zee News. 2020: Used to connect candidates with local residents during the Delhi assembly elections. 2019: Renamed from Gadfly to its current name. 2018: Used for Karnataka State Elections to get detailed information on candidates. 2017: Launched under the name Gadfly as a tool to connect citizens with their elected representatives.

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