AI Chat On Google

AI Chat On Google — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

Edits (app)

Edits is an American photo and short form video editing software service owned by Meta Platforms. It allows users to create videos and edit them by using features like green screens, and AI animation, and also provides real-time statistics to Instagram creators to track their accounts. Accounts directly from Instagram can be imported, and videos can be exported vice-versa. It is available solely on iOS and Android. On Apple, it supports over 32 different languages, including French, Spanish, and Chinese. It has been noted by critics as a direct competitor for apps like CapCut, owned by Chinese brand ByteDance. The Instagram head, Adam Mosseri, also acknowledged these similarities. Launched on April 22 for both iOS and Android. It received over 5M+ users on Apple and Android combined in its first 4 days since its launch. == History == On January 19, 2025, following the ban of all ByteDance Apps from the Google Play Store, and App Store, Instagram head Adam Mosseri announced on Threads that they would be launching the app in February for iOS, followed by an Android counterpart. He said the app is working with select people to test its features. In a separate post, he emphasized that the app is "more for creators than casual video makers". == Features == Edits contains many similar features to other competition of video editors like KineMaster, Inshot, and CapCut. When creating a video, users have the option to export in resolution of HD, 4K, and 2K, along with having HDR and SDR support. Like many traditional video editing software, it includes a timeline, and basic undo-redo buttons. On the bottom bar, 7 tabs for editing exist, namely the Split, Volume, Adjust, Speed, Delete, Filters, Green Screen, Voice FX, Extract Audio, Mirror, Slip, Replace and Duplicate bars. Basic features, like splitting, and adjusting speed and volume of clips are present, along with more advanced Green Screens, and AI features. Being a mobile video editor app, Edits also has drag-and-drop features to ease customer usage. Users have the ability to record videos directly within the app. This feature allows users to create content without needing extra software or devices. They can choose from several focal lengths, which affect how close or wide the shot appears. The app also supports different frame rates. Users have the ability to record videos directly within the app. This feature allows users to create content without needing extra software or devices. Once users are done filming your clips, they can simply transfer them into a project to start editing immediately. Upcoming features for the app include Keyframes, AI-powered modification, Collaboration, and Enhanced creativity. == Reception == Since its release, it received over 5 million downloads in 4 days. Critically, the app received great rankings from many. From users, the app received an average of 4.45 stars over Google Play Store and App Store in the first few days, with Google Play Store receiving the least stars. As in reviews, it was received mixed by the public. Many people praised the smoothness and intuivity of the app. "The app is more than just a basic editor, offering a full suite of creative tools, including a dedicated tab for inspiration and trending audio, as well as a tab for managing drafts," said a blogger. Some users were disappointed with the range of editing tools, some users have noted that it could benefit from more transition options between clips. Some even reported crashing between clips.
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Awwwards

Awwwards (Awwwards Online SL) is an organization that hosts web design competitions and conferences across Europe and the United States. Website owners and developers can participate by submitting their websites for review. Submissions are assessed by a jury, and top entries are presented and awarded prizes on a rotational basis. == Nomination process == Web designers submit their websites through Awwwards' platform for consideration for the Site of the Day. A jury, composed of industry professionals, and the Awwwards community evaluate the entries. The best daily sites are published annually in "The 365 Best Websites Around the World" book. == Jury == The jury consists of international designers, developers, and agencies who assess the creativity, technical skills, and insight of the submitted web projects. The panel's expertise ensures a comprehensive review process. === Developer Award === Awwwards, in partnership with Microsoft, created the Developer Award to recognize web developers who demonstrate excellence in creating websites that meet modern standards. The award highlights websites that work seamlessly across various platforms and devices, using best practices in HTML5, JavaScript, and CSS. == Annual winners == Some prominent Site of the Year winners include Mercedes-Benz, Bloomberg L.P., Bose Corporation, Warner Brothers, Volkswagen, Uber, and Google. == Awwwards conference == Awwwards also organizes two-day conferences featuring speakers from major tech companies and industry leaders such as Microsoft, Google, Spotify, Adobe, Opera, and Smashing Magazine. These events focus on the latest trends in web design and development. Speakers at Awwwards conferences have included notable figures in the design and technology industry such as Stefan Sagmeister, Paula Scher, and design leaders from companies including Wix. == Corporate affairs == === Platform === Awwwards operates an online platform where web designers and developers submit websites for evaluation and awards. Submitted projects are reviewed by a jury based on design, usability, creativity, and content. The platform also serves as a community hub for discovering digital trends, showcasing work, and accessing educational resources including talks and interviews. Design professionals from international companies have participated in Awwwards events and platform content. For example, Wix, a cloud-based web development company known for its website builder tools, has featured prominently in Awwwards conferences, with its design leadership contributing to discussions on design trends and creative thinking.
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General-Purpose Serial Interface

General-Purpose Serial Interface, also known as GPSI, 7-wire interface, or 7WS, is a 7 wire communications interface. It is used as an interface between Ethernet MAC and PHY blocks. Data is received and transmitted using separate data paths (TXD, RXD) and separate data clocks (TXCLK, RXCLK). Other signals consist of transmit enable (TXEN), receive carrier sense (CRS), and collision (COL).
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Web engineering

The World Wide Web has become a major delivery platform for a variety of complex and sophisticated enterprise applications in several domains. In addition to their inherent multifaceted functionality, these Web applications exhibit complex behaviour and place some unique demands on their usability, performance, security, and ability to grow and evolve. However, a vast majority of these applications continue to be developed in an ad hoc way, contributing to problems of usability, maintainability, quality and reliability. While Web development can benefit from established practices from other related disciplines, it has certain distinguishing characteristics that demand special considerations. In recent years, there have been developments towards addressing these considerations. Web engineering focuses on the methodologies, techniques, and tools that are the foundation of Web application development and which support their design, development, evolution, and evaluation. Web application development has certain characteristics that make it different from traditional software, information systems, or computer application development. Web engineering is multidisciplinary and encompasses contributions from diverse areas: systems analysis and design, software engineering, hypermedia/hypertext engineering, requirements engineering, human-computer interaction, user interface, data engineering, information science, information indexing and retrieval, testing, modelling and simulation, project management, and graphic design and presentation. Web engineering is neither a clone nor a subset of software engineering, although both involve programming and software development. While Web Engineering uses software engineering principles, it encompasses new approaches, methodologies, tools, techniques, and guidelines to meet the unique requirements of Web-based applications. == As a discipline == Proponents of Web engineering supported the establishment of Web engineering as a discipline at an early stage of Web. Major arguments for Web engineering as a new discipline are: Web-based Information Systems (WIS) development process is different and unique. Web engineering is multi-disciplinary; no single discipline (such as software engineering) can provide a complete theory basis, body of knowledge and practices to guide WIS development. Issues of evolution and lifecycle management when compared to more 'traditional' applications. Web-based information systems and applications are pervasive and non-trivial. The prospect of Web as a platform will continue to grow and it is worth being treated specifically. However, it has been controversial, especially for people in other traditional disciplines such as software engineering, to recognize Web engineering as a new field. The issue is how different and independent Web engineering is, compared with other disciplines. Main topics of Web engineering include, but are not limited to, the following areas: === Modeling disciplines === Business Processes for Applications on the Web Process Modelling of Web applications Requirements Engineering for Web applications B2B applications === Design disciplines, tools, and methods === UML and the Web Conceptual Modeling of Web Applications (aka. Web modeling) Prototyping Methods and Tools Web design methods CASE Tools for Web Applications Web Interface Design Data Models for Web Information Systems === Implementation disciplines === Integrated Web Application Development Environments Code Generation for Web Applications Software Factories for/on the Web Web 2.0, AJAX, E4X, ASP.NET, PHP and Other New Developments Web Services Development and Deployment === Testing disciplines === Testing and Evaluation of Web systems and Applications. Testing Automation, Methods, and Tools. === Applications categories disciplines === Semantic Web applications Document centric Web sites Transactional Web applications Interactive Web applications Workflow-based Web applications Collaborative Web applications Portal-oriented Web applications Ubiquitous and Mobile Web Applications Device Independent Web Delivery Localization and Internationalization of Web Applications Personalization of Web Applications == Attributes == === Web quality === Web Metrics, Cost Estimation, and Measurement Personalisation and Adaptation of Web applications Web Quality Usability of Web Applications Web accessibility Performance of Web-based applications === Content-related === Web Content Management Content Management System (CMS) Multimedia Authoring Tools and Software Authoring of adaptive hypermedia == Education == Master of Science: Web Engineering as a branch of study within the MSc program Web Sciences at the Johannes Kepler University Linz, Austria Diploma in Web Engineering: Web Engineering as a study program at the International Webmasters College (iWMC), Germany
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Weibo

Weibo (Chinese: 微博; pinyin: Wēibó), or Sina Weibo (Chinese: 新浪微博; pinyin: Xīnlàng Wēibó), is a Chinese microblogging (weibo) website. Launched by Sina Corporation on 14 August 2009, it is one of the biggest social media platforms in China, with over 582 million monthly active users (252 million daily active users) as of Q1 2022. The platform has been highly successful but has faced criticism for heavy censorship. Sina had gone public on the Nasdaq in 2000. In March 2014, Sina announced a spinoff of Weibo and filed an IPO under the symbol WB. Sina carved out 11% of Weibo in the IPO, with Alibaba owning 32% post-IPO. The company began trading publicly on 17 April 2014. In March 2017, Sina launched Sina Weibo International Version. In November 2018, Sina Weibo suspended its registration function for minors under the age of 14. In July 2019, Sina Weibo announced that it would launch a two-month campaign to clean up pornographic and vulgar information, named "Project Deep Blue" (蔚蓝计划). On 29 September 2020, the company announced it would go private again due to rising tensions between the US and China. == Name == "Weibo" (微博) is the Chinese word for "microblog". Sina Weibo launched its new domain name weibo.com on 7 April 2011, deactivating and redirecting from the old domain, t.sina.com.cn, to the new one. Due to its popularity, the media sometimes refers to the platform simply as "Weibo", despite the numerous other Chinese microblogging services including Tencent Weibo, Sohu Weibo, and NetEase Weibo. However, the latter three have stopped providing services. == Background == Sina Weibo is a platform based on fostering user relationships to share, disseminate, and receive information. Through the website or the mobile app, users can upload pictures and videos publicly for instant sharing, with other users being able to comment with text, pictures and videos, or use a multimedia instant messaging service. The company initially invited a large number of celebrities to join the platform at the beginning and has since invited many media personalities, government departments, businesses and non-governmental organizations to open accounts for the purpose of publishing and communicating information. To avoid the impersonation of celebrities, Sina Weibo uses verification symbols; celebrity accounts have an orange letter "V" and organizations' accounts have a blue letter "V". Sina Weibo has more than 500 million registered users; out of these, 313 million are monthly active users, 85% use the Weibo mobile app, 70% are college-aged, 50.10% are male and 49.90% are female. There are over 100 million messages posted by users each day. With more than 100 million followers, actress Xie Na holds the record for the most followers on the platform. Despite fierce competition among Chinese social media platforms, Sina Weibo remains the most popular. == History == After the July 2009 Ürümqi riots, China shut down most domestic microblogging services, including Fanfou, the very first weibo service. Many popular non-China-based microblogging services like Twitter, Facebook, and Plurk have since been blocked. Sina Corporation CEO Charles Chao considered this to be an opportunity, and on 14 August 2009, Sina launched the tested version of Sina Weibo. Basic functions including message, private message, comment and reposting were made available that September. A Sina Weibo–compatible API platform for developing third-party applications was launched on 28 July 2010. On 1 December 2010, the website experienced an outage, which administrators later said was due to the ever-increasing numbers of users and posts. Registered users surpassed 100 million in February 2011. Since 23 March 2011, t.cn has been used as Sina Weibo's official shortened URL in lieu of sinaurl.cn. On 7 April 2011, weibo.com replaced t.sina.com.cn as the new main domain name used by the website. The official logo was also updated. In June 2011, Sina announced an English-language version of Sina Weibo would be developed and launched, though content would still be governed by Chinese law. On 11 January 2013, Sina Weibo and Alibaba China (a subsidiary of Alibaba Group) signed a strategic cooperation agreement. With more and more foreign celebrities using Sina Weibo, language translation has become an urgent need for Chinese users who wish to communicate with their idols online, especially Korean. In January 2013, Sina Weibo and NetEase.com announced that they had reached a strategic cooperation agreement. When users browse foreign language content, they can now directly obtain translation results through the YouDao Dictionary. The Sina Weibo financial report in February 2013 showed that its total revenue was approximately US$66 million and that the number of registered users had exceeded the 500 million mark. In April 2013, Sina officially announced that Sina Weibo had signed a strategic cooperation agreement with Alibaba. The two sides conducted in-depth cooperation in areas such as user account interoperability, data exchange, online payment, and internet marketing. At the same time, Sina announced that Alibaba, through its wholly owned subsidiary, had purchased the preferred shares and common shares issued by Sina Weibo Company for US$586 million, which accounted for approximately 18% of Weibo's fully diluted and diluted total shares. === Ownership === On 9 April 2013, Alibaba Group announced that it would acquire 18% of Sina Weibo for US$586 million, with the option to buy up to 30% in the future. Alibaba exercised this option when Weibo was listed on the NASDAQ in April 2014. == Users == According to iResearch's report on 30 March 2011, Sina Weibo had 56.5% of China's microblogging market based on active users and 86.6% based on browsing time over competitors such as Tencent Weibo and Baidu. According to research by Sina Corporation, the number of active users reached over 400 million by Q1 2018, making Sina Weibo the 7th platform with at least 400 million active users, and daily usage increased by 21%. As of 2017, approximately 80% of its users were in their 20s and 30s. The top 100 users had over 485 million followers combined. More than 5,000 companies and 2,700 media organizations in China use Sina Weibo. The site is maintained by a growing microblogging department of 200 employees responsible for technology, design, operations, and marketing. Sina executives invited and persuaded many Chinese celebrities to join the platform. Users now include Asian celebrities, movie stars, singers, famous business and media figures, athletes, scholars, artists, organizations, religious figures, government departments, and officials from Hong Kong, Mainland China, Malaysia, Singapore, Taiwan, and Macau, as well as some famous foreign individuals and organizations, including Kevin Rudd, Boris Johnson, David Cameron, Narendra Modi, Toshiba, and the Germany national football team. Sina Weibo has a verification program for known people and organizations. Once an account is verified, a verification badge is added beside the account name. == Features == Many of Sina Weibo's features resemble those of Twitter. A user may post with a 140-character limit (increased to 2,000 as of January 2016 with the exception of reposts and comments). An analysis of 29 million Weibo posts found the median length was 14 characters. Users may mention or talk to other people using "@UserName" formatting, add hashtags, follow other users to make their posts appear in one's own timeline, re-post with "//@UserName" similar to Twitter's retweet function "RT @UserName", select posts for one's favorites list, and verify the account if the user is a celebrity, brand, business or otherwise of public interest. URLs are automatically shortened using the domain name t.cn, akin to Twitter's t.co. Official and third-party applications can access Sina Weibo from other websites or platforms. Users may: Submit up to 18 images/video files in every post Send personal messages to followers Follow others and be followed Post "stories" like on Instagram React to posts using different emojis Receive monetary rewards that can be used in a digital store linked to Weibo View posts identified as "hot" or popular Display the location they post from Hashtags differ slightly between Sina Weibo and Twitter, using the double-hashtag "#HashName#" format (the lack of spacing between Chinese characters necessitates a closing tag). Users can own a hashtag by requesting hashtag monitoring; the company reviews these requests and responds within one to three days. Once a user owns a hashtag, they have access to a wide variety of functions available only to them on the condition that they remain active (less than 1 post per calendar week revokes these privileges). Additionally, comments appear as a list below each post. A commenter can also choose to re-post the comment, quoting the whole original post, to their own page. Unregistered users can only browse a few post
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Remote scripting

Remote scripting is a technology which allows scripts and programs that are running inside a browser to exchange information with a server. The local scripts can invoke scripts on the remote side and process the returned information. The earliest form of asynchronous remote scripting was developed before XMLHttpRequest existed, and made use of very simple process: a static web page opens a dynamic web page (e.g. at other target frame) that is reloaded with new JavaScript content, generated remotely on the server side. The XMLHttpRequest and similar "client-side script remote procedure call" functions, open the possibility of use and triggering web services from the web page interface. The web development community subsequently developed a range of techniques for remote scripting in order to enable consistent results across different browsers. Early examples include JSRS library from 2000, the introduction of the Image/Cookie technique in 2000. == JavaScript Remote Scripting == JavaScript Remote Scripting (JSRS) is a web development technique for creating interactive web applications using a combination of: HTML (or XHTML) The Document Object Model manipulated through JavaScript to dynamically display and interact with the information presented A transport layer. Different technologies may be used, though using a script tag or an iframe is used the most because it has better browser support than XMLHttpRequest A data format. XML with WDDX can be used as well as JSON or any other text format. Schematic A similar approach is Ajax, though it depends on the XmlHttpRequest in newer web browsers. === Libraries === Brent Ashley's original JSRS library released in 2000 BlueShoes JSRS with added encoding and OO RPC abstractions Simple Tutorials: Javascript Remote Scripting with PHP at the Wayback Machine (archived 2006-04-14) MSDN article
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Packard Bell Statesman

The Packard Bell Statesman was an economy line of notebook-sized laptops introduced in 1993 by Packard Bell. They were slower in performance and lacked features compared to most competitor products, but they were lower in price. It was created in a collaboration between Packard Bell and Zenith Data Systems. The Statesman series was essentially a rebrand of Zenith Data Systems Z-Star 433 series, with the only notable difference of the logo in the middle and text on the front bezel. == History == In June 1993 Zenith Data Systems announced an alliance with Packard Bell. Zenith acquired about 20% of Packard Bell and they would both now work together to design and build PC's. Zenith would also provide Packard Bell with private-label versions of their portable PC's. The Packard Bell Statesman was a rebrand of the Zenith Z-Star notebook computer series. While the Statesman was being advertised by Packard Bell, the Z-Star series was also still being sold by Zenith. The Statesman was first introduced on October 4, 1993. Prices started at $1,500 for a monochrome or color DSTN model with a 33 MHz Cyrix Cx486SLC, 4 MB of RAM, 200 MB hard disk drive, internal 1.44 MB floppy disk drive, and MS-DOS 6.0 with Windows 3.1 for the included software. A "J mouse" pointing device was included, similar to the TrackPoint. The Statesman was expected to begin shipping within the next few weeks. == Specifications == === Hardware === CPU The first two models, the 200M and 200C, used the Cyrix Cx486SLC. This was Cyrix's first processor, which was a 386SX pin-compatible chip with on-board L1 cache and 486 instructions, being known as a "hybrid chip". The processor was clocked at 33 MHz and had 1 KB of L1 cache. It was a 16-bit processor and was pin compatible with the Intel 80386SX. On the bottom of the unit, the motherboard had an empty socket for a Cyrix FasMath co-processor, which could improve floating-point math performance. The 200M and 200C plus models had a Cyrix Cx486SLC2 clocked at 50 MHz, which was 50% faster than the original 486SLC. The SLC2 similarly had 1 KB of on-board cache and was pin compatible with the previous model. Graphics & Display For video all models used three versions of the Chips & Technologies 655xx, the CT65520, 65525, and 65530. The 65520 was first introduced in early 1992 as the first controller with Super VGA resolution. It supported resolutions up to 1024x768 in 16 colors or shades of gray. If in 800x600 resolution, it can display up to 256 colors. All 3 chips were the same, with the CT65525 identifying as a CT65530. The CT65530 had an ability of 5V and 3.3V mixed operation and linear video memory addressing. All models used a 9.5in 800x600 resolution DSTN LCD. The 200M and 200M Plus had a monochrome display, while the 200C and 200C Plus had a color display. Audio All models had only basic audio available, with just a piezo speaker soldered onto the motherboard and no sound controller. Memory Standard RAM included was 4-8 MB of EDO RAM. The RAM was on a proprietary SIPP package that could only be upgraded to 12 MB maximum if the user had compatible modules. Storage For storage all models used a hard drive with a size of 100 or 200 MB, and all models had an internal 1.44 MB floppy disk drive located on the side of the unit. The maximum capacity hard drive compatible if the user wanted to upgrade was 500 MB.Ports & Expansion For ports all models had 1x serial, 1x parallel, 1x VGA output, and 1x PS/2 keyboard/mouse input. For expansion all models only had one PCMCIA type II slot. Keyboard & Mouse All models used a small-scale keyboard with control keys. One interesting feature of the keyboard is that the J key also acted as a mouse, working similar to IBM's ThinkPad TrackPoint. On some models additional keys such as S, D, F, G and space let you do other mouse actions such as right click, left click, double click, and middle mouse click. === Software === The series shipped with MS-DOS and Windows 3.1 as the included operating system. == Model Comparison == Statesman 200M — The first Statesman model, it came with a DSTN monochrome screen, and a Nickel-cadmium battery pack which could last up to 4 hours. It weighed 7.4 lb and was $1500. Statesman 200C — The second Statesman model, it was the same as the 200M with the only notable differences of a DSTN color display rather than monochrome and a slightly decreased battery life of about 3 hours. It cost $700 more than the 200M at $2200. Statesman 200M/200C Plus — The 200M/200C Plus were both identical to their previous base models, with the only difference of them having a Cx486SLC2 running at 50 MHz. In 1994 it cost around $2,295 for the 200C plus with 4 MB of ram, with 8 MB costing an extra $400. == Reception == The Statesman received fair reception, with most reviewers giving positivity for the low price and high battery life, but mainly criticizing the performance and screen quality of the model line. A review by PC World writer Rex Farrance and Owen Linderholm said the 200M had a good price, being only $1500, and a good battery life which lasted about 4 hours. In benchmarks however, the 200M performed "noticeably below the average". It was noted that the 200M's worst feature was its monochrome display, being "cloudy and a bit dim for our tastes". The J mouse was considered a decent choice, and was said to be "highly usable" after some practice. The 200M was listed as number 3 on PC World's top 20 budget PC list. PC World also reviewed the 200C, saying the color display is only a "marginal, although an improvement on the monochrome version". The 200C placed 9 on the PC World top 20 budget PC list. Compute! Magazine reviewed the 200C Plus in September 1994 stating it "lagged far behind the others, especially the DXs, but then speed isn't everything". It was given pros for low cost and good display, but criticized for its low performance, not having a trackball, and poor external monitor support.
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Algorithmic curation

Algorithm curation is the selection of online media by technologies such as recommender systems and personalized search. Curation entails the selective sharing of online content and recommendations based on inferred interests. Curation algorithms implement different filter approaches, such as collaborative filtering and content-based filtering. Examples include search engine and social media products such as the Twitter feed, Facebook's News Feed, and Google Personalized Search. == History == === Early algorithmic curation === Online platforms use newsfeed algorithms to determine what content to present to each user. The volume of content published on social media platforms created a need for automated filtering, as manual review of all available content by users is not feasible. These systems function as a form of gatekeeper, shaping which new material users are exposed to and influencing knowledge, attention, and political exposure. ==== Information overload ==== Early ranking algorithms addressed information overload by surfacing the most recent or most popular posts. Later systems shifted toward ranking content based on predicted engagement, aiming to increase the time users spend on a platform. Research has found that these engagement-oriented systems can increase the spread of misinformation and contribute to political polarization as a side effect of optimising for user interaction. ==== How algorithm changes users' feeds over time ==== Algorithmic curation has been found to increase source diversity in some respects while simultaneously reducing the number of external links presented to users, which limits exposure to off-platform content. Research using agent-based modelling has examined how user behaviour, information quality, and algorithmic design interact with one another over time. === Emergence of AI === Platforms increasingly shifted from rule-based ranking systems toward machine-learning and AI-driven approaches, which allow feeds to be personalised at a larger scale and with greater responsiveness to user behaviour. For example, X (formerly Twitter) moved away from a chronological feed toward an AI-powered ranking system that personalises content for each user. These systems are capable of making ranking decisions across volumes of content and user interactions that would not be practical to handle manually. == Approach == === Filter types === ==== Collaborative filtering ==== Collaborative filtering (CF) methods create recommendations based on a person's usage patterns. CF predicts a person's preference for an item by matching their interests with those of users who have similar interests. This process allows for the sharing of ratings between users with similar profiles. CF is based on patterns of human behaviour rather than machine analysis of content itself. Users of CF systems rate items they have interacted with, and these ratings form a profile of interests. The CF system then matches that user with others who have similar profiles, and uses their ratings to generate recommendations. Collaborative filtering can be applied across various content types including text, images, music, and financial products, and can account for complex attributes such as taste and quality that are difficult to represent explicitly. ==== Content-based filtering ==== Content-based filtering (CBF) builds a user profile to represent the types of items a user has engaged with, based on keywords and attributes used to describe those items. Recommendations are generated by presenting items similar to those the user has previously engaged with or is currently viewing. The CBF method creates a profile for each item based on discrete attributes and features, and then constructs a content-based user profile using a weighted vector of those features derived from items the user has rated, purchased, or interacted with. The weights represent the relative importance of each feature, and can be computed using techniques such as Bayesian classifiers, cluster analysis, decision trees, and artificial neural networks, with the goal of estimating the probability that a user will engage with a suggested item. One application of content-based filtering is Pandora Radio, where users provide an artist, genre, or composer to generate a station, and the system surfaces music with similar attributes. == Technology == === Recommender system === Recommender systems rank and suggest content to users based on a combination of implicit and explicit user input. Implicit signals include time spent viewing or engaging with a specific item. Explicit signals include actions such as liking posts, saving store pages, reading news articles, or sharing content. === Personalized search === Personalized search aims to retrieve results most relevant to the user by incorporating contextual factors beyond the explicit query, such as past queries, browsing history, and inferred interests. Social media platforms such as X (formerly Twitter) and Bluesky generate recommendations based on similar users and the content those users interact with. Personalized search may also allow users to explicitly filter results by blocking content containing certain phrases or hashtags. For first-time users without prior history, personalized search may draw on content-based filtering to establish an initial context. Similar processes are used by search engines and retail platforms to tailor results and product recommendations to individual users. == AI contribution == Artificial intelligence contributes to algorithmic curation through machine-learning models capable of processing large volumes of data. Techniques such as deep learning and reinforcement learning allow curation algorithms to model user preferences with greater granularity alongside established filtering approaches. This enables platforms to adjust content rankings rapidly in response to user behaviour. In social media and streaming contexts, AI-driven systems arrange feeds according to predicted relevance, with the outputs shaped by patterns present in the training data. == Social media and potential impact == === Echo chambers === Social media algorithms, such as those used by X (formerly Twitter), recommend content that the system predicts a user will engage with positively. Content from accounts with differing perspectives is less likely to be surfaced, which may reduce source and topic diversity and contribute to the formation of echo chambers. For example, Facebook's news feed is designed to surface content aligned with users' prior engagement, which may reinforce existing views. This dynamic may contribute to filter bubbles, in which users are seldom exposed to content outside their existing interests. Users may further narrow their feeds by actively blocking certain content or accounts. === Over-representation === A pattern observed across social media platforms is the concentration of algorithmic visibility among a small subset of users. Content from the most active users, those with the largest followings, or those generating the most engagement tends to be surfaced more frequently, meaning a small number of accounts can account for a disproportionate share of what appears in other users' feeds.
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Conditional random field

Conditional random fields (CRFs) are a class of statistical modeling methods often applied in pattern recognition and machine learning and used for structured prediction. Whereas a classifier predicts a label for a single sample without considering "neighbouring" samples, a CRF can take context into account. To do so, the predictions are modelled as a graphical model, which represents the presence of dependencies between the predictions. The kind of graph used depends on the application. For example, in natural language processing, "linear chain" CRFs are popular, for which each prediction is dependent only on its immediate neighbours. In image processing, the graph typically connects locations to nearby and/or similar locations to enforce that they receive similar predictions. Other examples where CRFs are used are: labeling or parsing of sequential data for natural language processing or biological sequences, part-of-speech tagging, shallow parsing, named entity recognition, gene finding, peptide critical functional region finding, and object recognition and image segmentation in computer vision. == Description == CRFs are a type of discriminative undirected probabilistic graphical model. Lafferty, McCallum and Pereira define a CRF on observations X {\displaystyle {\boldsymbol {X}}} and random variables Y {\displaystyle {\boldsymbol {Y}}} as follows: Let G = ( V , E ) {\displaystyle G=(V,E)} be a graph such that Y = ( Y v ) v ∈ V {\displaystyle {\boldsymbol {Y}}=({\boldsymbol {Y}}_{v})_{v\in V}} , so that Y {\displaystyle {\boldsymbol {Y}}} is indexed by the vertices of G {\displaystyle G} . Then ( X , Y ) {\displaystyle ({\boldsymbol {X}},{\boldsymbol {Y}})} is a conditional random field when each random variable Y v {\displaystyle {\boldsymbol {Y}}_{v}} , conditioned on X {\displaystyle {\boldsymbol {X}}} , obeys the Markov property with respect to the graph; that is, its probability is dependent only on its neighbours in G and not its past states: P ( Y v | X , { Y w : w ≠ v } ) = P ( Y v | X , { Y w : w ∼ v } ) {\displaystyle P({\boldsymbol {Y}}_{v}|{\boldsymbol {X}},\{{\boldsymbol {Y}}_{w}:w\neq v\})=P({\boldsymbol {Y}}_{v}|{\boldsymbol {X}},\{{\boldsymbol {Y}}_{w}:w\sim v\})} , where w ∼ v {\displaystyle {\mathit {w}}\sim v} means that w {\displaystyle w} and v {\displaystyle v} are neighbors in G {\displaystyle G} . What this means is that a CRF is an undirected graphical model whose nodes can be divided into exactly two disjoint sets X {\displaystyle {\boldsymbol {X}}} and Y {\displaystyle {\boldsymbol {Y}}} , the observed and output variables, respectively; the conditional distribution p ( Y | X ) {\displaystyle p({\boldsymbol {Y}}|{\boldsymbol {X}})} is then modeled. === Inference === For general graphs, the problem of exact inference in CRFs is intractable. The inference problem for a CRF is basically the same as for an MRF and the same arguments hold. However, there exist special cases for which exact inference is feasible: If the graph is a chain or a tree, message passing algorithms yield exact solutions. The algorithms used in these cases are analogous to the forward-backward and Viterbi algorithm for the case of HMMs. If the CRF only contains pair-wise potentials and the energy is submodular, combinatorial min cut/max flow algorithms yield exact solutions. If exact inference is impossible, several algorithms can be used to obtain approximate solutions. These include: Loopy belief propagation Alpha expansion Mean field inference Linear programming relaxations === Parameter learning === Learning the parameters θ {\displaystyle \theta } is usually done by maximum likelihood learning for p ( Y i | X i ; θ ) {\displaystyle p(Y_{i}|X_{i};\theta )} . If all nodes have exponential family distributions and all nodes are observed during training, this optimization is convex. It can be solved for example using gradient descent algorithms, or Quasi-Newton methods such as the L-BFGS algorithm. On the other hand, if some variables are unobserved, the inference problem has to be solved for these variables. Exact inference is intractable in general graphs, so approximations have to be used. === Examples === In sequence modeling, the graph of interest is usually a chain graph. An input sequence of observed variables X {\displaystyle X} represents a sequence of observations and Y {\displaystyle Y} represents a hidden (or unknown) state variable that needs to be inferred given the observations. The Y i {\displaystyle Y_{i}} are structured to form a chain, with an edge between each Y i − 1 {\displaystyle Y_{i-1}} and Y i {\displaystyle Y_{i}} . As well as having a simple interpretation of the Y i {\displaystyle Y_{i}} as "labels" for each element in the input sequence, this layout admits efficient algorithms for: model training, learning the conditional distributions between the Y i {\displaystyle Y_{i}} and feature functions from some corpus of training data. decoding, determining the probability of a given label sequence Y {\displaystyle Y} given X {\displaystyle X} . inference, determining the most likely label sequence Y {\displaystyle Y} given X {\displaystyle X} . The conditional dependency of each Y i {\displaystyle Y_{i}} on X {\displaystyle X} is defined through a fixed set of feature functions of the form f ( i , Y i − 1 , Y i , X ) {\displaystyle f(i,Y_{i-1},Y_{i},X)} , which can be thought of as measurements on the input sequence that partially determine the likelihood of each possible value for Y i {\displaystyle Y_{i}} . The model assigns each feature a numerical weight and combines them to determine the probability of a certain value for Y i {\displaystyle Y_{i}} . Linear-chain CRFs have many of the same applications as conceptually simpler hidden Markov models (HMMs), but relax certain assumptions about the input and output sequence distributions. An HMM can loosely be understood as a CRF with very specific feature functions that use constant probabilities to model state transitions and emissions. Conversely, a CRF can loosely be understood as a generalization of an HMM that makes the constant transition probabilities into arbitrary functions that vary across the positions in the sequence of hidden states, depending on the input sequence. Notably, in contrast to HMMs, CRFs can contain any number of feature functions, the feature functions can inspect the entire input sequence X {\displaystyle X} at any point during inference, and the range of the feature functions need not have a probabilistic interpretation. == Variants == === Higher-order CRFs and semi-Markov CRFs === CRFs can be extended into higher order models by making each Y i {\displaystyle Y_{i}} dependent on a fixed number k {\displaystyle k} of previous variables Y i − k , . . . , Y i − 1 {\displaystyle Y_{i-k},...,Y_{i-1}} . In conventional formulations of higher order CRFs, training and inference are only practical for small values of k {\displaystyle k} (such as k ≤ 5), since their computational cost increases exponentially with k {\displaystyle k} . However, another recent advance has managed to ameliorate these issues by leveraging concepts and tools from the field of Bayesian nonparametrics. Specifically, the CRF-infinity approach constitutes a CRF-type model that is capable of learning infinitely-long temporal dynamics in a scalable fashion. This is effected by introducing a novel potential function for CRFs that is based on the Sequence Memoizer (SM), a nonparametric Bayesian model for learning infinitely-long dynamics in sequential observations. To render such a model computationally tractable, CRF-infinity employs a mean-field approximation of the postulated novel potential functions (which are driven by an SM). This allows for devising efficient approximate training and inference algorithms for the model, without undermining its capability to capture and model temporal dependencies of arbitrary length. There exists another generalization of CRFs, the semi-Markov conditional random field (semi-CRF), which models variable-length segmentations of the label sequence Y {\displaystyle Y} . This provides much of the power of higher-order CRFs to model long-range dependencies of the Y i {\displaystyle Y_{i}} , at a reasonable computational cost. Finally, large-margin models for structured prediction, such as the structured Support Vector Machine can be seen as an alternative training procedure to CRFs. === Latent-dynamic conditional random field === Latent-dynamic conditional random fields (LDCRF) or discriminative probabilistic latent variable models (DPLVM) are a type of CRFs for sequence tagging tasks. They are latent variable models that are trained discriminatively. In an LDCRF, like in any sequence tagging task, given a sequence of observations x = x 1 , … , x n {\displaystyle x_{1},\dots ,x_{n}} , the main problem the model must solve is how to assign a sequence of labels y = y 1 , … , y n {\displaystyle y_{1},\dots ,y_{n}} from one finite set
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Blue check

A blue check is used on social media platforms, notably X (formerly known as Twitter), to indicate the authenticity of an account. Since November 2022, Twitter users whose accounts are at least 90 days old and have a verified phone number receive verification upon subscribing to X Premium or Verified Organizations; this status persists as long as the subscription remains active. When introduced in June 2009, the system provided the site's readers with a means to distinguish genuine notable account holders, such as celebrities and organizations, from impostors or parodies. Until November 2022, a blue checkmark displayed against an account name indicated that Twitter had taken steps to ensure that the account was actually owned by the person or organization whom it claimed to represent. The checkmark does not imply endorsement from Twitter, and does not mean that tweets from a verified account are necessarily accurate or truthful in any way. People with verified accounts on Twitter are often colloquially referred to as "blue checks" on social media and by reporters. In November 2022, the verification program was modified heavily by new owner Elon Musk, extending verification to any account with a verified phone number and an active subscription to an eligible X Premium (formerly Twitter Blue) plan. These changes faced criticism from users and the media, who believed that the changes would ease impersonation, and allow accounts spreading misleading information to feign credibility. In a related change, Twitter introduced additional gold and gray checkmarks, used by Verified Organizations and government-affiliated accounts, respectively. Twitter claims that the changes to verification are required to "reduce fraudulent accounts and bots". Twitter users who had been verified through the previous system were known as "legacy verified" accounts; legacy verification was deprecated in April 2023, and stripped from accounts who do not meet the new payment requirements. Musk later implied that he had been personally paying for the X Premium subscriptions of several notable celebrities. == Until November 2022 == In June 2009, after being criticized by Kanye West and sued by Tony La Russa over unauthorized accounts run by impersonators, the company launched their "Verified Accounts" program. Twitter stated that an account with a "blue tick" verification badge indicates "we've been in contact with the person or entity the account is representing and verified that it is approved". After the beta period, the company stated in their FAQ that it "proactively verifies accounts on an ongoing basis to make it easier for users to find who they're looking for" and that they "do not accept requests for verification from the general public". Originally, Twitter took on the responsibility of reaching out to celebrities and other notable people to confirm their identities in order to establish a verified account. In July 2016, Twitter announced a public application process to grant verified status to an account "if it is determined to be of public interest" and that verification "does not imply an endorsement". In 2016, the company began accepting requests for verification, but it was discontinued the same year. Twitter explained that the volume of requests for verified accounts had exceeded its ability to cope; rather, Twitter determines on its own whom to approach about verified accounts, limiting verification to accounts which are "authentic, notable, and active". In November 2020, Twitter announced a relaunch of its verification system in 2021. According to the new policy, Twitter verifies six different types of accounts; for three of them (companies, brands, and influential individuals like activists), the existence of a Wikipedia page will be one criterion for showing that the account has "Off Twitter Notability". === Controversy === On June 21, 2014, actor William Shatner raised an issue with several Engadget editorial staff and their verification status on Twitter. Besides the site's social media editor, John Colucci, Shatner also targeted several junior members of the staff for being "nobodies", unlike some of his actor colleagues who did not bear such distinction. Shatner claimed Colucci and the team were bullying him when giving a text interview to Mashable. Over a month later, Shatner continued to discuss the issue on his Tumblr page, to which Engadget replied by defending its team and discussing the controversy surrounding the social media verification. Twitter's practice and process for verifying accounts came under scrutiny again in 2017 after the company verified the account of white supremacist and far-right political activist, Jason Kessler. Many who criticized Twitter's decision to verify Kessler's account saw this as a political act on the company's behalf. In response, Twitter put its verification process on hold. The company tweeted, "Verification was meant to authenticate identity & voice but it is interpreted as an endorsement or an indicator of importance. We recognize that we have created this confusion and need to resolve it. We have paused all general verifications while we work and will report back soon." As of November 2017, Twitter continued to deny verification of Julian Assange's account following his requests. In November 2019, Dalit activists of India alleged that higher-caste people get Twitter verification easily and trended hashtags #CancelAllBlueTicksInIndia and #CasteistTwitter. Critics have said that the company's verification process is not transparent and causes digital marginalisation of already marginalised communities. Twitter India rejected the allegations, calling them "impartial" and working on a "case-by-case" policy. == Since November 2022 == On April 20, 2023, Twitter (known as X since July 2023) began removing verification status for users of public interest, causing a controversy among Twitter users. The website's system was altered, allowing any individual to receive verification for a monthly fee, an act which saw significant criticism. Following the acquisition of Twitter by Elon Musk on October 28, 2022, Musk told Twitter employees to introduce paid verification by November 7 through Twitter Blue. The Verge reported that the updated Blue subscription would cost $19.99 per month, and users would lose their verification status if they did not join within 90 days. Following backlash, Musk tweeted, in response to author Stephen King, a lowered $8 price on November 1, 2022. Twitter confirmed the new price of $7.99 per month on November 5, 2022. The new verification system began rollout on November 9, 2022, a day after the 2022 United States elections. The decision to delay its rollout was to address concerns about users potentially spreading misinformation about voting results by posing as news outlets and lawmakers. At the same time, Twitter introduced a secondary gray "Official" label on some high-profile accounts, but removed them hours after launch. Less than 48 hours later, Twitter reinstated the gray "Official" label, after multiple users were suspended for deliberately impersonating reporters and high-profile athletes like LeBron James. A viral tweet from an account purporting to be the pharmaceutical company Eli Lilly and Company caused the company's stock to fall after announcing "insulin is free now". As a result, Twitter disabled new Blue subscriptions on November 11, 2022. === Announcement === In October 2022, Casey Newton of Platformer reported that executives at Twitter began discussing the possibility of users being forced to pay for Twitter Blue in order to keep their verification status. Musk publicly announced that verification was "being revamped right now" after Newton's article; according to The Verge, Twitter planned to increase the price of Twitter Blue from US$4.99 per month to US$19.99 per month. Users would have had 90 days to subscribe or face losing their verification status, and employees were told to implement paid verification by November 9 or risk getting fired. Upon the news that Twitter Blue would cost US$19.99 per month, author Stephen King expressed displeasure towards Twitter and stated that he would leave. Musk, replying to King's tweet, proposed that the service should cost US$7.99 instead. In a separate tweet, Musk wrote that Twitter Blue subscribers would receive priority in replies, mentions, and search, fewer advertisements, and longer audio and video. Although paid verification was expected to be launched by November 7, the reintroduction of Twitter Blue was delayed until after the 2022 United States elections on November 9, according to a memo obtained by The New York Times. The announcement of paid verification resulted in several accounts facetiously impersonating Musk, such as those of comedians Kathy Griffin and Sarah Silverman, being suspended. In response, Musk announced that impersonators using Twitter Blue "will be permanently suspended". An "official
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International Webmasters Association

The International Webmasters Association (IWA) is a non-profit association for education and certification of web professionals founded in 1996. It provides a Certified Web Professional certification. One of its objectives is to build a World Wide Web that is a true global community. According to the IWA, as of 2025 it has more than 100 official chapters with over 300,000 individual members in 106 countries. In 2001, the IWA merged with the HTML Writers Guild (HWG) and joined the World Wide Web Consortium (W3C). IWA's accomplishments include the publishing of the industry's first guidelines for ethical and professional standards, web certification and education programs, specialized employment resources, and technical assistance to individuals and businesses. IWA members participate to the activities of W3C WCAG Working Group, ATAG Working Group, and the XHTML Working Group. They have also participated in other initiatives such as the Multimodal Interaction Working Group which developed EMMA, the Extensible MultiModal Annotation markup language.
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Access-independent services

Access-independent service (AIS) is a service concept in which a service does not depend on guaranteed access network cooperation for service delivery. Telecommunications industry analyst Dean Bubley first used the term in a report on Telco-OTT in February 2012. Traditionally, most telecom company or internet service provider services are access-dependent, because they rely heavily on guaranteed access cooperation on the network the service is delivered over. For instance, traditional IP-based TV service (IPTV) delivered by a telecom company is generally a managed service. This means that IPTV service assumes the IPTV service provider has control over the access network that the IPTV service is delivered over, and network quality of service (QoS) guarantees are available for IPTV service delivery. As a result, the reach of a telecom company's IPTV service is generally restricted by the reach of the telecom company's access network. In contrast, services offered by non-traditional video content delivery service providers such as Netflix, Hulu, and Amazon Video are considered access-independent services. Netflix's video content streaming service, for example, dynamically adapts to network conditions in real-time to strive for the best overall quality of experience (QoE) and does not assume guaranteed cooperation from the underlying IP network, such as QoS. As a result, without considering content rights and different countries' government restrictions, the reach of Netflix's video content streaming service is, in theory, the reach of the Internet. Skype is another example of AIS, because Skype offers an IP-based telephony service over the Internet without depending on IP network cooperation guarantees other than basic IP network connectivity. In the context of telecom service delivery, the concept of access independent services is also commonly described by the term "over-the-top" (OTT) services. OTT service providers such as but not limited to Facebook, WeChat, and Netflix generally do not own or directly manage any wide-area access network to begin with, so they design their services for overall quality of experience, with no assumptions on guaranteed access network cooperation.
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Enterprise cognitive system

Enterprise cognitive systems (ECS) are part of a broader shift in computing, from a programmatic to a probabilistic approach, called cognitive computing. An Enterprise Cognitive System makes a new class of complex decision support problems computable, where the business context is ambiguous, multi-faceted, and fast-evolving, and what to do in such a situation is usually assessed today by the business user. An ECS is designed to synthesize a business context and link it to the desired outcome. It recommends evidence-based actions to help the end-user achieve the desired outcome. It does so by finding past situations similar to the current situation, and extracting the repeated actions that best influence the desired outcome. While general-purpose cognitive systems can be used for different outputs, prescriptive, suggestive, instructive, or simply entertaining, an enterprise cognitive system is focused on action, not insight, to help in assessing what to do in a complex situation. == Key characteristics == ECS have to be: Adaptive: They must learn as information changes, and as goals and requirements evolve. They must resolve ambiguity and tolerate unpredictability. They must be engineered to feed on dynamic data in real time, or near real time. In the Enterprise, near-real time learning from data requires an agile information federation approach to ingest incremental data updates as they occur, and an unsupervised learning approach to ensure that new best practice is leveraged across the organization in a timely manner. Interactive: They must interact easily with users so that those users can define their needs comfortably. They may also interact with other processors, devices, and Cloud services, as well as with people. In the Enterprise, interactions are controlled via existing workflows and UIs. Therefore, embedding best practices directly into these existing interfaces, in the context of a specific step, is critical to ensure maximum end-user adoption. Iterative and stateful: They must aid in defining a problem by asking questions or finding additional source input if a problem statement is ambiguous or incomplete. They must “remember” previous interactions in a process and return information that is suitable for the specific application at that point in time. In the Enterprise, business context is often structured by a business process, and therefore sufficiently data-rich to make relevant recommendations without significant iterations from the end-user. A stateful memory of overall interactions across communication channels is critical for understanding of context, as a static profile will not capture intent and outcome potential the way behavior does. Contextual: They must understand, identify, and extract contextual elements such as meaning, syntax, time, location, appropriate domain, regulations, user's profile, process, task and goal. They may draw on multiple sources of information, including both structured and unstructured digital information, as well as sensory inputs (visual, gestural, auditory, or sensor-provided). In the Enterprise, Context is fragmented and must be aggregated across data types, sources, and locations. In most business environments, such data is captured in existing enterprise information systems, and the effort is linked to quickly source and unify such information. It is rare to have to directly process sensor, audio or visual data in real-time as direct input into the enterprise cognitive system. Instead, these data types are captured by Enterprise Applications and pre-processed into a binary or text format prior to consumption by the System. == Business applications powered by an ECS == Bottlenose – trends and brands monitoring Cybereason – security threat monitoring Dataminr – social media monitoring
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VibeOS

VibeOS is an operating system built from scratch entirely by generative artificial intelligence, using code produced through prompts to Claude (vibe coding). It is capable of running on QEMU and was successfully tested on a Raspberry Pi Zero. It has been released under the MIT license. == Features == === Core === Custom kernel with cooperative multitasking (preemptive backup) FAT32 filesystem with long filename support Memory allocator, process scheduler, interrupt handling GIC-400 (QEMU) and BCM2836/BCM2835 (Pi) interrupt controllers Configurable boot (splash screen, boot target) === GUI === Desktop environment with draggable windows Menu bar, dock, window minimize/maximize/close Mouse and keyboard input Modern macOS-inspired aesthetic === Networking === Full TCP/IP stack (Ethernet, ARP, IP, ICMP, UDP, TCP) DNS resolver HTTP client TLS 1.2 with HTTPS support === Apps === Web browser with HTML/CSS rendering Terminal emulator with readline-style shell Text editor (vim clone) with syntax highlighting File manager with drag-and-drop Music player (MP3/WAV) Calculator, system monitor VibeCode IDE Doom port === Development === TCC (Tiny C Compiler) - compile C programs directly on VibeOS MicroPython interpreter with full kernel API bindings 60+ userspace programs (coreutils, games, GUI apps) === Hardware === Runs on Raspberry Pi Zero 2W USB keyboard and mouse via DWC2 driver SD card via EMMC driver 1920×1080 framebuffer == Further projects == There are other independent projects under the VibeOS name, including an independent development by Ben, also developed using vibe coding, aimed at creating a Unix-like operating system for educational purposes. Another project is Vib-OS, an operating system also built using vibe coding, capable of booting on a Raspberry Pi. It offers a desktop environment with a customizable wallpaper, a file manager, and a web browser currently in an early stage of development, a functional Doom port, among other features that are not very polished given the state of development.
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Amplified conference

An amplified conference is a conference or similar event in which the talks and discussions at the conference are 'amplified' through use of networked technologies in order to extend the reach of the conference deliberations. The term was originally coined by Lorcan Dempsey in a blog post. The term is now widely used within the academic and research community with Wankel proposing the following definition: The extension of a physical event (or a series of events) through the use of social media tools for expanding access to (aspects of) the event beyond physical and temporal bounds. Such amplification takes place in the context of intent to make the most of the intellectual content, discussion, networking, and discovery initiated by the event through the process of sharing with co-attendees, colleagues, friends and wider informed publics. A paper by Haider and others illustrates how amplified conferences are becoming mainstream in a discussion on "how social media have been employed as part of the project, particularly around event amplification". As described by Guy in the Ariadne ejournal the term is not a prescriptive one, but rather describes a pattern of behaviors which initially took place at IT and Web-oriented conferences once WiFi networks started to become available at conference venues and delegates started to bring with them networked devices such as laptops and, more recently, PDAs and mobile phones. == Different Approaches to 'Amplification' of Conferences == There are a number of ways in which conferences can be amplified through use of networked technologies: Amplification of the audiences' voice: Prior to the availability of real time chat technologies at events (whether use of IRC, Twitter, instant messaging clients, etc.) it was only feasible to discuss talks with immediate neighbours, and even then this may be considered rude. Amplification of the speaker's talk: The availability of video and audio-conferencing technologies make it possible for a speaker to be heard by an audience which isn't physically present at the conference. Although use of video technologies has been available to support conferences for some time, this has normally been expensive and require use of dedicated video-conferencing technologies. However the availability of lightweight desktop tools make it much easier to deploy such technologies, without even, requiring the involvement of conference organisers. Amplification across time: Video and audio technologies can also be used to allow a speaker's talk to be made available after the event, with use of podcasting or videocasting technologies allowing the talks to be easily syndicated to mobile devices as well as accessed on desktop computers. Amplification of the speaker's slides: The popularity of global repository services for slides, such as SlideShare, enable the slides used by a speaker to be more easily found, embedded on other Web sites and commented upon, in ways that were not possible when the slides, if made available at all, were only available on a conference Web site. Amplification of feedback to the speaker: Micro-blogging technologies, such as Twitter, are being used not only as a discussion channel for conference participants but also as a way of providing real-time feedback to a speaker during a talk. We are also now seeing dedicated microblogging technologies, such as Coveritlive and Scribblelive, being developed which aim to provide more sophisticated 'back channels' for use at conferences. Amplification of a conference's collective memory: The popularity of digital cameras and the photographic capabilities of many mobile phones is leading to many photographs being taken at conferences. With such photographs often being uploaded to popular photographic sharing services, such as Flickr, and such collections being made more easy to discover through agreed use of tags, we are seeing amplification of the memories of an event though the sharing of such resources. The ability of such photographic resources to be 'mashed up' with, say, accompanying music, can similarly help to enrich such collective experiences. Amplification of the learning: The ability to be able to follow links to resources and discuss the points made by a speaker during a talk can enrich the learning which takes place at an event, as described by Shabajee's article on "'Hot' or Not? Welcome to real-time peer review" published in the Times Higher Education Supplement in May 2003. Long term amplification of conference outputs: The availability in a digital format of conference resources, including 'official' resources such as slides, video and audio recordings, etc. which have been made by the conference organisers with the approval of speakers, together with more nebulous resources such as archives of conference back channels, and photographs and unofficial recordings taken at the event may help to provide a more authentic record of an event, which could potentially provide a valuable historical record. The amplification of conferences can be viewed as an example of how new technologies are altering standard practice. By using these techniques a different type of interaction is created at the conference itself, but also the boundaries around the conference can be seen as permeable, with remote participants engaging in discussion. An amplified conference also provides a considerably altered archive compared with a 'traditional' one. For the latter, the printed proceedings will be the main record, but for an amplified event this record is distributed across many media and takes in a wider range of content types, including the papers, videos of the presentations (for example on YouTube), the slides (e.g. on Slideshare), photos of the event (Flickr), interaction between participants (Twitter), reflections and comments (blogs), etc. The amplified conference represents an example of changing practice in digital scholarship.
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