AI App Inventor

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

  • Thinkfree Office

    Thinkfree Office

    Thinkfree Office is a web-based commercial office productivity suite developed by South Korea-based Thinkfree Inc. It includes Word (a word processor), Spreadsheet (a spreadsheet) and Presentation (a presentation program). They are compatible with Microsoft Office's Word, PowerPoint, and Excel. It also features collaborative editing. The product is hosted on the client's server. == Supported file formats == Thinkfree Office supports ISO/IEC international standard ISO/IEC 26300 Open Document Format for Office Applications (odf, odt, odp, ods, odg). It also supports Microsoft's XML formats (docx, pptx, xlsx) and Microsoft's legacy binary formats (doc, ppt, xls). == Naming == The software was previously marketed under different names, such as Thinkfree Server, Thinkfree Online, Hancom Office Online, and Hancom Office Web. Eventually, the brand was consolidated under the name Thinkfree Office. == History == In June 2000, Thinkfree Inc. released Thinkfree Office, based in Silicon Valley, California. It is recognized as the world's first online office editor (predating Google Docs and Microsoft 365) and attracted significant media coverage, including reports on CNN. In 2001, Microsoft CEO Steve Ballmer highlighted Thinkfree as a significant competitor in a magazine interview, considering it a potential threat to his company, second only to Linux. In November 2003, Hancom, a South Korean office software company, signed a memorandum of understanding and subsequently acquired Thinkfree. In January 2004, Thinkfree expanded into other foreign markets. Subsidiary Haansoft USA, Inc. was created in San Jose, California to begin formal commercial operations in the US market. At the same time, a partnership was established with Riverdeep with the purpose of improving marketshare. In February 2004, expansion into the Japanese market began. A commercial agency agreement was signed with PSI in Shinjuku, Japan, which allowed for localized distribution. In addition, a global agreement was entered into with Yamada Denki, one of the three main computer distributors in Japan, for a total of 180,000 units. In May 2006, Thinkfree Office received the "Product of the Year" award at the Well-Connected Awards, USA. In January 2009, Thinkfree Mobile was launched at CES 2009 in Las Vegas. In April 2009, Thinkfree Live, Korea's first web office service, was launched. In June 2018, a partnership was formed with Amazon Web Services to integrate Thinkfree Office into WorkDocs, an in-house office suite. In October 2023, Hancom split its online office business unit as "Thinkfree Inc.".

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  • Contact center telephony

    Contact center telephony

    In marketing, contact center telephony is the communication and collaboration system used by businesses to either manage high volumes of inbound queries or outbound telephone calls keeping their workforce or agents productive and in control to serve or acquire customers. This business communication system is an extension of computer telephony integration (CTI). == Overview == The interactions between callers and customer service representatives are supported by the collective system of computers, telephones and the Internet. The shift from CTI to contact center telephony is marked by the sheer change in the customer’s behavior when it comes to communication. Means customers are no longer confined only to voice-based communication i.e. phone to connect with their customer service departments. In addition, they are making use of email, SMS, chat, social media, and other virtual contact channels. This is also the reason for the shift in nomenclature from "call centers" to "contact centers", "contact" being a wider term than "call". Respecting the trend, contact center owners need to adopt unified communication or multi-channel approach to let customers get in touch with them via their preferred communication mediums, either voice or non-voice (data). Cloud-based phone system is a further advancement in the direction as it allows operators to access all the features and benefits of call center telephony over the Web against an affordable & flexible pay-as-you-go subscription model. Thus, in-house infrastructure deployment to manage public switched telephone networks, storage, communication applications, and collaboration servers is no more an obligation. Neither is the need to invest resources for their upgrade, repair, maintenance and security as cloud vendor would be responsible for the same. == India == India, a popular call center business process outsourcing destination, often uses a cloud-based phone system in order to cut operational expenses and downtime, and increase connectivity. == Promotion == Businesses can rely on contact center telephony services to respond to their customers’ queries over phone, email, chat, fax, etc. Integrating it with their customer relationship management tools, entire contact details of customers and their interaction sessions with different customer service representatives can be found at one place. The combination can manage not just sales and marketing but also deliver excellent post-sales customer service or technical support to allow customers derive the most from their products or services. Hence, it’s becoming instrumental in increasing customer satisfaction and loyalty and most of the call center services in India are taking refuge from it. The entire contact center telephony service can be availed by professionals over a browser. Hence, businesses can leverage the concept of BYOD (bring your own device) and mobility and serve their customers well using mobile applications. According to market analysts, BYOD increases satisfaction among workforce, and hence their individual and collective productivity as well. BYOD programme significantly reduces the TCO (total cost of ownership) as professionals prefer to work with their own devices rather than using company-provisioned devices. Next, they tend to be more caring towards such devices and can even shell out money to update and upgrade those when required. Integration of IM, along with audio and video conferencing services helps call center or contact center representatives to get real time assistance from their peers or seniors to resolve any complex issues. They can internally exchange information and knowledge articles as and when required. Real-time call monitoring/barging system can be used by quality assessment team to provide important guidelines to agents to maintain the standard of the service as per industry norms. Integrated recording feature is helpful for internal training and quality purposes to improve productivity and customer satisfaction in equal measures. It also helps in getting business insights and improving products or services to gain deeper penetration into the market.

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  • MX1 Ltd

    MX1 Ltd

    MX1 was a global media services provider founded in July 2016 from a merger between digital media services companies, RR Media and SES Platform Services, and a wholly owned subsidiary of global satellite owner and operator, SES. In September 2019, MX1 was merged into the SES Video division and the MX1 brand dropped. Broadcast and streamed content management, playout, distribution, and monetisation services from both MX1 and SES Video are now provided under the SES name. Before merger with SES, MX1 claimed to manage more than 5 million media assets and every day to distribute more than 3,600 TV channels, manage the playout of over 525 channels, distribute content to more than 120 subscription VOD platforms, and deliver over 8,400 hours of online video streaming and more than 620 hours of premium sports and live events. == Services == MX1 video and media services are provided through a single hybrid, cloud and on-premises solution, called MX1 360, which enables video and media solutions including content and metadata management, archiving, localisation solutions, channel playout, VOD, online video (OTT) and content distribution. Services provided by MX1 include: === Content aggregation === Acquisition of content via satellite, fibre or IP with satellite downlinking services (for encryption, re-encryption and re-muxing into different platforms), fibre reception from any location, and IP reception via the public Internet. Live sports, news and entertainment production (including in-studio, outside broadcasting, and SNG) with mobile live streaming and video contribution. === Content management === Digital mastering including scanning, conversion, restoration, quality control and localisation/versioning. Content archiving including secure, cloud and on-premises digital storage, and disaster recovery services. Metadata packaging and platform validation to enhance content discovery, searchability and cataloguing. Playout preparation and delivery to any format. === Channel origination and playout === Managed TV channel origination in SD, HD and UHD including 3D graphics, and video and audio effects, using cloud-based solution accessible from any location, with live content insertion and operation, and 24/7 monitoring. === Online video/VOD services === Content preparation and management for online video, VOD, live streaming services and Online video platforms using an ultra-high capacity content delivery network, including subscriber management, apps, DRM, social media, advertising tools, monetisation tools, metadata management, and analytics. === Content delivery === Delivery in all video formats over hybrid distribution network of satellite (using over 150 platforms), fibre (60 digital media hubs worldwide) and the Internet with complete downlink/uplink turnaround services and OTT content delivery. == Locations == MX1 has 16 offices worldwide, the most recent opened in March 2017 in Seoul, South Korea, as well as media centres in UK (London), US (Hawley, PA), Israel (Emeq Ha'Ela), Romania (Bucharest) and at the headquarters in Unterföhring near Munich, Germany. In the early part of 2017, significant upgrades were made to MX1's US media centre in Hawley, Pennsylvania, including expanding its capabilities for US based and global content aggregation, management and delivery to support US broadcasters and content providers. == History == RRsat was founded in Israel by David Rivel, an electronics, computers and communications engineer in 1981 as a communications provider, and in 2014 changed its name to RR Media to reflect its expanding global service offering. In 2015, RR Media acquired Eastern Space Systems (ESS), a Romanian provider of content management and content distribution services and satellite transmission services provider, SatLink Communications. Digital Playout Centre GmbH (DPC) was founded in 1996 by German media company, Kirch to provide playout, multiplexing, satellite uplinks and other broadcast services to Kirch's Premiere pay-TV platform (now Sky Deutschland) and other private and public German broadcasters. In 2005, SES Astra (a subsidiary of SES Global, now SES) bought 100% of DPC from Premiere and the company renamed ASTRA Platform Services GmbH (APS). In 2012, to reflect the company's expanding worldwide reach, the name was changed to SES Platform Services. In February 2016, it was announced that SES Platform Services had agreed, subject to regulatory approvals, to purchase RR Media. The acquisition was completed in July 2016, with the merged company renamed MX1 and headed by Avi Cohen, the former CEO of RR Media. In October 2017, Cohen was replaced as CEO by Wilfred Urner, the former CEO of SES Platform Services, CEO of SES subsidiary, HD+ and Head of Media Platforms and Product Development, SES Video.

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  • Interference (communication)

    Interference (communication)

    In telecommunications, an interference is that which modifies a signal in a disruptive manner, as it travels along a communication channel between its source and receiver. The term is often used to refer to the addition of unwanted signals to a useful signal. Common examples include: Electromagnetic interference (EMI) Co-channel interference (CCI), also known as crosstalk Adjacent-channel interference (ACI) Intersymbol interference (ISI) Inter-carrier interference (ICI), caused by doppler shift in OFDM modulation (multitone modulation). Common-mode interference (CMI) Conducted interference Noise is a form of interference but not all interference is noise. Radio resource management aims at reducing and controlling the co-channel and adjacent-channel interference. == Interference alignment == A solution to interference problems in wireless communication networks is interference alignment, which was crystallized by Syed Ali Jafar at the University of California, Irvine. A specialized application was previously studied by Yitzhak Birk and Tomer Kol for an index coding problem in 1998. For interference management in wireless communication, interference alignment was originally introduced by Mohammad Ali Maddah-Ali, Abolfazl S. Motahari, and Amir Keyvan Khandani, at the University of Waterloo, for communication over wireless X channels. Interference alignment was eventually established as a general principle by Jafar and Viveck R. Cadambe in 2008, when they introduced "a mechanism to align an arbitrarily large number of interferers, leading to the surprising conclusion that wireless networks are not essentially interference limited." This led to the adoption of interference alignment in the design of wireless networks. Jafar explained: My research group crystallized the concept of interference alignment and showed that through interference alignment, it is possible for everyone to access half of the total bandwidth free from interference. Initially this result was shown under a number of idealized assumptions that are typical in theoretical studies. We have since continued to work on peeling off these idealizations one at a time, to bring the theory closer to practice. Along the way we have made numerous discoveries through the lens of interference alignment, which reveal new and powerful signaling schemes. According to New York University senior researcher Paul Horn: Syed Jafar revolutionized our understanding of the capacity limits of wireless networks. He demonstrated the astounding result that each user in a wireless network can access half of the spectrum without interference from other users, regardless of how many users are sharing the spectrum. This is a truly remarkable result that has a tremendous impact on both information theory and the design of wireless networks.

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  • Autonomous logistics

    Autonomous logistics

    Autonomous logistics describes systems that provide unmanned, autonomous transfer of equipment, baggage, people, information or resources from point-to-point with minimal human intervention. Autonomous logistics is a new area being researched and currently there are few papers on the topic, with even fewer systems developed or deployed. With web enabled cloud software there are companies focused on developing and deploying such systems which will begin coming online in 2018. == Autonomous logistics vehicles == There are several subclasses of autonomous logistics vehicles: Ground autonomous logistics Based on Unmanned ground vehicle technology, a large autonomous logistics tracked carrier, which can be deployed in a tropical forest for day and night, has been developed. Another example is the TerraMax autonomous truck based on Oshkosh's Medium Tactical Vehicle Replacement (MTVR) military truck platform. Most recently, TerraMax competed in the 2007 Darpa Urban Challenge. The MTVR was designed for the U.S. Marine Corps with a 70% off-road mission profile. TerraMax's unmanned ground vehicle kit does not interfere with the conventional operation of the vehicle. A robust sensor suite allows for 360-degree situational awareness around TerraMax. Elements of the autonomous navigation kit could be used to enhance driver awareness. The complete kit could be used in applications such as snow removal on airport runways. Aerial autonomous logistics Based on unmanned aerial vehicle technology, aerial autonomous logistics (or logistics UAVs) provides transfer of resources and equipment in disaster relief situations, replenishment operations, reconnaissance operations where information is gathered, and general parcel or package delivery. Space autonomous logistics Describes the ability to provide logistics to and from space, be that orbital, lunar or beyond. Current space logistics vehicle examples are the Progress spacecraft, Russian expendable freighter uncrewed resupply spacecraft and the Automated Transfer Vehicle, expendable uncrewed resupply spacecraft developed by the European Space Agency. Above Water autonomous logistics Based on unmanned surface vehicle technology, this class of vehicles provides a range of surface fleet replenishment and equipment transfer capabilities. Subsea autonomous logistics Using autonomous underwater vehicle technology, these vehicles provide re-supply to underwater facilities, reconnaissance of underwater structures, emergency recovery capability, and so on. == Agent-based logistics == Shipping containers handle most of today's intercontinental transport of packaged goods. Managing them in terms of planning and scheduling is a challenging task due to the complexity and dynamics of the involved processes. Hence, recent developments show an increasing trend towards autonomous control with software agents acting on behalf of the logistic objects. Despite the high degree of autonomy it is still necessary to cooperate in order to achieve certain goals. The current trends and recent changes in logistics lead to new, complex and partially conflicting requirements for logistic planning and control systems. Due to the distributed nature of logistics, the usage of agent technology is promising. Due to the mobile nature of logistics, the usage of mobile agent technology is promising as well. Scenarios of usage of mobile agents in logistics has been envisioned.

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  • Enterprise bookmarking

    Enterprise bookmarking

    Enterprise bookmarking is a method for Web 2.0 users to tag, organize, store, and search bookmarks of both web pages on the Internet and data resources stored in a distributed database or fileserver. This is done collectively and collaboratively in a process by which users add tag (metadata) and knowledge tags. In early versions of the software, these tags are applied as non-hierarchical keywords, or terms assigned by a user to a web page, and are collected in tag clouds. Examples of this software are Connectbeam and Dogear. New versions of the software such as Jumper 2.0 and Knowledge Plaza expand tag metadata in the form of knowledge tags that provide additional information about the data and are applied to structured and semi-structured data and are collected in tag profiles. == History == Enterprise bookmarking is derived from Social bookmarking that got its modern start with the launch of the website del.icio.us in 2003. The first major announcement of an enterprise bookmarking platform was the IBM Dogear project, developed in Summer 2006. Version 1.0 of the Dogear software was announced at Lotusphere 2007, and shipped later that year on June 27 as part of IBM Lotus Connections. The second significant commercial release was Cogenz in September 2007. Since these early releases, Enterprise bookmarking platforms have diverged considerably. The most significant new release was the Jumper 2.0 platform, with expanded and customizable knowledge tagging fields. == Differences == === Versus social bookmarking === In a social bookmarking system, individuals create personal collections of bookmarks and share their bookmarks with others. These centrally stored collections of Internet resources can be accessed by other users to find useful resources. Often these lists are publicly accessible, so that other people with similar interests can view the links by category or by the tags themselves. Most social bookmarking sites allow users to search for bookmarks which are associated with given "tags", and rank the resources by the number of users which have bookmarked them. Enterprise bookmarking is a method of tagging and linking any information using an expanded set of tags to capture knowledge about data. It collects and indexes these tags in a web-infrastructure knowledge base server residing behind the firewall. Users can share knowledge tags with specified people or groups, shared only inside specific networks, typically within an organization. Enterprise bookmarking is a knowledge management discipline that embraces Enterprise 2.0 methodologies to capture specific knowledge and information that organizations consider proprietary and are not shared on the public Internet. === Tag management === Enterprise bookmarking tools also differ from social bookmarking tools in the way that they often face an existing taxonomy. Some of these tools have evolved to provide Tag management which is the combination of uphill abilities (e.g. faceted classification, predefined tags, etc.) and downhill gardening abilities (e.g. tag renaming, moving, merging) to better manage the bottom-up folksonomy generated from user tagging.

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  • Radio code

    Radio code

    A radio code is any code that is commonly used over a telecommunication system such as Morse code, brevity codes and procedure words. == Brevity code == Brevity codes are designed to convey complex information with a few words or codes. Specific brevity codes include: ACP-131 Aeronautical Code signals ARRL Numbered Radiogram Multiservice tactical brevity code Ten-code Phillips Code NOTAM Code === Operating signals === Brevity codes that are specifically designed for use between communications operators and to support communication operations are referred to as "operating signals". These include: Prosigns for Morse code 92 Code, Western Union telegraph brevity codes Q code, initially developed for commercial radiotelegraph communication, later adopted by other radio services, especially amateur radio. Used since circa 1909. QN Signals, published by the ARRL and used by Amateur radio operators to assist in the transmission of ARRL Radiograms in the National Traffic System. R and S brevity codes, published by the British Post Office in 1908 for coastal wireless stations and ships, superseded in 1912 by Q codes X code, used by European military services as a wireless telegraphy code in the 1930s and 1940s Z code, also used in the early days of radiotelegraph communication. == Other == Morse code is commonly used in amateur radio. Morse code abbreviations are a type of brevity code. Procedure words used in radiotelephony procedure, are a type of radio code. Spelling alphabets, including the ICAO spelling alphabet, are commonly used in communication over radios and telephones. == Other meanings == Many car audio systems (car radios) have a so-called 'radio code' number which needs to be entered after a power disconnection. This was introduced as a measure to deter theft of these devices. If the code is entered correctly, the radio is activated for use. Entering the code incorrectly several times in a row will cause a temporary or permanent lockout. Some car radios have another check which operates in conjunction with car electronics. If the VIN or another vehicle ID matches the previously stored one, the radio is activated. If the radio cannot verify the vehicle, it is considered to be moved into another vehicle. The radio will then request for the code number or simply refuse to operate and display an error message such as "CANCHECK" or "SECURE".

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  • Algorithmic radicalization

    Algorithmic radicalization

    Algorithmic radicalization is the concept that recommender algorithms on popular social media sites, such as YouTube and Facebook, drive users toward progressively more extreme content over time, leading to the development of radicalized extremist political views. Algorithms meticulously record user interactions, encompassing likes, dislikes and the duration of time watching content, with the objective of generating an endless stream of media designed to sustain user engagement. The phenomenon of echo chamber channels has been demonstrated to exacerbate the polarization of consumers, primarily through the reinforcement of media preferences and the validation of one's existing beliefs. Algorithmic radicalization remains a controversial phenomenon as it is often not in the best interest of social media companies to remove echo chamber channels. To what extent recommender algorithms are actually responsible for radicalization remains disputed. Studies have found contradictory results regarding the promotion of extremist content by algorithms. == Social media echo chambers and filter bubbles == Social media platforms learn the interests and likes of the user to modify their experiences in their feed to keep them engaged and scrolling, known as a filter bubble. An echo chamber is formed when users come across beliefs that magnify or reinforce their thoughts and form a group of like-minded users in a closed system. Echo chambers spread information without any opposing beliefs and can possibly lead to confirmation bias. According to group polarization theory, an echo chamber can potentially lead users and groups towards more extreme radicalized positions. According to the National Library of Medicine, "Users online tend to prefer information adhering to their worldviews, ignore dissenting information, and form polarized groups around shared narratives. Furthermore, when polarization is high, misinformation quickly proliferates." == By site == === Facebook === Facebook's algorithm focuses on recommending content that makes the user want to interact. They rank content by prioritizing popular posts by friends, viral content, and sometimes divisive content. Each feed is personalized to the user's specific interests which can sometimes lead users towards an echo chamber of troublesome content. Users can find their list of interests the algorithm uses by going to the "Your ad Preferences" page. According to a Pew Research study, 74% of Facebook users did not know that list existed until they were directed towards that page in the study. It is also relatively common for Facebook to assign political labels to their users. In recent years, Facebook has started using artificial intelligence to change the content users see in their feed and what is recommended to them. A document known as The Facebook Files has revealed that their AI system prioritizes user engagement over everything else. The Facebook Files has also demonstrated that controlling the AI systems has proven difficult to handle. In an August 2019 internal memo leaked in 2021, Facebook has admitted that "the mechanics of our platforms are not neutral", concluding that in order to reach maximum profits, optimization for engagement is necessary. In order to increase engagement, algorithms have found that hate, misinformation, and politics are instrumental for app activity. As referenced in the memo, "The more incendiary the material, the more it keeps users engaged, the more it is boosted by the algorithm." According to a 2018 study, "false rumors spread faster and wider than true information... They found falsehoods are 70% more likely to be retweeted on Twitter than the truth, and reach their first 1,500 people six times faster. This effect is more pronounced with political news than other categories." === YouTube === YouTube has been around since 2005 and has more than 2.5 billion monthly users. YouTube discovery content systems focus on the user's personal activity (watched, favorites, likes) to direct them to recommended content. YouTube's algorithm is accountable for roughly 70% of users' recommended videos and what drives people to watch certain content. According to a 2022 study by the Mozilla Foundation, users have little power to keep unsolicited videos out of their suggested recommended content. This includes videos about hate speech, livestreams, etc. YouTube has been identified as an influential platform for spreading radicalized content. Al-Qaeda and similar extremist groups have been linked to using YouTube for recruitment videos and engaging with international media outlets. In a research study published by the American Behavioral Scientist Journal, they researched "whether it is possible to identify a set of attributes that may help explain part of the YouTube algorithm's decision-making process". The results of the study showed that YouTube's algorithm recommendations for extremism content factor into the presence of radical keywords in a video's title. In February 2023, in the case of Gonzalez v. Google, the question at hand is whether or not Google, the parent company of YouTube, is protected from lawsuits claiming that the site's algorithms aided terrorists in recommending ISIS videos to users. Section 230 is known to generally protect online platforms from civil liability for the content posted by its users. Multiple studies have found little to no evidence to suggest that YouTube's algorithms direct attention towards far-right content to those not already engaged with it. === TikTok === TikTok is a platform that recommends videos to a user's 'For You Page' (FYP), making every users' page different. With the nature of the algorithm behind the app, TikTok's FYP has been linked to showing more explicit and radical videos over time based on users' previous interactions on the app. Since TikTok's inception, the app has been scrutinized for misinformation and hate speech as those forms of media usually generate more interactions to the algorithm. Various extremist groups, including jihadist organizations, have utilized TikTok to disseminate propaganda, recruit followers, and incite violence. The platform's algorithm, which recommends content based on user engagement, can expose users to extremist content that aligns with their interests or interactions. As of 2022, TikTok's head of US Security has put out a statement that "81,518,334 videos were removed globally between April – June for violating our Community Guidelines or Terms of Service" to cut back on hate speech, harassment, and misinformation. Studies have noted instances where individuals were radicalized through content encountered on TikTok. For example, in early 2023, Austrian authorities thwarted a plot against an LGBTQ+ pride parade that involved two teenagers and a 20-year-old who were inspired by jihadist content on TikTok. The youngest suspect, 14 years old, had been exposed to videos created by Islamist influencers glorifying jihad. These videos led him to further engagement with similar content, eventually resulting in his involvement in planning an attack. Another case involved the arrest of several teenagers in Vienna, Austria, in 2024, who were planning to carry out a terrorist attack at a Taylor Swift concert. The investigation revealed that some of the suspects had been radicalized online, with TikTok being one of the platforms used to disseminate extremist content that influenced their beliefs and actions. == Self-radicalization == The U.S. Department of Justice defines 'Lone-wolf' (self) terrorism as "someone who acts alone in a terrorist attack without the help or encouragement of a government or a terrorist organization". Through social media outlets on the internet, 'Lone-wolf' terrorism has been on the rise, being linked to algorithmic radicalization. Through echo-chambers on the internet, viewpoints typically seen as radical were accepted and quickly adopted by other extremists. These viewpoints are encouraged by forums, group chats, and social media to reinforce their beliefs. == References in media == === The Social Dilemma === The Social Dilemma is a 2020 docudrama about how algorithms behind social media enables addiction, while possessing abilities to manipulate people's views, emotions, and behavior to spread conspiracy theories and disinformation. The film repeatedly uses buzz words such as 'echo chambers' and 'fake news' to prove psychological manipulation on social media, therefore leading to political manipulation. In the film, Ben falls deeper into a social media addiction as the algorithm found that his social media page has a 62.3% chance of long-term engagement. This leads into more videos on the recommended feed for Ben and he eventually becomes more immersed into propaganda and conspiracy theories, becoming more polarized with each video. == Proposed solutions == === United States: Weakening Section 230 protections === In the Communications Decency Act, Section 230 states t

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  • Machine learning

    Machine learning

    Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without being explicitly programmed. Advances in the field of deep learning have allowed neural networks, a class of statistical algorithms, to surpass many previous machine learning approaches in performance. Statistics and mathematical optimisation methods compose the foundations of machine learning. Data mining is a related field of study, focusing on exploratory data analysis (EDA) through unsupervised learning. From a theoretical viewpoint, probably approximately correct learning provides a mathematical and statistical framework for describing machine learning. Most traditional machine learning and deep learning algorithms can be described as empirical risk minimisation under this framework. == History == The term machine learning was coined in 1959 by Arthur Samuel, an IBM employee and pioneer in the field of computer gaming and artificial intelligence. The synonym self-teaching computers was also used during this time period. The earliest machine learning program was introduced in the 1950s, when Samuel invented a computer program that calculated the chance of winning in checkers for each side, but the history of machine learning is rooted in decades of efforts to study human cognitive processes. In 1949, Canadian psychologist Donald Hebb published the book The Organization of Behavior, in which he introduced a theoretical neural structure formed by certain interactions among nerve cells. The Hebbian theory of neuron interaction set the groundwork for how many machine learning algorithms work, with connected artificial neurons changing the strength of their connections based on data. Other researchers who have studied human cognitive systems contributed to the modern machine learning technologies as well, including Walter Pitts and Warren McCulloch, who proposed the first mathematical model of neural networks including algorithms that mirror human thought processes. By the early 1960s, an experimental "learning machine" with punched tape memory, called Cybertron, had been developed by Raytheon Company to analyse sonar signals, electrocardiograms, and speech patterns using rudimentary reinforcement learning. It was repetitively "trained" by a human operator/teacher to recognise patterns and equipped with a "goof" button to cause it to reevaluate incorrect decisions. A representative book on research into machine learning during the 1960s was Nils Nilsson's book "Learning Machines", dealing mostly with machine learning for pattern classification. Interest related to pattern recognition continued into the 1970s, as described by Duda and Hart in 1973. In 1981, a report was given on using teaching strategies so that an artificial neural network learns to recognise 40 characters (26 letters, 10 digits, and 4 special symbols) from a computer terminal. Tom M. Mitchell provided a widely quoted, more formal definition of the algorithms studied in the machine learning field: "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E." This definition of the tasks in which machine learning is concerned is fundamentally operational rather than defining the field in cognitive terms. This follows Alan Turing's proposal in his paper "Computing Machinery and Intelligence", in which the question, "Can machines think?", is replaced by asking whether machines can convincingly imitate a human in its responses to human-posed questions. In 2014 Ian Goodfellow and others introduced generative adversarial networks (GANs) which could produce realistic synthetic data. By 2016 AlphaGo had won against top human players in Go using reinforcement learning techniques. == Relationships to other fields == === Artificial intelligence === As a scientific endeavour, machine learning grew out of the quest for artificial intelligence (AI). In the early days of AI as an academic discipline, some researchers were interested in having machines learn from data. They attempted to approach the problem with various symbolic methods, as well as what were then termed "neural networks"; these were mostly perceptrons and other models that were later found to be reinventions of the generalised linear models of statistics. Probabilistic reasoning was also employed, especially in automated medical diagnosis. However, an increasing emphasis on the logical, knowledge-based approach caused a rift between AI and machine learning. Probabilistic systems were plagued by theoretical and practical problems of data acquisition and representation. By 1980, expert systems had come to dominate AI, and statistics was out of favour. Work on symbolic/knowledge-based learning continued within AI, leading to inductive logic programming (ILP), but the more statistical line of research was now outside the field of AI proper, in pattern recognition and information retrieval. Neural network research was abandoned by AI and computer science around the same time. This subfield, termed "connectionism", was continued by researchers from other disciplines, including John Hopfield, David Rumelhart, and Geoffrey Hinton. Their main success came in the mid-1980s with the reinvention of backpropagation. Machine learning (ML), reorganised and recognised as its own field, started to flourish in the 1990s. The field changed its goal from achieving artificial intelligence to tackling solvable problems of a practical nature. It shifted focus away from the symbolic approaches it had inherited from AI, and toward methods and models borrowed from statistics, fuzzy logic, and probability theory. === Data compression === === Data mining === Machine learning and data mining often employ the same methods and overlap significantly, but while machine learning focuses on prediction based on known properties learned from the training data, data mining focuses on the discovery of previously unknown properties in the data (this is the analysis step of knowledge discovery in databases). Data mining uses many machine learning methods, but with different goals; on the other hand, machine learning also employs data mining methods as "unsupervised learning" or as a preprocessing step to improve learner accuracy. Much of the confusion between these two research communities comes from the basic assumptions they work with: in machine learning, performance is usually evaluated with respect to the ability to reproduce known knowledge, while in knowledge discovery and data mining (KDD) the key task is the discovery of previously unknown knowledge. Evaluated with respect to known knowledge, an uninformed (unsupervised) method will easily be outperformed by other supervised methods, while in a typical KDD task, supervised methods cannot be used due to the unavailability of training data. Machine learning also has intimate ties to optimization: Many learning problems are formulated as minimisation of some loss function on a training set of examples. Loss functions express the discrepancy between the predictions of the model being trained and the actual problem instances (for example, in classification, one wants to assign a label to instances, and models are trained to correctly predict the preassigned labels of a set of examples). === Generalization === Characterizing the generalisation of various learning algorithms is an active topic of current research, especially for deep learning algorithms. === Statistics === Machine learning and statistics are closely related fields in terms of methods, but distinct in their principal goal: statistics draws population inferences from a sample, while machine learning finds generalisable predictive patterns. Conventional statistical analyses require the a priori selection of a model most suitable for the study data set. In addition, only significant or theoretically relevant variables based on previous experience are included for analysis. In contrast, machine learning is not built on a pre-structured model; rather, the data shape the model by detecting underlying patterns. The more variables (input) used to train the model, the more accurate the ultimate model will be. Leo Breiman distinguished two statistical modelling paradigms: the data model and the algorithmic model, wherein "algorithmic model" means more or less the machine learning algorithms like Random forest. Some statisticians have adopted methods from machine learning, producing the field of statistical learning. === Statistical physics === Analytical and computational techniques derived from deep-rooted physics of disordered systems can be extended to large-scale problems, including machine learning, e.g., to analyse the weight space of deep neural networks. Statistical physics is thus

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  • Democratization of technology

    Democratization of technology

    Democratization of technology is the process by which access to technology rapidly extends to an ever-broader audience, especially from a select group of people to the average public. New technologies and improved user experiences have empowered those outside of the technical industry to access and use technological products and services. At an increasing scale, consumers have greater access to use and purchase technologically sophisticated products, as well as to participate meaningfully in the development of these products. Industry innovation and user demand have been associated with more affordable, user-friendly products. This is an ongoing process, beginning with the development of mass production and increasing dramatically as digitization became commonplace. Thomas Friedman argued that the era of globalization has been characterized by the democratization of technology, democratization of finance, and democratization of information. Technology has been critical in the latter two processes, facilitating the rapid expansion of access to specialized knowledge and tools, as well as changing the way that people view and demand such access. A counter argument is that this is just a process of 'massification' - more people can use banks, technology, have access to information, but it does not mean there is any more democratic influence over its production, or that this massification promotes Democracy. == History == Scholars and social critics often cite the invention of the printing press as a major invention that changed the course of history. The force of the printing press rested not in its impact on the printing industry or inventors, but on its ability to transmit information to a broader public by way of mass production. This event is so widely recognized because of its social impact – as a democratizing force. The printing press is often seen as the historical counterpart to the Internet. After the development of the Internet in 1969, its use remained limited to communications between scientists and within government, although use of email and boards gained popularity among those with access. It did not become a popular means of communication until the 1990s. In 1993 the US federal government opened the Internet to commerce and the creation of HTML formed the basis for universal accessibility. === Major innovations === The Internet has played a critical role in modern life as a typical feature of most Western households, and has been key in the democratization of knowledge. It not only constitutes arguably the most critical innovation in this trend thus far; it has also allowed users to gain knowledge of and access to other technologies. Users can learn of new developments more quickly, and purchase high-tech products otherwise only actively marketed to recognized experts. Social media has also empowered and emboldened users to become contributors and critics of technological developments. Some have argued that cloud computing is having a major effect by allowing users greater access through mobility and pay-as-you-use capacity. The open-source model allows users to participate directly in development of software, rather than indirect participation, through contributing opinions. By being shaped by the user, development is directly responsive to user demand and can be obtained for free or at a low cost. In a comparable trend, arduino and littleBits have made electronics more accessible to users of all backgrounds and ages. The development of 3D printers has the potential to increasingly democratize production. Generative artificial intelligence tools have the potential to democratize the process of innovation by improving the ability of individuals to specify and visualize ideas. The democratization of artificial intelligence refers to the transition from AI as a high-cost, specialized field to one accessible to non-experts and smaller organizations. This process is driven by the release of open-weights models, the availability of cloud computing for model training, and the emergence of no-code development platforms. While early AI development was concentrated within Big Tech firms and elite research universities, the 2020s saw a proliferation of public tools like ChatGPT and repositories such as Hugging Face, which lowered the technical barriers to entry. However, the trend has faced criticism as the "illusion of democratization," as the underlying GPU hardware remains concentrated among a few global providers. == Cultural impact == This trend is linked to the spread of knowledge of and ability to perform high-tech tasks, challenging previous conceptions of expertise. Widespread access to technology, including lower costs, was critical to the transition to the new economy. Similarly, democratization of technology was also fuelled by this economic transition, which produced demands for technological innovation and optimism in technology-driven progress. Since the 1980s, a spreading constructivist conception of technology has emphasized that the social and technical domains are critically intertwined. Scholars have argued that technology is non-neutral, defined contextually and locally by a certain relationship with society. Andrew Feenberg, a central thinker in the philosophy of technology, argued that democratizing technology means expanding technological design to include alternative interests and values. When successful in doing so, this can be a tool for increasing inclusiveness. This also suggests an important participatory role for consumers if technology is to be truly democratic. Feenberg asserts that this must be achieved by consumer intervention in a liberated design process. Improved access to specialized knowledge and tools has been associated with an increase in the "do it yourself" (DIY) trend. This has also been associated with consumerization, whereby personal or privately owned devices and software are also used for business purposes. Some have argued that this is linked to reduced dependence on traditional information technology departments. Astra Taylor, the author of the book The People's Platform: Taking Back Power and Culture in the Digital Age, argues, "The promotion of Internet-enabled amateurism is a lazy substitute for real equality of opportunity." === Industry impact === In some ways, democratization of technology has strengthened this industry. Markets have broadened and diversified. Consumer feedback and input is available at a very low or no cost. However, related industries are experiencing decreased demand for qualified professionals as consumers are able to fill more of their demands themselves. Users of a range of types and status have access to increasingly similar technology. Because of the decreased costs and expertise necessary to use products and software, professionals (e.g. in the audio industry) may experience loss of work. In some cases, technology is accessible but sufficiently complex that most users without specialized training are able to operate it without necessarily understanding how it works. Additionally, the process of consumerization has led to an influx in the number of devices in businesses and accessing private networks that IT departments cannot control or access. While this can lead to lowered operating costs and increased innovation, it is also associated with security concerns that most businesses are unable to address at the pace of the spread of technology. === Political impact === Some scholars have argued that technological change will bring about a third wave of democracy. The Internet has been recognized for its role in promoting increased citizen advocacy and government transparency. Jesse Chen, a leading thinker in democratic engagement technologies, distinguishes the democratizing effects of technology from democracy itself. Chen has argued that, while the Internet may have democratizing effects, the Internet alone cannot deliver democracy at all levels of society unless technologies are purposely designed for the nuances of democracy, specifically the engagement of large groups of people in between elections in and beyond government. The spread of the Internet and other forms of technology has led to increased global connectivity. Many scholars believe that it has been associated in the developing world not only with increased Western influence, but also with the spread of democracy through increased communication, efficiency, and access to information. Scholars have drawn associations between the level of technological connectedness and democracy in many nations. Technology can enhance democracy in the developed world as well. In addition to increased communication and transparency, some electorates have implemented online voting to accommodate an increased number of citizens.

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  • Enterprise social software

    Enterprise social software

    Enterprise social software (also known as or regarded as a major component of Enterprise 2.0), comprises social software as used in "enterprise" (business/commercial) contexts. It includes social and networked modifications to corporate intranets and other classic software platforms used by large companies to organize their communication. In contrast to traditional enterprise software, which imposes structure prior to use, enterprise social software tends to encourage use prior to providing structure. Carl Frappaolo and Dan Keldsen defined Enterprise 2.0 in a report written for Association for Information and Image Management (AIIM) as "a system of web-based technologies that provide rapid and agile collaboration, information sharing, emergence and integration capabilities in the extended enterprise". == Applications == === Functionality === Social software for an enterprise must (according to Andrew McAfee, Associate Professor, Harvard Business School) have the following functionality to work well: Search: allowing users to search for other users or content Links: grouping similar users or content together Authoring: including blogs and wikis Tags: allowing users to tag content Extensions: recommendations of users; or content based on profile Signals: allowing people to subscribe to users or content with RSS feeds McAfee recommends installing easy-to-use software which does not impose any rigid structure on users. He envisages an informal roll-out, but on a common platform to enable future collaboration between areas. He also recommends strong and visible managerial support to achieve this. In 2007 Dion Hinchcliffe expanded the list above by adding the following four functions: Freeform function: no barriers to authorship (meaning free from a learning curve or from restrictions) Network-oriented function, requiring web-addressable content in all cases Social function: stressing transparency (to access), diversity (in content and community members) and openness (to structure) Emergence function: requiring the provision of approaches that detect and leverage the collective wisdom of the community Enterprise search differs from a typical web search in its focus on "use within an organization by employees seeking information held internally, in a variety of formats and locations, including databases, document management systems, and other repositories". === Criticism === There has been recent criticism that the adaptation of the social paradigm (e.g. openness and altruistic behavior) does not always work well for the enterprise setting, which led some authors to question the proper functioning of enterprise social software. The findings from a novel study suggests that free and non-anonymous sharing of trusted information (beyond marketing or product information) is significantly influenced by concerns from business users.

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  • Honeywell JetWave

    Honeywell JetWave

    Honeywell's JetWave is a piece of satellite communications hardware produced by Honeywell that enables global in-flight internet connectivity. Its connectivity is provided using Inmarsat’s GX Aviation network. The JetWave platform is used in business and general aviation, as well as defense and commercial airline users. == History == In 2012, Honeywell announced it would provide Inmarsat with the hardware for its GX Ka-band in-flight connectivity network. The Ka-band (pronounced either "kay-ay band" or "ka band") is a portion of the microwave part of the electromagnetic spectrum defined as frequencies in the range 27.5 to 31 gigahertz (GHz). In satellite communications, the Ka-band allows higher bandwidth communication. In 2017, after five years and more than 180 flight hours and testing, JetWave was launched as part of GX Aviation with Lufthansa Group. Honeywell’s JetWave was the exclusive terminal hardware option for the Inmarsat GX Aviation network; however, the exclusivity clause in that contract has expired. In July 2019, the United States Air Force selected Honeywell’s JetWave satcom system for 70 of its C-17 Globemaster III cargo planes. In December 2019, it was reported that six AirAsia aircraft had been fitted with Inmarsat’s GX Aviation Ka-band connectivity system and is slated to be implemented fleetwide across AirAsia’s Airbus A320 and A330 models in 2020, requiring installation of JetWave atop AirAsia’s fuselages. Today, Honeywell’s JetWave hardware is installed on over 1,000 aircraft worldwide. In August 2021, the Civil Aviation Administration of China approved a validation of Honeywell’s MCS-8420 JetWave satellite connectivity system for Airbus 320 aircraft. In December 2021, Honeywell, SES, and Hughes Network Systems demonstrated multi-orbit high-speed airborne connectivity for military customers using Honeywell’s JetWave MCX terminal with a Hughes HM-series modem, and SES satellites in both medium Earth orbit (MEO) and geostationary orbit (GEO). The tests achieved full duplex data rates of more than 40 megabits per second via a number of SES' (GEO) satellites including GovSat-1, and the high-throughput, low-latency O3b MEO satellite constellation, with connections moving between GEO/MEO links in under 30 sec. == Uses == === Commercial aviation === Honeywell’s JetWave enables air transport and regional aircraft to connect to Inmarsat’s GX Aviation network. The multichannel satellite (MSC) JetWave terminals share the same antenna controller, modem and router hardware with the business market, but have an MCS-8200 fuselage-mounted antenna. === Business aviation === Honeywell’s JetWave hardware allows users to connect to Inmarsat’s Jet ConneX, a business aviation broadband connectivity offering to provide Wi-Fi for connected devices. JetWave offers a tail-mount antenna for business jets. === Defense === Honeywell’s JetWave satellite communications system for defense allows users to connect to the Inmarsat GX network, offering global coverage for military airborne operators, including over water, over nontraditional flight paths and in remote areas. JetWave and the Inmarsat GX network enable mission-critical applications like real-time weather; videoconferencing; large file transfers; encryption capabilities; in-flight briefings; intelligence, surveillance, and reconnaissance video; and secure communications. JetWave is configurable for a variety of military platforms and offers antennas for large and small airframes.

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  • Aldus PhotoStyler

    Aldus PhotoStyler

    Aldus PhotoStyler was a graphics software program developed by the Taiwanese company Ulead. Released in June 1991 as the first 24 bit image editor for Windows, it was bought the same year by the Aldus Prepress group. Its main competition was Adobe Photoshop. Version 2.0 (late 1993) introduced a new user interface and improved color calibration. PhotoStyler SE - lacking some features of the version 2.0 - was bundled with scanners like HP ScanJet. The product disappeared from the Adobe product line after Adobe acquired Aldus in 1994.

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  • Digital signage

    Digital signage

    Digital signage is a segment of electronic signage that uses digital display technologies to present multimedia content in both public and private environments. Content may include video, images, text, or interactive media and is typically displayed for purposes such as advertising, information dissemination, branding, or entertainment. Digital signage systems can be either networked or standalone. Networked systems are managed through centralized content management systems (CMS), often cloud-based, enabling remote updates, scheduling, real-time data integration, and dynamic content delivery. These systems may also incorporate audience analytics, IoT sensors, or AI-driven personalization. Standalone systems, by contrast, operate without a network connection. They rely on local media playback via USB drives, SD cards, or internal storage. These solutions are simpler and suitable for locations where connectivity is limited or content changes infrequently. == Applications of digital signage == Digital signage is widely used in transportation hubs, retail stores, restaurants, corporate buildings, hotels, educational institutions, healthcare facilities, and public spaces. One prominent application of digital signage is Digital Out-of-Home (DOOH) advertising, which leverages digital signage displays in public spaces to deliver targeted advertisements to people outside of their homes. DOOH has become a significant segment of digital signage, providing advertisers with a dynamic and contextually relevant way to engage with audiences. == Components == === Hardware components === Digital signage hardware includes the physical equipment used to show multimedia content in public and private spaces. ==== Display devices ==== Display devices are the most prominent components of a digital signage system, serving as the primary medium for presenting content. Display devices come in various technologies, such as LCD, LED, and OLED formats, each offering different advantages in terms of clarity, color reproduction, and energy efficiency. In addition to flat-panel displays, projectors are also commonly used in digital signage, particularly in large-scale settings. Projectors can cast large-format visuals onto walls, screens, or other surfaces, providing flexibility in display size and positioning. Screen sizes vary widely to suit different applications. Smaller panels are often used in kiosks and point-of-sale systems, while larger displays, such as video walls and projection surfaces, are deployed in venues like stadiums, auditoriums, and other public spaces. Many digital signage displays are also equipped with touchscreen capabilities, allowing for interactive applications. These interactive displays are commonly used in information kiosks, wayfinding systems, and self-service applications. ==== Playback devices ==== Playback devices are specialized hardware components that manage the storage, processing, and transmission of multimedia content to digital signage displays and projectors. They serve as the crucial link between the content management system (CMS) and the visual output, ensuring seamless playback of static images, video files, animated graphics, and real-time content, such as news feeds. Playback devices can be standalone units or integrated into display hardware using System-on-Chip (SoC) technology. The latter reduces hardware complexity and installation time, making the system more efficient. These devices support remote or local content updates, allowing digital signage operators to manage networks effectively. Content can be updated via cloud-based platforms for centralized control or through direct interfaces on-site, depending on the system's configuration and deployment requirements. ==== Mounting systems ==== Mounting systems provide structural support for digital signage displays, enabling deployment across diverse environments. Typical configurations include wall mounts, ceiling mounts, and floor stands each engineered to meet specific spatial and functional requirements. === Software components === Digital signage software is responsible for content creation, scheduling, and management. It enables users to manage and distribute content to one or more playback devices. ==== Software compatibility ==== Digital signage software supports various operating systems, including Android, Windows, Linux, iOS, tvOS, webOS, Tizen, ChromeOS, macOS, and others. This allows customers to choose the hardware and software solution that best suits their digital signage needs. == Interactivity == Interactivity in digital signage allows users to interact directly with displays using input methods like touch, gestures, voice, or proximity sensors. This feature enables real-time responses and personalized content, improving the user experience. Interactive digital signage is commonly used in places like retail, transportation, education, and public spaces to create engaging and informative interactions. Additionally, self-service kiosks are often integrated into interactive signage solutions, allowing users to perform tasks such as ordering products, checking in for flights, accessing information, or making payments. These kiosks empower users to complete transactions or obtain services independently, improving efficiency and convenience in high-traffic locations. == Audience measurement and context-aware content adaptation == === Audience measurement === Cameras can be integrated into digital signage systems to enable audience measurement. They are used to detect and count viewers, estimate demographics such as age and gender, measure dwell time and attention, and sometimes analyze emotional reactions using computer vision techniques. This process is valuable for understanding audience behavior and refining business strategies. Privacy concerns are addressed by anonymizing collected data and avoiding the storage of personally identifiable information. === Context-aware digital signage === Context-aware digital signage refers to systems that adjust content based on environmental or audience data. The infrastructure supporting context awareness, including sensors and analytics systems, also facilitates the collection of audience insights. While these insights may be primarily used for reporting, optimization, or planning future campaigns rather than immediate content adjustments, they play a crucial role in the overall context-aware ecosystem. ==== Contextual information ==== Contextual information in the realm of context-aware digital signage refers to data about the environment, audience, and other factors that influence how digital signage content is displayed. This information helps the system to deliver more relevant, timely, and personalized content to its audience. Contextual information can include, but is not limited to: Audience demographics — this can involve detecting the age, gender, or even emotional state of viewers through cameras or sensors. It helps tailor content to specific audience segments, improving engagement. Time and weather — the system may adjust content based on the time of day or current weather conditions. For example, weather-appropriate content (like a raincoat ad on a rainy day) or time-specific content (like dinner menu promotions in the evening) can be shown. Emergency information — in situations of emergency, systems can prioritize displaying urgent notifications such as fire alerts, disaster warnings, or evacuation instructions. This can be crucial for public safety in crowded environments or densely populated areas. The system may adapt content in real-time to inform and guide individuals to safety, offering location-specific instructions or emergency service contacts. == Challenges == === Display blindness === Digital signage in public spaces has been found to lose visibility, significantly diminishing its ability to capture attention. This issue, known as "Display Blindness", was identified by Müller et al. and refers to the phenomenon where digital advertisements are largely overlooked by passersby. Observations indicate that many of these advertisements fail to resonate with their audience, often being irrelevant or unengaging, which leads to passive reception and reduced interaction. == Comparison with print signage == Digital signage and traditional print signage serve similar purposes by delivering visual information to a target audience, but they differ significantly in terms of flexibility, cost, maintenance, and environmental impact. Digital signage is advantageous in low-light or nighttime environments, where its internal illumination ensures visibility without the need for external lighting, unlike printed signs, which may require additional fixtures to be seen after dark. === Content and flexibility === Digital signage allows for dynamic and real-time content updates, often controlled remotely through content management systems. This makes it well-suited for environments where information chan

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  • FutureMedia

    FutureMedia

    FutureMedia is a program that analyzes the state and future of digital, social, and mobile media. It functions as a collaborative initiative at Georgia Tech and the Georgia Tech Research Institute. FutureMedia consults approximately 500 faculty members working in those fields. == History == In 2019, Future Media expanded into the Direct-To-Consumer market by acquiring Australian watchmaker Oak & Jackal. == Programs == === FutureMedia Fest === The organization most recently hosted FutureMedia Fest 2010, a four-day conference (Oct 4–7, 2010) with a keynote addresses from Michael Jones, the chief technology advocate at Google. The event featured panels, workshops, and technology demonstrations. === FutureMedia Outlook === Contemporaneous with FutureMedia Fest 2010, the organization released the FutureMedia Outlook, an analysis of the future of media, concentrating on six major trends in those fields, including information overload, personalization, data integrity, an expectation of multimedia, augmented reality, and collaborative software.

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