The DARPA Grand Challenge is a prize competition for American autonomous vehicles, funded by the Defense Advanced Research Projects Agency, the most prominent research organization of the United States Department of Defense. Congress has authorized DARPA to award cash prizes to further DARPA's mission to sponsor revolutionary, high-payoff research that bridges the gap between fundamental discoveries and military use. The initial DARPA Grand Challenge in 2004 was created to spur the development of technologies needed to create the first fully autonomous ground vehicles capable of completing a substantial off-road course within a limited time. The third event, the DARPA Urban Challenge in 2007, extended the initial Challenge to autonomous operation in a mock urban environment. The 2012 DARPA Robotics Challenge, focused on autonomous emergency-maintenance robots, and new Challenges are still being conceived. The DARPA Subterranean Challenge was tasked with building robotic teams to autonomously map, navigate, and search subterranean environments. Such teams could be useful in exploring hazardous areas and in search and rescue. In addition to the challenges in autonomous technology, DARPA has also conducted prize competitions in other areas of technology. == History and background == Fully autonomous vehicles have been an international pursuit for many years, from endeavors in Japan (starting in 1977), Germany (Ernst Dickmanns and VaMP), Italy (the ARGO Project), the European Union (EUREKA Prometheus Project), the United States of America, and other countries. DARPA funded the development of the first fully autonomous robot beginning in 1966 with the Shakey the robot project at Stanford Research Institute, now SRI International. The first autonomous ground vehicle capable of driving on and off roads was developed by DARPA as part of the Strategic Computing Initiative beginning in 1984 leading to demonstrations of autonomous navigation by the Autonomous Land Vehicle and the Navlab. The Grand Challenge was the first long distance competition for driverless cars in the world; other research efforts in the field of driverless cars take a more traditional commercial or academic approach. The U.S. Congress authorized DARPA to offer prize money ($1 million) for the first Grand Challenge to facilitate robotic development, with the ultimate goal of making one-third of ground military forces autonomous by 2015. Following the 2004 event, Dr. Tony Tether, the director of DARPA, announced that the prize money had been increased to $2 million for the next event, which was claimed on October 9, 2005. The first, second and third places in the 2007 Urban Challenge received $2 million, $1 million, and $500,000, respectively. 14 new teams have qualified in year 2015. The competition was open to teams and organizations from around the world, as long as there was at least one U.S. citizen on the roster. Teams have participated from high schools, universities, businesses and other organizations. More than 100 teams registered in the first year, bringing a wide variety of technological skills to the race. In the second year, 195 teams from 36 U.S. states and 4 foreign countries entered the race. == 2004 Grand Challenge == The first competition of the DARPA Grand Challenge was held on March 13, 2004 in the Mojave Desert region of the United States, along a 150-mile (240 km) route that follows along the path of Interstate 15 from just before Barstow, California to just past the California–Nevada border in Primm. None of the robot vehicles finished the route. Carnegie Mellon University's Red Team and car Sandstorm (a converted Humvee) traveled the farthest distance, completing 11.78 km (7.32 mi) of the course before getting hung up on a rock after making a switchback turn. No winner was declared, and the cash prize was not given. Therefore, a second DARPA Grand Challenge event was scheduled for 2005. == 2005 Grand Challenge == The second competition of the DARPA Grand Challenge began at 6:40 am on October 8, 2005. All but one of the 23 finalists in the 2005 race surpassed the 11.78 km (7.32 mi) distance completed by the best vehicle in the 2004 race. Five vehicles successfully completed the 212 km (132 mi) course: Vehicles in the 2005 race passed through three narrow tunnels and negotiated more than 100 sharp left and right turns. The race concluded through Beer Bottle Pass, a winding mountain pass with a sheer drop-off on one side and a rock face on the other. Although the 2004 course required more elevation gain and some very sharp switchbacks (Daggett Ridge) were required near the beginning of the route, the course had far fewer curves and generally wider roads than the 2005 course. The natural rivalry between the teams from Stanford and Carnegie Mellon (Sebastian Thrun, head of the Stanford team was previously a faculty member at Carnegie Mellon and colleague of Red Whittaker, head of the CMU team) was played out during the race. Mechanical problems plagued H1ghlander before it was passed by Stanley. Gray Team's entry was a miracle in itself, as the team from the suburbs of New Orleans was caught in Hurricane Katrina a few short weeks before the race. The fifth finisher, Terramax, a 30,000 pound entry from Oshkosh Truck, finished on the second day. The huge truck spent the night idling on the course, but was particularly nimble in carefully picking its way down the narrow roads of Beer Bottle Pass. == 2007 Urban Challenge == The third competition of the DARPA Grand Challenge, known as the "Urban Challenge", took place on November 3, 2007 at the site of the now-closed George Air Force Base (currently used as Southern California Logistics Airport), in Victorville, California (Google map). The course involved a 96 km (60 mi) urban area course, to be completed in less than 6 hours. Rules included obeying all traffic regulations while negotiating with other traffic and obstacles and merging into traffic. Unlike previous challenges, the 2007 Urban Challenge organizers divided competitors into two "tracks", A and B. All Track A and Track B teams were part of the same competition circuit, but the teams chosen for the Track A program received US $1 million in funding. These 11 teams largely represented major universities and large corporate interests such as CMU teaming with GM as Tartan Racing, Stanford teaming with Volkswagen, Virginia Tech teaming with TORC Robotics as VictorTango, Oshkosh Truck, Honeywell, Raytheon, Caltech, Autonomous Solutions, Cornell University, and MIT. One of the few independent entries in Track A was the Golem Group. DARPA has not publicly explained the rationale behind the selection of Track A teams. Teams were given maps sparsely charting the waypoints that defined the competition courses. At least one team, Tartan Racing, enhanced the maps through the insertion of additional extrapolated waypoints for improved navigation. A debriefing paper published by Team Jefferson illustrates graphically the contrast between the course map it was given by DARPA and the course map used by Tartan Racing. Tartan Racing claimed the $2 million prize with their vehicle "Boss", a Chevy Tahoe. The second-place finisher earning the $1 million prize was the Stanford Racing Team with their entry "Junior", a 2006 Volkswagen Passat. Coming in third place was team VictorTango, winning the $500,000 prize with their 2005 Ford Escape hybrid, "Odin". MIT placed 4th, with Cornell University and University of Pennsylvania/Lehigh University also completing the course. The six teams that successfully finished the entire course: While the 2004 and 2005 events were more physically challenging for the vehicles, the robots operated in isolation and only encountered other vehicles on the course when attempting to pass. The Urban Challenge required designers to build vehicles able to obey all traffic laws while they detect and avoid other robots on the course. This is a particular challenge for vehicle software, as vehicles must make "intelligent" decisions in real time based on the actions of other vehicles. Other than previous autonomous vehicle efforts that focused on structured situations such as highway driving with little interaction between the vehicles, this competition operated in a more cluttered urban environment and required the cars to perform sophisticated interactions with each other, such as maintaining precedence at a 4-way stop intersection. == 2012 Robotics Challenge == The DARPA Robotics Challenge is an ongoing competition focusing on humanoid robotics. The primary goal of the program is to develop ground robotic capabilities to execute complex tasks in dangerous, degraded, human-engineered environments. It launched in October 2012, and hosted the Virtual Robotics Competition in June 2013. Two more competitions are planned: the DRC Trials in December 2013, and the DRC Finals in December 2014. Unlike prior Challenges, the construction of the "vehicles" w
Adversarial machine learning
Adversarial machine learning is the study of the attacks on machine learning algorithms, and of the defenses against such attacks. Machine learning techniques are mostly designed to work on specific problem sets, under the assumption that the training and test data are generated from the same statistical distribution (IID). However, this assumption is often violated in practical high-stake applications, where users may intentionally supply fabricated data that violates the statistical assumption. Most common attacks in adversarial machine learning include evasion attacks, data poisoning attacks, Byzantine attacks and model extraction. == History == At the MIT Spam Conference in January 2004, John Graham-Cumming showed that a machine-learning spam filter could be used to defeat another machine-learning spam filter by automatically learning which words to add to a spam email to get the email classified as not spam. In 2004, Nilesh Dalvi and others noted that linear classifiers used in spam filters could be defeated by simple "evasion attacks" as spammers inserted "good words" into their spam emails. (Around 2007, some spammers added random noise to fuzz words within "image spam" in order to defeat OCR-based filters.) In 2006, Marco Barreno and others published "Can Machine Learning Be Secure?", outlining a broad taxonomy of attacks. As late as 2013 many researchers continued to hope that non-linear classifiers (such as support vector machines and neural networks) might be robust to adversaries, until Battista Biggio and others demonstrated the first gradient-based attacks on such machine-learning models (2012–2013). In 2012, deep neural networks began to dominate computer vision problems; starting in 2014, Christian Szegedy and others demonstrated that deep neural networks could be fooled by adversaries, again using a gradient-based attack to craft adversarial perturbations. Further work would show that adversarial attacks are harder to produce in uncontrolled environments, due to the different environmental constraints that cancel out the effect of noise. For example, any small rotation or slight illumination on an adversarial image can destroy the adversariality. In addition, researchers such as Google Brain's Nick Frosst point out that it is much easier to make self-driving cars miss stop signs by physically removing the sign itself, rather than creating adversarial examples. Frosst also believes that the adversarial machine learning community incorrectly assumes models trained on a certain data distribution will also perform well on a completely different data distribution. He suggests that a new approach to machine learning should be explored, and is currently working on a unique neural network that has characteristics more similar to human perception than state-of-the-art approaches. While adversarial machine learning continues to be heavily rooted in academia, large tech companies such as Google, Microsoft, and IBM have begun curating documentation and open source code bases to allow others to concretely assess the robustness of machine learning models and minimize the risk of adversarial attacks. === Examples === Examples include attacks in spam filtering, where spam messages are obfuscated through the misspelling of "bad" words or the insertion of "good" words; attacks in computer security, such as obfuscating malware code within network packets or modifying the characteristics of a network flow to mislead intrusion detection; attacks in biometric recognition where fake biometric traits may be exploited to impersonate a legitimate user; or to compromise users' template galleries that adapt to updated traits over time. Researchers showed that by changing only one-pixel it was possible to fool deep learning algorithms. Others 3-D printed a toy turtle with a texture engineered to make Google's object detection AI classify it as a rifle regardless of the angle from which the turtle was viewed. Creating the turtle required only low-cost commercially available 3-D printing technology. A machine-tweaked image of a dog was shown to look like a cat to both computers and humans. A 2019 study reported that humans can guess how machines will classify adversarial images. Researchers discovered methods for perturbing the appearance of a stop sign such that an autonomous vehicle classified it as a merge or speed limit sign. A data poisoning filter called Nightshade was released in 2023 by researchers at the University of Chicago. It was created for use by visual artists to put on their artwork to corrupt the data set of text-to-image models, which usually scrape their data from the internet without the consent of the image creator. McAfee attacked Tesla's former Mobileye system, fooling it into driving 50 mph over the speed limit, simply by adding a two-inch strip of black tape to a speed limit sign. Adversarial patterns on glasses or clothing designed to deceive facial-recognition systems or license-plate readers, have led to a niche industry of "stealth streetwear". An adversarial attack on a neural network can allow an attacker to inject algorithms into the target system. Researchers can also create adversarial audio inputs to disguise commands to intelligent assistants in benign-seeming audio; a parallel literature explores human perception of such stimuli. Clustering algorithms are used in security applications. Malware and computer virus analysis aims to identify malware families, and to generate specific detection signatures. In the context of malware detection, researchers have proposed methods for adversarial malware generation that automatically craft binaries to evade learning-based detectors while preserving malicious functionality. Optimization-based attacks such as GAMMA use genetic algorithms to inject benign content (for example, padding or new PE sections) into Windows executables, framing evasion as a constrained optimization problem that balances misclassification success with the size of the injected payload and showing transferability to commercial antivirus products. Complementary work uses generative adversarial networks (GANs) to learn feature-space perturbations that cause malware to be classified as benign; Mal-LSGAN, for instance, replaces the standard GAN loss with a least-squares objective and modified activation functions to improve training stability and produce adversarial malware examples that substantially reduce true positive rates across multiple detectors. == Challenges in applying machine learning to security == Researchers have observed that the constraints under which machine-learning techniques function in the security domain are different from those of common benchmark domains. Security data may change over time, include mislabeled samples, or reflect adversarial behavior, which complicates evaluation and reproducibility. === Data collection issues === Security datasets vary across formats, including binaries, network traces, and log files. Studies have reported that the process of converting these sources into features can introduce bias or inconsistencies. In addition, time-based leakage can occur when related malware samples are not properly separated across training and testing splits, which may lead to overly optimistic results. === Labeling and ground truth challenges === Malware labels are often unstable because different antivirus engines may classify the same sample in conflicting ways. Ceschin et al. note that families may be renamed or reorganized over time, causing further discrepancies in ground truth and reducing the reliability of benchmarks. === Concept drift === Because malware creators continuously adapt their techniques, the statistical properties of malicious samples also change. This form of concept drift has been widely documented and may reduce model performance unless systems are updated regularly or incorporate mechanisms for incremental learning. === Feature robustness === Researchers differentiate between features that can be easily manipulated and those that are more resistant to modification. For example, simple static attributes, such as header fields, may be altered by attackers, while structural features, such as control-flow graphs, are generally more stable but computationally expensive to extract. === Class imbalance === In realistic deployment environments, the proportion of malicious samples can be extremely low, ranging from 0.01% to 2% of total data. This unbalanced distribution causes models to develop a bias towards the majority class, achieving high accuracy but failing to identify malicious samples. Prior approaches to this problem have included both data-level solutions and sequence-specific models. Methods like n-gram and Long Short-Term Memory (LSTM) networks can model sequential data, but their performance has been shown to decline significantly when malware samples are realistically proportioned in the training set, demonstrating the limitations in
Online exhibition
An online exhibition, also referred to as a virtual exhibition, online gallery, cyber-exhibition, is an exhibition whose venue is cyberspace. Museums and other organizations create online exhibitions for many reasons. For example, an online exhibition may: expand on material presented at, or generate interest in, or create a durable online record of, a physical exhibition; save production costs (insurance, shipping, installation); solve conservation/preservation problems (e.g., handling of fragile or rare objects); reach lots more people: "Access to information is no longer restricted to those who can afford travel and museum visits, but is available to anyone who has access to a computer with an Internet connection. Unlike physical exhibitions, online exhibitions are not restricted by time; they are not forced to open and close but may be available 24 hours a day. In the nonprofit world, many museums, libraries, archives, universities, and other cultural organizations create online exhibitions. A database of such exhibitions is Library and Archival Exhibitions on the Web. Online exhibition organizers may use techniques such as marquee text, display advertisements, and in-event emails to engage patrons. Various guides have been published to help organizations create effective online exhibitions. The earliest museum with a physical existence to create a programme of substantial online exhibitions with high resolution images of artefacts was the Museum of the History of Science in Oxford, the first of which, The Measurers: a Flemish Image of Mathematics in the Sixteenth Century and an exhibition of early photographs, were published on 21 August 1995. == Examples of online exhibitions == International Museum of Women is an online-only museum that does not have a physical building and instead offers online exhibitions about women's issues globally as well as an online community. Online exhibitions include "Imagining Ourselves" (launched 2006) about women's identity, "Women, Power and Politics" (2008), and "Economica: Women and the Global Economy" (2009). Tucson LGBTQ Museum is an online-only museum that does not have a physical building and instead offers online exhibitions about LGBTQ history. The online photographic, audio, video, text, and other historical exhibitions include exhibits from the 1700s to the present day. The effort began in the summer of 1967 and spanned almost 50 years. International New Media Gallery (INMG) is an online museum specialising in moving image and screen-based art. The INMG is dedicated to exploring current debates and topics in art history: touching on areas such as migration, war, environmental activism and the internet itself. The gallery publishes extensive academic catalogues alongside its exhibitions. It also hosts spaces for discussion and debate, both online and offline. Virtual Museum of Modern Nigerian Art – the VMMNA is the first of its kind in Africa. Hosted by the Pan-African University, Lagos, Nigeria this virtual museum offers a good view of the development on Nigerian Art in the past fifty years.
Giditraffic
GidiTraffic (or GIDITRAFFIC) is an online social service started on 23 September 2011. Based primarily on social media, the service employs crowdsourcing as its primary means of providing real-time traffic updates to subscribers on its platform. The service, delivered free of charge, affords its users access to various types of information. Though its broadest category of users is road users and motorists, GIDITRAFFIC lends itself as a platform for answering inquiries from anyone who requires information on any subject of interest. GIDITRAFFIC's core competence is in vehicular traffic reports, however, the service also handles all other forms of traffic (going by the fact that the word traffic also means "the mutual exchange of information"). == Operation == Users of the service log on to its Twitter feed to get up-to-date traffic information or to post a general inquiry, which GIDITRAFFIC then publishes to all subscribers. Through crowdsourced replies, a requester receives numerous responses from other subscribers who have seen the question and can provide a relevant answer. In addition, updates are provided by subscribers to the platform via their mobile devices, thereby making the service effective in delivering traffic updates as they occur, and providing timely answers to other user inquiries. This informs GIDITRAFFIC's motto of "Lending each other an eye", alluding to the collaboration and cooperation between the platform's users in making the service indispensable to its users. == Reception == On Twitter, which is its primary platform, the service caters to over 1,800,000 subscribers, with the number increasing daily. The popularity of the platform stems from the fact that it not only keeps its subscribers abreast of the traffic situation in Lagos, the commercial capital city of Nigeria (well known for its many traffic jams), but users in other parts of the world. For a regular user of the platform, knowing where to avoid getting to a set destination in good time is well worth the two or three minutes it takes to access and scroll through the GIDITRAFFIC feed for updates. Another interesting aspect of this platform is the identity of the person behind it. The sustained anonymity of this individual has sparked many discussions centering on his or her possible identity. Online, GIDITRAFFIC continuously publishes traffic updates and user questions, while keeping up witty interactions with the platform's followers round the clock – adding to the mystery and persona of the GIDITRAFFIC owner. == Awards and recognition == In early 2012, GIDITRAFFIC received a nomination for a Shorty Award in the Life-Saving Hero category. Although this did not translate into a win, it brought recognition and wider exposure for the service from international news outlets such as the BBC, Washington Post. and New York Times. Back home in Nigeria, also in 2012, GIDITRAFFIC was honored with a Future Award for Best Use of New Media in recognition of the huge impact the service has had in terms of helping Lagos residents better manage time spent in traffic. == Mobile Applications == In 2012, GIDITRAFFIC partnered with telecommunications company Nokia to produce a downloadable mobile traffic application (the GIDITRAFFIC application, available for Nokia Asha phones on Nokia's online store). There are plans to extend the application to a wider range of mobile phone platforms. On 4 September 2013, the GIDITRAFFIC application for Nokia Lumia phones using Windows Phone 8 was launched on the Windows App Store.
Digital studio
A digital studio provides both a technology-equipped space and technological/rhetorical support to students (commonly at a university) working individually or in groups on a variety of digital projects, such as designing a website, developing an electronic portfolio for a class, creating a blog, making edits, selecting images for a visual essay, or writing a script for a podcast. == History/theory == === Overview === Digital Studios are places with different names but similar objectives. They have risen in response to the need for resources dedicated to improving students' interactions with digital technologies for rhetorical ends. Digital Studios have often been theoretically and administratively linked to writing centers, which are sites where students can seek assistance with their text-based projects. The academic term that has been used for this kind of site (i.e. a writing center with a focus on digital and new media) is multiliteracy center. Besides having a multimodal focus, Digital Studios also make a departure from writing center model in allowing students the freedom to work in the Studio without one-on-one interaction with a writing tutor. === The rise of technology === ==== Computer literacy in popular culture ==== As early as 1983, computer literacy was being hailed in The New York Times as the "new goal in schools." As computer technology became more ubiquitous, and the World Wide Web became more popular and accessible, and as the teaching of computer skills became official US policy with the enactment of the "Technology Literacy Challenge" by the Clinton Administration in 1996, educators across disciplines began to investigate with renewed vigor the role of computer technology in curriculum as both a means and an end. ==== Scholarly interest in 'multiliteracies' ==== The same year that President Clinton initiated the "Technology Literacy Challenge", the New London Group (NLG) issued a call for scholars of literacy pedagogy to account for the burgeoning variety of text forms associated with information and multimedia technologies. This includes understanding and competent control of representational forms that are becoming increasingly significant in the overall communications environment, such as visual images and their relationship to the written word – for instance, visual design in desktop publishing or the interface of visual and linguistic meaning in multimedia. This account for new text forms, combined with a similar account for "increasingly globalized societies," is called 'multiliteracies' by the NLG. ==== Technological literacy in rhetoric and composition ==== Two years later, during the 1998 CCCC Chair's Address, Cynthia Selfe (who founded the peer-reviewed journal Computers and Composition in 1983) addressed professionals in the field of Rhetoric and Composition with an objective similar to that of the NLG, arguing that as a field, composition scholars had "paid technology issues precious little focused attention over the years." She called this lack of attention "dangerously shortsighted." What was needed, Selfe claimed, was for teachers to "pay attention" to "how technology is now inextricably linked with literacy and literacy education in this country." In a way, Selfe's call marked the beginning of a new scholarly interest in what Selfe called "critical technological literacy": Composition teachers, language arts teachers, and other literacy specialists need to recognize that the relevance of technology in the English studies disciplines is not simply a matter of helping students work effectively with communication software and hardware, but, rather, also a matter of helping them to understand and to be able to assess – to pay attention to – the social, economic, and pedagogical implications of new communication technologies and technological initiatives that affect their lives. Scholars who took up this call included Barbara Blakely Duffelmeyer, who conducted studies involving the incorporation of "critical computer literacy" (an adaptation of Selfe's term) into first-year composition. ==== Communications across media, inside and outside school ==== The years following Selfe's address saw more rapid advancements in mobile technologies, social media, and Web 2.0, creating even more new venues of composing for teachers to pay attention to. In her own CCCC Chair's Address in 2004, Kathleen Blake Yancey cited these new venues in her argument as a "new curriculum for the 21st century," one that would bring "together the writing outside of school and that of inside." Such a curriculum, she said: is located in a new vocabulary, a new set of practices, and a new set of outcomes; it will focus our research in new and provocative ways; it has as its goal the creation of thoughtful, informed, technologically adept writing publics. A professor at Clemson at the time of her speech, Yancey also argued for the creation of an undergraduate major in composition and rhetoric. She soon moved to Florida State University, where she helped to establish a new major in line with the one she argued for at CCCC called Editing, Writing, and Media (EWM). As teachers and administrators across the country looked to incorporate more digital technology into their curriculum, the need for spaces for digital composition and for support with the innumerable digital composing platforms became apparent. A Digital Studio is one such space. === Link with writing centers === With the need for support for students who would engage with digital writing and multimedia projects, professionals involved with work in writing centers began to draw comparisons between their traditional work — assisting students with alphabetic texts on the page — and a new kind of work: assisting students with their multimedia projects on the screen. John Trimbur predicted in 2000: My guess is that writing centers will more and more define themselves as multiliteracy centers. Many are already doing so – tutoring oral presentations, adding online tutorials, offering workshops in evaluating web sources, and being more conscious of document design. To my mind, new digital literacies will increasingly be incorporated into writing centers not just as sources of information or delivery systems for tutoring but as productive arts in their own right, and writing center work will, if anything, become more rhetorical in paying attention to the practices and effects of design in written and visual communication — more product-oriented and perhaps less like the composing conferences of the process movement. Later, just months before Yancey delivered her CCCC Chair's Address, Michael Pemberton, writing in the Writing Center Journal, asked: As we enter an era when electronic publishing and computer-mediated discourse are the norm, an era when new literary genres and new forms of communication emerge on, seemingly, a weekly basis, we must ask ourselves whether writing centers should continue to dwell exclusively in the linear, non-linked world of the printed page or whether they should plan to redefine themselves – and retrain themselves – to take residence in the emerging world of multimedia, hyperlinked, digital documents. To put it plainly, should we be preparing tutors to conference with students about hypertexts? Pemberton also surveyed (by his account) the forty-year history of how "writing centers [have] viewed new technologies," observing that "the relationship between writing centers and computer technology has been, overall, only a cordial one." Pemberton's article is evidence of the continuing discussion among writing center professionals about the need for support for students' digital creations, support which they saw as analogous to work in writing centers. In 2010, a collection edited by David Sheridan and James Inman, Multiliteracy Centers: Writing Center Work, New Media, and Multimodal Rhetoric, was published. Many of the chapters therein cite the above Trimbur and Pemberton quotes as they work to explain the exigence for the collection, the instances in which multiliteracy centers have been established (the founding of the Clemson Class of 1941 Studio for Student Communication is the subject of two chapters), and both theoretical and practical analyses of potential futures of such work. === 'Studio' vs. 'Center:' A break from the model === The conflation of digital studios and writing centers into multiliteracy centers is helpful in some respects, for example, administratively the two may be managed in similar ways and staffed by the same people. In other respects, it has been said that it is better to separate them into two distinct kinds of facilities. The very choice of naming a "writing center" or "digital studio" by either (or another) title, for instance, ought (according to some) to be informed by what kinds of student-activities are expected to take place there. A writing center is a place for individual students to seek help from individual writing
Artificial intelligence and elections
As artificial intelligence (AI) has become more mainstream, there is growing concern about how this will influence elections. Potential targets of AI include election processes, election offices, election officials and election vendors. There are also global efforts to improve elections using AI. == Tactics == Generative AI capabilities allow creation of misleading content. Examples of this include text-to-video, deepfake videos, text-to-image, AI-altered images, text-to-speech, voice cloning, and text-to-text. In the context of an election, a deepfake video of a candidate may propagate information that the candidate does not endorse. Chatbots could spread misinformation related to election locations, times or voting methods. In contrast to malicious actors in the past, these techniques require little technical skill and can spread rapidly. LLM-generated messages have the capacity to persuade humans on political issues. Researchers have begun to investigate how people rate messages that LLMs generate for how persuasive they are. When it came to policy issues, the LLM-generated messages received a 2.91 compared to a 2.80 when it came to smartness between the AI and humans. The LLM-generated messages were often more technical and analytical than human-generated messages. Generative AI has been used to micro-target people during tight political elections. The generation of targeted large language models has triggered concern that they will be used to leverage readily scale microtargeting. Rephrasing inputs have been used to generate fraudulent emails and phishing websites. Rephrasing inputs in a microtargeting does not violate the terms of OpenAI usage. There are no safeguards to prevent the use of rephrasing and creation of fraudulent emails. Political campaign managers have access to this allowing for them to create targeted content. == Usage by country == === Argentina === ==== 2023 elections ==== During the 2023 Argentine primary elections, Javier Milei's team distributed AI generated images including a fabricated image of his rival Sergio Massa and drew 3 million views. The team also created an unofficial Instagram account entitled "AI for the Homeland." Sergio Massa's team also distributed AI generated images and videos. === Bangladesh === ==== 2024 elections ==== In the run up to the 2024 Bangladeshi general election, deepfake videos of female opposition politicians appeared. Rumin Farhana was pictured in a bikini while Nipun Ray was shown in a swimming pool. === Canada === ==== 2025 elections ==== In the run up to the 2025 Canadian federal election, the use of AI tools is likely to figure prominently. India, Pakistan and Iran are all expected to make efforts to subvert the national vote using disinformation campaigns to deceive voters and sway diaspora communities. In a report by the Canadian Centre for Cyber Security called "Cyber Threats to Canada's Democratic Process: 2025 Update", it states that malicious actors including China and Russia: "are most likely to use generative AI as a means of creating and spreading disinformation, designed to sow division among Canadians and push narratives conducive to the interests of foreign states". === France === ==== 2024 elections ==== In the 2024 French legislative election, deepfake videos appeared claiming: i) That they showed the family of Marine le Pen. In the videos, young women, supposedly Le Pen's nieces, are seen skiing, dancing and at the beach "while making fun of France’s racial minorities": However, the family members don't exist. On social media there were over 2 million views. ii) In a video seen on social media, a deepfake video of a France24 broadcast appeared to report that the Ukrainian leadership had "tried to lure French president Emmanuel Macron to Ukraine to assassinate him and then blame his death on Russia". === Ghana === ==== 2024 elections ==== During the months before the December 2024 Ghanaian general election, a network of at least 171 fake accounts has been used to spam social media. Posts have been used by a group identified as "@TheTPatriots" to promote the New Patriotic Party, although it is not known whether the two are connected. All the networks' posts were "highly likely" to have been generated by ChatGPT and appear to be the "first secretly partisan network using AI to influence elections in Ghana". The opposition National Democratic Congress was also criticized with its leader John Mahama being called a drunkard. === India === ==== 2024 elections ==== In the 2024 Indian general election, politicians used deepfakes in their campaign materials. These deepfakes included politicians who had died prior to the election. Mathuvel Karunanidhi's party posted with his likeness even though he had died 2018. A video The All-India Anna Dravidian Progressive Federation party posted showed an audio clip of Jayaram Jayalalithaa even though she had died in 2016. The Deepfakes Analysis Unit (DAU) is an open source platform created in March 2024 for the public to share misleading content and assess if it had been AI-generated. AI was also used to translate political speeches in real time. This translating ability was widely used to reach more voters. === Indonesia === ==== 2024 elections ==== In the 2024 Indonesian presidential election, Prabowo Subianto made extensive use of AI-generated art in his campaign, which ranged from images of himself as an adorable child to various child portrayals in his advertisements. The Indonesian Children's Protection Commission condemned these ads, labeling them as a form of misuse. Other candidates, Anies Baswedan and Ganjar Pranowo, also incorporated AI art into their campaigns. Throughout the election period, all presidential candidates faced attacks from deepfakes, both in video and audio formats. === Ireland === ==== 2024 elections ==== In the last weeks of the 2024 Irish general election a spoof election poster appeared in Dublin featuring "an AI-generated candidate with three arms". The candidate is called Aidan Irwin, but no-one stood in the election with that name. A slogan on the poster says "put matters into artificial intelligence’s hands". The convincing election poster shows a man that "has six fingers on one hand, three arms, and a distorted thumb". === New Zealand === ==== 2023 elections ==== In May 2023, ahead of the 2023 New Zealand general election in October 2023, the New Zealand National Party published a "series of AI-generated political advertisements" on its Instagram account. After confirming that the images were faked, a party spokesperson said that it was "an innovative way to drive our social media". === Pakistan === ==== 2024 elections ==== AI has been used by the imprisoned ex-Prime Minister Imran Khan and his media team in the 2024 Pakistani general election: i) An AI generated audio of his voice was added to a video clip and was broadcast at a virtual rally. ii) An op-ed in The Economist written by Khan was later claimed by himself to have been written by AI which was later denied by his team. The article was liked and shared on social media by thousands of users. === South Africa === ==== 2024 elections ==== In the 2024 South African general election, there were several uses of AI content: i) A deepfaked video of Joe Biden emerged on social media showing him saying that "The U.S. would place sanctions on SA and declare it an enemy state if the African National Congress (ANC) won". ii) In a deepfake video, Donald Trump was shown endorsing the uMkhonto weSizwe party. It was posted to social media and was viewed more than 158,000 times. iii) Less than 3 months before the elections, a deepfake video showed U.S. rapper Eminem endorsing the Economic Freedom Fighters party while criticizing the ANC. The deepfake was viewed on social media more than 173,000 times. === South Korea === ==== 2022 elections ==== In the 2022 South Korean presidential election, a committee for one presidential candidate Yoon Suk Yeol released an AI avatar 'Al Yoon Seok-yeol' that would campaign in places the candidate could not go. The other presidential candidate Lee Jae-myung introduced a chatbot that provided information about the candidate's pledges. ==== 2024 elections ==== Deepfakes were used to spread misinformation before the 2024 South Korean legislative election with one source reporting 129 deepfake violations of election laws within a two week period. Seoul hosted the 2024 Summit for Democracy, a virtual gathering of world leaders initiated by US President Joe Biden in 2021. The focus of the summit was on digital threats to democracy including artificial intelligence and deepfakes. === Taiwan === ==== 2024 elections ==== AI-generated content was used during the 2024 Taiwanese presidential election. Among the media were: i) A deepfake video of General Secretary of the Chinese Communist Party Xi Jinping which showed him supporting the presidential elections. Created on social media, the video was "widely circulated
Digital content
Digital content is any content that exists in the form of digital data. Digital content is stored on digital media or analog storage in specific formats. Forms of digital content include information that is digitally broadcast, streamed, or contained in computer files. Viewed narrowly, digital content includes popular media types, while a broader approach considers any type of digital information (e. g. digitally updated weather forecasts, GPS maps, and so on) as digital content. Digital content has increased as more households have accessed the Internet. Expanded access has made it easier for people to receive their news and watch TV online, challenging the popularity of traditional platforms. Increased access to the Internet has also led to the mass publication of digital content through individuals in the form of eBooks, blog posts, and even Facebook posts. == History == At the beginning of the Digital Revolution, computers facilitated the discovery, retrieval, and creation of new information in every field of human knowledge. As information became increasingly more accessible, the Digital Revolution also facilitated the creation of digital content. Despite an evolution to digital technology, which occurred somewhere between the late 1970s, distribution of digital content did not begin until the late 1990s with the rise in popularity of the Internet. In the past, digital content was primarily distributed through computers and the Internet. Methods of distribution are rapidly changing as the Digital Revolution brings new channels, such as mobile apps and eBooks. These new technologies will create challenges for content creators, as they determine the best channel to bring content to their consumers. Despite the benefits, new technologies have created new intellectual property issues. Users can easily share, modify, and redistribute content outside of the creator's control. While new technologies have made digital content available to large audiences, managing copyright and limiting content movement will continue to be an issue that digital content creators face in the future. == Types of digital content == Examples include: Video – Types of video content include home videos, music videos, TV shows, and movies. Many of these can be viewed on websites such as YouTube, Hulu, Paramount+, Disney+, HBO Max, and so on, in which people and companies alike can post content. However, many movies and television shows are not available for free legally, but rather can be purchased from sites such as iTunes and Amazon. Audio – Music is the most common form of audio. Spotify has emerged as a popular way for people to listen to music either over the Internet or from their computer desktop. Digital content in the form of music is also available through Pandora and last.fm, both of which allow listeners to listen to music online for no charge. Images – Photo and image sharing is another example of digital content. Popular sites used for this type of digital content includes Imgur, where people share self-created pictures, Flickr, where people share their photo albums, and DeviantArt, where people share their artwork. Popular apps that are used for images include Instagram and Snapchat. Visual Stories - Stories are a new type of digital content that got introduced by Snapchat. Since then, stories as a format has been introduced in a couple of other platforms such as Facebook and Linkedin. In 2018, Google introduced their AMP Stories, which provides content publishers with a mobile-focused format for delivering news and information as visually rich, tap-through stories. Text - Type of digital content which is available in text or written format. Blog websites which store data in form of textual format. === Paid digital content === In order to have access to more premium digital goods, consumers usually have to pay an upfront charge for digital content, or a subscription based fee. Video – Many licensed videos, such as movies and television shows, require money in order to be viewed or downloaded. Popular services used by many include streaming giant Netflix and Amazon's streaming service, as well as recent notice put forth by the online video platform YouTube. Audio – While songs can be streamed for free, generally in order to download most licensed music, consumers need to purchase songs from web stores, such as the popular iTunes. However, Spotify Premium is emerging as a new model for purchasing digital content on the web: consumers pay a monthly fee to unlimited streaming and downloading from Spotify's music library. According to a report done by IHS Inc. in 2013, the global consumer spending on digital content grew to over $57 billion in 2013, which was up almost 30% from $44 billion in 2012. In past years, the US has always been a leader in consumer expenditure on digital content, but as of 2013, many countries have emerged with great consumer expenditure. South Korea's overall digital spend per capita is now greater than the US. ==== Consolidation ==== According to research firm Ampere Analysis, in 2024, a small group of six media conglomerates; Disney, Comcast, Google, Warner Bros. Discovery, Netflix, and Paramount Global—are poised to dominate the global content market. These companies are projected to account for 51% of all global spending on content, a significant increase from 47% in 2020. Disney, in particular, is a major player, with an estimated $35.8 billion investment in television and film content, representing 14% of global spending. This significant increase, fueled by Disney's full ownership of Hulu, highlights the company's strategic focus on streaming services. A substantial portion of the projected $126 billion global content spending is allocated to streaming platforms. === Non-purchasable digital content === Not all digital content is purchasable, and is simply anything published digitally. This would include: News – in recent years newspapers have attempted to expand their readership by creating access to their newspapers digitally. As of 2012, 39% of readers learned about news from online formats, making news a prevalent form of digital content. Advertisements – as media consumers increasingly use digital formats to watch TV, check the weather, and search for content, advertisements have shifted to digital forms to keep up with their viewership. Advertisements are now being made digitally and placed on sites ranging from Facebook to YouTube. Question and Answer sites – these sites are a type of Internet forum where people can post questions they want answered, or provide responses to previous inquiries. With millions of questions posted each day, anyone has the ability to create content on these sites, so the information provided may not be 100% reliable or accurate. Popular sites include Yahoo! Answers, WikiAnswers and Quora. Web mapping – sites such as MapQuest and Google Maps provide users with map content. These sites give people the ability to quickly look up the location of a landmark and create routes to a destination. Online maps are a form of free content provided by companies such as Google and AOL, serving as much more efficient alternatives to the traditional Thomas Guide. == Business implications == === Digital companies === Digital content businesses can include news, information, and entertainment distributed over the Internet and consumed digitally by both consumers and businesses. Based on revenue, the leading digital businesses are ranked Google, China Mobile, Bloomberg, Reed Elsevier, and Apple. The 50 companies with the highest revenue are split between those offering free and paid digital content, but these top 50 companies combined generate revenue of $150 billion. === Educational opportunities === Programs such as CUNY's Macaulay Honors College in their New Media Lab, run by industry professional Robert Small, is set up to train and introduce students to the various disciplines within the digital content industry. The goal is to offer information and access to professional work opportunities. They also explore within an incubator how to create businesses and start ups within the world of digital content. There are many educational events in support of choosing digital content as a career. === Government support === The Irish government adopted a "Strategy for the Digital Content Industry in Ireland" in 2002.