The European Conference on Computer Vision (ECCV) is a biennial research conference with the proceedings published by Springer Science+Business Media. Similar to ICCV in scope and quality, it is held those years which ICCV is not. It is considered to be one of the top conferences in computer vision, alongside CVPR and ICCV, with an 'A' rating from the Australian Ranking of ICT Conferences and an 'A1' rating from the Brazilian ministry of education. The acceptance rate for ECCV 2010 was 24.4% for posters and 3.3% for oral presentations. Like other top computer vision conferences, ECCV has tutorial talks, technical sessions, and poster sessions. The conference is usually spread over five to six days with the main technical program occupying three days in the middle, and tutorial and workshops, focused on specific topics, being held in the beginning and at the end. The ECCV presents the Koenderink Prize annually to recognize fundamental contributions in computer vision. == Location == The conference is usually held in autumn in Europe.
Competition in artificial intelligence
Competition in artificial intelligence refers to the rivalry among companies, research institutions, and governments to develop and deploy the most capable artificial intelligence (AI) systems. The competition spans multiple domains, including large language models (LLMs), autonomous vehicles, robotics, computer vision systems, natural language processing (NLP), and AI-optimized hardware. == Background == Competition in AI is driven by potential economic, strategic, and scientific advantages. Breakthroughs in AI can enhance productivity, enable new products and services, and provide geopolitical leverage. The field has experienced rapid progress since the mid-2010s, particularly in machine learning and artificial neural networks, leading to intense rivalry among leading actors. == Corporate competition == Major technology companies are among the most visible competitors in AI. In the United States, firms such as OpenAI, Google DeepMind, Meta Platforms, Microsoft, Anthropic, and Nvidia compete in building advanced LLMs, generative AI platforms, and AI-optimized graphics processing units (GPUs). In China, companies such as Baidu, Alibaba Group, Tencent, and startups such DeepSeek have become leaders in AI deployment, often with state backing. The "[war for talent]" in AI research has become a defining feature of corporate competition. Leading firms often recruit top AI researchers from rivals, sometimes offering multi-million-dollar compensation packages. == National competition == Governments see leadership in AI as a strategic priority. The United States has funded AI research for military, economic, and societal applications, while China has set a target to lead the world in AI by 2030 through its "New Generation Artificial Intelligence Development Plan". Other nations, including the UK, India, Israel, Russia, South Korea, and members of the European Union, have launched national AI strategies. In February 2026 Anthropic said Chinese companies - DeepSeek, Moonshot AI, and MiniMax - were conducting "distillation attacks" in an attempt to copy their model's capabilities, and warned that business wars were closely tied to geopolitical ones: "foreign labs that illicitly distill American models can remove safeguards, feeding model capabilities into their own military, intelligence, and surveillance systems." == Sectors of competition == === Large language models and chatbots competition === Competition to produce the most capable generative text models, with benchmarks such as MMLU and ARC used to evaluate performance has been on scale since the emergence of AI. These systems leverage deep learning, especially transformer architectures, to understand and generate human-like language. Companies and research groups globally compete to develop chatbots that are more capable, reliable, and context-aware. Among the most well-known chatbots is ChatGPT, developed by OpenAI. Since its public release in 2022, ChatGPT has rapidly gained widespread attention for its ability to engage in coherent and versatile conversations, assist with creative writing, and solve complex problems. In response, technology firms introduced competing chatbots aiming to challenge or surpass ChatGPT's capabilities. Notably, DeepSeek, a Chinese AI company, launched an advanced chatbot integrated with their R1 language model, emphasizing strong natural language understanding and multilingual support. Similarly, Grok, developed by xAI (company), integrates conversational AI into vehicles and digital assistants, combining natural language processing with real-time data for personalized user interaction. These chatbots not only compete in language tasks but also demonstrate strategic reasoning capabilities by playing complex games such as chess and Go. This form of competition is reminiscent of historic AI milestones set by programs such as Deep Blue and AlphaGo. The OpenAI’s ChatGPT has been tested in playing chess at various levels, while DeepSeek’s chatbot showcased its prowess in online chess tournaments in early 2024, winning several matches against human and AI opponents. Grok, leveraging Tesla's vast data infrastructure, has demonstrated real-time strategic decision-making in simulation environments that include chess-like games. The competition pushes rapid innovation, with firms racing to improve chatbot conversational depth, reduce biases, increase factual accuracy, and integrate multimodal inputs like images and videos. At the same time, the competition raises questions about AI safety, ethical use, and the societal impacts of increasingly human-like chatbots. === Autonomous vehicles === Companies such as Waymo, Tesla, and Baidu are racing to deploy safe and reliable self-driving car technology. === AI chips === Rivalry between Nvidia, AMD, Intel, and Huawei in designing processors optimized for AI workloads. === Military applications === Development of AI-enabled drones, surveillance systems, and decision-support tools, with associated ethical debates. == Events == In 2023, OpenAI released GPT-4, prompting competitors such as Google DeepMind to accelerate the release of their own models, including Gemini. In 2024, Chinese AI company DeepSeek launched the R1 model, leading OpenAI to release an open-source system, GPT-OSS, as a strategic countermeasure. In 2022, Tesla and Waymo both expanded autonomous taxi services in U.S. cities, competing for regulatory approval and public trust. The U.S. Department of Defense's Project Maven and China's AI-enabled surveillance programs have been cited as examples of military AI rivalry. In 2025, Microsoft hired several senior engineers from Google DeepMind, highlighting the ongoing "talent poaching" competition in the AI sector. == Risks and concerns == Critics warn that unrestrained competition in AI can undermine safety, ethics, and governance. Concerns include the proliferation of biased or unsafe models, escalation in autonomous weapons, and reduced cooperation on safety standards.
International Teletraffic Congress
The International Teletraffic Congress (ITC) is the first international conference in networking science and practice. It was created in 1955 by Arne Jensen to initially cater to the emerging need to understand and model traffic in telephone networks using stochastic methodologies, and to bring together researchers with these considerations as a common theme. Up through World War II, teletraffic research was done mainly by engineers and mathematicians working in telephone companies. Most of their work was published in local or company journals. In 1955, however, the field acquired a formal, international, institutional structure, with the organization of the first International Teletraffic Congress (ITC). Over the years, it has broaden its scope to address a wide spectrum ranging from the mathematical theory of traffic processes, stochastic system modelling and analysis, traffic and performance measurements, network management, traffic engineering to network capacity planning and cost optimization, including network economics and reliability for various types of networks. ITC served as a forum for all theoretical fundamentals and engineering practices for large-scale deployment and operation of telecommunications networks. Since its inception, ITC witnessed the evolution of communications and networking: the influence of computer science on telecommunication, the advent of the Internet and the massive deployment of mobile communications and optics, the appearance of peer-to-peer networking and social networks, the ever increasing speed and flexibility of new communication technologies, networks, user devices, and applications, and the ever changing operation challenges arising from this development. ITC documented this evolution with contemporary measurement studies, performance analyses of new technologies, recommendations for provisioning and configuration, and greatly contributed to the methodological toolbox of network scientists. Today, with its conferences, specialist seminars, regional seminars, training courses and publications, the ITC aims at a worldwide forum for all questions related to network and service performance, management, and assessment, both present and futuristic. The notion of traffic is broadly used to encompass data traffic from the MAC layer all the way to application traffic in the application layer. The scope of ITC is thus ranging all issues embedding operations, design, planning, economics and performance analysis of current and emerging communication networks and services, to be addressed by applying a variety of tools from different fields, such as Stochastic Processes, Information theory, Control theory, Signal and Processing, Game theory and optimization techniques, Statistical methodologies and Artificial Intelligence techniques. The target audience of such issues is experts from research organizations, universities, equipment vendors and suppliers, network operators, service providers, system integrators and international technical organizations, guaranteeing a well-balanced contribution from theory, application, and practice. The general goal remains to bring researchers and practitioners together toward operational understanding of all types of current and future networks. The ITC is ruled by the International Advisory Council (IAC) which gathers a number of technical experts, from universities and the research arms of key corporations in the industry, from countries having a strong tradition in teletraffic development. The IAC responsibilities are to disseminate information on teletraffic which is of interest for the whole community and: to select the locations of Plenary Congresses and to ensure their high-level technical programme to support Specialist Seminars on specific topics of current interest to promote Regional Seminars for the dissemination of teletraffic concepts in developing countries to facilitate the liaison activity with the ITU through participation in the standardization process and in the Development Programme The technical program and the organization of each ITC event remains within the responsibilities of the hosting country, but with significant IAC support to guarantee that the event is consistent with the quality standards established during the previous congresses. The ITC Plenary Congresses were scheduled tri-annually from 1955 until 1995 when the interval became bi-annual to account for the ever-accelerating development of network technologies, products and services and the associated dramatic increases in network demands. Similarly, to better cover the impact of dramatic changes undergoing in the field of computer and communication systems, networks and usage, it has been decided to hold the Plenary Congress on an annual basis from 2009. == Content == Teletraffic science is the traditional term for all theoretical fundamentals and engineering practices to describe data flows in telecommunication networks, the performance of the usage of network resources, procedures for sizing of resources and engineering the networks for given traffic load and quality of service requirements. For more than 50 years of the 20th century, traffic or teletraffic has been identified primarily with telephone networks. With the huge development of computers, stored program control of network nodes and computer communication, the traditional teletraffic science field naturally extended to computer networks, mobile and wireless/optical networks, and for a wide spectrum of new applications. The convergence between the voice network, the Internet, the television and mobility raised new questions that request new models and tools to be developed. In addition, the development of community networks, home networking, multiple access networking technologies, and the advent of pervasive and ambient communications dictates new challenges to be addressed. Today, ITC addresses the emerging paradigms such as an increasing diversity of distributed applications and services over various media like mobile/optical networks, enabling new markets and economy. ITC has steered the evolutions in communications since its creation in 1955 and remains at the forefront of innovation regarding modeling and performance. The scientific roots of communications traffic are based on the theory of probability and stochastic processes, modelling and performance evaluation. Modelling is the key for the mathematical description and quantitative performance analysis. Traffic flows are described by stochastic processes with complex dependencies which have to be validated by traffic measurements. Modelling also includes operational properties of resource control reflected by service strategies such as queueing disciplines, admission control, and routing. The results of such performance analyses are used for resource dimensioning (sizing), resource management, and network optimization while providing targeted Quality of Service. Teletraffic science is closely related to methods of operation research (queueing theory, optimization, forecasting) and computational sciences (simulation technology distributed systems). In this context, ITC represents a wide community of researchers and practitioners and is regularly organizing events like Congresses, Specialist Seminars and Workshops in order to discuss the latest changes in the modelling, design and performance of communication systems, networks and services. === The evolution of technologies of the 20th century === ITC has been witnessing the change of communication and networking technologies which are reflected in the proceedings and programs of the congresses. The specialist seminars and the motto of the congresses thereby reflect the hot topics of that time and the evolution. Selected topics of the 70's, 80's and 90's were 1998: Traffic Issues related to Multimedia and Nomadic Communications 1995: Traffic Modeling and Measurement in Broadband and Mobile Communications 1990: Broadband Technologies: Architectures, Applications, Control and Performance 1986: ISDN Traffic Issues 1984: Fundamentals of Teletraffic Theory 1977: Modeling of SPC Exchanges and Data Networks === Recent topics in the 21st century === With the rise of the Internet, new networking paradigms and technologies but also new challenges emerged: 2020: Teletraffic in the era of beyond-5G and AI 2019: Networked Systems and Services 2018: Teletraffic in the Smart World 2017: Ubiquitous, software-based, and sustainable networks and services 2016: Digital Connected World 2015: Traffic, Performance and Big Data 2014: Towards a Sustainable World 2013: Energy Efficient and Green Networking 2010: Multimedia Applications - Traffic, Performance and QoE 2009: Network Virtualization - Concepts and Performance 2008: Future Internet Design and Experimental Facilities 2008: Quality of Experience 2002: Internet Traffic Engineering and Traffic Management == Arne Jensen Lifetime Achievement Awards == The Arne Jensen Lifetime A
Artificial Intelligence for Digital Response
Artificial Intelligence for Digital Response (AIDR) is a free and open source platform to filter and classify social media messages related to emergencies, disasters, and humanitarian crises. It has been developed by the Qatar Computing Research Institute and awarded the Grand Prize for the 2015 Open Source Software World Challenge. Muhammad Imran stated that he and his team "have developed novel computational techniques and technologies, which can help gain insightful and actionable information from online sources to enable rapid decision-making" - according to him the system "combines human intelligence with machine learning techniques, to solve many real-world challenges during mass emergencies and health issues". == How to use == It can be used by logging in with ones Twitter credentials and by collecting tweets by specifying keywords or hashtags, like #ChileEarthquake, and possibly a geographical region as well. == Use == It has been deployed in conjunction with UNICEF in Zambia to classify short messages related to AIDS/HIV received through the U-Report platform. AIDR was used for the first time during the 2010 Pakistan floods. The first real test of AIDR took place during the 2014 Iquique earthquake in Chile. == Related talks and events == Muhammad Imran delivered a keynote talk on the science behind the AIDR system at the International Conference on Information Systems for Crisis Response And Management (ISCRAM). Abdelkader Lattab and Ji Lucas also presented the system at the 2016 QCRI-IBM Data Science Connect event.
Content Credentials
Content Credentials (also known as C2PA signatures) are a digital media metadata specification. They aim to provide provenance information about a piece of media (such as an image or a video) and help prove its authenticity. They are described as the equivalent of nutrition labels for digital media. One of the stated goal of this specification is to fight online disinformation. The specification is written and maintained by the Coalition for Content Provenance and Authenticity (C2PA), a group of many media and tech organizations including Adobe, Amazon, the BBC, Google, Meta, Microsoft, OpenAI and Sony. Another organization, the Content Authenticity Initiative (CAI), is responsible for promoting the standard and accelerate its adoption. The standard relies on cryptographic digital signatures. == Adoption == There are two main stakeholders who can implement Content Credentials: Producers (softwares and hardwares that produce or modify digital media) and publishers (softwares that show digital media to users). === Producers === ==== Adobe ==== Adobe is one of the first companies to implement the specification, announcing support in Photoshop in 2021. Content Credentials can be enabled and the complete history of edits is kept. ==== Google ==== Google announced support for Content Credentials on its Pixel 10 phones in August 2025. The Content Credentials are embedded on each picture taken from the Pixel Camera, and modifications done using Google Photos. Information include picture timestamp and a non-identifiable signature that proves it was taken from a Pixel 10. As for Google Photos, a list of AI and non-AI edits are kept. Google is the first company to introduce support for Content Credentials on either phones or consumer-grade devices, and also the first company to make it available for free to all users. ==== Nikon ==== Nikon announced in 2024 that their Z6 III camera would support embedding Content Credentials in its photos. However, in 2025, a vulnerability was discovered in the software of the camera that allowed to combine unauthentic images with authentic photos and still have the resulting image with a valid digital signature. Nikon revoked the certificates. ==== Media organizations ==== CBC/Radio-Canada and the BBC both have started attaching Content Credentials to media they produce or verify. ==== OpenAI ==== OpenAI embeds Content Credentials on the images and videos it generates that includes that the media was created by AI using their platforms. ==== Sony ==== In June 2025, Sony announced the release of its Camera Verify system for press photographers and news editors using C2PA digital signatures. Initially, the system will be limited to still images, high‑end cameras, and selected news agencies. Registration with Sony Creators' Cloud is also required. === Publishers === ==== LinkedIn ==== In 2024, LinkedIn started showing a "CR" icon on images that contain Content Credentials of AI-generated images. In 2025, they announced a partnership with Adobe to allow photographers to prove ownership of images using Content Credentials. ==== TikTok ==== TikTok announced in 2024 that an "AI-generated" label would be applied to videos containing Content Credentials if they were AI-generated. In 2025, they announced that users could control the amount of AI-generated content they see, using self-reported labels, Content Credentials and an invisible, proprietary AI watermark embedded in videos by their AI editor tool. ==== YouTube ==== In 2024, YouTube started showing to users a label that reads "captured with a camera" on videos that show authentic, unedited videos taken by Content Credentials-compatible cameras.
Boyfriend Maker
Boyfriend Maker was a dating sim, romance chatbot smartphone app for iOS (iPhone) and Android devices, developed by Japanese studio 36 You Games (styled as 36You) and distributed under the freemium business model. Boyfriend Maker incorporated advanced artificial intelligence chat technology a decade before products such as ChatGPT. According to the developer's website, Boyfriend Maker is an "app that lets you interact and chat with quirky virtual boyfriends". While each virtual boyfriend has certain unique characteristics, the various instances of the boyfriend are powered by a chat engine, that (at least within a language and market) can utilise vocabulary and knowledge acquired in a chat with one user in subsequent chats with other users. == Gameplay == Users gain experience points and in-game coins. Users can customize their virtual boyfriend's appearance by selecting items such as hair, clothing, face, and a necklace. == Apple delisting and reintroduction == In late November 2012, the original iOS Boyfriend Maker app was delisted from the Apple App Store due to "ribald" chat, according to the New York Times. Boyfriend Maker was removed by Apple due to "reports of references to violent sexual acts and pedophilia". Boyfriend Maker had an age rating of 4+, even though the chat bot "responds with often strange and explicit text unsuitable for young children". User-posted chat excerpts indicate that the virtual boyfriend would sometimes transition abruptly to sexual chat in response to a seemingly innocent question. In one user-posted example, in response to the question, "what kind of wedding cake will we have" the boyfriend responds, "a good sex ima be on top of u u gonna ride oon me bitin the pillow gurrl ima fuck da shit out of u". The developer's use of the SimSimi-created third-party chat engine may be responsible for the sexual text. As the virtual boyfriend converses with human users, the SimSimi chat engine acquires vocabulary from users of the game and applies this "learned" vocabulary in chats with other users. The chat engine might also employ lines harvested from human-human chat logs, song lyrics, movies or TV shows. In April 2013, a detuned and presumably tamer version of the app, titled Boyfriend Plus, was permitted on Apple's App Store.
Flat-panel display
A flat-panel display (FPD) is an electronic display used to display visual content such as text or images. It is present in consumer, medical, transportation, and industrial equipment. Flat-panel displays are thin, lightweight, provide better linearity and are capable of higher resolution and contrast than typical consumer-grade TVs from earlier eras. They are usually less than 10 centimetres (3.9 in) thick. While the highest resolution for consumer-grade CRT televisions is 1080i, many interactive flat panels in the 2020s are capable of 1080p and 4K resolution. In the 2010s, portable consumer electronics such as laptops, mobile phones, and portable cameras have used flat-panel displays since they consume less power and are lightweight. As of 2016, flat-panel displays have almost completely replaced CRT displays. Most 2010s-era flat-panel displays use LCD or light-emitting diode (LED) technologies, sometimes combined. Most LCD screens are back-lit with color filters used to display colors. In many cases, flat-panel displays are combined with touch screen technology, which allows the user to interact with the display in a natural manner. For example, modern smartphone displays often use OLED panels, with capacitive touch screens. Flat-panel displays can be divided into two display device categories: volatile and static. The former requires that pixels be periodically electronically refreshed to retain their state (e.g. liquid-crystal displays (LCD)), and can only show an image when it has power. On the other hand, static flat-panel displays rely on materials whose color states are bistable, such as displays that make use of e-ink technology, and as such retain content even when power is removed. == History == The first engineering proposal for a flat-panel TV was by General Electric in 1954 as a result of its work on radar monitors. The publication of their findings gave all the basics of future flat-panel TVs and monitors. But GE did not continue with the R&D required and never built a working flat panel at that time. The first production flat-panel display was the Aiken tube, developed in the early 1950s and produced in limited numbers in 1958. This saw some use in military systems as a heads up display and as an oscilloscope monitor, but conventional technologies overtook its development. Attempts to commercialize the system for home television use ran into continued problems and the system was never released commercially. Dennis Gabor, better known as the inventor of holography, patented a flat-screen CRT in 1958. This was substantially similar to Aiken's concept, and led to a years-long patent battle. By the time the lawsuits were complete, with Aiken's patent applying in the US and Gabor's in the UK, the commercial aspects had long lapsed, and the two became friends. Around this time, Clive Sinclair came across Gabor's work and began an ultimately unsuccessful decade-long effort to commercialize it. The Philco Predicta featured a relatively flat (for its day) cathode-ray tube setup and would be the first commercially released "flat panel" upon its launch in 1958; the Predicta was a commercial failure. The plasma display panel was invented in 1964 at the University of Illinois, according to The History of Plasma Display Panels. === Liquid-crystal displays (LC displays, or LCDs) === The MOSFET (metal–oxide–semiconductor field-effect transistor, or MOS transistor) was invented by Mohamed M. Atalla and Dawon Kahng at Bell Labs in 1959, and presented in 1960. Building on their work, Paul K. Weimer at RCA developed the thin-film transistor (TFT) in 1962. It was a type of MOSFET distinct from the standard bulk MOSFET. The idea of a TFT-based LCD was conceived by Bernard J. Lechner of RCA Laboratories in 1968. B.J. Lechner, F.J. Marlowe, E.O. Nester and J. Tults demonstrated the concept in 1968 with a dynamic scattering LCD that used standard discrete MOSFETs. The first active-matrix addressed electroluminescent display was made using TFTs by T. Peter Brody's Thin-Film Devices department at Westinghouse Electric Corporation in 1968. In 1973, Brody, J. A. Asars and G. D. Dixon at Westinghouse Research Laboratories demonstrated the first thin-film-transistor liquid-crystal display. Brody and Fang-Chen Luo demonstrated the first flat active-matrix liquid-crystal display (AM LCD) using TFTs in 1974. By 1982, pocket LCD TVs based on LCD technology were developed in Japan. The 2.1-inch Epson ET-10 Epson Elf was the first color LCD pocket TV, released in 1984. In 1988, a Sharp research team led by engineer T. Nagayasu demonstrated a 14-inch full-color LCD, which convinced the electronics industry that LCD would eventually replace CRTs as the standard television display technology. As of 2013, all modern high-resolution and high-quality electronic visual display devices use TFT-based active-matrix displays. === LED displays === The first usable LED display was developed by Hewlett-Packard (HP) and introduced in 1968. It was the result of research and development (R&D) on practical LED technology between 1962 and 1968, by a research team under Howard C. Borden, Gerald P. Pighini, and Mohamed M. Atalla, at HP Associates and HP Labs. In February 1969, they introduced the HP Model 5082-7000 Numeric Indicator. It was the first alphanumeric LED display, and was a revolution in digital display technology, replacing the Nixie tube for numeric displays and becoming the basis for later LED displays. In 1977, James P Mitchell prototyped and later demonstrated what was perhaps the earliest monochromatic flat-panel LED television display. Ching W. Tang and Steven Van Slyke at Eastman Kodak built the first practical organic LED (OLED) device in 1987. In 2003, Hynix produced an organic EL driver capable of lighting in 4,096 colors. In 2004, the Sony Qualia 005 was the first LED-backlit LCD. The Sony XEL-1, released in 2007, was the first OLED television. == Common types == === Liquid-crystal display (LCD) === Field-effect LCDs are lightweight, compact, portable, cheap, more reliable, and easier on the eyes than CRT screens. LCD screens use a thin layer of liquid crystal, a liquid that exhibits crystalline properties. It is sandwiched between two glass plates carrying transparent electrodes. Two polarizing films are placed at each side of the LCD. By generating a controlled electric field between electrodes, various segments or pixels of the liquid crystal can be activated, causing changes in their polarizing properties. These polarizing properties depend on the alignment of the liquid-crystal layer and the specific field-effect used, being either twisted nematic (TN), in-plane switching (IPS) or vertical alignment (VA). Color is produced by applying appropriate color filters (red, green and blue) to the individual subpixels. LC displays are used in various electronics like watches, calculators, mobile phones, TVs, computer monitors and laptops screens etc. === LED-LCD === Most earlier large LCD screens were back-lit using a number of CCFL (cold-cathode fluorescent lamps). However, small pocket size devices almost always used LEDs as their illumination source. With the improvement of LEDs, almost all new displays are now equipped with LED backlight technology. The image is still generated by the LCD layer. === Plasma panel === A plasma display consists of two glass plates separated by a thin gap filled with a gas such as neon. Each of these plates has several parallel electrodes running across it. The electrodes on the two plates are at right angles to each other. A voltage applied between the two electrodes one on each plate causes a small segment of gas at the two electrodes to glow. The glow of gas segments is maintained by a lower voltage that is continuously applied to all electrodes. By 2010, consumer plasma displays had been discontinued by numerous manufacturers. === Electroluminescent panel === In an electroluminescent display, the image is created by applying electrical signals to the plates which make the phosphor glow. === Organic light-emitting diode === An OLED (organic light-emitting diode) is a light-emitting diode (LED) in which the emissive electroluminescent layer is a film of organic compound which emits light in response to an electric current. This layer of organic semiconductor is situated between two electrodes; typically, at least one of these electrodes is transparent. OLEDs are used to create digital displays in devices such as television screens, computer monitors, portable systems such as mobile phones, handheld game consoles and PDAs. === Quantum-dot light-emitting diode === QLED or quantum dot LED is a flat panel display technology introduced by Samsung under this trademark. Other television set manufacturers such as Sony have used the same technology to enhance the backlighting of LCD TVs already in 2013. Quantum dots create their own unique light when illuminated by a light source of shorter wavelength such as blue LEDs. Th