In artificial neural networks, a hidden layer is a layer of artificial neurons that is neither an input layer nor an output layer. The simplest examples appear in multilayer perceptrons (MLP), as illustrated in the diagram. An MLP without any hidden layer is essentially just a linear model. With hidden layers and activation functions, however, nonlinearity is introduced into the model. In typical machine learning practice, the weights and biases are initialized, then iteratively updated during training via backpropagation.
AI Overviews
AI Overviews is an artificial intelligence (AI) feature integrated into Google Search that produces AI-generated summaries of search results. The feature has been criticized for its inaccuracy and for reducing website traffic. == History and development == AI Overviews were first introduced as part of Google's Search Generative Experience (SGE), which was unveiled at the Google I/O conference in May 2023. In May 2024 at Google I/O 2024, the feature was rebranded as AI Overviews and launched in the United States. The introduction of AI Overviews was seen as a strategic move to compete with other generative AI advancements, including OpenAI's ChatGPT. By August 2024, AI Overviews was rolled out to several other countries, including the United Kingdom, India, Japan, Brazil, Mexico, and Indonesia, with support for multiple languages. In October 2024, Google expanded the feature globally, making it available in over 100 countries. In December 2024, Botify x Demandsphere released findings stating that when AI Overviews and featured snippets appear together on the search engine results page, they take up approximately 67.1% of the screen on desktop and 75.7% on mobile. Even if content is ranking in the #1 position, it may not be visible to consumers if other visual elements on the results page are more prominent. In March 2025, Google started testing an "AI Mode", where the search results page is AI-generated. The company was also considering adding advertisements to the AI Mode, as they already exist in AI Overviews. As of May 2025, AI Overviews are available in over 200 countries and territories and in more than 40 languages. As of March 2026, Google AI Overviews appear on more than 48% of total Google Search queries, compared to just 6.49% in the previous year (58% year-over-year growth). == Functionality == The AI Overviews feature uses large language models to generate summaries from web content. The overviews are designed to be concise, providing a snapshot of relevant information about the queried topic. Google allows users to adjust the language complexity in summaries, offering both simplified and detailed options. The overviews also include links to sources. According to a June 2025 study by Semrush, the most cited source is Quora, followed by Reddit. == Reception == The feature has faced criticism for inaccuracies, including instances where erroneous or nonsensical content was generated. Depending on what is searched for, the overview may also consist of hallucinated content, such as when searching for idioms that do not exist. In May 2024, Google temporarily restricted the AI tool after it provided suggestions that were seen as nonsensical and harmful, such as telling users to eat rocks or apply glue on pizza. Concerns were also raised by content publishers, who feared a decline in web traffic as users relied on the summaries instead of visiting source websites. A Google patent from 2026 raised the concern of webmasters that Google could entirely replace the landing page of websites by an AI optimized copy of the website in its results. There is also apprehension about the ethical implications of AI-driven content aggregation, including its impact on intellectual property rights and the visibility of smaller content providers. The European Commission announced in December 2025 that they were investigating whether AI Overviews breached European competition law. In response, Google has stated its commitment to improve content validation and refine the algorithms used to filter unreliable information. Google implemented measures to prioritize link placement within AI Overviews, aiming to balance user convenience with the needs of content creators. In January 2026, Google restricted AI Overviews on certain health-related searches following an investigation by The Guardian. == Lawsuits == On February 24, 2025, Chegg sued Alphabet over the AI Overviews feature, claiming that it was leading to students preferring "low-quality, unverified AI summaries", thus violating antitrust law. Chegg also said it was considering either a sale or a take-private transaction. In September 2025, Penske Media Corporation, the publisher of Rolling Stone and The Hollywood Reporter, sued Google, claiming that AI Overviews illegally regurgitate content from their websites and drive off potential site visitors by always appearing on top of the search results while leaving little incentive to see the linked sources. The company stated that "the future of digital media and [...] its integrity [...] is threatened by Google's current actions", alleging that 20% of searches that link to Penske-owned websites show AI Overviews and that the figure is expected to rise. Google spokesperson José Castañeda called the claims "meritless" and stated that "AI Overviews send traffic to a greater diversity of sites." In 2026, Canadian musician Ashley MacIsaac filed a lawsuit against Google claiming that the AI Overview feature had wrongly stated that MacIsaac had been convicted of numerous criminal offences and was on the sex offender registry. He claims this incorrect information led to the cancellation of a December 2025 gig organized by the Sipekne'katik First Nation.
Georges Giralt PhD Award
The Georges Giralt PhD Award is a European scientific prize for extraordinary contributions to robotics. It is awarded yearly at the European Robotics Forum by euRobotics AISBL, a non-profit organisation based in Brussels with the objective of turning robotics beneficial for Europe’s economy and society. Georges Giralt received his PhD in 1958, from Paul Sabatier University, in the domain of electrical machines, and soon afterwards became a pioneer in robotics, in Europe and worldwide. He was especially instrumental in bringing in scientific foundations and methodology when the domain was still young, and a loose coupling of mechanical and electrical engineering, adopting the early results of automatic control. The high reputation of the Georges Giralt PhD Award is based on the prominent role of the awarding institution euRobotics. With more than 250 member organisations, euRobotics represents the academic and industrial robotics community in Europe. Moreover, it provides the European robotics community with a legal entity to engage in a public/private partnership with the European Commission. The award is covered by various media. Entitled for participation in the Georges Giralt PhD Award are all robotics-related dissertations which have been successfully defended at a European university. The US-American counterpart is the Dick Volz Award. == Award winners == 2026: Antonio González Morgado 2025: Erfan Shahriari 2024: Manuel Keppler 2023: Antonio Andriella, Ribin Balachandran 2022: Antonio Loquercio, Michael Lutter 2021: Giuseppe Averta, Bernd Henze 2020: Cosimo Della Santina 2019: Grazioso Stanislao, Teodor Tomic 2018: Frank Bonnet, Daniel Leidner 2017: Johannes Englsberger 2016: Alexander Dietrich, Mark Müller 2015: Jörg Stückler 2014: Manuel Catalano, Fabien Expert, Rainer Jaekel 2013: Jens Kober 2012: Sami Haddadin 2011: Mario Pratts 2010: Ludovic Righetti 2009: Alejandro-Dizan Vasquez-Govea 2008: Cyrill Stachniss, Eduardo Rocon 2007: Pierre Lamon 2006: Martijn Wisse 2005: Juan Andrade Cetto 2004: Gilles Duchemin 2003: Ralf Koeppe 2002: Gianluca Antonelli, Jens-Steffen Gutmann
Office automation
Office automation refers to the varied computer machinery and software used to digitally create, collect, store, manipulate, and relay office information needed for accomplishing basic tasks. Raw data storage, electronic transfer, and the management of electronic business information comprise the basic activities of an office automation system. Office automation helps in optimizing or automating existing office procedures. The backbone of office automation is a local area network, which allows users to transfer data, mail and voice across the network. All office functions, including dictation, typing, filing, copying, fax, telex, microfilm and records management, telephone and telephone switchboard operations, fall into this category. Office automation was a popular term in the 1970s and 1980s as the desktop computer exploded onto the scene. Advantages of office automation include that it can get many tasks accomplished faster, it eliminates the need for a large staff, less storage is required to store data, and multiple people can update data simultaneously in the event of changes in schedule. == Outline == Businesses can easily purchase and stock their wares with the aid of technology. Many of the manual tasks that used to be done by hand can now be done through hand held devices and UPC and SKU coding. In the retail setting, automation also increases choice. Customers can easily process their payments through automated credit card machines and no longer have to wait in line for an employee to process and manually type in the credit card numbers. Office payrolls have been automated, which means no one has to manually cut checks, and those checks that are cut can be printed through computer programs. Direct deposit can be automatically set up and this further reduces the manual process, and most employees who participate in direct deposit often find their paychecks come earlier than if they'd have to wait for their checks to be written and then cleared by the bank. Other ways automation has reduced employee manpower on tasks is automated voice direction. Through the use of prompts, automated phone menus and directed calls, the need for employees to be dedicated to answer the phones has been reduced, and in some cases, eliminated.
Echo Lake (software)
Echo Lake (AKA Family Album Creator) was the most notable multimedia software product produced by Delrina, which debuted in June 1995. It was touted internally as a "cross [of] Quark Xpress and Myst". It featured an immersive 3D environment where a user could go to a virtual desktop in a virtual office and assemble video and audio clips along with images, and then print them out as either a virtual book other users of the program could use, or for print. It was a highly innovative product for its time, and ultimately was hampered by the inability of many users able to input their own multimedia content easily into a computer from that period. Creative Wonders bought the rights to the Echo Lake multimedia product, which was re-shaped as an introductory program on multimedia and re-released as Family Album Creator in 1996.
Floyd–Steinberg dithering
Floyd–Steinberg dithering is an image dithering algorithm first published in 1976 by Robert W. Floyd and Louis Steinberg. It is commonly used by image manipulation software, for example, when converting an image from a Truecolor 24-bit PNG format into a GIF format, which is restricted to a maximum of 256 colors. == Implementation == The algorithm achieves dithering using error diffusion, meaning it pushes (adds) the residual quantization error of a pixel onto its neighboring pixels, to be quantized after. It spreads the debt out according to the distribution (shown as a map of the neighboring pixels): [ ∗ 7 16 … … 3 16 5 16 1 16 … ] {\displaystyle {\begin{bmatrix}&&&{\frac {\displaystyle 7}{\displaystyle 16}}&\ldots \\\ldots &{\frac {\displaystyle 3}{\displaystyle 16}}&{\frac {\displaystyle 5}{\displaystyle 16}}&{\frac {\displaystyle 1}{\displaystyle 16}}&\ldots \\\end{bmatrix}}} The pixel indicated with a star () indicates the pixel currently being scanned, and the blank pixels are the previously scanned pixels. The specific values (7/16, 3/16, 5/16, 1/16) were originally found by trial-and-error, "guided by the desire to have a region of desired density 0.5 come out as a checkerboard pattern". The algorithm scans the image from left to right, top to bottom, quantizing pixel values one by one. Each time, the quantization error is transferred to the neighboring pixels, while not affecting the pixels that already have been quantized. Hence, if a number of pixels have been rounded downwards, it becomes more likely that the next pixel is rounded upwards, such that on average, the quantization error is close to zero. The diffusion coefficients have the property that if the original pixel values are exactly halfway in between the nearest available colors, the dithered result is a checkerboard pattern. For example, 50% grey data could be dithered as a black-and-white checkerboard pattern. For optimal dithering, the counting of quantization errors should be in sufficient accuracy to prevent rounding errors from affecting the result. For correct results, all values should be linearized first, rather than operating directly on sRGB values as is common for images stored on computers. In some implementations, the horizontal direction of scan alternates between lines; this is called "serpentine scanning" or boustrophedon transform dithering. The algorithm described above is in the following pseudocode. This works for any approximately linear encoding of pixel values, such as 8-bit integers, 16-bit integers or real numbers in the range [0, 1]. for each y from top to bottom do for each x from left to right do oldpixel := pixels[x][y] newpixel := find_closest_palette_color(oldpixel) pixels[x][y] := newpixel quant_error := oldpixel - newpixel pixels[x + 1][y ] := pixels[x + 1][y ] + quant_error × 7 / 16 pixels[x - 1][y + 1] := pixels[x - 1][y + 1] + quant_error × 3 / 16 pixels[x ][y + 1] := pixels[x ][y + 1] + quant_error × 5 / 16 pixels[x + 1][y + 1] := pixels[x + 1][y + 1] + quant_error × 1 / 16 When converting grayscale pixel values from a high to a low bit depth (e.g. 8-bit grayscale to 1-bit black-and-white), find_closest_palette_color() may perform just a simple rounding, for example: find_closest_palette_color(oldpixel) = round(oldpixel / 255) The pseudocode can result in pixel values exceeding the valid values (such as greater than 255 in 8-bit grayscale images). Such values should ideally be handled by the find_closest_palette_color() function, rather than clipping the intermediate values, since a subsequent error may bring the value back into range. However, if fixed-width integers are used, wrapping of intermediate values would cause inversion of black and white, and so should be avoided. The find_closest_palette_color() implementation is nontrivial for a palette that is not evenly distributed, however small inaccuracies in selecting the correct palette color have minimal visual impact due to error being propagated to future pixels. A nearest neighbor search in 3D is frequently used.
Imaging
Imaging is the process of creating visual representations of objects, scenes, or phenomena. The term encompasses both the formation of images through physical processes and the technologies used to capture, store, process, and display them. While traditional imaging relies on visible light, modern imaging systems can visualize information across the electromagnetic spectrum and through other physical phenomena such as sound waves, magnetic fields, and particle emissions, enabling the visualization of subjects invisible to the human eye. Imaging science is the multidisciplinary field concerned with the theoretical foundations and practical applications of image creation and analysis. The field draws on physics, mathematics, electrical engineering, computer science, computer vision, and perceptual psychology to develop systems that generate, collect, duplicate, analyze, modify, and visualize images. == Principles == === The imaging chain === The imaging chain is a conceptual framework describing the interconnected components of any imaging system. Understanding each link in this chain allows engineers and scientists to optimize system performance for specific applications. The chain begins with the subject and its observable properties, typically energy that is emitted, reflected, or transmitted. A light source or other energy source may illuminate the subject to make these properties detectable. The capture device then collects this energy using appropriate sensors: optical systems for electromagnetic radiation, transducers for acoustic waves, or antenna arrays for radio frequencies. In digital systems, a processor converts the captured signals into a format suitable for rendering, applying algorithms for noise reduction, enhancement, or reconstruction. Finally, a display renders the processed information as a visible image on media such as paper, screens, or projection surfaces. Throughout this process, the characteristics of the human visual system inform design decisions, as the ultimate purpose of most imaging systems is to convey information to human observers. === Coherent and non-coherent imaging === Imaging systems are often classified by whether they use coherent or non-coherent illumination. Coherent imaging employs an active source that produces waves with a consistent phase relationship, as in radar, synthetic aperture radar, medical ultrasound, and optical coherence tomography. These systems can capture phase information in addition to amplitude, enabling techniques such as holography and interferometry. Non-coherent imaging systems, including conventional photography, fluorescence microscopy, and telescopes, rely on illumination sources where light waves have random phase relationships. == Methods and applications == Imaging methods span a wide range of physical principles, each suited to particular applications. Optical imaging encompasses photography, cinematography, microscopy, and telescopic observation. These methods capture electromagnetic radiation in or near the visible spectrum and form the basis of most consumer and scientific imaging. Extensions include thermography, which visualizes infrared radiation to reveal temperature distributions, and multispectral imaging, which captures data across multiple wavelength bands for applications in remote sensing and materials analysis. Medical imaging comprises techniques designed to visualize the interior of the human body for diagnostic and therapeutic purposes. Radiography and computed tomography use X-rays to image dense structures such as bone. Magnetic resonance imaging exploits nuclear magnetic properties to produce detailed soft-tissue images without ionizing radiation. Ultrasound imaging uses high-frequency sound waves and is particularly valuable for real-time imaging and fetal monitoring. Nuclear medicine techniques such as positron emission tomography track radioactive tracers to reveal metabolic activity. Emerging modalities include photoacoustic imaging, which combines optical and acoustic principles, and Magneto-acousto-electrical tomography, which maps electrical conductivity in biological tissues. Acoustic imaging uses sound waves to create images. Beyond medical ultrasound, applications include sonar for underwater navigation and mapping, seismic imaging for geological exploration, and industrial non-destructive testing. Radar and microwave imaging employ radio waves to detect and image objects. Synthetic aperture radar produces high-resolution images from aircraft or satellites regardless of weather or lighting conditions, making it essential for Earth observation and reconnaissance. Ground-penetrating radar images subsurface structures for archaeological and engineering applications. Electron and particle imaging use beams of electrons or other particles to achieve resolutions far beyond the diffraction limit of visible light. Electron microscopes can image individual atoms, enabling advances in materials science and structural biology. Chemical imaging combines spectroscopy with spatial imaging to map the chemical composition of samples, with applications in pharmaceutical development, food safety, and forensics. LIDAR (Light Detection and Ranging) measures distances using laser pulses to create three-dimensional representations of surfaces and objects, widely used in autonomous vehicles, topographic mapping, and forestry. Computational and digital imaging encompasses image processing, computer graphics, three-dimensional rendering, and digital image restoration. Computer vision applies algorithmic analysis to extract information from images automatically. == History == Photography and imaging have always been intertwined. When Joseph Nicéphore Niépce created the first permanent photograph using heliography in 1826, and Louis Daguerre refined the process into the daguerreotype a decade later, they weren't just inventing a new art form, they were laying the groundwork for an entire scientific discipline built on silver halide chemistry. For most of the nineteenth century, photography remained the province of specialists. That changed with George Eastman's Kodak camera, introduced in 1888 with the slogan "You press the button, we do the rest." Suddenly, anyone could take pictures. Around the same time, Wilhelm Röntgen stumbled onto X-rays in 1895, an accident that would spawn the entire field of medical imaging. World War II proved to be a turning point. Radar technology, developed frantically on both sides of the conflict, introduced concepts that engineers would later adapt for synthetic aperture radar and medical ultrasound. Then the charge-coupled device came: Willard Boyle and George E. Smith built the first one at Bell Labs in 1969, and within a few decades it had made film nearly obsolete. Magnetic resonance imaging arrived in the 1970s, offering doctors something X-rays never could, detailed views of soft tissue without any radiation. Digital cameras took over fast. By the 2000s, film was already in decline; by the 2010s, smartphones had put a surprisingly capable camera in nearly every pocket. Features that once required real skill, proper exposure, sharp focus, accurate color, became automatic. Today, billions of photos get uploaded to social media every day. As a result, a growing issue is that generative artificial intelligence can fabricate photorealistic images from scratch. What counts as a "real" photograph is no longer necessarily obvious.