Color digital images are made of pixels, and pixels are made of combinations of primary colors represented by a series of code. A channel in this context is the grayscale image of the same size as a color image, made of just one of these primary colors. For instance, an image from a standard digital camera will have a red, green and blue channel. A grayscale image has just one channel. In geographic information systems, channels are often referred to as raster bands. Another closely related concept is feature maps, which are used in convolutional neural networks. == Overview == In the digital realm, there can be any number of conventional primary colors making up an image; a channel in this case is extended to be the grayscale image based on any such conventional primary color. By extension, a channel is any grayscale image of the same dimension as and associated with the original image. Channel is a conventional term used to refer to a certain component of an image. In reality, any image format can use any algorithm internally to store images. For instance, GIF images actually refer to the color in each pixel by an index number, which refers to a table where three color components are stored. However, regardless of how a specific format stores the images, discrete color channels can always be determined, as long as a final color image can be rendered. The concept of channels is extended beyond the visible spectrum in multispectral and hyperspectral imaging. In that context, each channel corresponds to a range of wavelengths and contains spectroscopic information. The channels can have multiple widths and ranges. Three main channel types (or color models) exist, and have respective strengths and weaknesses. === RGB images === An RGB image has three channels: red, green, and blue. RGB channels roughly follow the color receptors in the human eye, and are used in computer displays and image scanners. If the RGB image is 24-bit (the industry standard as of 2005), each channel has 8 bits, for red, green, and blue—in other words, the image is composed of three images (one for each channel), where each image can store discrete pixels with conventional brightness intensities between 0 and 255. If the RGB image is 48-bit (very high color-depth), each channel has 16-bit per pixel color, that is 16-bit red, green, and blue for each per pixel. ==== RGB color sample ==== Notice how the grey trees have similar brightness in all channels, the red dress is much brighter in the red channel than in the other two, and how the green part of the picture is shown much brighter in the green channel. === YUV === YUV images are an affine transformation of the RGB colorspace, originated in broadcasting. The Y channel correlates approximately with perceived intensity, whilst the U and V channels provide colour information. === CMYK === A CMYK image has four channels: cyan, magenta, yellow, and key (black). CMYK is the standard for print, where subtractive coloring is used. A 32-bit CMYK image (the industry standard as of 2005) is made of four 8-bit channels, one for cyan, one for magenta, one for yellow, and one for key color (typically is black). 64-bit storage for CMYK images (16-bit per channel) is not common, since CMYK is usually device-dependent, whereas RGB is the generic standard for device-independent storage. ==== CMYK color sample ==== === HSV === HSV, or hue saturation value, stores color information in three channels, just like RGB, but one channel is devoted to brightness (value), and the other two convey colour information. The value channel is similar to (but not exactly the same as) the CMYK black channel, or its negative. HSV is especially useful in lossy video compression, where loss of color information is less noticeable to the human eye. == Alpha channel == The alpha channel stores transparency information—the higher the value, the more opaque that pixel is. No camera or scanner measures transparency, although physical objects certainly can possess transparency, but the alpha channel is extremely useful for compositing digital images together. Bluescreen technology involves filming actors in front of a primary color background, then setting that color to transparent, and compositing it with a background. The GIF and PNG image formats use alpha channels on the World Wide Web to merge images on web pages so that they appear to have an arbitrary shape even on a non-uniform background. == Other channels == In 3D computer graphics, multiple channels are used for additional control over material rendering; e.g., controlling specularity and so on. == Bit depth == In digitizing images, the color channels are converted to numbers. Since images contain thousands of pixels, each with multiple channels, channels are usually encoded in as few bits as possible. Typical values are 8 bits per channel or 16 bits per channel. Indexed color effectively gets rid of channels altogether to get, for instance, 3 channels into 8 bits (GIF) or 16 bits. == Optimized channel sizes == Since the brain does not necessarily perceive distinctions in each channel to the same degree as in other channels, it is possible that differing the number of bits allocated to each channel will result in more optimal storage; in particular, for RGB images, compressing the blue channel the most and the red channel the least may be better than giving equal space to each. Among other techniques, lossy video compression uses chroma subsampling to reduce the bit depth in color channels (hue and saturation), while keeping all brightness information (value in HSV). 16-bit HiColor stores red and blue in 5 bits, and green in 6 bits.
Pooling layer
In neural networks, a pooling layer is a kind of network layer that downsamples and aggregates information that is dispersed among many vectors into fewer vectors. It has several uses. It removes redundant information, thus reducing the amount of computation and memory required, which makes the model more robust to small variations in the input; and it increases the receptive field of neurons in later layers in the network. == Convolutional neural network pooling == Pooling is most commonly used in convolutional neural networks (CNN). Below is a description of pooling in 2-dimensional CNNs. The generalization to n-dimensions is immediate. As notation, we consider a tensor x ∈ R H × W × C {\displaystyle x\in \mathbb {R} ^{H\times W\times C}} , where H {\displaystyle H} is height, W {\displaystyle W} is width, and C {\displaystyle C} is the number of channels. A pooling layer outputs a tensor y ∈ R H ′ × W ′ × C ′ {\displaystyle y\in \mathbb {R} ^{H'\times W'\times C'}} . We define two variables f , s {\displaystyle f,s} called "filter size" (aka "kernel size") and "stride". Sometimes, it is necessary to use a different filter size and stride for horizontal and vertical directions. In such cases, we define 4 variables: f H , f W , s H , s W {\displaystyle f_{H},f_{W},s_{H},s_{W}} . The receptive field of an entry in the output tensor, y {\displaystyle y} , are all the entries in x {\displaystyle x} that can affect that entry. === Max pooling === Max Pooling (MaxPool) is commonly used in CNNs to reduce the spatial dimensions of feature maps. Define M a x P o o l ( x | f , s ) 0 , 0 , 0 = max ( x 0 : f − 1 , 0 : f − 1 , 0 ) {\displaystyle \mathrm {MaxPool} (x|f,s)_{0,0,0}=\max(x_{0:f-1,0:f-1,0})} where 0 : f − 1 {\displaystyle 0:f-1} means the range 0 , 1 , … , f − 1 {\displaystyle 0,1,\dots ,f-1} . Note that we need to avoid the off-by-one error. The next input is M a x P o o l ( x | f , s ) 1 , 0 , 0 = max ( x s : s + f − 1 , 0 : f − 1 , 0 ) {\displaystyle \mathrm {MaxPool} (x|f,s)_{1,0,0}=\max(x_{s:s+f-1,0:f-1,0})} and so on. The receptive field of y i , j , c {\displaystyle y_{i,j,c}} is x i s + f − 1 , j s + f − 1 , c {\displaystyle x_{is+f-1,js+f-1,c}} , so in general, M a x P o o l ( x | f , s ) i , j , c = m a x ( x i s : i s + f − 1 , j s : j s + f − 1 , c ) {\displaystyle \mathrm {MaxPool} (x|f,s)_{i,j,c}=\mathrm {max} (x_{is:is+f-1,js:js+f-1,c})} If the horizontal and vertical filter size and strides differ, then in general, M a x P o o l ( x | f , s ) i , j , c = m a x ( x i s H : i s H + f H − 1 , j s W : j s W + f W − 1 , c ) {\displaystyle \mathrm {MaxPool} (x|f,s)_{i,j,c}=\mathrm {max} (x_{is_{H}:is_{H}+f_{H}-1,js_{W}:js_{W}+f_{W}-1,c})} More succinctly, we can write y k = max ( { x k ′ | k ′ in the receptive field of k } ) {\displaystyle y_{k}=\max(\{x_{k'}|k'{\text{ in the receptive field of }}k\})} . If H {\displaystyle H} is not expressible as k s + f {\displaystyle ks+f} where k {\displaystyle k} is an integer, then for computing the entries of the output tensor on the boundaries, max pooling would attempt to take as inputs variables off the tensor. In this case, how those non-existent variables are handled depends on the padding conditions, illustrated on the right. Global Max Pooling (GMP) is a specific kind of max pooling where the output tensor has shape R C {\displaystyle \mathbb {R} ^{C}} and the receptive field of y c {\displaystyle y_{c}} is all of x 0 : H , 0 : W , c {\displaystyle x_{0:H,0:W,c}} . That is, it takes the maximum over each entire channel. It is often used just before the final fully connected layers in a CNN classification head. === Average pooling === Average pooling (AvgPool) is similarly defined A v g P o o l ( x | f , s ) i , j , c = a v e r a g e ( x i s : i s + f − 1 , j s : j s + f − 1 , c ) = 1 f 2 ∑ k ∈ i s : i s + f − 1 ∑ l ∈ j s : j s + f − 1 x k , l , c {\displaystyle \mathrm {AvgPool} (x|f,s)_{i,j,c}=\mathrm {average} (x_{is:is+f-1,js:js+f-1,c})={\frac {1}{f^{2}}}\sum _{k\in is:is+f-1}\sum _{l\in js:js+f-1}x_{k,l,c}} Global Average Pooling (GAP) is defined similarly to GMP. It was first proposed in Network-in-Network. Similarly to GMP, it is often used just before the final fully connected layers in a CNN classification head. === Interpolations === There are some interpolations of max pooling and average pooling. Mixed Pooling is a linear sum of max pooling and average pooling. That is, M i x e d P o o l ( x | f , s , w ) = w M a x P o o l ( x | f , s ) + ( 1 − w ) A v g P o o l ( x | f , s ) {\displaystyle \mathrm {MixedPool} (x|f,s,w)=w\mathrm {MaxPool} (x|f,s)+(1-w)\mathrm {AvgPool} (x|f,s)} where w ∈ [ 0 , 1 ] {\displaystyle w\in [0,1]} is either a hyperparameter, a learnable parameter, or randomly sampled anew every time. Lp Pooling is similar to average pooling, but uses Lp norm average instead of average: y k = ( 1 N ∑ k ′ in the receptive field of k | x k ′ | p ) 1 / p {\displaystyle y_{k}=\left({\frac {1}{N}}\sum _{k'{\text{ in the receptive field of }}k}|x_{k'}|^{p}\right)^{1/p}} where N {\displaystyle N} is the size of receptive field, and p ≥ 1 {\displaystyle p\geq 1} is a hyperparameter. If all activations are non-negative, then average pooling is the case of p = 1 {\displaystyle p=1} , and max pooling is the case of p → ∞ {\displaystyle p\to \infty } . Square-root pooling is the case of p = 2 {\displaystyle p=2} . Stochastic pooling samples a random activation x k ′ {\displaystyle x_{k'}} from the receptive field with probability x k ′ ∑ k ″ x k ″ {\displaystyle {\frac {x_{k'}}{\sum _{k''}x_{k''}}}} . It is the same as average pooling in expectation. Softmax pooling is like max pooling, but uses softmax, i.e. ∑ k ′ e β x k ′ x k ′ ∑ k ″ e β x k ″ {\displaystyle {\frac {\sum _{k'}e^{\beta x_{k'}}x_{k'}}{\sum _{k''}e^{\beta x_{k''}}}}} where β > 0 {\displaystyle \beta >0} . Average pooling is the case of β ↓ 0 {\displaystyle \beta \downarrow 0} , and max pooling is the case of β ↑ ∞ {\displaystyle \beta \uparrow \infty } Local Importance-based Pooling generalizes softmax pooling by ∑ k ′ e g ( x k ′ ) x k ′ ∑ k ″ e g ( x k ″ ) {\displaystyle {\frac {\sum _{k'}e^{g(x_{k'})}x_{k'}}{\sum _{k''}e^{g(x_{k''})}}}} where g {\displaystyle g} is a learnable function. === Other poolings === Spatial pyramidal pooling applies max pooling (or any other form of pooling) in a pyramid structure. That is, it applies global max pooling, then applies max pooling to the image divided into 4 equal parts, then 16, etc. The results are then concatenated. It is a hierarchical form of global pooling, and similar to global pooling, it is often used just before a classification head. Region of Interest Pooling (also known as RoI pooling) is a variant of max pooling used in R-CNNs for object detection. It is designed to take an arbitrarily-sized input matrix, and output a fixed-sized output matrix. Covariance pooling computes the covariance matrix of the vectors { x k , l , 0 : C − 1 } k ∈ i s : i s + f − 1 , l ∈ j s : j s + f − 1 {\displaystyle \{x_{k,l,0:C-1}\}_{k\in is:is+f-1,l\in js:js+f-1}} which is then flattened to a C 2 {\displaystyle C^{2}} -dimensional vector y i , j , 0 : C 2 − 1 {\displaystyle y_{i,j,0:C^{2}-1}} . Global covariance pooling is used similarly to global max pooling. As average pooling computes the average, which is a first-degree statistic, and covariance is a second-degree statistic, covariance pooling is also called "second-order pooling". It can be generalized to higher-order poolings. Blur Pooling means applying a blurring method before downsampling. For example, the Rect-2 blur pooling means taking an average pooling at f = 2 , s = 1 {\displaystyle f=2,s=1} , then taking every second pixel (identity with s = 2 {\displaystyle s=2} ). == Vision Transformer pooling == In Vision Transformers (ViT), there are the following common kinds of poolings. BERT-like pooling uses a dummy [CLS] token, "classification". For classification, the output at [CLS] is the classification token, which is then processed by a LayerNorm-feedforward-softmax module into a probability distribution, which is the network's prediction of class probability distribution. This is the one used by the original ViT and Masked Autoencoder. Global average pooling (GAP) does not use the dummy token, but simply takes the average of all output tokens as the classification token. It was mentioned in the original ViT as being equally good. Multihead attention pooling (MAP) applies a multi headed attention block to pooling. Specifically, it takes as input a list of vectors x 1 , x 2 , … , x n {\displaystyle x_{1},x_{2},\dots ,x_{n}} , which might be thought of as the output vectors of a layer of a ViT. It then applies a feedforward layer F F N {\displaystyle \mathrm {FFN} } on each vector, resulting in a matrix V = [ F F N ( v 1 ) , … , F F N ( v n ) ] {\displaystyle V=[\mathrm {FFN} (v_{1}),\dots ,\mathrm {FFN} (v_{n})]} . This is then sent to a multi-headed attention, resulting in M u l t i h e a d e d A
Opposition to AI data centers
Since 2024, dozens of local community-led protest campaigns have emerged in opposition to AI data centers. == Motivations == Organized opposition to AI data centers has been driven by concerns about energy use, energy costs, noise pollution, air pollution, and water waste. Opposition sentiment is widespread with a Gallup poll conducted in March 2026 showing that 70% of respondents oppose the construction of new AI data centers in their neighborhood. == Impact == In 2025, local opposition to AI data centers led to the delay or cancellation of projects totalling US$156 billion. == Specific protests and outcomes in the United States == According to Data Center Watch, there are has been a wave of dozens of protests against AI data centers since 2022. Below is a non-exhaustive list of some notable examples. === Goodyear and Buckeye, Arizona: Tract AI Data Center Proposal === In Goodyear and Buckeye, Arizona, a $14 billion project by developer Tract was withdrawn after local authorities blocked necessary rezoning in response to pressure from resident organizers. Opponest stiff resistance due to concerns over building heights, noise pollution, and the potential strain on local utilities. However, the company announced a revised project near the Buckeye airport in August 2024, with the backing of local officials and the mayor. === Peculiar, Missouri: Diode Ventures Harper Road Technology Park Proposal === In Peculiar, Missouri, residents from the group "Peaceful Peculiar" organized to stop a data center proposal from Diode Ventures called Harper Road Technology Park. Citing concerns around noise and light pollution, health, environmental impacts, jobs, property values, and energy use, organizers attended local planning and zoning meetings in large numbers and lobbied councilors to reject the proposal. Ultimately, the city council unanimously rejected the proposal in September 2024. === Chesterton, Indiana: Provident Realty Advisors Proposal === In Chesterton, Indiana, the Texas-based company Provident Reality Advisors applied for a $1.3 billion construction of a data center complex on the Brassie Golf Club property. Provident Realty Advisors wanted to purchase the 200 acres owned by PPM Chesterton LLC in 2024 order to build a data center complex, with eight buildings and an end user of a hyperscaler. The Town Council of Chesterton released a statement saying that they would never support this project, at least not at the scale and location it was planned for. They cited fears of added noise for locals, electrical or water management concerns, the intrusiveness of a data center built next to houses, and more. Provident released a statement shortly after rescinding their plan, because it was clear than the town of Chesterton would not support them. === Cascade Locks, Oregon: Roundhouse Digital Infrastructure Proposal === Startup data center developer Roundhouse Digital Infrastructure had planned to build out a 10-megawatt data center using a vacant industrial building and nearby 10-acre site in the Port of Cascade Locks, Oregon. After significant organized community opposition, the project was abandoned. === Forth Worth, Texas: WUSF 5 Rock Creek East Proposal === In September 2024, the City Council of Fort Worth, Texas approved a zoning change that would allow construction of a data center. In responses, neighbors mounted opposition citing concerns about traffic, light pollution, energy consumption, water use, and noise issues if the data center were to be built. In response to extensive public comments opposing a tax break for the data center, a city councilor withdrew his motion to approve the tax break. As of April, 2026, the future of the project is still uncertain. === Santa Clara, California: GI Partners Proposal === GI partners sought to build a new AI data center in Santa Clara, California, which is already home to many data centers, by acquiring a conditional permit use that would have allowed the developer to knock down a property and replace it with a data center. To obtain this permit they were required to go before members of the Planning Commission. Ultimately, the project was delayed with the Planning Commission requiring GI partners to do more public outreach. === Virginia === ==== Richmond: DC Blox Proposal ==== After residents organized to lobby the municipal government to block the proposal to avoid noise pollution and higher energy use, commissioners denied the company's permit. ==== Catlett Station: Headwaters Site Proposal ==== In Catlett, Virginia, developer Headwaters proposed construction of a data center complex just north of the town in 2020. In response, a residents' organization called "Protect Catlett" was formed to oppose the project. Arguments against the data center involved its impacts on water and power availability, its noise as a residential disturbance, and its destruction of historic and community heritage buildings. Arguments in favor cited job creation and $20 million in local tax revenue if the project were to go through. Protect Catlett utilized town halls and public comments to mobilize opposition to the project. They also dedicated time to educating other residents about the project's negative impacts and canvassing door-to-door in order to garner even more opposition to the project. Ultimately, after fervent opposition from most town residents, the project was canceled by the town and the developer. ==== Culpeper County: Culpeper Acquisitions Proposal ==== Culpeper Acquisitions, LLC, proposed a massive $12 billion data center project in Culpeper County, Virginia, designed to feature 4.6 million square feet of space across nine multi-story buildings. Coalition to Save Culpeper (C2SC) is an activist organization formed to resist the development of the project. C2SC has been active on many fronts including, messaging on social media, reaching out to local officials, and organizing meetings to bring community members with aligned interests together. Ultimately, the project was delayed due to unanimous denial by the Culpeper County Planning Commission on June 12, 2024, which was driven by intense opposition from C2SC. C2SC was successful in their mission largely because they were able to get so many people from the community behind it, and put enough pressure on local officials to take action. ==== Midlothian: Province Group Proposal ==== In late October 2025, the Powhatan County Board of Supervisors in Virginia voted unanimously to approve the $3 billion data center, despite the county's Planning Commission having unanimously recommended denial several days earlier. The reasoning behind their support for the center is that it will generate substantial tax revenue, reducing the county's reliance on residential property taxes. This appeal of lowering residential property taxes is the major selling point for the center's development. The developer, California-based Province Group, incentivized the Board by being agreeable to its conditions for building the center. The center is still on track for development, but faces local resistance, though little information is available on specific groups opposing it. ==== Warrenton: Amazon Proposal ==== Citizens for Farquier County (CFFC) advocates to "preserve the natural, historic and agricultural resources" of their county. Historically, this has meant opposing the building of a dam or lights in front of fast food stores. This group has recently mobilized in opposition of a plan to build data centers for Amazon. They first filed a suit to stop the construction in 2023 and it has been in litigation ever since. The case hinges on opposition to a 2021 zoning amendment which allowed data centers to be built in town. CFFC's lawyer, Dale Mullen, argues that this amendment violates state law, which requires such amendments to state their "public purpose". They argue that the permit for the Amazon data center was "void from the beginning". The CFFC also organized to vote out town council members who approved the first data center and were up for reelection, replacing them with candidates who opposed the data center. In May 2025, after attending town council meetings to speak out against the data center, the planning commission voted 4–1 to remove the zoning amendment allowing data center construction in town, citing public opposition. Currently, CFFC is advocating along with Piedmont Environmental Group, for phasing out data center tax breaks at the state level. ==== France: Marseille opposition ==== In France, local opposition materialised in response to proposed data centre developments, especially in and around the city of Marseille. Opposition came from activists, such as "Clouds Were Under Our Feet" group, residents ,and local politicians. Issues raised related to energy use, environmental impact, and limited local benefits (such as the creation of a few jobs only). == Legislation in the United States == Legal limits and moratoriums on the construction of new d
Argument technology
Argument technology is a sub-field of collective intelligence and artificial intelligence that focuses on applying computational techniques to the creation, identification, analysis, navigation, evaluation and visualisation of arguments and debates. In the 1980s and 1990s, philosophical theories of arguments in general, and argumentation theory in particular, were leveraged to handle key computational challenges, such as modeling non-monotonic and defeasible reasoning and designing robust coordination protocols for multi-agent systems. At the same time, mechanisms for computing semantics of Argumentation frameworks were introduced as a way of providing a calculus of opposition for computing what it is reasonable to believe in the context of conflicting arguments. With these foundations in place, the area was kick-started by a workshop held in the Scottish Highlands in 2000, the result of which was a book coauthored by philosophers of argument, rhetoricians, legal scholars and AI researchers. Since then, the area has been supported by various dedicated events such as the International Workshop on Computational Models of Natural Argument (CMNA) which has run annually since 2001; the International Workshop on Argument in Multi Agent Systems (ArgMAS) annually since 2004; the Workshop on Argument Mining, annually since 2014, and the Conference on Computational Models of Argument (COMMA), biennially since 2006. Since 2010, the field has also had its own journal, Argument & Computation, which was published by Taylor & Francis until 2016 and since then by IOS Press. One of the challenges that argument technology faced was a lack of standardisation in the representation and underlying conception of argument in machine readable terms. Many different software tools for manual argument analysis, in particular, developed idiosyncratic and ad hoc ways of representing arguments which reflected differing underlying ways of conceiving of argumentative structure. This lack of standardisation also meant that there was no interchange between tools or between research projects, and little re-use of data resources that were often expensive to create. To tackle this problem, the Argument Interchange Format set out to establish a common standard that captured the minimal common features of argumentation which could then be extended in different settings. Since about 2018, argument technology has been growing rapidly, with, for example, IBM's Grand Challenge, Project Debater, results for which were published in Nature in March 2021; German research funder, DFG's nationwide research programme on Robust Argumentation Machines, RATIO, begun in 2019; and UK nationwide deployment of The Evidence Toolkit by the BBC in 2019. A 2021 video narrated by Stephen Fry provides a summary of the societal motivations for work in argument technology. Argument technology has applications in a variety of domains, including education, healthcare, policy making, political science, intelligence analysis and risk management and has a variety of sub-fields, methodologies and technologies. == Technologies == === Argument assistant === An argument assistant is a software tool which support users when writing arguments. Argument assistants can help users compose content, review content from one other, including in dialogical contexts. In addition to Web services, such functionalities can be provided through the plugin architectures of word processor software or those of Web browsers. Internet forums, for instance, can be greatly enhanced by such software tools and services. === Argument blogging === ArguBlogging is software which allows its users to select portions of hypertext on webpages in their Web browsers and to agree or disagree with the selected content, posting their arguments to their blogs with linked argument data. It is implemented as a bookmarklet, adding functionality to Web browsers and interoperating with blogging platforms such as Blogger and Tumblr. === Argument mapping === Argument maps are visual, diagrammatic representations of arguments. Such visual diagrams facilitate diagrammatic reasoning and promote one's ability to grasp and to make sense of information rapidly and readily. Argument maps can provide structured, semi-formal frameworks for representing arguments using interactive visual language. One avenue of research and development is the design of online platforms to leverage collective intelligence to populate such maps and to integrate data, optimize and assess arguments. === Argument mining === Argument mining, or argumentation mining, is a research area within the natural language processing field. The goal of argument mining is the automatic extraction and identification of argumentative structures from natural language text with the aid of computer programs. === Argument search === An argument search engine is a search engine that is given a topic as a user query and returns a list of arguments for and against the topic or about that topic. Such engines could be used to support informed decision-making or to help debaters prepare for debates. === Automated argumentative essay scoring === The goal of automated argumentative essay scoring systems is to assist students in improving their writing skills by measuring the quality of their argumentative content. === Debate technology === Debate technology focuses on human-machine interaction and in particular providing systems that support, monitor and engage in debate. One of the most high-profile examples of debating technology is IBM's Project Debater which combines scripted communication with very large-scale processing of news articles to identify and construct arguments on the fly in a competitive debating setting. Debating technology also encompasses tools aimed at providing insight into debates, typically using techniques from data science. These analytics have been developed in both academic and commercial settings. === Decision support system === Argument technology can reduce both individual and group biases and facilitate more accurate decisions. Argument-based decision support systems do so by helping users to distinguish between claims and the evidence supporting them, and express their confidence in and evaluate the strength of evidence of competing claims. They have been used to improve predictions of housing market trends, risk analysis, ethical and legal decision making. ==== Ethical decision support system ==== An ethical decision support system is a decision support system which supports users in moral reasoning and decision-making. ==== Legal decision support system ==== A legal decision support system is a decision support system which supports users in legal reasoning and decision-making. === Explainable artificial intelligence === An explainable or transparent artificial intelligence system is an artificial intelligence system whose actions can be easily understood by humans. === Intelligent tutoring system === An intelligent tutoring system is a computer system that aims to provide immediate and customized instruction or feedback to learners, usually without requiring intervention from a human teacher. The intersection of argument technology and intelligent tutoring systems includes computer systems which aim to provide instruction in: critical thinking, argumentation, ethics, law, mathematics, and philosophy. === Legal expert system === A legal expert system is a domain-specific expert system that uses artificial intelligence to emulate the decision-making abilities of a human expert in the field of law. === Machine ethics === Machine ethics is a part of the ethics of artificial intelligence concerned with the moral behavior of artificially intelligent beings. As humans argue with respect to morality and moral behavior, argument can be envisioned as a component of machine ethics systems and moral reasoning components. === Proof assistant === In computer science and mathematical logic, a proof assistant or interactive theorem prover is a software tool to assist with the development of formal proofs by human-machine collaboration. This involves some sort of interactive proof editor, or other interface, with which a human can guide the search for proofs, the details of which are stored in, and some steps provided by, a computer. === Ethical considerations === Ethical considerations of argument technology include privacy, transparency, societal concerns, and diversity in representation. These factors cut across different levels such as technology, user interface design, user, service context, and society. There is concern about unethical misuse for "generating arguments on controversial topics with specific stances and deploying them on social platforms". Another issue may concern the design of conclusion-making algorithms, such as e.g. enabling such to conclude that certain key data is needed instead of only making lists of best-fit conclusions or enabling the generation of multi
The Future of Truth (Rosenbaum book)
The Future of Truth: How AI Reshapes Reality is a 2026 book by American filmmaker and author Steven Rosenbaum about how artificial intelligence affects the concept of truth. It was published by Matt Holt Books on May 12, 2026, to positive media attention; on May 19, in response to an inquiry from The New York Times, Rosenbaum acknowledged that the book itself contains multiple misattributed or false quotes that were hallucinated by AIs. == Synopsis == == Development == Rosenbaum has said that he developed the book using AI chatbots as research tools, indicating in his notes what information came from AI and sending those claims to a fact-checker affiliated with the publisher. He has said that he did not use AI tools to write the book itself. He has described AI tools as "a delightful writing companion ... strangely creative and crafty and unusual in all these ways", while acknowledging that sometimes "then it betrays you in ways that are just really quite horrible". Journalist and Nobel laureate Maria Ressa wrote the book's foreword. Taylor Lorenz, Michael Wolff, and Nicholas Thompson wrote blurbs promoting it. == Release and reception == The Future of Truth was published by Matt Holt Books, an imprint of BenBella Books, and distributed by Simon & Schuster. The book's release on May 12, 2026, was described by Futurism as "buzzy" and by The New York Times as "to great fanfare". On May 14, an excerpt was published in Wired under the title "Gen Z Is Pioneering a New Understanding of Truth". On May 17, the Times contacted Rosenbaum regarding a number of quotes that appeared to be falsified or misattributed; the following evening he confirmed that they were the result of AI hallucinations:As I disclosed in the book's acknowledgments, I used AI tools ChatGPT and Claude during the research, writing and editing process. That does not excuse these errors, of which I take full responsibility. I am now working with the editors to thoroughly review and quickly correct any affected passages; any future editions will be corrected. The Times documented several of the errors, including a quote from Kara Swisher that Swisher described as making it "sound like I have a stick up my butt" and a quote from Lisa Feldman Barrett that Barrett described as misrepresenting her views on the nature of emotions, social signals, and truth. The book also misattributed a quote by Meredith Broussard from an interview with Marketplace Tech as having been from her book Artificial Unintelligence and hallucinated several words in a quote from Lee McIntyre, although according to McIntyre it did not misrepresent his views. Wired's editors, in an addendum to the excerpt they published, said that all quotes included in it had been verified as part of their fact-checking process. Rosenbaum told the Times that the series of errors "serves as a warning about the risks of AI-assisted research and verification, that is why I wrote the book. These AI errors do not, in fact, diminish the larger questions that the book raises about truth, trust and AI and its impact on society, democracy and editorial." Maggie Harrison Dupré in Futurism expressed skepticism, writing "The risk of AI hallucinations ... is well-known. If you're going to literally write the book on post-AI truth, you should probably put some more elbow grease into fact-checking your AI-assisted research." Kyle Orland in Ars Technica, responding to Rosenbaum's statement that his error "demonstrates the problem more vividly than any abstract argument could", was similarly skeptical, writing that "if we accept this take, every avoidably obvious mess in the world might be a disguised good because it really helps illuminate the huge mistake. And that can't be right; sometimes 'negligence' is just that." Subsequent comments by Rosenbaum placed more blame on the chatbots, which he told The Atlantic "fucked up the book". Rosenbaum told Ars Technica that fact-checking occurred "incredibly effectively, but not a hundred percent"; Orland observed that "it's worth noting that most writers manage to include zero made-up quotes when they write a book". Rosenbaum said that he had "learned a lesson" and would be "much more suspicious" of AI in the future, but would continue to use AI in his research. Orland responded to Rosenbaum's characterization of AI as "magical" by comparing it to the One Ring from The Lord of the Rings, in that it "convinces many of those who use it that they can control its power properly" when many cannot. Orland highlighted the limits of traditional fact-checking regarding AI, given that fact-checkers are used to assuming that direct quotes are copied word-for-word from the source. Rosenbaum told Orland that the future of fact-checking for AI-researched works "probably includes mandatory source tracing for quotations, better provenance tracking, clearer standards around AI-assisted research, and potentially (more irony here) AI tools that audit citations against primary materials". Patrick Redford in Defector criticized Rosenbaum, alongside other artists tricked by AI, for failing to recognize AI as "the enemy". Will Oremus in The Atlantic described Redford's approach of stigmatizing AI writing as "reasonable", noting the presence of low-quality, seemingly AI-generated verbiage in The Future of Truth—a claim denied by Rosenbaum—before saying that the greater issue is finding the line at which AI assistance in writing becomes a problem. Oremus concluded, "The scandal can't just be that [Rosenbaum] used AI while working on his book, because he acknowledged that up front. He got in trouble because he had used AI badly, failing to check its work on a task at which it is famously unreliable."
Artificial psychology
Artificial psychology (AP) has had multiple meanings dating back to 19th century, with recent usage related to artificial intelligence (AI).Artificial psychology is a theoretical field related to artificial intelligence, cognitive science, and psychology, which explores how advanced AI systems may develop human-like decision-making processes. In 1999, Zhiliang Wang and Lun Xie presented a theory of artificial psychology based on artificial intelligence. They analyze human psychology using information science research methods and artificial intelligence research to probe deeper into the human mind. == Main Theory == Dan Curtis (b. 1963) proposed AP is a theoretical discipline. The theory considers the situation when an artificial intelligence approaches the level of complexity where the intelligence meets two conditions: Condition I A: Makes all of its decisions autonomously B: Is capable of making decisions based on information that is New Abstract Incomplete C: The artificial intelligence is capable of reprogramming itself based on the new data, allowing it to evolve. D: And is capable of resolving its own programming conflicts, even in the presence of incomplete data. This means that the intelligence autonomously makes value-based decisions, referring to values that the intelligence has created for itself. Condition II All four criteria are met in situations that are not part of the original operating program When both conditions are met, then, according to this theory, the possibility exists that the intelligence will reach irrational conclusions based on real or created information. At this point, the criteria are met for intervention which will not necessarily be resolved by simple re-coding of processes due to extraordinarily complex nature of the codebase itself; but rather a discussion with the intelligence in a format which more closely resembles classical (human) psychology. If the intelligence cannot be reprogrammed by directly inputting new code, but requires the intelligence to reprogram itself through a process of analysis and decision based on information provided by a human, in order for it to overcome behavior which is inconsistent with the machines purpose or ability to function normally, then artificial psychology is by definition, what is required. The level of complexity that is required before these thresholds are met is currently a subject of extensive debate. The theory of artificial psychology does not address the specifics of what those levels may be, but only that the level is sufficiently complex that the intelligence cannot simply be recoded by a software developer, and therefore dysfunctionality must be addressed through the same processes that humans must go through to address their own dysfunctionalities. Along the same lines, artificial psychology does not address the question of whether or not the intelligence is conscious. As of 2022, the level of artificial intelligence does not approach any threshold where any of the theories or principles of artificial psychology can even be tested, and therefore, artificial psychology remains a largely theoretical discipline. Even at a theoretical level, artificial psychology remains an advanced stage of artificial intelligence.
TCEC Season 14
The 14th season of the Top Chess Engine Championship took place between 17 November 2018 and 24 February 2019. Stockfish was the defending champion, having defeated Komodo in the previous season's superfinal. The season is notable for two things: the emergence of two strong, new engines, the Komodo variant Komodo Monte Carlo tree search (MCTS) and the neural network engine Leela Chess Zero, and the dramatic superfinal. Komodo MCTS and Leela fought their way from Division 4 and Division 3 respectively to the Premier Division, with Leela further qualifying for the superfinal against Stockfish. The superfinal was a topsy-turvy affair with the lead changing hands several times. It finished as the closest superfinal TCEC has ever seen, with Stockfish winning by a single game, 50.5–49.5 (+10 =81 -9). == Overview == === Structure === The season comprised five divisions: from the lowest Division 4 to the Premier Division. The top two engines of each division promote to the division above, while the bottom two engines relegate. The top two engines of the Premier Division contest a 100-game superfinal. The lengths of the opening books used increases as the divisions progress. The superfinal itself used a custom opening book designed by Jeroen Noomen. === Rules === The TCEC draw and win rules were slightly modified for Season 14. The game is now adjudicated as drawn if, after move 30, both engines have evals ±0.08 for five consecutive moves, and there are neither pawn moves nor a capture. Win adjudication now occurs if both engines have an eval of ±10 for five consecutive moves. Following the controversy over DeusX's participation last season, the uniqueness rule for neural networks was modified such that at least two of the following three hallmarks must be unique: The code for training the neural network The neural network (and weights file) itself The engine that executes this network This change meant DeusX did not meet the uniqueness criteria and therefore did not participate. Aside from this change, the season used the standard rules of the TCEC. == Results == === Division 4 === New entrant Komodo MCTS dominated Division 4, winning by a clear four points, although it did lose a game to second-place finisher rofChade. Fellow new entrant Scorpio NN performed badly and finished last, drawing only one game and losing the rest. === Division 3 === The neural network engine Leela Chess Zero had just missed promotion to Division 2 in the previous season. Since its relatively weak performance last season was partly due to hardware problems, and since it had shown a lot of improvement in strength, it was the hot favourite in this division. Leela lived up to its billing by comprehensively defeating everyone else. In a portent of future divisions however, Leela surprisingly dropped a game to third-place Arasan. Komodo MCTS was also improving quickly, and an updated version finished second behind Leela. The gap between second and third was 6.5 points, illustrating the gulf in class. === Division 2 === Although Division 2 engines are significantly stronger than Division 3, Leela and Komodo MCTS continued to dominate the competition, and again finished first and second. Komodo MCTS only lost one game to Leela, while Leela's tendency to occasionally lose to weaker engines saw her losing a game to 4th-placed Booot. Third place finisher Xiphos gave Leela and Komodo MCTS a run for their money, and was in the running up until the final rounds when it lost a crucial game to Leela. This loss left it one point behind Komodo MCTS in the final standings. === Division 1 === Leela and Komodo MCTS's rampage through the lower divisions continued, and they again finished first and second. In a demonstration of how much it had improved, Leela scored 20/28 in this division, the same score it had achieved in Division 2. This was also a TCEC points record for this division. However, Leela dropped a game against fourth-place finisher Chiron. Komodo MCTS, which had yet to lose a game in the lower divisions except to Leela, also conceded its first loss to third-place Fizbo. At the other end of the table, former champions Jonny and Fritz, which had not been updated, found themselves outclassed and finished second-last and last respectively; however with fellow competitor Ginkgo crashing five times (and therefore being disqualified), Jonny managed to stay in the division. The penultimate game for this division set a new TCEC moves record for a decisive game: 308 moves before Leela defeated Fritz. === Premier division === This was the strongest premier division ever, with multiple-time champions Stockfish, Komodo, and Houdini in the mix. Right from the start it became clear that Stockfish was in a league of its own, and it dominated the division, scoring wins against every other engine without losing a game. Second place however was a hotly-contested affair, with Leela, Komodo and Houdini neck-and-neck for most of the division. Houdini took the early lead, but Komodo gained second after winning two games by forfeit when its sibling Komodo MCTS crashed. This led to murmurs of a "Konspiracy". However, when both Komodo and Houdini failed to score more wins against the lower half of the field, Leela was able to take the lead. Halfway through the division the race was upended again when Leela went through a bad streak, losing three games in a row to Stockfish, Komodo, and Fire. This led to Komodo regaining second place, only for Komodo MCTS to crash yet again. By TCEC rules this meant Komodo MCTS was disqualified and all its scores were zeroed out, which put Leela back in second place. With three games left, Leela missed a win against Andscacs, which would've more or less secured her a place in the superfinal. Meanwhile, Komodo kept the division interesting by winning two of its last three games. Because Komodo had superior tiebreakers to Leela, this meant Komodo would qualify for the superfinal unless Leela managed to hold Stockfish to a draw with Black in the last game of the division. In a tense final game, Stockfish came close to winning, but missed the winning line. Leela managed to draw and qualified for the superfinal. At the other end of the table, it was quickly apparent that Ethereal and Andscacs were the weakest engines and would likely relegate. However, when Komodo MCTS was disqualified (and therefore relegated), it threw both engines a lifeline, since they could now stay in the division by beating the other. Andscacs was able to score a head-to-head win against Ethereal, but was crushed by Stockfish (+0 =2 -4) and Leela (+0 =3 -3). Ethereal didn't manage to score a win in the entire division, but did manage to score more draws than Andscacs, condemning Andscacs to relegation. === Superfinal === Going into the superfinal expectations were high for Leela: she had received a new network and had just won her first major competition when she defeated Houdini in the second TCEC cup. However, she had won the tournament without having played Stockfish (who had been surprisingly eliminated by Houdini in the semifinals). That, plus the fact that Stockfish dominated Premier Division and had never lost a match to Leela, left it unclear which engine was superior, although most spectators favored Stockfish. The superfinal turned out to be a roller-coaster. It began with Stockfish drawing first blood in game 7, and then scoring another win in game 10. Leela hit back with wins in game 11 and 13, but then lost games 20, 21, and 22. This gave Stockfish a 3-point lead. However, in the next 30 games, Leela was the only one to score wins: it first equalized by winning games 25, 27, and 29, and then took the lead by winning games 49 and 53. Stockfish won game 56, but Leela won game 63, maintaining her lead. There followed two dramatic games. In game 65, Leela built up a winning position. Stockfish showed a +153 evaluation, indicating that it had found a forced line leading to an endgame tablebase win; indeed analysis with 7-piece tablebases showed that Leela's position was winning. Under previous seasons' rules, the game would have been adjudicated as a win because Leela's evaluation was above 6.5. However under the new rules, Leela's +8.92 evaluation was not enough to adjudicate. It turned out that Leela could not see the winning line, and shuffled her pieces aimlessly, leading to a 50-move draw. In game 66, Stockfish was given a substantial advantage by the opening, but failed to make the most of it. The evaluations were leveling out to zero when the internet connection to the GPU servers was cut off. By tournament rules, this meant the game was replayed from scratch. After a further internet disconnection and restart, Stockfish handled the opening better and won, leaving Leela with a 1-point lead. In the last third of the superfinal, there followed more drama as Leela often built up strong advantages, but Stockfish showed great resourcefulness in defending inferior positions. Meanwh