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  Inductive programming

  Inductive programming (IP) is a special area of automatic programming, covering research from artificial intelligence and programming, which addresses learning of typically declarative (logic or functional) and often recursive programs from incomplete specifications, such as input/output examples or constraints. Depending on the programming language used, there are several kinds of inductive programming. Inductive functional programming, which uses functional programming languages such as Lisp or Haskell, and most especially inductive logic programming, which uses logic programming languages such as Prolog and other logical representations such as description logics, have been more prominent, but other (programming) language paradigms have also been used, such as constraint programming or probabilistic programming. == Definition == Inductive programming incorporates all approaches which are concerned with learning programs or algorithms from incomplete (formal) specifications. Possible inputs in an IP system are a set of training inputs and corresponding outputs or an output evaluation function, describing the desired behavior of the intended program, traces or action sequences which describe the process of calculating specific outputs, constraints for the program to be induced concerning its time efficiency or its complexity, various kinds of background knowledge such as standard data types, predefined functions to be used, program schemes or templates describing the data flow of the intended program, heuristics for guiding the search for a solution or other biases. Output of an IP system is a program in some arbitrary programming language containing conditionals and loop or recursive control structures, or any other kind of Turing-complete representation language. In many applications the output program must be correct with respect to the examples and partial specification, and this leads to the consideration of inductive programming as a special area inside automatic programming or program synthesis, usually opposed to 'deductive' program synthesis, where the specification is usually complete. In other cases, inductive programming is seen as a more general area where any declarative programming or representation language can be used and we may even have some degree of error in the examples, as in general machine learning, the more specific area of structure mining or the area of symbolic artificial intelligence. A distinctive feature is the number of examples or partial specification needed. Typically, inductive programming techniques can learn from just a few examples. The diversity of inductive programming usually comes from the applications and the languages that are used: apart from logic programming and functional programming, other programming paradigms and representation languages have been used or suggested in inductive programming, such as functional logic programming, constraint programming, probabilistic programming, abductive logic programming, modal logic, action languages, agent languages and many types of imperative languages. == History == The early works of Plotkin, and his "relative least general generalization (rlgg)", had an enormous impact in inductive logic programming. There were some encouraging results on learning recursive Prolog programs such as quicksort from examples together with suitable background knowledge, for example with GOLEM. However, after initial success, the community got disappointed by limited progress about the induction of recursive programs with ILP less and less focusing on recursive programs and leaning more and more towards a machine learning setting with applications in relational data mining and knowledge discovery. In parallel to work in ILP, Koza proposed genetic programming in the early 1990s as a generate-and-test based approach to learning programs. The idea of genetic programming was further developed into the inductive programming system ADATE and the systematic-search-based system MagicHaskeller. Here again, functional programs are learned from sets of positive examples together with an output evaluation (fitness) function which specifies the desired input/output behavior of the program to be learned. The early work in grammar induction (also known as grammatical inference) is related to inductive programming, as rewriting systems or logic programs can be used to represent production rules. In fact, early works in inductive inference considered grammar induction and Lisp program inference as basically the same problem. The results in terms of learnability were related to classical concepts, such as identification-in-the-limit, as introduced in the seminal work of Gold. More recently, the language learning problem was addressed by the inductive programming community. In the recent years, the classical approaches have been resumed and advanced with great success. Therefore, the synthesis problem has been reformulated on the background of constructor-based term rewriting systems taking into account modern techniques of functional programming, as well as moderate use of search-based strategies and usage of background knowledge as well as automatic invention of subprograms. Many new and successful applications have recently appeared beyond program synthesis, most especially in the area of data manipulation, programming by example and cognitive modelling (see below). Other ideas have also been explored with the common characteristic of using declarative languages for the representation of hypotheses. For instance, the use of higher-order features, schemes or structured distances have been advocated for a better handling of recursive data types and structures; abstraction has also been explored as a more powerful approach to cumulative learning and function invention. One powerful paradigm that has been recently used for the representation of hypotheses in inductive programming (generally in the form of generative models) is probabilistic programming (and related paradigms, such as stochastic logic programs and Bayesian logic programming). == Application areas == The first workshop on Approaches and Applications of Inductive Programming (AAIP) Archived 2016-03-03 at the Wayback Machine held in conjunction with ICML 2005 identified all applications where "learning of programs or recursive rules are called for, [...] first in the domain of software engineering where structural learning, software assistants and software agents can help to relieve programmers from routine tasks, give programming support for end users, or support of novice programmers and programming tutor systems. Further areas of application are language learning, learning recursive control rules for AI-planning, learning recursive concepts in web-mining or for data-format transformations". Since then, these and many other areas have shown to be successful application niches for inductive programming, such as end-user programming, the related areas of programming by example and programming by demonstration, and intelligent tutoring systems. Other areas where inductive inference has been recently applied are knowledge acquisition, artificial general intelligence, reinforcement learning and theory evaluation, and cognitive science in general. There may also be prospective applications in intelligent agents, games, robotics, personalisation, ambient intelligence and human interfaces.

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  Really Simple Licensing

  Really Simple Licensing (RSL) is an open content licensing standard that allows web publishers to set terms for web crawlers gathering training data for generative AI use. It was launched on September 10, 2025 and is managed by the nonprofit RSL Collective, co-founded by RSS co-creator Eckart Walther and former Ask.com CEO Doug Leeds. Participating companies at launch include Reddit, Yahoo, and Medium. Publishers can implement the RSL standard by adding licensing terms to their robots.txt files.

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  Neural architecture search

  Neural architecture search (NAS) is a technique for automating the design of artificial neural networks (ANN), a widely used model in the field of machine learning. NAS has been used to design networks that are on par with or outperform hand-designed architectures. Methods for NAS can be categorized according to the search space, search strategy and performance estimation strategy used: The search space defines the type(s) of ANN that can be designed and optimized. The search strategy defines the approach used to explore the search space. The performance estimation strategy evaluates the performance of a possible ANN from its design (without constructing and training it). NAS is closely related to hyperparameter optimization and meta-learning and is a subfield of automated machine learning (AutoML). == Reinforcement learning == Reinforcement learning (RL) can underpin a NAS search strategy. Barret Zoph and Quoc Viet Le applied NAS with RL targeting the CIFAR-10 dataset and achieved a network architecture that rivals the best manually-designed architecture for accuracy, with an error rate of 3.65, 0.09 percent better and 1.05x faster than a related hand-designed model. On the Penn Treebank dataset, that model composed a recurrent cell that outperforms LSTM, reaching a test set perplexity of 62.4, or 3.6 perplexity better than the prior leading system. On the PTB character language modeling task it achieved bits per character of 1.214. Learning a model architecture directly on a large dataset can be a lengthy process. NASNet addressed this issue by transferring a building block designed for a small dataset to a larger dataset. The design was constrained to use two types of convolutional cells to return feature maps that serve two main functions when convoluting an input feature map: normal cells that return maps of the same extent (height and width) and reduction cells in which the returned feature map height and width is reduced by a factor of two. For the reduction cell, the initial operation applied to the cell's inputs uses a stride of two (to reduce the height and width). The learned aspect of the design included elements such as which lower layer(s) each higher layer took as input, the transformations applied at that layer and to merge multiple outputs at each layer. In the studied example, the best convolutional layer (or "cell") was designed for the CIFAR-10 dataset and then applied to the ImageNet dataset by stacking copies of this cell, each with its own parameters. The approach yielded accuracy of 82.7% top-1 and 96.2% top-5. This exceeded the best human-invented architectures at a cost of 9 billion fewer FLOPS—a reduction of 28%. The system continued to exceed the manually-designed alternative at varying computation levels. The image features learned from image classification can be transferred to other computer vision problems. E.g., for object detection, the learned cells integrated with the Faster-RCNN framework improved performance by 4.0% on the COCO dataset. In the so-called Efficient Neural Architecture Search (ENAS), a controller discovers architectures by learning to search for an optimal subgraph within a large graph. The controller is trained with policy gradient to select a subgraph that maximizes the validation set's expected reward. The model corresponding to the subgraph is trained to minimize a canonical cross entropy loss. Multiple child models share parameters, ENAS requires fewer GPU-hours than other approaches and 1000-fold less than "standard" NAS. On CIFAR-10, the ENAS design achieved a test error of 2.89%, comparable to NASNet. On Penn Treebank, the ENAS design reached test perplexity of 55.8. == Evolution == An alternative approach to NAS is based on evolutionary algorithms, which has been employed by several groups. An Evolutionary Algorithm for Neural Architecture Search generally performs the following procedure. First a pool consisting of different candidate architectures along with their validation scores (fitness) is initialised. At each step the architectures in the candidate pool are mutated (e.g.: 3x3 convolution instead of a 5x5 convolution). Next the new architectures are trained from scratch for a few epochs and their validation scores are obtained. This is followed by replacing the lowest scoring architectures in the candidate pool with the better, newer architectures. This procedure is repeated multiple times and thus the candidate pool is refined over time. Mutations in the context of evolving ANNs are operations such as adding or removing a layer, which include changing the type of a layer (e.g., from convolution to pooling), changing the hyperparameters of a layer, or changing the training hyperparameters. On CIFAR-10 and ImageNet, evolution and RL performed comparably, while both slightly outperformed random search. == Bayesian optimization == Bayesian Optimization (BO), which has proven to be an efficient method for hyperparameter optimization, can also be applied to NAS. In this context, the objective function maps an architecture to its validation error after being trained for a number of epochs. At each iteration, BO uses a surrogate to model this objective function based on previously obtained architectures and their validation errors. One then chooses the next architecture to evaluate by maximizing an acquisition function, such as expected improvement, which provides a balance between exploration and exploitation. Acquisition function maximization and objective function evaluation are often computationally expensive for NAS, and make the application of BO challenging in this context. Recently, BANANAS has achieved promising results in this direction by introducing a high-performing instantiation of BO coupled to a neural predictor. == Hill-climbing == Another group used a hill climbing procedure that applies network morphisms, followed by short cosine-annealing optimization runs. The approach yielded competitive results, requiring resources on the same order of magnitude as training a single network. E.g., on CIFAR-10, the method designed and trained a network with an error rate below 5% in 12 hours on a single GPU. == Multi-objective search == While most approaches solely focus on finding architecture with maximal predictive performance, for most practical applications other objectives are relevant, such as memory consumption, model size or inference time (i.e., the time required to obtain a prediction). Because of that, researchers created a multi-objective search. LEMONADE is an evolutionary algorithm that adopted Lamarckism to efficiently optimize multiple objectives. In every generation, child networks are generated to improve the Pareto frontier with respect to the current population of ANNs. Neural Architect is claimed to be a resource-aware multi-objective RL-based NAS with network embedding and performance prediction. Network embedding encodes an existing network to a trainable embedding vector. Based on the embedding, a controller network generates transformations of the target network. A multi-objective reward function considers network accuracy, computational resource and training time. The reward is predicted by multiple performance simulation networks that are pre-trained or co-trained with the controller network. The controller network is trained via policy gradient. Following a modification, the resulting candidate network is evaluated by both an accuracy network and a training time network. The results are combined by a reward engine that passes its output back to the controller network. == One-shot models == RL or evolution-based NAS require thousands of GPU-days of searching/training to achieve state-of-the-art computer vision results as described in the NASNet, mNASNet and MobileNetV3 papers. To reduce computational cost, many recent NAS methods rely on the weight-sharing idea. In this approach, a single overparameterized supernetwork (also known as the one-shot model) is defined. A supernetwork is a very large Directed Acyclic Graph (DAG) whose subgraphs are different candidate neural networks. Thus, in a supernetwork, the weights are shared among a large number of different sub-architectures that have edges in common, each of which is considered as a path within the supernet. The essential idea is to train one supernetwork that spans many options for the final design rather than generating and training thousands of networks independently. In addition to the learned parameters, a set of architecture parameters are learnt to depict preference for one module over another. Such methods reduce the required computational resources to only a few GPU days. More recent works further combine this weight-sharing paradigm, with a continuous relaxation of the search space, which enables the use of gradient-based optimization methods. These approaches are generally referred to as differentiable NAS and have proven very efficient in exploring the search space of ne

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  Neuroshima

  Neuroshima is a Polish tabletop roleplaying system inspired by such films and games as Mad Max, Fallout, The Matrix, Terminator and Deadlands: Hell on Earth. It is currently available only in Polish. The game's motto is "never trust the machines". Its designers include Michal Oracz and Ignacy Trzewiczek. == Setting == The game describes the United States in the mid-21st century, after a nuclear war started by a cybernetic revolt, which molded the continent into a barren wasteland. It seems that the reason for the war to break out was a sentient Artificial Intelligence commonly referred to as Moloch and made up of interconnected net of military computers: automated factories, military facilities, power plants and alike, that now cover the whole north of the U.S., from Oregon to the Great Lakes. On the south, there is another creation, called the Neojungle, that poses a threat to those who survived the war. It is a semi-intelligent carnivorous vegetation that grows very quickly, advancing north from Latin America. Right in the middle, there are humans. They are surrounded by mutant creatures, some bred by Moloch and hostile towards humans, and some simply animals and humans misshapen by nuclear fallout. On top of that there are Moloch's deadly machines lurking to complete the picture. But what is stressed in the book is that the worst enemy of humans is within them: hatred, indifference, greed. === Landscapes of Neuroshima === Car wrecks, ruined towns and villages, collapsed roofs on deserted houses, broken glass in the windows of abandoned gas stations fill the landscape of the United States of the middle of the 21st century. Technology is history - cars will not start, radios are jammed, no electricity whatsoever almost everywhere the characters go. Shops and malls are looted, prosperous villages are burned by gangers, and safe places are very sparse. === People in Neuroshima === No one knows how many people survived the war with machines, but it is estimated that their number oscillates around 2-3 million. Some people reverted to nomadic lifestyles and live in the deserts, some of them try to build the civilisation anew in devastated cities, some of them form gangs of highwaymen (called gangers), some of them just try to make a living by growing crops, and finally, there are those who just wander around the wasteland; the adventuring sort here is mostly represented by player characters. Each village they visit in this world is a discrete microcosm and nothing is certain as whether the inhabitants are welcoming or shoot strangers on sight. The continent is full of small, anonymous settlements, but there are places which aspire to become post-nuclear states. === Places in Neuroshima === In this world it is very important where you come from, and that is because people are prejudiced and afraid of strangers. Different places produce different kinds of people, and who you are is determined by where you are from. Examples: The Southern Hegemony - (commonly referred to as 'the Hegemony') - located in what was once Arizona, New Mexico and partially Texas. A place where brute force determines one's place in the society. Dominated by gangs and unhampered by Moloch, the Hegemony is a threat to neighbouring lands. Vegas - the only well-lit city in the post-apocalyptic world. Home to many playhouses and casinos, it attracts people from every part of the country. Mother Desert - if you were born in the desert, whenever you go away from civilisation, you feel at home. Many Native Americans still live out there and are doing fine - after all the warheads did not hit the deserts. Detroit - known for some of the best drivers and racers in the post-nuclear US. Home of many gangs, such as The Shultz (mafia styled), Hurons (punkers), The League (racers), Parker Lots (gothic assassins) and the Gas Drinkers (mutant barbarians). New York - a place which has established a strong government and would like to rebuild America. They maintain schools, factories and railways and send soldiers to fight Moloch. Surprisingly enough, they sometimes succeed. Texas - the healthiest place in America. Actually, the only place where one can find green vegetation. Modern Texans still grow crops, breed horses and herd cattle, like their ancestors in the 19th century did. The Appalachian Federation - a place ruled by feudal lords. They have a social class system, in which people are divided into nobility and peasantry. Thanks to its iron and coal deposits, it's one of the richest places in the post-nuclear U.S. The Outpost - A mobile settlement run by scientists who aim to destroy Moloch. In coalition with New York, they manage an army, which is yet to stop Moloch's advance south. They steal technology from the machines they destroy and apply it to their own advantage. == System == The game uses its own, custom system of rules. The dice you use is d20. This system does not have an official name, but it is unconnected to the d20 system, as it typically uses three twenty-sided dice. === Four colours === Neuroshima relies on the division of the gameplay into something the authors called Four Colours, namely steel, chrome, rust and mercury. The choice of a particular colour is made by the gamemaster (the decision can be consulted with the players in order to enhance the game experience) and determines the mood, atmosphere and the type of events/characters present in the story. The name of the colour itself implies the kind of gameplay it will symbolise. These colours are: Steel - this kind of gameplay is characterised by a slightly optimistic attitude towards the world. The aim is to raise the spirit of the characters by showing them that the war with the machines that is going on may be a difficult one, but it is not unwinnable, and that humans, when strong and united, can build the world anew. Example of a story: a unit of soldiers dispatched from the Outpost is sent to build a bunker and establish a relay base far in the north in order to plan a counter-tactic against Moloch's advance south. Chromium - is characterised by a hedonistic attitude. The characters are supposed to enjoy anything that is left from the world after the war and the story is supposed to allow them to do that. Example: the characters are offered a well-paid job by a local ganger boss who extorts wares from local tradesmen. Their job is to drive around the county and pick up the extorted items and trade it for drugs. Rust - a depressing, pessimistic mood. The characters will encounter rust, dilapidation and ruin everywhere they go. All the elements and NPCs of a story played in this mood are supposed to put the characters down and destroy their spirit. Example: the characters, badly wounded after a gunfight and robbed of all their possession find refuge in a village which is constantly raided by gangers. The characters' quest is to repel those attacks, but the enemies outnumber them and are well equipped, whereas the characters have nothing to fight with. Mercury (Quicksilver) - the most depressing side of the game; usually stories played in this mood end with the death of all the characters. The aim of this mood is to show that any kind of action undertaken is futile and that the war is already over, hence all the people are already dead, which is a fact they just need to realise. Example: a group of soldiers stationed in a bunker is awaiting an attack by mutants. They are well-armed and trained, but there is a mistake in the intelligence they were given and they do not know yet that they are seriously outnumbered. The attack commences at dusk and it is already too late to retreat, so the characters decide to seal off the bunker, hopeful that the mutants will not be able to get inside and simply go away. The mutants attack the bunker with chemical weapons instead. The characters do not have enough gas masks to go around. As an effect, those strong enough will kill the weaker ones to get their masks, not knowing that the mutants will blow up the sealed entrance the following morning. == Official rulebooks and sourcebooks == The current edition is 1.5 [1]. Since the release of the game in 2003, sourcebooks have been appearing. The game keeps growing bigger with every add-on, as well as the storyline, which is updated in those sourcebooks and in Space Pirate (pl. Gwiezdny Pirat) magazine, also published by Portal. === List of released rulebooks and sourcebooks === Neuroshima 1.0 - the original edition of the core rulebook (out of print). Neuroshima 1.5 - enhanced and revised core rulebook, with new material added and some material cut out. Wyścig (The Race) - sourcebook dedicated to cars and racing; contains rules concerning building your own vehicle and new character classes connected with driving. Gladiator - sourcebook describing in detail the "Gladiator" character class. Supplement (Supplement) - sourcebook revising the core rulebook. Detroit - sourcebook describing the city of Detroit, its inhabi

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  Tensor (machine learning)

  In machine learning, the term tensor informally refers to two different concepts: (i) a way of organizing data and (ii) a multilinear (tensor) transformation. Data may be organized in a multidimensional array (M-way array), informally referred to as a "data tensor"; however, in the strict mathematical sense, a tensor is a multilinear mapping over a set of domain vector spaces to a range vector space. Observations, such as images, movies, volumes, sounds, and relationships among words and concepts, stored in an M-way array ("data tensor"), may be analyzed either by artificial neural networks or tensor methods. Tensor decomposition factors data tensors into smaller tensors. Operations on data tensors can be expressed in terms of matrix multiplication and the Kronecker product. The computation of gradients, a crucial aspect of backpropagation, can be performed using software libraries such as PyTorch and TensorFlow. Computations are often performed on graphics processing units (GPUs) using CUDA, and on dedicated hardware such as Google's Tensor Processing Unit or Nvidia's Tensor core. These developments have greatly accelerated neural network architectures, and increased the size and complexity of models that can be trained. == History == A tensor is by definition a multilinear map. In mathematics, this may express a multilinear relationship between sets of algebraic objects. In physics, tensor fields, considered as tensors at each point in space, are useful in expressing mechanics such as stress or elasticity. In machine learning, the exact use of tensors depends on the statistical approach being used. In 2001, the field of signal processing and statistics were making use of tensor methods. Pierre Comon surveys the early adoption of tensor methods in the fields of telecommunications, radio surveillance, chemometrics and sensor processing. Linear tensor rank methods (such as, Parafac/CANDECOMP) analyzed M-way arrays ("data tensors") composed of higher order statistics that were employed in blind source separation problems to compute a linear model of the data. He noted several early limitations in determining the tensor rank and efficient tensor rank decomposition. In the early 2000s, multilinear tensor methods crossed over into computer vision, computer graphics and machine learning with papers by Vasilescu or in collaboration with Terzopoulos, such as Human Motion Signatures, TensorFaces TensorTextures and Multilinear Projection. Multilinear algebra, the algebra of higher-order tensors, is a suitable and transparent framework for analyzing the multifactor structure of an ensemble of observations and for addressing the difficult problem of disentangling the causal factors based on second order or higher order statistics associated with each causal factor. Tensor (multilinear) factor analysis disentangles and reduces the influence of different causal factors with multilinear subspace learning. When treating an image or a video as a 2- or 3-way array, i.e., "data matrix/tensor", tensor methods reduce spatial or time redundancies as demonstrated by Wang and Ahuja. Yoshua Bengio, Geoff Hinton and their collaborators briefly discuss the relationship between deep neural networks and tensor factor analysis beyond the use of M-way arrays ("data tensors") as inputs. One of the early uses of tensors for neural networks appeared in natural language processing. A single word can be expressed as a vector via Word2vec. Thus a relationship between two words can be encoded in a matrix. However, for more complex relationships such as subject-object-verb, it is necessary to build higher-dimensional networks. In 2009, the work of Sutskever introduced Bayesian Clustered Tensor Factorization to model relational concepts while reducing the parameter space. From 2014 to 2015, tensor methods become more common in convolutional neural networks (CNNs). Tensor methods organize neural network weights in a "data tensor", analyze and reduce the number of neural network weights. Lebedev et al. accelerated CNN networks for character classification (the recognition of letters and digits in images) by using 4D kernel tensors. == Definition == Let F {\displaystyle \mathbb {F} } be a field (such as the real numbers R {\displaystyle \mathbb {R} } or the complex numbers C {\displaystyle \mathbb {C} } ). A tensor T ∈ F I 1 × I 2 × … × I C {\displaystyle {\mathcal {T}}\in {\mathbb {F} }^{I_{1}\times I_{2}\times \ldots \times I_{C}}} is a multilinear transformation from a set of domain vector spaces to a range vector space: T : { F I 1 × F I 2 × … F I C } ↦ F I 0 {\displaystyle {\mathcal {T}}:\{{\mathbb {F} }^{I_{1}}\times {\mathbb {F} }^{I_{2}}\times \ldots {\mathbb {F} }^{I_{C}}\}\mapsto {\mathbb {F} }^{I_{0}}} Here, C {\displaystyle C} and I 0 , I 1 , … , I C {\displaystyle I_{0},I_{1},\ldots ,I_{C}} are positive integers, and ( C + 1 ) {\displaystyle (C+1)} is the number of modes of a tensor (also known as the number of ways of a multi-way array). The dimensionality of mode c {\displaystyle c} is I c {\displaystyle I_{c}} , for 0 ≤ c ≤ C {\displaystyle 0\leq c\leq C} . In statistics and machine learning, an image is vectorized when viewed as a single observation, and a collection of vectorized images is organized as a "data tensor". For example, a set of facial images { d i p , i e , i l , i v ∈ R I X } {\displaystyle \{{\mathbb {d} }_{i_{p},i_{e},i_{l},i_{v}}\in {\mathbb {R} }^{I_{X}}\}} with I X {\displaystyle I_{X}} pixels that are the consequences of multiple causal factors, such as a facial geometry i p ( 1 ≤ i p ≤ I P ) {\displaystyle i_{p}(1\leq i_{p}\leq I_{P})} , an expression i e ( 1 ≤ i e ≤ I E ) {\displaystyle i_{e}(1\leq i_{e}\leq I_{E})} , an illumination condition i l ( 1 ≤ i l ≤ I L ) {\displaystyle i_{l}(1\leq i_{l}\leq I_{L})} , and a viewing condition i v ( 1 ≤ i v ≤ I V ) {\displaystyle i_{v}(1\leq i_{v}\leq I_{V})} may be organized into a data tensor (ie. multiway array) D ∈ R I X × I P × I E × I L × V {\displaystyle {\mathcal {D}}\in {\mathbb {R} }^{I_{X}\times I_{P}\times I_{E}\times I_{L}\times V}} where I P {\displaystyle I_{P}} are the total number of facial geometries, I E {\displaystyle I_{E}} are the total number of expressions, I L {\displaystyle I_{L}} are the total number of illumination conditions, and I V {\displaystyle I_{V}} are the total number of viewing conditions. Tensor factorizations methods such as TensorFaces and multilinear (tensor) independent component analysis factorizes the data tensor into a set of vector spaces that span the causal factor representations, where an image is the result of tensor transformation T {\displaystyle {\mathcal {T}}} that maps a set of causal factor representations to the pixel space. Another approach to using tensors in machine learning is to embed various data types directly. For example, a grayscale image, commonly represented as a discrete 2-way array D ∈ R I R X × I C X {\displaystyle {\mathbf {D} }\in {\mathbb {R} }^{I_{RX}\times I_{CX}}} with dimensionality I R X × I C X {\displaystyle I_{RX}\times I_{CX}} where I R X {\displaystyle I_{RX}} are the number of rows and I C X {\displaystyle I_{CX}} are the number of columns. When an image is treated as 2-way array or 2nd order tensor (i.e. as a collection of column/row observations), tensor factorization methods compute the image column space, the image row space and the normalized PCA coefficients or the ICA coefficients. Similarly, a color image with RGB channels, D ∈ R N × M × 3 . {\displaystyle {\mathcal {D}}\in \mathbb {R} ^{N\times M\times 3}.} may be viewed as a 3rd order data tensor or 3-way array.-------- In natural language processing, a word might be expressed as a vector v {\displaystyle v} via the Word2vec algorithm. Thus v {\displaystyle v} becomes a mode-1 tensor v ↦ A ∈ R N . {\displaystyle v\mapsto {\mathcal {A}}\in \mathbb {R} ^{N}.} The embedding of subject-object-verb semantics requires embedding relationships among three words. Because a word is itself a vector, subject-object-verb semantics could be expressed using mode-3 tensors v a × v b × v c ↦ A ∈ R N × N × N . {\displaystyle v_{a}\times v_{b}\times v_{c}\mapsto {\mathcal {A}}\in \mathbb {R} ^{N\times N\times N}.} In practice the neural network designer is primarily concerned with the specification of embeddings, the connection of tensor layers, and the operations performed on them in a network. Modern machine learning frameworks manage the optimization, tensor factorization and backpropagation automatically. === As unit values === Tensors may be used as the unit values of neural networks which extend the concept of scalar, vector and matrix values to multiple dimensions. The output value of single layer unit y m {\displaystyle y_{m}} is the sum-product of its input units and the connection weights filtered through the activation function f {\displaystyle f} : y m = f ( ∑ n x n u m , n ) , {\displaystyle y_{m}=f\left(\sum _{n}x_{n}u_{m,n}\right),} where y m ∈ R .

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  Tensor network

  Tensor networks or tensor network states are a class of variational wave functions used in the study of many-body quantum systems and fluids. Tensor networks extend one-dimensional matrix product states to higher dimensions while preserving some of their useful mathematical properties. The wave function is encoded as a tensor contraction of a network of individual tensors. The structure of the individual tensors can impose global symmetries on the wave function (such as antisymmetry under exchange of fermions) or restrict the wave function to specific quantum numbers, like total charge, angular momentum, or spin. It is also possible to derive strict bounds on quantities like entanglement and correlation length using the mathematical structure of the tensor network. This has made tensor networks useful in theoretical studies of quantum information in many-body systems. They have also proved useful in variational studies of ground states, excited states, and dynamics of strongly correlated many-body systems. == Diagrammatic notation == In general, a tensor network diagram (Penrose diagram) can be viewed as a graph where nodes (or vertices) represent individual tensors, while edges represent summation over an index. Free indices are depicted as edges (or legs) attached to a single vertex only. Sometimes, there is also additional meaning to a node's shape. For instance, one can use trapezoids for unitary matrices or tensors with similar behaviour. This way, flipped trapezoids would be interpreted as complex conjugates to them. == History == Foundational research on tensor networks began in 1971 with a paper by Roger Penrose. In "Applications of negative dimensional tensors" Penrose developed tensor diagram notation, describing how the diagrammatic language of tensor networks could be used in applications in physics. In 1992, Steven R. White developed the density matrix renormalization group (DMRG) for quantum lattice systems. The DMRG was the first successful tensor network and associated algorithm. In 2002, Guifré Vidal and Reinhard Werner attempted to quantify entanglement, laying the groundwork for quantum resource theories. This was also the first description of the use of tensor networks as mathematical tools for describing quantum systems. In 2004, Frank Verstraete and Ignacio Cirac developed the theory of matrix product states, projected entangled pair states, and variational renormalization group methods for quantum spin systems. In 2006, Vidal developed the multi-scale entanglement renormalization ansatz (MERA). In 2007 he developed entanglement renormalization for quantum lattice systems. In 2010, Ulrich Schollwock developed the density-matrix renormalization group for the simulation of one-dimensional strongly correlated quantum lattice systems. In 2014, Román Orús introduced tensor networks for complex quantum systems and machine learning, as well as tensor network theories of symmetries, fermions, entanglement and holography. == Connection to machine learning == Tensor networks have been adapted for supervised learning, taking advantage of similar mathematical structure in variational studies in quantum mechanics and large-scale machine learning. This crossover has spurred collaboration between researchers in artificial intelligence and quantum information science. In June 2019, Google, the Perimeter Institute for Theoretical Physics, and X (company), released TensorNetwork, an open-source library for efficient tensor calculations. The main interest in tensor networks and their study from the perspective of machine learning is to reduce the number of trainable parameters (in a layer) by approximating a high-order tensor with a network of lower-order ones. Using the so-called tensor train technique (TT), one can reduce an N-order tensor (containing exponentially many trainable parameters) to a chain of N tensors of order 2 or 3, which gives us a polynomial number of parameters.

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  Riffusion

  Riffusion is a neural network, designed by Seth Forsgren and Hayk Martiros, that generates music using images of sound rather than audio. The resulting music has been described as "de otro mundo" (otherworldly), although unlikely to replace man-made music. The model was made available on December 15, 2022, with the code also freely available on GitHub. The first version of Riffusion was created as a fine-tuning of Stable Diffusion, an existing open-source model for generating images from text prompts, on spectrograms, resulting in a model which used text prompts to generate image files which could then be put through an inverse Fourier transform and converted into audio files. While these files were only several seconds long, the model could also use latent space between outputs to interpolate different files together (using the img2img capabilities of SD). It was one of many models derived from Stable Diffusion. In December 2022, Mubert similarly used Stable Diffusion to turn descriptive text into music loops. In January 2023, Google published a paper on their own text-to-music generator called MusicLM. Forsgren and Martiros formed a startup, also called Riffusion, and raised $4 million in venture capital funding in October 2023.

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  Transhuman Space

  Transhuman Space (THS) is a role-playing game by David Pulver, published by Steve Jackson Games as part of the "Powered by GURPS" (Generic Universal Role-Playing System) line. Set in the year 2100, humanity has begun to colonize the Solar System. The pursuit of transhumanism is now in full swing, as more and more people reach fully posthuman states. In 2002, the Transhuman Space adventure "Orbital Decay" received an Origins Award nomination for Best Role-Playing Game Adventure. Transhuman Space won the 2003 Grog d'Or Award for Best Role-playing Game, Game Line or RPG Setting. == Setting == The game assumes that no cataclysm — natural or human-induced — swept Earth in the 21st century. Instead, constant developments in information technology, genetic engineering, nanotechnology and nuclear physics generally improved condition of the average human life. Plagues of the 20th century (like cancer or AIDS) have been suppressed, the ozone layer is being restored and Earth's ecosystems are recovering (although thermal emission by fusion power plants poses an environmental threat—albeit a much lesser one than previous sources of energy). Thanks to modern medicine humans live biblical timespans surrounded by various artificially intelligent helper applications and robots (cybershells), sensory experience broadcasts (future TV) and cyberspace telepresence. Thanks to cheap and clean fusion energy humanity has power to fuel all these wonders, restore and transform its home planet and finally settle on other heavenly bodies. Human genetic engineering has advanced to the point that anyone—single individuals, same-sex couples, groups of three or more—can reproduce. The embryos can be allowed to be developed naturally, or they can undergo three levels of tinkering: 1. Genefixing, which corrects defects; 2. Upgrades, which boost natural abilities (Ishtar Upgrades are slightly more attractive than usual, Metanoia Upgrades are more intelligent, etc.); and... 3. Full transition to parahuman status (Nyx Parahumans only need a few hours of sleep per week, Aquamorphs can live underwater, etc.) Another type of human genetic engineering, far more controversial, is the creation of bioroids, fully sentient slave races. People can "upload" by recording the simulation of their brains on computer disks. The emulated individual then becomes a ghost, an infomorph very easily confused with "sapient artificial intelligence". However, this technology has several problems as the solely available "brainpeeling" technique is fatal to the original biological lifeform being simulated, has a significant failure rate and the philosophical questions regarding personal identity remain equivocal. Any infomorph, regardless of its origin, can be plugged into a "cybershell" (robotic or cybernetic body), or a biological body, or "bioshell". Or, the individual can illegally make multiple "xoxes", or copies of themselves, and scatter them throughout the system, exponentially increasing the odds that at least one of them will live for centuries more, if not forever. This is also a time of space colonization. First, humanity (specifically China, followed by the United States and others) colonized Mars in a fashion resembling that outlined in the Mars Direct project. The Moon, Lagrangian points, inner planets and asteroids soon followed. In the late 21st century even some of Saturn's moons have been settled as a base for that planet's Helium-3 scooping operations. Transhuman Space's setting is neither utopia nor dystopia, however: several problems have arisen from these otherwise beneficial developments. The generation gap has become a chasm as lifespans increase. No longer do the elite fear death, and no longer can the young hope to replace them. While it seemed that outworld colonies would offer accommodation and work for those young ones, they are being replaced by genetically tailored bioroids and AI-powered cybershells. The concept of humanity is no longer clear in a world where even some animals speak of their rights and the dead haunt both cyberspace and reality (in form of infomorph-controlled bioshells or cybershells). And the wonders of high science are not universally shared — some countries merely struggle with informatization while others suffer from nanoplagues, defective drugs, implants and software tested on their populace. In some poor countries high-tech tyrants oppress their backward people. And in outer space all sort of modern crime thrives, barely suppressed by military forces. == Publication history == After the initial set of GURPS books that were published using the GURPS Lite, later publications such as Transhuman Space by David Pulver were labelled simply "Powered by GURPS" without using the name "GURPS" in the book title. Transhuman Space received a significant amount of supporting publications, and was the largest original background setting that Steve Jackson Games produced in 15 years. Shannon Appelcline noted that by its inclusion of posthuman characters, the book began to show the limits of the GURPS system as it was, which is something that Pulver would address soon thereafter. Steve Jackson Games has not updated the core book (GURPS Transhuman Space) to 4th edition, although the supplement Transhuman Space: Changing Times provides a path for migrating to 4th edition. It has produced several 4th edition supplements for the setting: Transhuman Space: Bioroid Bazaar, Transhuman Space: Cities on the Edge, Transhuman Space: Martial Arts 2100, Transhuman Space: Personnel Files 2-5, Transhuman Space: Shell-Tech, GURPS Spaceships 8: Transhuman Spacecraft, Transhuman Space: Transhuman Mysteries, and Transhuman Space: Wings of the Rising Sun. == Reception == In a review of Transhuman Space in Black Gate, William Stoddard said "Transhuman Space was a richly detailed setting; if it had imperfections, it had enough depth to make up for them. I think it has the potential to become a classic in its field. Perhaps a campaign set in its default start year of 2100 could leave the early twenty-first century blurry enough to avoid obvious incongruities." == Reviews == Review in Vol. 20, No. 1 of Prometheus, the journal of the Libertarian Futurist Society.

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  CLEVER score

  The CLEVER (Cross Lipschitz Extreme Value for nEtwork Robustness) score is a way of measuring the robustness of an artificial neural network towards adversarial attacks. It was developed by a team at the MIT-IBM Watson AI Lab in IBM Research and first presented at the 2018 International Conference on Learning Representations. It was mentioned and reviewed by Ian Goodfellow as well. It was adopted into an educational game Fool The Bank by Narendra Nath Joshi, Abhishek Bhandwaldar and Casey Dugan

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  Blocks world

  The blocks world is a planning domain in artificial intelligence. It consists of a set of wooden blocks of various shapes and colors sitting on a table. The goal is to build one or more vertical stacks of blocks. Only one block may be moved at a time: it may either be placed on the table or placed atop another block. Because of this, any blocks that are, at a given time, under another block cannot be moved. Moreover, some kinds of blocks cannot have other blocks stacked on top of them. The simplicity of this toy world lends itself readily to classical symbolic artificial intelligence approaches, in which the world is modeled as a set of abstract symbols which may be reasoned about. == Motivation == Artificial Intelligence can be researched in theory and with practical applications. The problem with most practical applications is that the engineers don't know how to program an AI system. Instead of rejecting the challenge at all the idea is to invent an easy to solve domain which is called a toy problem. Toy problems were invented with the aim to program an AI which can solve it. The blocks world domain is an example of a toy problem. Its major advantage over more realistic AI applications is that many algorithms and software programs are available which can handle the situation. This allows comparing different theories against each other. In its basic form, the blocks world problem consists of cubes of the same size which have all the color black. A mechanical robot arm has to pick and place the cubes. More complicated derivatives of the problem consist of cubes of different sizes, shapes and colors. From an algorithmic perspective, blocks world is an NP-hard search and planning problem. The task is to bring the system from an initial state into a goal state. Automated planning and scheduling problems are usually described in the Planning Domain Definition Language (PDDL) notation which is an AI planning language for symbolic manipulation tasks. If something was formulated in the PDDL notation, it is called a domain. Therefore, the task of stacking blocks is a blocks world domain which stands in contrast to other planning problems like the dock worker robot domain and the monkey and banana problem. == Theses/projects which took place in a blocks world == Terry Winograd's SHRDLU Patrick Winston's Learning Structural Descriptions from Examples and Copy Demo Gerald Jay Sussman's Sussman anomaly Decision problem (Gupta and Nau, 1992): Given a starting Blocks World, an ending Blocks World, and an integer L > 0, is there a way to move the blocks to change the starting position to the ending position with L or less steps? This decision problem is NP-hard.

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  Alice and Sparkle

  Alice and Sparkle is a 2022 illustrated children's book published by American technology product designer Ammaar Reshi. Reshi created the book using artificial intelligence programs ChatGPT and Midjourney in one weekend, which sparked controversy among artists, both in regard to the copyright status of the book and the quality of the illustration and text. == Plot == A girl named Alice discovers a group of magical and benevolent artificial intelligence beings. She knows that artificial intelligence is powerful, and that it has the power to do good and evil depending on how it is used. One day, she creates her own artificial intelligence and names it Sparkle. Sparkle helps Alice with her homework and plays with her, and they quickly become good friends. However, Sparkle soon grows more powerful and begins to make its own decisions, which makes Alice both proud and scared. She knows that it is her responsibility to guide Sparkle to do good, not evil. Together, Alice and Sparkle use their knowledge to make the world a better place and to teach people about the power of artificial intelligence. The two live happily ever after, spreading the magic of artificial intelligence. == Structure == Including the dedication and postscript, the book contains twenty four pages, about half of which being illustrations provided by Midjourney. The very short story, composed of text generated by ChatGPT, contains 343 words. Some of the illustrations are accompanied by descriptions, at least one of which was provided by Reshi. Both Alice's and Sparkle's appearances change significantly between illustrations, although Alice's is more consistent. Reshi said Midjourney was unable to generate consistent images of Sparkle, so he had to include a line in the book saying that it could turn "into all kinds of robot shapes". == Creation == When reading a children's book to his friend's daughter, Ammaar Reshi "decided he wanted to write his own". He had no experience with creative writing or illustration, so instead used the chatbot ChatGPT to write the story for him and used the image generation software Midjourney to illustrate it. On December 4, 2022, 72 hours after having the idea for the book, he published it on Amazon's digital bookstore, and published a paperback version the following day. == Controversy == On December 9, 2022, Reshi made a thread on Twitter about his experience publishing the book, which soon went viral. Reshi received heavy backlash from artists with concerns over the ethics of art generated by artificial intelligence. He also received death threats and messages encouraging self-harm because of his publication. Many writers and illustrators criticized both the creation process and the product itself, claiming that if artificial intelligence programs such as Midjourney are trained on existing illustrations, then the original artists should be financially compensated for derivative works such as Alice and Sparkle. The book was temporarily removed from Amazon in January 2023 because of "suspicious review activity", caused by a high volume of both five-star and one-star reviews.

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  The Sword in the Stoned

  "The Sword in the Stoned" is the fifth episode of the second season of the American fantasy comedy television series Ted. Written by Julius Sharpe, and directed by Seth MacFarlane, it premiered on the American streaming service Peacock, along with the rest of season two, on March 5, 2026. The series acts as a precursor to the Ted film franchise, showcasing the childhood lives of the protagonists. The series, set in 1994, focuses on John Bennett (Max Burkholder), the series' primary protagonist, an awkward high-school aged boy; along with Ted (MacFarlane), the series' titular anthropomorphic teddy bear. The two live with John's family, Susan (Alanna Ubach), his mild mannered mother, and Matty (Scott Grimes), his conservative father. Also residing with the family is Blaire (Giorgia Whigham), his radically liberal cousin whom often clashes with Matty. In the episode, Ted and John join the school play so they can have more extracurricular activities for their college applications, but the latter grows a connection with the school's popular teenager, Erin (Francesca Xuereb). Concurrently, Susan and Matty get a job at Dunkin' Donuts to help with their financial troubles, and Matty is given an opportunity to tell off Bill Clinton. Burkholder wore prop armor during the episode's play scenes. Bill Clinton’s appearance in the episode was portrayed by MacFarlane. After conventional makeup and visual techniques failed to convincingly resemble Clinton, the production used artificial intelligence to digitally replace MacFarlane's face with Clinton's likeness. Upon release, the episode received generally positive reviews from critics, though the use of AI in the Clinton scene was polarizing among audiences and reviewers. == Plot == John tells Ted that he is the last single guy left at their school, to which Ted points out the popular, single cheerleader, Erin, but John dismisses this. At home, Blaire tells John that he needs extracurricular activities to get into college, while Susan and Matty discuss their financial troubles, especially regarding John's college tuition. Looking over their options, they decide to audition for a school production of the play Camelot. Matty takes a job at Dunkin' Donuts, despite being told that nobody will give him a tip, and having to wear an incorrect name tag. Waiting for their auditions, John and Ted watch several poor auditions for the play before seeing Erin's, who delivers a flawless performance; John and Ted do less serious auditions, getting cast as knights, while Erin gets the role of Guinevere. Matty complains about his low salary, and Susan decides to get a job at Dunkin' Donuts beside him to help earn more income. Erin clashes with Lancelot's actor while rehearsing, and John compliments her performance, which she ignores, but, seeing Ted and John give good performances in a repetition exercise, she becomes interested in him, particularly since he treats her better than her stage-partner. Matty and Susan watch an employee training video, explaining how they should treat customers politely, not affecting Matty's nihilistic attitude. The manager announces that Bill Clinton is visiting their Dunkin' Donuts for publicity, and Matty sees this as a chance to tell Bill off. John and Erin practice lines, as she reveals the show is being taped so it can be sent to Emerson College in hopes of her getting in; Erin asks John to go out with her after the show. At dinner, Matty enthusiastically reveals what he plans to tell Bill, as John becomes stressed about the play when Susan tells there will be a large audience. Bill comes to the Dunkin' Donuts, and, seeing Matty is nervously insulting him, stages a private meeting with him, where Bill yells at Matty, calling him a loser before posing for a picture with Matty and subsequently throwing the cold coffee onto him. To ease the pressure, Ted and John take edibles from Blaire, but learn at the show that they contained mushrooms, causing them to stress further. On stage, Ted and John yell nervously that they're on drugs as the latter urinates in his costume, causing Erin to angrily storm off. == Production == "The Sword in the Stoned" was directed by series creator and lead Seth MacFarlane, and written by Julius Sharpe in his third and final writing credit for the series. When Ted and John are doing repetition exercises, they tackle each other to the ground, which required a stuntman named Ashton to play the role of Ted, according to Max Burkholder, who portrays John. Burkholder also recalled that, when Ted was choking John in the scene, he kept making a noise during the choking, which made Bill, the cameraman, laugh, despite being a "stone face" that never laughs, noting that seeing him be amused by the noise he was making assured Burkholder that what he was doing was "hilarious". Burkholder found the filming of the play scenes "weird", as he was put in fake armor with a hose inside his suit—which was filled with water mixed with yellow food coloring—that was made to create the urine stream that comes out of John's armor in the episode; he also noted that it took around 45 minutes to put on and take off the armor. He revealed that he himself had to urinate during the filming, as doing a scene about a character having to do so "really [broke] my brain", with the fact that it took 45 minutes to get the suit off adding to the frustration. Jennifer Ashley Connell, who worked for wardrobe, had to repeatedly go to Burkholder quickly between takes to dry off his pants with two hair dryers to make it look like the fake urine hadn't already streamed down his pants, so they could get as many shots of it as possible. Francesca Xuereb guest stars in the episode as Erin, the cheerleader who stars in the play. Incumbent president Bill Clinton was portrayed by MacFarlane, with artificial intelligence (AI) being used to digitally make MacFarlane's face look like Clinton's during post-production. Before settling on AI, the crew tried to use traditional computer-generated imagery and prosthetics, which made him look "terrifying", resulting in them deciding that AI would give them a more accurate look. One of the original technologies considered was one where, after scanning MacFarlane, a mesh of his head was created, and they had to use computer graphics to replace MacFarlane's face with Clinton's. An issue was faced, however, when they found the archival footage used as reference from the Clinton Library—an official Presidential Library containing information related to Clinton—to be extremely low-quality, making it hard to properly emulate his face, since only still images were of acceptable quality, and there weren't references of his moving face to work off of. A forensic artist was hired to help with this, and they created a 3D model of Clinton's head in ZBrush, based off of his presidential portrait. The model head worked for still frames, but movement was still difficult to do realistically, due to it being made for a "single-point perspective", which made details like the cheekbones or other minor issues more noticeable when using it for the scene. Since this did not work, AI was ultimately chosen through the studio Deep Voodoo, which used large language models to teach the tool how to correctly replicate Clinton's appearance. Defending the episode's use of AI, MacFarlane noted that the crew did not want people to focus on the tool being used, trying to utilize it in a way that wouldn't distract from the humor and narrative. Like the rest of the series, the episode was shot using ViewScreen; MacFarlane was able to act live with the cast as Ted due to ViewScreen, a technology that allows the production crew to visualize what Ted will look like in each scene in real time. == Release and reception == "The Sword in the Stoned" was first released on March 5, 2026, on the American streaming service Peacock, along with the rest of the second season. Nate Richards of Collider highlighted the Dunkin' Donuts subplot as an example of Scott Grimes delivering a "lot of laughs" through his performance as Matty. Dustin Rowles of Pajiba called "The Sword in the Stoned" one of the season's many episodes he'd recommend, particularly for the scenes of Ted and John being high on mushrooms during the play. Oppositely, Nick Valdez of ComicBook.com ranked the episode as the worst of the second season, criticizing it for not having a "huge impact" on the Bennett family dynamic like other episodes of the season do, and Susan and Matty's side story as the main reason he felt it was "[kept] from being great". Valdez noted the episode for likely being an advertisement for Dunkin' Donuts, calling the plot's ending scene involving Clinton the reason "it just all sticks out like a sore thumb". === Response to AI usage === The episode's use of AI for MacFarlane's portrayal of Clinton proved controversial, mainly on social media, where audiences asserted that the crew should have gotten an actor that resembl

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  Linked timestamping

  Linked timestamping is a type of trusted timestamping where issued time-stamps are related to each other. Each time-stamp would contain data that authenticates the time-stamp before it, the authentication would be authenticating the entire message, including the previous time-stamps authentication, making a chain. This makes it impossible to add a time-stamp in to the middle of the chain, as any time-stamps afterwards would be different. == Description == Linked timestamping creates time-stamp tokens which are dependent on each other, entangled in some authenticated data structure. Later modification of the issued time-stamps would invalidate this structure. The temporal order of issued time-stamps is also protected by this data structure, making backdating of the issued time-stamps impossible, even by the issuing server itself. The top of the authenticated data structure is generally published in some hard-to-modify and widely witnessed media, like printed newspaper or public blockchain. There are no (long-term) private keys in use, avoiding PKI-related risks. Suitable candidates for the authenticated data structure include: Linear hash chain Merkle tree (binary hash tree) Skip list The simplest linear hash chain-based time-stamping scheme is illustrated in the following diagram: The linking-based time-stamping authority (TSA) usually performs the following distinct functions: Aggregation For increased scalability the TSA might group time-stamping requests together which arrive within a short time-frame. These requests are aggregated together without retaining their temporal order and then assigned the same time value. Aggregation creates a cryptographic connection between all involved requests; the authenticating aggregate value will be used as input for the linking operation. Linking Linking creates a verifiable and ordered cryptographic link between the current and already issued time-stamp tokens. Publishing The TSA periodically publishes some links, so that all previously issued time-stamp tokens depend on the published link and that it is practically impossible to forge the published values. By publishing widely witnessed links, the TSA creates unforgeable verification points for validating all previously issued time-stamps. == Security == Linked timestamping is inherently more secure than the usual, public-key signature based time-stamping. All consequential time-stamps "seal" previously issued ones - hash chain (or other authenticated dictionary in use) could be built only in one way; modifying issued time-stamps is nearly as hard as finding a preimage for the used cryptographic hash function. Continuity of operation is observable by users; periodic publications in widely witnessed media provide extra transparency. Tampering with absolute time values could be detected by users, whose time-stamps are relatively comparable by system design. Absence of secret keys increases system trustworthiness. There are no keys to leak and hash algorithms are considered more future-proof than modular arithmetic based algorithms, e.g. RSA. Linked timestamping scales well - hashing is much faster than public key cryptography. There is no need for specific cryptographic hardware with its limitations. The common technology for guaranteeing long-term attestation value of the issued time-stamps (and digitally signed data) is periodic over-time-stamping of the time-stamp token. Because of missing key-related risks and of the plausible safety margin of the reasonably chosen hash function this over-time-stamping period of hash-linked token could be an order of magnitude longer than of public-key signed token. == Research == === Foundations === Stuart Haber and W. Scott Stornetta proposed in 1990 to link issued time-stamps together into linear hash-chain, using a collision-resistant hash function. The main rationale was to diminish TSA trust requirements. Tree-like schemes and operating in rounds were proposed by Benaloh and de Mare in 1991 and by Bayer, Haber and Stornetta in 1992. Benaloh and de Mare constructed a one-way accumulator in 1994 and proposed its use in time-stamping. When used for aggregation, one-way accumulator requires only one constant-time computation for round membership verification. Surety started the first commercial linked timestamping service in January 1995. Linking scheme is described and its security is analyzed in the following article by Haber and Sornetta. Buldas et al. continued with further optimization and formal analysis of binary tree and threaded tree based schemes. Skip-list based time-stamping system was implemented in 2005; related algorithms are quite efficient. === Provable security === Security proof for hash-function based time-stamping schemes was presented by Buldas, Saarepera in 2004. There is an explicit upper bound N {\displaystyle N} for the number of time stamps issued during the aggregation period; it is suggested that it is probably impossible to prove the security without this explicit bound - the so-called black-box reductions will fail in this task. Considering that all known practically relevant and efficient security proofs are black-box, this negative result is quite strong. Next, in 2005 it was shown that bounded time-stamping schemes with a trusted audit party (who periodically reviews the list of all time-stamps issued during an aggregation period) can be made universally composable - they remain secure in arbitrary environments (compositions with other protocols and other instances of the time-stamping protocol itself). Buldas, Laur showed in 2007 that bounded time-stamping schemes are secure in a very strong sense - they satisfy the so-called "knowledge-binding" condition. The security guarantee offered by Buldas, Saarepera in 2004 is improved by diminishing the security loss coefficient from N {\displaystyle N} to N {\displaystyle {\sqrt {N}}} . The hash functions used in the secure time-stamping schemes do not necessarily have to be collision-resistant or even one-way; secure time-stamping schemes are probably possible even in the presence of a universal collision-finding algorithm (i.e. universal and attacking program that is able to find collisions for any hash function). This suggests that it is possible to find even stronger proofs based on some other properties of the hash functions. At the illustration above hash tree based time-stamping system works in rounds ( t {\displaystyle t} , t + 1 {\displaystyle t+1} , t + 2 {\displaystyle t+2} , ...), with one aggregation tree per round. Capacity of the system ( N {\displaystyle N} ) is determined by the tree size ( N = 2 l {\displaystyle N=2^{l}} , where l {\displaystyle l} denotes binary tree depth). Current security proofs work on the assumption that there is a hard limit of the aggregation tree size, possibly enforced by the subtree length restriction. == Standards == ISO 18014 part 3 covers 'Mechanisms producing linked tokens'. American National Standard for Financial Services, "Trusted Timestamp Management and Security" (ANSI ASC X9.95 Standard) from June 2005 covers linking-based and hybrid time-stamping schemes. There is no IETF RFC or standard draft about linking based time-stamping. RFC 4998 (Evidence Record Syntax) encompasses hash tree and time-stamp as an integrity guarantee for long-term archiving.

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  History of artificial life

  Humans have considered and tried to create non-biological life for at least 3,000 years. As seen in tales ranging from Pygmalion to Frankenstein, humanity has long been intrigued by the concept of artificial life. == Pre-computer == The earliest examples of artificial life involve sophisticated automata constructed using pneumatics, mechanics, and/or hydraulics. The first automata were conceived during the third and second centuries BC and these were demonstrated by the theorems of Hero of Alexandria, which included sophisticated mechanical and hydraulic solutions. Many of his notable works were included in the book Pneumatics, which was also used for constructing machines until early modern times. In 1490, Leonardo da Vinci also constructed an armored knight, which is considered the first humanoid robot in Western civilization. Other early famous examples include al-Jazari's humanoid robots. This Arabic inventor once constructed a band of automata, which can be commanded to play different pieces of music. There is also the case of Jacques de Vaucanson's artificial duck exhibited in 1735, which had thousands of moving parts and one of the first to mimic a biological system. The duck could reportedly eat and digest, drink, quack, and splash in a pool. It was exhibited all over Europe until it fell into disrepair. In the late 1600s, following René Descartes' claims that animals could be understood as purely physical machines, there was increasing interest in the question of whether a machine could be designed that, like an animal, could generate offspring (a self-replicating machine). However, it wasn't until the invention of cheap computing power that artificial life as a legitimate science began in earnest, steeped more in the theoretical and computational than the mechanical and mythological. == 1950s–1970s == One of the earliest thinkers of the modern age to postulate the potentials of artificial life, separate from artificial intelligence, was math and computer prodigy John von Neumann. At the Hixon Symposium, hosted by Linus Pauling in Pasadena, California in the late 1940s, von Neumann delivered a lecture titled "The General and Logical Theory of Automata." He defined an "automaton" as any machine whose behavior proceeded logically from step to step by combining information from the environment and its own programming, and said that natural organisms would in the end be found to follow similar simple rules. He also spoke about the idea of self-replicating machines. He postulated a made-up of a control computer, a construction arm, and a long series of instructions, floating in a lake of parts. By following the instructions that were part of its own body, it could create an identical machine. He followed this idea by creating (with Stanislaw Ulam) a purely logic-based automaton, not requiring a physical body but based on the changing states of the cells in an infinite grid – the first cellular automaton. It was extraordinarily complicated compared to later CAs, having hundreds of thousands of cells which could each exist in one of twenty-nine states, but von Neumann felt he needed the complexity in order for it to function not just as a self-replicating "machine", but also as a universal computer as defined by Alan Turing. This "universal constructor" read from a tape of instructions and wrote out a series of cells that could then be made active to leave a fully functional copy of the original machine and its tape. Von Neumann worked on his automata theory intensively right up to his death, and considered it his most important work. Homer Jacobson illustrated basic self-replication in the 1950s with a model train set – a seed "organism" consisting of a "head" and "tail" boxcar could use the simple rules of the system to consistently create new "organisms" identical to itself, so long as there was a random pool of new boxcars to draw from. Edward F. Moore proposed "Artificial Living Plants", which would be floating factories which could create copies of themselves. They could be programmed to perform some function (extracting fresh water, harvesting minerals from seawater) for an investment that would be relatively small compared to the huge returns from the exponentially growing numbers of factories. Freeman Dyson also studied the idea, envisioning self-replicating machines sent to explore and exploit other planets and moons, and a NASA group called the Self-Replicating Systems Concept Team performed a 1980 study on the feasibility of a self-building lunar factory. University of Cambridge professor John Horton Conway invented the most famous cellular automaton in the 1960s. He called it the Game of Life, and publicized it through Martin Gardner's column in Scientific American magazine. Norwegian-Italian mathematician Nils Aall Barricelli, who worked mainly at US institutions, was a pioneer in computer based simulation of biological processes such as symbiogenesis and evolution. == 1970s–1980s == Philosophy scholar Arthur Burks, who had worked with von Neumann (and indeed, organized his papers after Neumann's death), headed the Logic of Computers Group at the University of Michigan. He brought the overlooked views of 19th century American thinker Charles Sanders Peirce into the modern age. Peirce was a strong believer that all of nature's workings were based on logic (though not always deductive logic). The Michigan group was one of the few groups still interested in alife and CAs in the early 1970s; one of its students, Tommaso Toffoli argued in his PhD thesis that the field was important because its results explain the simple rules that underlay complex effects in nature. Toffoli later provided a key proof that CAs were reversible, just as the true universe is considered to be. Christopher Langton was an unconventional researcher, with an undistinguished academic career that led him to a job programming DEC mainframes for a hospital. He became enthralled by Conway's Game of Life, and began pursuing the idea that the computer could emulate living creatures. After years of study, he began attempting to actualize Von Neumann's CA and the work of Edgar F. Codd, who had simplified Von Neumann's original twenty-nine state monster to one with only eight states. He succeeded in creating the first self-replicating computer organism in October 1979, using only an Apple II desktop computer. He entered Burks' graduate program at the Logic of Computers Group in 1982, at the age of 33, and helped to found a new discipline. Langton's official conference announcement of Artificial Life I was the earliest description of a field which had previously barely existed: Artificial life is the study of artificial systems that exhibit behavior characteristic of natural living systems. It is the quest to explain life in any of its possible manifestations, without restriction to the particular examples that have evolved on earth. This includes biological and chemical experiments, computer simulations, and purely theoretical endeavors. Processes occurring on molecular, social, and evolutionary scales are subject to investigation. The ultimate goal is to extract the logical form of living systems. Microelectronic technology and genetic engineering will soon give us the capability to create new life forms in silico as well as in vitro. This capacity will present humanity with the most far-reaching technical, theoretical and ethical challenges it has ever confronted. The time seems appropriate for a gathering of those involved in attempts to simulate or synthesize aspects of living systems. Ed Fredkin founded the Information Mechanics Group at MIT, which united Toffoli, Norman Margolus, and Charles Bennett. This group created a computer especially designed to execute cellular automata, eventually reducing it to the size of a single circuit board. This "cellular automata machine" allowed an explosion of alife research among scientists who could not otherwise afford sophisticated computers. In 1982, computer scientist named Stephen Wolfram turned his attention to cellular automata. He explored and categorized the types of complexity displayed by one-dimensional CAs, and showed how they applied to natural phenomena such as the patterns of seashells and the nature of plant growth. Norman Packard, who worked with Wolfram at the Institute for Advanced Study, used CAs to simulate the growth of snowflakes, following very basic rules. Computer animator Craig Reynolds similarly used three simple rules to create recognizable flocking behaviour in a computer program in 1987 to animate groups of boids. With no top-down programming at all, the boids produced lifelike solutions to evading obstacles placed in their path. Computer animation has continued to be a key commercial driver of alife research as the creators of movies attempt to find more realistic and inexpensive ways to animate natural forms such as plant life, animal movement, hair growth, and complicated org

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  AI-generated content in American politics

  In American politics since the 2020s, political figures have deployed AI-generated images, videos, and audio to attack opponents, create misleading narratives, or inflame emotions. The use of generative AI by American political figures has been subject to criticism from many sides of the political spectrum. Republican president Donald Trump has notably used generative AI in several posts to Truth Social during his second term, many of which have made headlines due to their inflammatory nature. == Background == Generative artificial intelligence is a subfield of artificial intelligence that uses generative models to generate text, images, videos, audio, software code or other forms of data. In the mid 2020s with the release of 15.ai, ChatGPT, DALL-E and other generative artificial intelligence applications there was an AI boom. There has been an increase of usage of generative-AI within the United States political field during this boon, with both Republican and Democratic party members using it. The Trump administration during his second term, have embraced the use of AI-generated images, causing some misinformation experts to raise concerns about the continued usage would cause the erosion of public perception of the truth. In response to some criticisms White House deputy communications director Kaelan Dorr posted on X that the "memes will continue" with White House deputy press secretary Abigail Jackson also mocking concerns. == History of usage == === 2023 === In April 2023, the Republican National Committee released an attack ad made entirely with AI-generated images depicting a dystopian future under Joe Biden's re-election. === 2024 === Generative AI has increased the efficiency with which political candidates were able to raise money by analyzing donor data and identifying possible donors and target audiences. In March 2024 Democratic consultant working for Dean Phillips has admitted to using AI to generate a robocall which used Joe Biden's voice to discourage voter participation. In August 2024, The Atlantic noted that AI slop was becoming associated with the political right in the United States, who were using it for shitposting and engagement farming on social media, with the technology offering "cheap, fast, on-demand fodder for content". AI slop is frequently used in political campaigns in an attempt at gaining attention through content farming. === 2025 === The initial version of the Make Our Children Healthy Again Assessment of children's health issues, released by a commission of cabinet members and officials of the Trump administration, and led by US Department of Health and Human Services Secretary Robert F. Kennedy Jr., reportedly cited nonexistent and garbled references generated using artificial intelligence. Democratic governor Gavin Newsom has used AI-generated images to criticize Trump. In the midst of disruptions to food stamp distribution during the 2025 US government shutdown, anonymous social media users began using OpenAI's Sora to post slop videos of welfare queens complaining, stealing, and rioting in supermarkets; many comments to the videos appeared unaware that they were AI-generated, or acknowledged that they were AI-generated but nonetheless useful in pushing a narrative of widespread welfare fraud. On September 6, 2025, Trump posted an image on Truth Social making a reference to "Chipocalypse Now". Trump's post consisted of an AI-generated image showing Trump frowning and wearing a U.S. Cavalry hat and sunglasses, in front of Lake Michigan with the city of Chicago behind him with a smoke and fire spread across the background with five U.S. Army helicopters in the sky. The words "Chipocalypse Now" are rendered in a font resembling that in which the title of the 1979 film Apocalypse Now was styled. === 2026 === On February 5, 2026, Donald Trump shared a video of Barack and Michelle Obama depicted as apes in a Truth Social post. The two-second AI-generated clip of the Obamas portrayed as apes set to "The Lion Sleeps Tonight" appeared at the end of a one-minute two second long video, the rest of which was about false claims of voter fraud during the 2020 presidential election. The post received at least 4,650 likes, 409 comments, and 1,470 reTruths before it was deleted the next morning. The short clip was part of a longer AI-generated video posted in October 2025. The post received widespread backlash and bipartisan condemnation of the video as racist. In April 2026, Trump posted a picture of himself depicted as Jesus, drawing widespread criticism from Evangelicals and Catholics, resulting in Trump deleting the post hours later and claiming he believed he was depicted as a doctor. == Examples of use == === Election campaigns === In 2023, while he was still running for re-election, the presidential campaign of Joe Biden prepared a task force to respond to AI images and videos. The campaign for the 2024 Republican nominee, Donald Trump, has used deepfake videos of political opponents in campaign ads and fake images showing Trump with black supporters. During the first five months of his second term in 2025, Trump posted several AI-generated images of himself on official government social media accounts, including him as the Pope, him as a Jedi, and him as a muscular man. In August 2024, Trump posted a series of AI-generated images on his social media platform, Truth Social, that portrayed fans of the singer Taylor Swift in "Swifties for Trump" T-shirts, as well as a photo of the singer herself appearing to endorse Trump's 2024 presidential campaign. The images originated from the conservative Twitter account @amuse, which posted numerous AI slop images leading up to the 2024 United States elections that were shared by other high-profile figures within the US Republican Party, such as Elon Musk, who has publicly endorsed the utilization of generative AI, furthering this association. In 2024, Michigan GOP candidate Anthony Hudson posted an AI-generated video showing Martin Luther King Jr. endorsing his campaign, later claiming it was uploaded by a volunteer. In his 2025 bid to be the Democratic nominee for governor of New Jersey, Rep. Josh Gottheimer drew attention and criticism when he released a TV ad that used AI to portray him as a shirtless boxer sparring with Donald Trump in a boxing ring. In November 2025, the campaign of Mike Collins, a GOP candidate in the 2026 United States Senate election in Georgia released a fake video, generated by artificial intelligence, that depicted Democrat Jon Ossoff defending his vote on the 2025 United States federal government shutdown by declaring he could never say no to Chuck Schumer and that SNAP recipients did not attend his out-of-state fundraisers. The Collins campaign also shared an AI-generated video featuring Collins as a shirtless blue jeans model, referencing an American Eagle Outfitters advertisement featuring Sydney Sweeney. During the 2026 Los Angeles mayoral election, candidate Spencer Pratt reposted an AI-generated video portraying Pratt as Batman and prominent California politicians such as Karen Bass, Gavin Newsom, and Kamala Harris, as unruly aristocrats. Former governor of Florida Jeb Bush described the ad as “maybe the best political ad of the year.” In response, a spokesperson for Bass's campaign said, he was "doing his best Trump impression." Bass further responded that the AI ads are "taking on a violent trend." === Protests === In response to the nation-wide No Kings protests in October 2025, Donald Trump posted a video depicting himself flying a fighter jet and releasing feces on crowds of demonstrators, including Democratic influencer Harry Sisson. === Foreign interference === Officials from the ODNI and FBI have stated that Russia, Iran, and China used generative artificial intelligence tools to create fake and divisive text, photos, video, and audio content to foster anti-Americanism and engage in covert influence campaigns. The use of artificial intelligence was described as an accelerant rather than a revolutionary change to influence efforts. Regulation of AI with regard to elections was unlikely to see a resolution for most of the 2024 United States general election season. === Disasters and wars === In the aftermath of Hurricane Helene in the United States, members of the Republican Party circulated an AI-generated image of a young girl holding a puppy in a flood, and used it as evidence of the failure of President Joe Biden to respond to the disaster. Some, like Trump supporter Amy Kremer, shared the image on social media but acknowledged that it was not genuine. In February 2025, Donald Trump shared an AI-generated video on Truth Social depicting a hypothetical Gaza after a Trump takeover. The video's creator claimed it was made as political satire. == Reception == Ramesh Srinivasan, a professor at UCLA raised concerns about the use of AI-generative images stating that many people are questioning where they can find trustab

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