AI Image Generators

Explore the best AI Image Generators — independent reviews, comparisons, pricing and step-by-step how-to guides, curated by Aizhi.

  • Software durability

    Software durability

    In software engineering, software durability means the solution ability of serviceability of software and to meet user's needs for a relatively long time. Software durability is important for user's satisfaction. For a software security to be durable, it must allow an organization to adjust the software to business needs that are constantly evolving, often in impulsive ways. Durability of software depends on four characteristics mainly; i.e. software trustworthiness, Human Trust for Serviceability, software dependability and software usability.

    Read more →
  • Deepfake

    Deepfake

    Deepfakes (a portmanteau of 'deep learning' and 'fake') are images, videos, or audio that have been edited or generated using artificial intelligence, AI-based tools or audio-video editing software. They may depict real or fictional people and are considered a form of synthetic media, that is media that is usually created by artificial intelligence systems by combining various media elements into a new media artifact. While the act of creating fake content is not new, deepfakes uniquely leverage machine learning and artificial intelligence techniques, including facial recognition algorithms and artificial neural networks such as variational autoencoders and generative adversarial networks (GANs). In turn, the field of image forensics has worked to develop techniques to detect manipulated images. Deepfakes have garnered widespread attention for their potential use in creating child sexual abuse material, celebrity pornographic videos, revenge porn, fake news, hoaxes, bullying, and financial fraud. Academics have raised concerns about the potential for deepfakes to promote disinformation and hate speech, as well as interfere with elections. In response, the information technology industry and governments have proposed recommendations and methods to detect and mitigate their use. Academic research has also delved deeper into the factors driving deepfake engagement online as well as potential countermeasures to malicious application of deepfakes. From traditional entertainment to gaming, deepfake technology has evolved to be increasingly convincing and available to the public, allowing for the disruption of the entertainment and media industries. == History == Photo manipulation was developed in the 19th century and soon applied to motion pictures. Technology steadily improved during the 20th century, and more quickly with the advent of digital video. Deepfake technology has been developed by researchers at academic institutions beginning in the 1990s, and later by amateurs in online communities. More recently, the methods have been adopted by industry. The development of generative adversarial networks (GANs) in the mid-2010s represented a key technical turning point in the evolution of deepfakes. GANs allowed for the creation of highly realistic fake images and videos by training competing neural networks, achieving a much improved visual fidelity over previous methods of creating the content using rules or by using autoencoders, and formed the basis for modern deepfake methods. === Academic research === Academic research related to deepfakes is split between the field of computer vision, a sub-field of computer science, which develops techniques for creating and identifying deepfakes, and humanities and social science approaches that study the social, ethical, aesthetic implications as well as journalistic and informational implications of deepfakes. As deepfakes have risen in prominence in popularity with innovations provided by AI tools, significant research has gone into detection methods and defining the factors driving engagement with deepfakes on the internet. Deepfakes have been shown to appear on social media platforms and other parts of the internet for purposes ranging from entertainment and education related to deepfakes to misinformation to elicit strong reactions. There are gaps in research related to the propagation of deepfakes on social media. Negativity and emotional response are the primary driving factors for users sharing deepfakes. === Social science and humanities approaches to deepfakes === In cinema studies, deepfakes illustrate how "the human face is emerging as a central object of ambivalence in the digital age". Video artists have used deepfakes to "playfully rewrite film history by retrofitting canonical cinema with new star performers". Film scholar Christopher Holliday analyses how altering the gender and race of performers in familiar movie scenes destabilizes gender classifications and categories. The concept of "queering" deepfakes is also discussed in Oliver M. Gingrich's discussion of media artworks that use deepfakes to reframe gender, including British artist Jake Elwes' Zizi: Queering the Dataset, an artwork that uses deepfakes of drag queens to intentionally play with gender. The aesthetic potentials of deepfakes are also beginning to be explored. Theatre historian John Fletcher notes that early demonstrations of deepfakes are presented as performances, and situates these in the context of theater, discussing "some of the more troubling paradigm shifts" that deepfakes represent as a performance genre. While most English-language academic studies of deepfakes focus on the Western anxieties about disinformation and pornography, digital anthropologist Gabriele de Seta has analyzed the Chinese reception of deepfakes, which are known as huanlian, which translates to "changing faces". The Chinese term does not contain the "fake" of the English deepfake, and de Seta argues that this cultural context may explain why the Chinese response has centered on practical regulatory measures to "fraud risks, image rights, economic profit, and ethical imbalances". === Computer science research on deepfakes === A landmark early project was the "Video Rewrite" program, published in 1997. The program modified existing video footage of a person speaking to depict that person mouthing the words from a different audio track. It was the first system to fully automate this kind of facial reanimation, and it did so using machine learning techniques to make connections between the sounds produced by a video's subject and the shape of the subject's face. Contemporary academic projects have focused on creating more realistic videos and improving deepfake techniques. The "Synthesizing Obama" program, published in 2017, modifies video footage of former president Barack Obama to depict him mouthing the words contained in a separate audio track. The project lists as a main research contribution to its photorealistic technique for synthesizing mouth shapes from audio. The "Face2Face" program, published in 2016, modifies video footage of a person's face to depict them mimicking another person's facial expressions. The project highlights its primary research contribution as the development of the first method for re-enacting facial expressions in real time using a camera that does not capture depth, enabling the technique to work with common consumer cameras. Researchers have also shown that deepfakes are expanding into other domains such as medical imagery. In this work, it was shown how an attacker can automatically inject or remove lung cancer in a patient's 3D CT scan. The result was so convincing that it fooled three radiologists and a state-of-the-art lung cancer detection AI. To demonstrate the threat, the authors successfully performed the attack on a hospital in a White hat penetration test. A survey of deepfakes, published in May 2020, provides a timeline of how the creation and detection of deepfakes have advanced over the last few years. The survey identifies that researchers have been focusing on resolving the following challenges of deepfake creation: Generalization. High-quality deepfakes are often achieved by training on hours of footage of the target. This challenge is to minimize the amount of training data and the time to train the model required to produce quality images and to enable the execution of trained models on new identities (unseen during training). Paired Training. Training a supervised model can produce high-quality results, but requires data pairing. This is the process of finding examples of inputs and their desired outputs for the model to learn from. Data pairing is laborious and impractical when training on multiple identities and facial behaviors. Some solutions include self-supervised training (using frames from the same video), the use of unpaired networks such as Cycle-GAN, or the manipulation of network embeddings. Identity leakage. This is where the identity of the driver (i.e., the actor controlling the face in a reenactment) is partially transferred to the generated face. Some solutions proposed include attention mechanisms, few-shot learning, disentanglement, boundary conversions, and skip connections. Occlusions. When part of the face is obstructed with a hand, hair, glasses, or any other item then artifacts can occur. A common occlusion is a closed mouth which hides the inside of the mouth and the teeth. Some solutions include image segmentation during training and in-painting. Temporal coherence. In videos containing deepfakes, artifacts such as flickering and jitter can occur because the network has no context of the preceding frames. Some researchers provide this context or use novel temporal coherence losses to help improve realism. As the technology improves, the interference is diminishing. Overall, deepfakes are expected to have several implications in media and society, med

    Read more →
  • T-norm fuzzy logics

    T-norm fuzzy logics

    T-norm fuzzy logics are a family of non-classical logics, informally delimited by having a semantics that takes the real unit interval [0, 1] for the system of truth values and functions called t-norms for permissible interpretations of conjunction. They are mainly used in applied fuzzy logic and fuzzy set theory as a theoretical basis for approximate reasoning. T-norm fuzzy logics belong in broader classes of fuzzy logics and many-valued logics. In order to generate a well-behaved implication, the t-norms are usually required to be left-continuous; logics of left-continuous t-norms further belong in the class of substructural logics, among which they are marked with the validity of the law of prelinearity, (A → B) ∨ (B → A). Both propositional and first-order (or higher-order) t-norm fuzzy logics, as well as their expansions by modal and other operators, are studied. Logics that restrict the t-norm semantics to a subset of the real unit interval (for example, finitely valued Łukasiewicz logics) are usually included in the class as well. Important examples of t-norm fuzzy logics are monoidal t-norm logic (MTL) of all left-continuous t-norms, basic logic (BL) of all continuous t-norms, product fuzzy logic of the product t-norm, or the nilpotent minimum logic of the nilpotent minimum t-norm. Some independently motivated logics belong among t-norm fuzzy logics, too, for example Łukasiewicz logic (which is the logic of the Łukasiewicz t-norm) or Gödel–Dummett logic (which is the logic of the minimum t-norm). == Motivation == As members of the family of fuzzy logics, t-norm fuzzy logics primarily aim at generalizing classical two-valued logic by admitting intermediary truth values between 1 (truth) and 0 (falsity) representing degrees of truth of propositions. The degrees are assumed to be real numbers from the unit interval [0, 1]. In propositional t-norm fuzzy logics, propositional connectives are stipulated to be truth-functional, that is, the truth value of a complex proposition formed by a propositional connective from some constituent propositions is a function (called the truth function of the connective) of the truth values of the constituent propositions. The truth functions operate on the set of truth degrees (in the standard semantics, on the [0, 1] interval); thus the truth function of an n-ary propositional connective c is a function Fc: [0, 1]n → [0, 1]. Truth functions generalize truth tables of propositional connectives known from classical logic to operate on the larger system of truth values. T-norm fuzzy logics impose certain natural constraints on the truth function of conjunction. The truth function ∗ : [ 0 , 1 ] 2 → [ 0 , 1 ] {\displaystyle \colon [0,1]^{2}\to [0,1]} of conjunction is assumed to satisfy the following conditions: Commutativity, that is, x ∗ y = y ∗ x {\displaystyle xy=yx} for all x and y in [0, 1]. This expresses the assumption that the order of fuzzy propositions is immaterial in conjunction, even if intermediary truth degrees are admitted. Associativity, that is, ( x ∗ y ) ∗ z = x ∗ ( y ∗ z ) {\displaystyle (xy)z=x(yz)} for all x, y, and z in [0, 1]. This expresses the assumption that the order of performing conjunction is immaterial, even if intermediary truth degrees are admitted. Monotony, that is, if x ≤ y {\displaystyle x\leq y} then x ∗ z ≤ y ∗ z {\displaystyle xz\leq yz} for all x, y, and z in [0, 1]. This expresses the assumption that increasing the truth degree of a conjunct should not decrease the truth degree of the conjunction. Neutrality of 1, that is, 1 ∗ x = x {\displaystyle 1x=x} for all x in [0, 1]. This assumption corresponds to regarding the truth degree 1 as full truth, conjunction with which does not decrease the truth value of the other conjunct. Together with the previous conditions this condition ensures that also 0 ∗ x = 0 {\displaystyle 0x=0} for all x in [0, 1], which corresponds to regarding the truth degree 0 as full falsity, conjunction with which is always fully false. Continuity of the function ∗ {\displaystyle } (the previous conditions reduce this requirement to the continuity in either argument). Informally this expresses the assumption that microscopic changes of the truth degrees of conjuncts should not result in a macroscopic change of the truth degree of their conjunction. This condition, among other things, ensures a good behavior of (residual) implication derived from conjunction; to ensure the good behavior, however, left-continuity (in either argument) of the function ∗ {\displaystyle } is sufficient. In general t-norm fuzzy logics, therefore, only left-continuity of ∗ {\displaystyle } is required, which expresses the assumption that a microscopic decrease of the truth degree of a conjunct should not macroscopically decrease the truth degree of conjunction. These assumptions make the truth function of conjunction a left-continuous t-norm, which explains the name of the family of fuzzy logics (t-norm based). Particular logics of the family can make further assumptions about the behavior of conjunction (for example, Gödel–Dummett logic requires its idempotence) or other connectives (for example, the logic IMTL (involutive monoidal t-norm logic) requires the involutiveness of negation). All left-continuous t-norms ∗ {\displaystyle } have a unique residuum, that is, a binary function ⇒ {\displaystyle \Rightarrow } such that for all x, y, and z in [0, 1], x ∗ y ≤ z {\displaystyle xy\leq z} if and only if x ≤ y ⇒ z . {\displaystyle x\leq y\Rightarrow z.} The residuum of a left-continuous t-norm can explicitly be defined as ( x ⇒ y ) = sup { z ∣ z ∗ x ≤ y } . {\displaystyle (x\Rightarrow y)=\sup\{z\mid zx\leq y\}.} This ensures that the residuum is the pointwise largest function such that for all x and y, x ∗ ( x ⇒ y ) ≤ y . {\displaystyle x(x\Rightarrow y)\leq y.} The latter can be interpreted as a fuzzy version of the modus ponens rule of inference. The residuum of a left-continuous t-norm thus can be characterized as the weakest function that makes the fuzzy modus ponens valid, which makes it a suitable truth function for implication in fuzzy logic. Left-continuity of the t-norm is the necessary and sufficient condition for this relationship between a t-norm conjunction and its residual implication to hold. Truth functions of further propositional connectives can be defined by means of the t-norm and its residuum, for instance the residual negation ¬ x = ( x ⇒ 0 ) {\displaystyle \neg x=(x\Rightarrow 0)} or bi-residual equivalence x ⇔ y = ( x ⇒ y ) ∗ ( y ⇒ x ) . {\displaystyle x\Leftrightarrow y=(x\Rightarrow y)(y\Rightarrow x).} Truth functions of propositional connectives may also be introduced by additional definitions: the most usual ones are the minimum (which plays a role of another conjunctive connective), the maximum (which plays a role of a disjunctive connective), or the Baaz Delta operator, defined in [0, 1] as Δ x = 1 {\displaystyle \Delta x=1} if x = 1 {\displaystyle x=1} and Δ x = 0 {\displaystyle \Delta x=0} otherwise. In this way, a left-continuous t-norm, its residuum, and the truth functions of additional propositional connectives determine the truth values of complex propositional formulae in [0, 1]. Formulae that always evaluate to 1 are called tautologies with respect to the given left-continuous t-norm ∗ , {\displaystyle ,} or ∗ - {\displaystyle {\mbox{-}}} tautologies. The set of all ∗ - {\displaystyle {\mbox{-}}} tautologies is called the logic of the t-norm ∗ , {\displaystyle ,} as these formulae represent the laws of fuzzy logic (determined by the t-norm) that hold (to degree 1) regardless of the truth degrees of atomic formulae. Some formulae are tautologies with respect to a larger class of left-continuous t-norms; the set of such formulae is called the logic of the class. Important t-norm logics are the logics of particular t-norms or classes of t-norms, for example: Łukasiewicz logic is the logic of the Łukasiewicz t-norm x ∗ y = max ( x + y − 1 , 0 ) {\displaystyle xy=\max(x+y-1,0)} Gödel–Dummett logic is the logic of the minimum t-norm x ∗ y = min ( x , y ) {\displaystyle xy=\min(x,y)} Product fuzzy logic is the logic of the product t-norm x ∗ y = x ⋅ y {\displaystyle xy=x\cdot y} Monoidal t-norm logic MTL is the logic of (the class of) all left-continuous t-norms Basic fuzzy logic BL is the logic of (the class of) all continuous t-norms It turns out that many logics of particular t-norms and classes of t-norms are axiomatizable. The completeness theorem of the axiomatic system with respect to the corresponding t-norm semantics on [0, 1] is then called the standard completeness of the logic. Besides the standard real-valued semantics on [0, 1], the logics are sound and complete with respect to general algebraic semantics, formed by suitable classes of prelinear commutative bounded integral residuated lattices. == History == Some particular t-norm fuzzy logics have been introduced and investigated long before the family was re

    Read more →
  • DialogOS

    DialogOS

    DialogOS is a graphical programming environment to design computer system which can converse through voice with the user. Dialogs are clicked together in a Flowchart. DialogOS includes bindings to control Lego Mindstorms robots by voice and has bindings to SQL databases, as well as a generic plugin architecture to integrate with other types of backends. DialogOS is used in computer science courses in schools and universities to teach programming and to introduce beginners in the basic principles of human/computer interaction and dialog design. It has also been used in research systems. DialogOS was initially developed commercially by CLT Sprachtechnologie GmbH until its liquidation in 2017. The rights were then acquired by Saarland University and the software was released as open-source. == Bindings to Lego Mindstorms NXT == DialogOS can control the LEGO Mindstorms NXT Series. It uses sensor-nodes to obtain values for the following sensors: noise sensor ultrasonic sensor touch sensor luminosity sensor

    Read more →
  • Glossary of robotics

    Glossary of robotics

    Robotics is the branch of technology that deals with the design, construction, operation, structural disposition, manufacture and application of robots. Robotics is related to the sciences of electronics, engineering, mechanics, and software. The following is a list of common definitions related to the Robotics field. == A == Actuator: a motor that translates control signals into mechanical movement. The control signals are usually electrical but may, more rarely, be pneumatic or hydraulic. The power supply may likewise be any of these. It is common for electrical control to be used to modulate a high-power pneumatic or hydraulic motor. Aerobot: a robot capable of independent flight on other planets. A type of aerial robot. Arduino: The current platform of choice for small-scale robotic experimentation and physical computing. Artificial intelligence: is the intelligence of machines and the branch of computer science that aims to create it. Aura (satellite): a robotic spacecraft launched by NASA in 2004 which collects atmospheric data from Earth. Automaton: an early self-operating robot, performing exactly the same actions, over and over. Autonomous vehicle: a vehicle equipped with an autopilot system, which is capable of driving from one point to another without input from a human operator. == B == Biomimetic: See Bionics. Bionics: also known as biomimetics, biognosis, biomimicry, or bionical creativity engineering is the application of biological methods and systems found in nature to the study and design of engineering systems and modern technology. == C == CAD/CAM (computer-aided design and computer-aided manufacturing): These systems and their data may be integrated into robotic operations. Čapek, Karel: Czech author who coined the term 'robot' in his 1921 play, Rossum's Universal Robots. Chandra X-ray Observatory: a robotic spacecraft launched by NASA in 1999 to collect astronomical data. Cloud robotics: robots empowered with more capacity and intelligence from cloud. Combat, robot: a hobby or sport event where two or more robots fight in an arena to disable each other. This has developed from a hobby in the 1990s to several TV series worldwide. Cruise missile: a robot-controlled guided missile that carries an explosive payload. Cyborg: also known as a cybernetic organism, a being with both biological and artificial (e.g. electronic, mechanical or robotic) parts. == D == Degrees of freedom: the extent to which a robot can move itself; expressed in terms of Cartesian coordinates (x, y, and z) and angular movements (yaw, pitch, and roll). Delta robot: a tripod linkage, used to construct fast-acting manipulators with a wide range of movement. Drive Power: The energy source or sources for the robot actuators. == E == Emergent behaviour, a complicated resultant behaviour that emerges from the repeated operation of simple underlying behaviours. Envelope (Space), Maximum The volume of space encompassing the maximum designed movements of all robot parts including the end-effector, workpiece, and attachments. Explosive ordnance disposal robot A mobile robot designed to assess whether an object contains explosives; some carry detonators that can be deposited at the object and activated after the robot withdraws. == F == FIRST(For Inspiration and Recognition of Science and Technology): an organization founded by inventor Dean Kamen in 1989 in order to develop ways to inspire students in engineering and technology fields. Forward chaining: a process in which events or received data are considered by an entity to intelligently adapt its behavior. == G == Gynoid: A humanoid robot designed to look like a human female. == H == Haptic: tactile feedback technology using the operator's sense of touch. Also sometimes applied to robot manipulators with their own touch sensitivity. Hexapod (platform): A movable platform using six linear actuators. Often used in flight simulators and fairground rides, they also have applications as a robotic manipulator. Hexapod (walker): A six-legged walking robot, using a simple insect-like locomotion. Human–computer interaction. Humanoid: A robotic entity designed to resemble a human being in form, function, or both. Hydraulics: the control of mechanical force and movement, generated by the application of liquid under pressure. cf. pneumatics. == I == Industrial robot: A reprogrammable, multifunctional manipulator designed to move material, parts, tools, or specialized devices through variable programmed motions for the performance of a variety of tasks. Insect robot: A small robot designed to imitate insect behaviors rather than complex human behaviors. == K == Kalman filter: a mathematical technique to estimate the value of a sensor measurement, from a series of intermittent and noisy values. Kinematics: the study of motion, as applied to robots. This includes both the design of linkages to perform motion, their power, control and stability; also their planning, such as choosing a sequence of movements to achieve a broader task. Inverse Kinematics: the process of determining joint angles required for a robot's end-effector to reach a desired position and orientation in space. Used in motion planning to calculate motor commands from target positions. == L == Linear actuator A form of motor that generates a linear movement directly. == M == Manipulator or gripper: A robotic 'hand'. Mobile robot: A self-propelled and self-contained robot that is capable of moving over a mechanically unconstrained course. Muting: The deactivation of a presence-sensing safeguarding device during a portion of the robot cycle. Mecanum wheel: A wheel fitted with angled rollers that enables a robot vehicle to move in multiple directions, including sideways. == O == Ornithopter – An aerial robot or drone that achieves flight through a flapping-wing mechanism rather than rotating blades or fixed wings, often utilized for highly maneuverable flight. == P == Parallel manipulator: an articulated robot or manipulator based on a number of kinematic chains, actuators and joints, in parallel. cf. serial manipulator. Pendant: Any portable control device that permits an operator to control the robot from within the restricted envelope (space) of the robot. Pneumatics: the control of mechanical force and movement, generated by the application of compressed gas. cf. hydraulics. Powered exoskeleton: is a wearable mobile machine that allow for limb movement with increased strength and endurance. Prosthetic robots: programmable manipulators or devices for missing human limbs. == R == Remote manipulator: A manipulator under direct human control, often used for work with hazardous materials. Robonaut: a development project conducted by NASA to create humanoid robots capable of using space tools and working in similar environments to suited astronauts. == S == Sensor fusion:The process of combining data from multiple sensors, such as LiDAR, cameras, global positioning systems (GPS), and inertial measurement units (IMUs), to produce a more accurate and reliable understanding of an environment than using a single sensor alone. It is widely used in robotics and autonomous systems to improve perception, localization, and decision-making. Serial manipulator: an articulated robot or manipulator with a single series kinematic chain of actuators. cf. parallel manipulator. Service robots are machines that extend human capabilities. Servo, a motor that moves to and maintains a set position under command, rather than continuously moving. Servomechanism An automatic device that uses error-sensing negative feedback to correct the performance of a mechanism. Single Point of Control The ability to operate the robot such that initiation or robot motion from one source of control is possible only from that source and cannot be overridden from another source. Slow Speed Control A mode of robot motion control where the velocity of the robot is limited to allow persons sufficient time either to withdraw the hazardous motion or stop the robot. Snake robot A robot component resembling a tentacle or elephant's trunk, where many small actuators are used to allow continuous curved motion of a robot component, with many degrees of freedom. This is usually applied to snake-arm robots, which use this as a flexible manipulator. A rarer application is the snakebot, where the entire robot is mobile and snake-like, so as to gain access through narrow spaces. Stepper motor Stewart platform A movable platform using six linear actuators, hence also known as a Hexapod. Subsumption architecture A robot architecture that uses a modular, bottom-up design beginning with the least complex behavioral tasks. Surgical robot, a remote manipulator used for keyhole surgery Swarm robotics involve large numbers of mostly simple physical robots. Their actions may seek to incorporate emergent behavior observed in social insects (swarm intelligence). Synchro == T == Teach Mode: The control state that al

    Read more →
  • Niceaunties

    Niceaunties

    Niceaunties is the pseudonym of a Singapore-based artist and designer whose work incorporates generative artificial intelligence, video, and digital installation. Her practice centers around the figure of the "auntie", a common term for older women in Southeast Asian contexts, and explores themes such as aging, care, domesticity, and gender roles. Her work has been featured in exhibitions and media platforms including TED, Christie's Art + Tech, Expanded.Art, and publications such as The Guardian, The Straits Times. == Early life and career == Niceaunties was born in 1981 in Singapore. She attributes her inspiration for "auntie culture" to the matriarchal environment and older women of her household, including her grandmother, while growing up. She is also an architectural designer with Spark Architect. The Niceaunties project began in 2023 after she encountered AI-generated images in her work as an architect. It draws inspiration from women in the artist's family and broader Southeast Asian cultural dynamics. Her work often features AI-generated visuals created with tools such as DALL-E, Krea, RunwayML, and SORA. Her imagery and narratives center on the fictional "Auntieverse", which features older women in imagined settings involving community, ecology, and labor. Her notable works include 'Auntlantis', a five-part video series imagining older women engaged in ocean clean-up and collective ritual, and 'Goddess,' a video created with Sora, featuring a character who gradually forgets her divine identity through years of domestic labor. == Exhibitions == 2024 – Expanded.Art, Berlin – Auntiedote solo exhibition 2024 – TED (conference), Vancouver – Speaker and screening 2024 – Victoria and Albert Museum, London – Digital Art Weekend 2024 – Louisiana Museum of Modern Art, Denmark – Ocean exhibition 2025 – Christie's Augmented Intelligence Auction, New York == Reception == In 2024, Niceaunties gave a TED Talk titled The Weird and Wonderful Art of Niceaunties. Journalist Rebecca Ratcliffe, writing for The Guardian, described her work as combining AI with "the surreal and the political," noting her focus on older women as central characters. Her work has also received criticism for being reliant on generative AI, which many feel exploits and steals from traditional artists.

    Read more →
  • Gundam Build Divers Re:Rise

    Gundam Build Divers Re:Rise

    Gundam Build Divers Re:Rise (Japanese: ガンダムビルドダイバーズRe:RISE, Hepburn: Gandamu Birudo Daibāzu Re:Raizu) is a Japanese original net animation anime series produced by Sunrise Beyond, and the fourth series within the Gundam Build Series sub-series. A sequel to the 2018 anime Gundam Build Divers, it is the first Gundam anime series to be released in the Reiwa period, released to celebrate the franchise's 40th anniversary. The series is directed by Shinya Watada and written by Yasuyuki Muto. Initially announced at the Gundam 40th anniversary video, the series aired on its Gundam Channel YouTube channel from October 10 to December 26, 2019. A TV airing of the ONA began on BS11 on October 12, 2019, and on January 28, 2020, on Tokyo MX. A second season aired from April 9 to August 27, 2020. Two spinoffs of the series were later serialized in Kadokawa's Gundam Ace magazine and Hobby Japan. == Plot == Two years have passed since the EL-Diver Incident, an event that almost destroyed the Gunpla Battle Nexus Online (GBN) game until it was resolved by the force group known as "Build Divers", and soon after more EL-Divers were discovered. In order to make the game more secure, a newer version of the game was rolled out in order to prevent the same incident from happening again and with newer experiences that would make the gameplay more immersive to players. The story focuses on Hiroto Kuga, a high schooler who is a rogue mercenary Gunpla Diver in GBN, who goes in the game and wanders throughout its countless dimensions while helping out other Divers whether it is on insistence or by hire. Despite his selfless act, he chooses to remain unaffiliated with anyone and refuses rewards and Force (Diver parties) group invites, isolating himself from other people even in real life. His primary goal as a Diver is to be reunited with a mysterious girl from his past named Eve, who was in fact the very first EL-Diver to appear in the game. But after a special request mission, Hiroto is united with three other active Divers in a strange world named "Eldora" and forms the Force group "BUILD DiVERS" in what appears to be just another GBN gamespace event, until they learn the truth about Eldora and its consequences not only for GBN, but for the entire world. == Characters == === BUILD DiVERS === Hiroto Kuga (クガ・ヒロト, Kuga Hiroto) / Hiroto (ヒロト, Hiroto) Voiced by: Chiaki Kobayashi (Japanese); Billy Kametz (English) The main protagonist of the series and a high-school builder, veteran diver, and a former ace member of the Force group Avalon, who lives in Yokohama. He was one of the first minors to make it to the deep end of GBN, due to his conviction of being a person who does his best to help others. He was active prior and during the events of the previous series. Now working as a rogue diver for hire after leaving Avalon, he wanders the GBN gamespace alone, harboring regrets, resentments, and suffering from trauma after the death of his close friend and lover, the EL-Diver Eve. He is very calm and a man of few words, usually refusing others' reward and help, especially on joining other forces, but this stoic persona is a mental mask to hide his condition from everyone, including his parents. But when a special mission done by Freddie united him with Kazami, May and Parviz, they accidentally formed the force team named "BUILD DiVERS" to protect the Eldorans from the One-Eyes army. Currently he is the ace of his unit and the leader of the overall force. Hiroto uses the PFF-X7 Core Gundam as his main Gunpla, based on the RX-78-2 Gundam from the original Mobile Suit Gundam series. Its special armament system called the "core-change" gimmick and his first theme invented from that gimmick is the "Planets System". This allows the Core Gundam to be equipped with various types of armor and weapons, each for a different situation named after the eight planets. Hiroto later upgrades his Gunpla into the PFF-X7II Core Gundam II. This new Core Gundam can transform into the "Core Flyer", in a similar fashion to the original Gundam's FF-X7 Core Fighter for increased mobility and like its predecessor, it can also use the Planets System: Earth Armor (PFF-X7/E3 Earthree Gundam): Core Gundam's default blue armor, focused on traditional all-around combat. Mars Armor (PFF-X7/M4 Marsfour Gundam): A red armor whose focus is on fragments of four styles of close combat, hence "Cross-Combat". Venus Armor (PFF-X7/V2 Veetwo Gundam): A green armor whose focus is commando style ranged and bombardment combat, additionally with option works. Mercury Armor (PFF-X7/M1 Mercuone Gundam): A navy armor whose focus is underwater combat. Jupiter Armor (PFF-X7/J5 Jupitive Gundam): A white armor whose focus is fast orbital combat. Uranus Armor (PFF-X7II/U7 Uraven Gundam): An indigo armor focused on reconnaissance and high powered sniping. Saturn Armor (PFF-X7II/S6 Saturnix Gundam): An orange armor focused in demolition style close combat without beam weapons, originally developed to counter Gundam Frames. Neptune Armor (PFF-X7II/N8 Nepteight Gundam): An aqua-green armor equipped with a customized Volture Lumiere system similar to the one from Mobile Suit Gundam SEED C.E. 73: Stargazer, intended to be used for traveling through GBN's space in a short amount of time, but was used for launching into orbit instead of maneuvering in deep space. It is ultimately discarded in Eldora's orbit due to the strain of leaving Eldora's gravitational field. Pluto Armor (PFF-X7II+/P9 Plutine Gundam): Appearing only on Gundam Build Metaverse, the black colored armor is used for close combat and dueling purposes with its color scheme reminiscent of that of EcoPla. PFF-X7II/BUILD DiVERS Re:Rising Gundam: A special combination of the Core Gundam II with the WoDom Pod + and parts from the Gundam Aegis Knight and the EX Valkylander, armed with two giant beam sabers, eight miracle wings born from Eve's blessings, and the "Grand Cross Cannon", Hiroto's first special move, made with the help of his team. In one occasion, Hiroto changes his avatar to a Haro to pilot the Mobile Builder Haro Loader to help with the repairs on Cuadorn by making a prosthetic wing out of gunpla parts. During the Gunpla Battle Royal, he pilots an unmodified ASW-G-08 Gundam Barbatos Lupus Rex from Mobile Suit Gundam: Iron-Blooded Orphans. In Battlelogue, it is revealed that he has made a second Core Gundam II that he leaves on Eldora with the colors of the Gundam MK-II Titan. Another variant of this Gunpla sports the old "Gundam G3" colors with his team's personal crest, which is most likely to represent Sarah since the color of her hair, eyes, and dress embody Hiroto's time with Eve before they joined Avalon and to symbolize how he has officially befriended the original Build Divers. Each of the two units have unique advancements, the Titan color specializes in ground and underwater combat and the G3 color specializes in aerial and space combat. May (メイ, Mei) Voiced by: Mai Fuchigami (Japanese); Lauren Landa (English) A seemingly late teens female diver who prefers to play solo, she is a very calm and no-nonsense girl whose interest is in battles alone. However, she is not a fan of those who engage their opponents head on and prefers to implement a strategic approach. She is mature and has a strong sense of justice, and can be impulsive rushing into situations, especially for those in danger. Later in the series, she is revealed to be one of the 87 EL-Divers, however she was not one of those who were saved after the EL-Diver incident two years ago, she was born shortly after. After she was born she was given her own Mobile Doll body similar to Sarah, that is when she first met her, Koichi, Tsukasa, and Nanami. During the Lotus Challenge Eldoran style rehearsal battle it is revealed that she, as a new sister of Sarah, addresses the latter as the older since Sarah is chronologically older, regardless of her maturity. In the final episode, she is revealed to have been born with the remnant data originating from Eve, the first born EL-Diver who Hiroto befriended and fell in love with several years ago, and carries Eve's earring on her armband. In Battlelogue, it's implied that she is currently living with Hiroto IRL and in GBN is his attendant. May uses the JMA0530-MAY WoDom Pod as her main Gunpla, which is a customized JMA-0530 Walking Dome from Turn A Gundam. In the later episodes, the mobile suit is revealed to be a disguise for its true form, the HER-SELF Mobile Doll May. May later upgrades her WoDom Pod into the JMA0530-MAYBD WoDom Pod +. During the Gunpla Battle Royal, she uses her Mobile Doll (albeit with a new color scheme and the Gundam Base logo) along with an unmodified NZ-999 II Neo Zeong mobile armor from Mobile Suit Gundam Narrative. Kazami Torimachi (トリマチ・カザミ, Torimachi Kazami) / Kazami (カザミ, Kazami) Voiced by: Masaaki Mizunaka (Japanese); Ray Chase (English) A diver who was a former member of the diver group "Mu Dish". He is a very energet

    Read more →
  • Context-sensitive user interface

    Context-sensitive user interface

    A context-sensitive user interface offers the user options based on the state of the active program. Context sensitivity is ubiquitous in current graphical user interfaces, often in context menus. A user-interface may also provide context sensitive feedback, such as changing the appearance of the mouse pointer or cursor, changing the menu color, or with auditory or tactile feedback. == Reasoning and advantages of context sensitivity == The primary reason for introducing context sensitivity is to simplify the user interface. Advantages include: Reduced number of commands required to be known to the user for a given level of productivity. Reduced number of clicks or keystrokes required to carry out a given operation. Allows consistent behaviour to be pre-programmed or altered by the user. Reduces the number of options needed on screen at one time. === Disadvantages === Context sensitive actions may be perceived as dumbing down of the user interface, leaving the operator at a loss as to what to do when the computer decides to perform an unwanted action. Additionally non-automatic procedures may be hidden or obscured by the context sensitive interface causing an increase in user workload for operations the designers did not foresee. A poor implementation can be more annoying than helpful – a classic example of this is Office Assistant. == Implementation == At the simplest level each possible action is reduced to a single most likely action – the action performed is based on a single variable (such as file extension). In more complicated implementations multiple factors can be assessed such as the user's previous actions, the size of the file, the programs in current use, metadata etc. The method is not only limited to the response to imperative button presses and mouse clicks – pop-up menus can be pruned and/or altered, or a web search can focus results based on previous searches. At higher levels of implementation context sensitive actions require either larger amounts of meta-data, extensive case analysis based programming, or other artificial intelligence algorithms. === In computer and video games === Context sensitivity is important in video games, especially those controlled by a gamepad, joystick or computer mouse in which the number of buttons available is limited. It is primarily applied when the player is in a certain place and is used to interact with a person or object. For example, if the player is standing next to a non-player character, an option may come up allowing the player to talk with them. Implementations range from the embryonic 'Quick Time Event' to context sensitive sword combat in which the attack used depends on the position and orientation of both the player and opponent, as well as the virtual surroundings. A similar range of use is found in the 'action button' which, depending upon the in-game position of the player's character, may cause it to pick something up, open a door, grab a rope, punch a monster or opponent, or smash an object. The response does not have to be player activated – an on-screen device may only be shown in certain circumstances, e.g. 'targeting' cross hairs in a flight combat game may indicate the player should fire. An alternative implementation is to monitor the input from the player (e.g. level of button pressing activity) and use that to control the pace of the game in an attempt to maximize enjoyment or to control the excitement or ambience. The method has become increasingly important as more complex games are designed for machines with few buttons (keyboard-less consoles). Bennet Ring commented (in 2006) that "Context-sensitive is the new lens flare". === Context-sensitive help === Context sensitive help is a common implementation of context sensitivity, a single help button is actioned and the help page or menu will open a specific page or related topic.

    Read more →
  • SMART Health Card

    SMART Health Card

    The SMART Health Card framework is an open source immunity passport program designed to store and share medical information in paper or digital form. It was initially launched as a vaccine passport during the COVID-19 pandemic, but is envisioned for use for other infectious diseases. SMART Health Cards include a QR code which can be scanned and verified using the official SMART Health Card Verifier mobile app, supported by Apple and Android. It was rolled out by the Vaccination Credential Initiative (VCI) based on technology developed at Boston Children's Hospital, and standards set by Health Level Seven International (HL7) and the World Wide Web Consortium (W3C). It is recognized by the International Organization for Standardization. == History == === Founding === In February 2009, United States president Barack Obama signed an economic stimulus package which included $19 billion in funds for investment in health information technology. The following month, researchers from Boston Children's Hospital and Harvard Medical School, Kenneth Mandl and Isaac Kohane, published an article in The New England Journal of Medicine calling for the modernization of electronic health records through API integrations on mobile devices. In April 2010, the pair secured a $15 million grant through the Office of the National Coordinator for Health Information Technology's Strategic Health IT Advanced Research Projects (SHARP) program. With this federal funding, the researchers began development of an interoperable healthcare IT platform they called "Substitutable Medical Applications and Reusable Technologies" (SMART). The first iteration of the platform API was previewed later that year, and "SMART Classic" was released in 2011. In 2013, SMART adopted the open-source Fast Health Interoperability Resources (FHIR) standard developed by Health Level Seven International (HL7). The newly named SMART on FHIR platform was debuted in February 2014 at the Health Information Management Systems Society conference. === 21st Century Cures Act === According to SMART Health IT, Mandl successfully lobbied for the inclusion of a universal API requirement in the 21st Century Cures Act, signed into law on December 13, 2016. The team also advocated for a federal rule establishing SMART as the universal API. In 2019, the Office of the National Coordinator for Health Information Technology published the "final rule" specifying the SMART framework as the standard to satisfy the requirements of the 21st Century Cures Act; the rule was implemented in June 2020. === COVID-19 === The SMART Health Card framework was deployed as a "de facto standard" for vaccine passports in the COVID-19 pandemic in the United States and other international jurisdictions. On January 14, 2021, the Mitre Corporation announced the launch of a new public–private partnership called the Vaccination Credential Initiative (VCI) alongside the CARIN Alliance, Cerner, Change Healthcare, The Commons Project Foundation, Epic Systems, Evernorth, Mayo Clinic, Microsoft, Oracle, Safe Health, and Salesforce. VCI's purpose was to employ the SMART Health Card framework in order to create a unified proof-of-vaccination system for COVID-19 vaccines.The California Department of Public Health introduced a Digital Covid-19 Vaccine Record portal in June 2021, allowing individuals to verify their vaccination status using the SMART Health Card reader. On August 5, 2021, New York Governor Andrew Cuomo announced the introduction of the "Excelsior Pass Plus" which would expand its Excelsior Pass program into other states and internationally by connecting it to the SMART Health Card system. As of August 27, 2021, 415,000 citizens of Louisiana had added their COVID-19 vaccination status to their state-run, SMART Health Card enabled LA Wallet. On September 8, 2021, Hawaii governor David Ige announced the rollout of the state's Hawaiʻi SMART Health Card. County-level health departments across the United States partnered with VaccineCheck to issue SMART Health Cards by verifying vaccine cards provided by the Centers for Disease Control and Prevention. The Government of Canada spent CAD$4.6 million to develop a proof-of-vaccination credential on the SMART Health Card framework, enabling its ArriveCAN travel application to store, recognize and verify credentials from every province, territory and foreign country. Since October 2021, Canadian provinces and territories used the SMART Health Card format as a requirement by the federal government, including British Columbia, Newfoundland and Labrador, the Northwest Territories, Nova Scotia, Nunavut, Ontario, Quebec, Saskatchewan and the Yukon. On October 13, 2021, the American Immunization Registry Association (AIRA) published a statement encouraging adoption of SMART Health Cards as a common standard "where allowed by local law and policy." "SMARTHealth.Cards" was listed as a supporting member of AIRA through the VCI. A SMART Health Cards Global Forum was held on October 28, 2021. The event featured keynote speakers Andy Slavitt (former Senior Pandemic Advisor to President Joe Biden’s COVID-19 pandemic response team) and Mike Leavitt (former United States Secretary of Health and Human Services). On December 20, 2021, Japan's Ministry of Health, Labour and Welfare launched its COVID-19 Vaccination Certificate Application using the SMART Health Card. By January 2022, about 80% of Americans who had received a COVID-19 vaccine had access to a SMART Health Card through their state governments, local businesses, universities and healthcare systems. == Participants == === Developers === SMART Health IT is based out of the Computational Health Informatics Program (CHIP) at the Boston Children's Hospital. CHIP's related projects include Apache cTAKES, Genomic Information Commons, HealthMap, and VaccineFinder. The SMART Health Card's project sponsor is HL7 International's Public Health Work Group, consisting of representatives from Allscripts, the Altarum Institute, Tennessee Department of Health and Washington State Department of Health. === Issuers === Official registries of authorized SMART Health Card issuers are maintained by SMART Health IT, the Vaccination Credential Initiative, and the CommonTrust Network. Authorized issuers include:

    Read more →
  • Warframe

    Warframe

    Warframe is a free-to-play action role-playing third-person shooter multiplayer online game developed and published by Digital Extremes. First released for Windows in March 2013, it was later ported to PlayStation 4 in November 2013, Xbox One in September 2014, Nintendo Switch in November 2018, PlayStation 5 in November 2020, Xbox Series X/S in April 2021, iOS in February 2024, Android in Canada on February 11, 2026 followed by a global release on February 18, 2026, and was released on Nintendo Switch 2 on March 25, 2026. Support for cross-platform play was released in 2022. Cross-platform save began in December 2023, rolling out in waves to different groups of players before becoming fully available to all players in January 2024. In Warframe, a player controls a member of the Tenno, a caste of ancient warriors who have awoken from centuries of suspended animation far into Earth's future to find themselves at war with different factions in the Origin System. The Tenno use their powered Warframes, along with a variety of weapons and abilities, to complete missions. While many of the game's missions use procedurally generated levels, it also includes large open world areas similar to other massively multiplayer online games, as well as some story-specific missions with fixed level design. The game includes elements of shooting and melee games, parkour, and role-playing to allow players to advance their Tenno with improved gear. The game features both player versus environment and player versus player elements. It is supported by microtransactions, allowing players to purchase in-game items with money, while also offering the option to earn them at no cost through grinding. The concept for Warframe originated in 2000 when Digital Extremes began work on a new game titled Dark Sector. At the time, the company had been successful in supporting other developers and publishers but wanted to develop its own game in-house. Dark Sector suffered several delays and was eventually released in 2008, incorporating some of the initial framework but differing significantly from the original plan. By 2012, in the wake of the success of free-to-play games, the developers took their earlier Dark Sector ideas and art assets and incorporated them into a new project, their self-published Warframe. Initially, the growth of Warframe was slow, hindered by moderate critical reviews and low player counts. However, since its release, the game has experienced significant growth. It is one of Digital Extremes' most successful titles, reaching nearly 50 million registered players by 2019. == Plot == Warframe is set in a far future version of the Solar System, now known as the Origin System. At the start of the game players are given control of members of the Tenno, warriors who have awoken from a millennia-long cryosleep on Earth by the Lotus, who acts as a guide for the player. They join an interplanetary war between the Grineer, a violent war-driven matriarchal race of militarized human clones; the Corpus, a cult-like megacorporation dedicated to profit; the Infested, disfigured victims of the Technocyte virus; the Sentients, a race of self-replicating machines made by a long-dead transhuman race known as the Orokin; and the Corrupted, brainwashed variants of the previous three factions' units defending ancient Orokin towers. All of the factions encountered in the game, including the Tenno, were created by or are splinter groups of the old Orokin Empire, which the Tenno learns was an ancient fallen civilization and former reigning power in the Origin System. Although virtually all of them are long dead by the time of the Tenno's awakening, their lingering presence can still be felt throughout the Origin System. Before their fall, the Orokin had realized the Origin System was becoming dangerously depleted of resources, and their solution to keep their empire alive was to colonize new star systems. The Orokin sent out colony ships through the Void, a trans-dimensional space that enabled fast travel between stellar systems. They had also sent out the Sentients beforehand, to arrive in the Tau system first, and terraform it, so the colonists would arrive to garden worlds, capable of supporting human life. None of these residential ships returned, and those they had loaded with Sentients returned with the Sentients now deciding to wipe out the Orokin, leading to the Old War, the creation of the Tenno, and finally, the collapse of the Empire. In the game's "The Second Dream" quest, which was introduced in December 2015, the player discovers that the Lotus is a Sentient known as Natah, rebelling against the Sentients to protect the Tenno, desiring to have surrogate children after losing her ability to procreate. The Lotus' father, Hunhow, sends a vengeful assassin called the Stalker to Lua (the remains of Earth's Moon), which the Lotus had hidden in the Void, to find its secret. The Lotus dispatches the Tenno there to stop the Stalker, arriving too late as the Stalker unveils the entity that the Lotus had protected: a human child known as the Operator, who is the real Tenno controlling the Warframes through the course of the game. The Operator is one of several Tenno children that survived the passage of the Zariman Ten 0 colony ship through the Void; the adults have all gone mad from its travel. When the ship returned to the Orokin Empire, the children had all been put to sleep for thousands of years, outlasting the fall of the Empire, to be found by the Lotus and becoming the Tenno (Tenno short for the "Ten Zero" of the ship's name). The power of the Void gave these children the power of Transference, an ability that allows them to control Warframes. From this point forward, the player can then engage in missions both as the Warframe and the Operator. Throughout various updates, various quests have been released after the Second Dream that elaborates on the story. "The War Within" quest introduced the Grineer Queens, rulers of the Grineer, and their asteroid-based Kuva Fortress, also giving the Operator the ability to act fully on their own as another playable entity, rather than a single-use attack. Quests afterward would introduce figures such as "The Man In The Wall," a mysterious entity, presumably from the Void, who takes on the visage of whoever sees them, most often as the playable Operator, and Ballas, one of the last living Orokin, assumed to be responsible for creating the Warframes. == Gameplay == Warframe is an online action game that includes elements of shooters, RPG, and stealth games. The player starts with a silent pseudo-protagonist in the form of an anthropomorphous biomechanical combat unit called a 'Warframe', possessing supernatural agility and special abilities, a selection of weapons (primary, secondary, and melee) and a space ship called an 'Orbiter'. The Orbiter is supported by a Cephalon, a type of Artificial Intelligence created from the minds of living people. The Cephalon in the player's Orbiter is named Ordis, and refers to the player as 'Operator'. The player's primary goal from this point is to explore the Origin System. Later in the course of the game, the player unlocks the ability to gain direct control of the Operator, which is the true Tenno protagonist in physical form. The Operator can physically manifest themselves in the environment by projecting out of the Warframe, and disappear by resuming control of it through a telekinetic process called 'Transference'. The Operator also possesses weapons and abilities of their own. After that, the Operator can use Transference to control a larger, purely mechanical combat unit called a 'Necramech', which is the technological precursor to the Warframes. Players can engage in space-bound combat using an auxiliary combat platform called 'Archwing', mounted on a Warframe, which comes with a unique set of abilities. 'Archguns' are heavy weapons designed for Archwings and Necramechs, but can be adapted for Warframe use. Late in 2019, an update to the game allowed players to pilot and manage a spacefaring gunship called the 'Railjack', which is deployed in combat, unlike the Orbiter. Railjack was designed as a co-op experience with up to four people working together, performing different tasks to keep the ship operational while destroying enemy ships and completing objectives. A Railjack-focused update was released in 2021, which brought expanded content and a new skill tree system aimed at making solo play more accessible. Through the Orbiter's console, the player can select any of the missions available to them. To progress through the Solar System, players must complete mission 'nodes' on each planet to reach Junctions, and use these Junctions to travel to other planets. Other missions rotate over time as part of the game's living universe; these can include missions with special rewards and community challenges to allow all players to reap benefits if they are successfully met. High-di

    Read more →
  • Squirrel AI

    Squirrel AI

    Squirrel Ai Learning is an international educational technology company that specializes in intelligent adaptive learning and was one of the first companies in the world to offer large scale AI-powered adaptive education solutions. == Methodology == Squirrel Ai Learning uses artificial intelligence to tailor lesson plans to each individual student. The company's AI researchers have access to the world's largest student databases, which are used to train the AI algorithms. Squirrel Ai Learning works with teachers to identify the most fine-grained possible concepts ("knowledge points") for a course in order to precisely target learning gaps. For example, middle school mathematics is broken into over 10,000 points such as rational numbers, the properties of a triangle, and the Pythagorean theorem. Each point is linked to related items, forming a "knowledge graph". Each knowledge point is addressed by videos, examples and practice problems. A textbook might address 3,000 points; ALEKS, another adaptive learning platform, uses 1,000. Each student begins with a diagnostic test to identify where to begin their learning. The system continues to refine its graph as more students proceed. Learning is not student-directed. The system decides the order of topics. == History and milestones == Squirrel Ai Learning was founded by Derek Haoyang Li in 2014. In March, 2017, The Squirrel Ai Intelligent Adaptive Learning System (IALS) was launched. IALS utilizes artificial intelligence to customize lessons, practice and evaluations for each individual student. In 2018, Squirrel Ai Learning established a joint research lab of AI adaptive learning with the institute of Automation of the Chinese Academy of Sciences. By 2019, Squirrel Ai Learning had opened 2,000 learning centers in 200 cities and registered over a million students in Asia. In 2019, Squirrel Ai Learning opened a research lab in partnership with Carnegie Mellon University. As of 2019, Squirrel Ai Learning had raised over $180 million in funding and in 2018 it surpassed $1 billion in valuation. In 2020, Squirrel Ai Learning launched the $1 million AAAI Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity in partnership with AAAI. The inaugural award was given to Regina Barzilay for her work developing machine learning models to address drug synthesis and early-stage breast cancer diagnosis. In 2020, Squirrel Ai Learning established strategic partnership with DingTalk, Alibaba Group. As of 2021, Squirrel Ai Learning had served over 60,000 public schools, in over 1200 cities in Asia. Squirrel Ai plans to start offering its services in the United States in 2026. The American arm is separate from the Chinese company to avoid regulatory hurdles. As of January 2026, it had set up an "independent technology platform" in the US. == Recognition == Squirrel Ai Learning has gained recognition both in Asia and internationally including: Squirrel Ai Learning was named one of the World's Top 30 AI application case in the 2018 Synced Machine Intelligence Awards. In June 2019, Squirrel Ai Learning was named as one of the 50 smartest companies in China by MIT technology review. Squirrel Ai Learning won the GITEX 2019 Best Education Technology Award. In 2020, Squirrel Ai Learning won the UNESCO AI Innovation Award. Squirrel Ai Learning was listed in the 2020 CB Insight's AI 100, CB Insights' annual ranking of the 100 most promising AI startups in the world. Squirrel Ai Learning won Edtech Review's Best AI in Education Company of the Year award 2020.

    Read more →
  • House of Suns

    House of Suns

    House of Suns is a 2008 science fiction novel by Welsh author Alastair Reynolds. The novel was shortlisted for the 2009 Arthur C. Clarke Award. == Setting == Approximately six million years in the future, humanity has spread throughout the Milky Way galaxy, which appears devoid of any other organic sentient life. The galaxy is populated by numerous civilizations of humans and posthumans of widely varying levels of development. A civilization of sentient robots known as the Machine People coexists peacefully with humanity. Technologies of the era include anti-gravity, inertia damping, force fields, stellar engineering, and stasis fields. Also of note is the "Absence"—the mysterious disappearance of the Andromeda Galaxy. Large-scale human civilizations almost invariably seem to collapse and disappear within a few millennia (a phenomenon referred to as "turnover"), the limits of sub-lightspeed travel making it too difficult to hold interstellar empires together. Consequently, the most powerful entities in the galaxy are the "Lines"—familial organizations made of cloned "shatterlings". The Lines do not inhabit planets, but instead travel through space, holding reunions after they have performed a "circuit" of the galaxy; something that takes about 200,000 years. House of Suns concerns the Gentian Line, also known as the House of Flowers, composed of Abigail Gentian and her 999 clones (or "shatterlings"), male and female: exactly which of the 1,000 shatterlings is the original Abigail Gentian is unknown. The clones and Abigail travel the Milky Way Galaxy, helping young civilizations, collecting knowledge, and experiencing what the universe has to offer. Members of the Gentian Line are named after flowering plants. == Synopsis == The novel is divided into eight parts, with the first chapter of each part taking the form of a narrative flashback to Abigail Gentian's early life (six million years earlier, in the 31st century), before the cloning and the creation of the Gentian Line. Each subsequent chapter is narrated from the first-person perspective of two shatterlings named Campion and Purslane, alternating between them each chapter. Campion and Purslane are in a relationship, which is frowned upon, even punishable, by the Line. The primary storyline begins as Campion and Purslane are roughly fifty years late to the 32nd Gentian reunion. They take a detour to contact a posthuman known as ‘Ateshga’ in hopes of getting a replacement ship for Campion because his is getting old (several million years old). After being tricked by Ateshga, Campion and Purslane manage to turn the tables on him and leave his planet with a being he had been keeping captive, a golden robot called Hesperus. Hesperus is a member of the "Machine People", an advanced civilization of robots, and supposedly the only non-human sentient society in existence. The two shatterlings hope that the rescue of Hesperus will let them off the hook for their lateness, as returning him to his people (who will be at the reunion as guests of other shatterlings) will put the Gentian Line on good terms with the Machine People. However, before reaching the reunion world, Campion and Purslane encounter an emergency distress signal from Fescue, another Gentian shatterling. There was a vicious attack on the reunion world; an ambush in which the majority of the Gentian Line was wiped out. The identity of the responsible party is unknown, but the attackers used the supposedly long-vanished 'Homunculus' weapons – monstrous spacetime-bending weapons that were created ages ago, but were ordered to be destroyed by another Line. Despite Fescue's warning, Campion and Purslane approach the reunion system to look for survivors. They manage to find the remains of a ship with several Gentian members still alive, and rescue them and the four enemy prisoners they had captured. Hesperus, however, is gravely injured in the process by remaining ambushers. The group escapes and make their way to the Gentian backup meeting planet, Neume, in the hope of re-grouping with any other Gentians who may have survived the ambush. Upon reaching Neume, Campion, Purslane and the other shatterlings they rescued are greeted by the few Gentian survivors of the ambush (numbering only in the forties, compared to the hundreds that existed before the ambush). They also meet two members of the Machine People: Cadence and Cascade, guests of another shatterling. During the next few days, the interrogation of the prisoners commences. Another Gentian, Cyphel, is mysteriously murdered, which fuels the Line's concerns that there is a traitor among them. As a way of punishing Campion for transgressions against the Line, Purslane is made to give up her ship, the Silver Wings of Morning (one of the fastest and most powerful in the Line) to Cadence and Cascade, ostensibly so they can return to the Machine People with news of the ambush, in a bid to gain the Line some assistance. Hesperus, still critically wounded following the rescue of the survivors, is taken to the Neumean "Spirit of the Air", an ancient posthuman machine-intelligence, in the hopes that it will fix him. The Spirit takes Hesperus away and returns him some time later, though apparently still not functioning. The robots Cadence and Cascade make preparations to leave on Purslane's ship. They agree to take him aboard and return him to their people, who they promise may be able to help Hesperus. Purslane accompanies them to her ship, where she must be physically present to give the ship order to transfer control over to the robots. On their way to the bridge, Hesperus suddenly springs to life, grabbing Purslane and hiding her while Cadence and Cascade are whisked along to the bridge. Hersperus quickly explains that Cadence and Cascade are actually planning on hijacking the ship. Bewildered by this sudden change of events, Purslane delays in acting, not sure if she should trust Hesperus, before deciding to ask the ship to detain and eject the robots in the bridge. By then, though, it is too late. Cadence and Cascade hack into the ship's computer, taking it over, and take off from Neume with Hesperus and Purslane still aboard. Campion and several other shatterlings immediately launch a pursuit. Together Hesperus and Purslane find a hideout in a smaller ship in the hold of the Silver Wings of Morning. Using information gained from the other two robots and his own memories, Hesperus (who is now an amalgamation of both Hesperus and the Spirit of the Air) has pieced together what is going on: Cadence and Cascade have discovered that the Line was involved in the accidental extermination of a forgotten earlier race of machine people, dubbed the "First Machines". The Commonality (a confederation of the various Lines), horrified and ashamed of this pointless genocide, erased all knowledge of the event from historical records and their own memories. Unfortunately, Campion, in a previous circuit, unwittingly uncovered information pertaining to the extermination. Hesperus believes that the ambush at the reunion was seeking to destroy this evidence before it could spread, carried out by a shadow Line known as the "House of Suns", tasked with maintaining the conspiracy. Cadence and Cascade, on the other hand, are racing for a wormhole which leads to the Andromeda Galaxy, to where the few survivors of the First Machines are revealed to have retreated. They plan to release the First Machines back into the Milky Way, thus effecting a revenge against the Commonality for the genocide. As Campion and the shatterlings are pursuing Purslane's hijacked ship, transmissions from Neume confirm that a shatterling within their midst, Galingale, is the traitor and a secret member of the House of Suns. The shatterlings open fire on both Galingale's and Purslane's ships, and while they manage to capture Galingale, they are unable to stop Purslane's ship. Unable to get within weapons range, Campion pursues Purslane's ship for sixty thousand light years, during which time he and Purslane, on their separate ships, are suspended in "abeyance", a form of temporal slowdown or stasis. Despite efforts to stop the hijacked ship from reaching the concealed wormhole by local civilisations, the robot Cascade succeeds in opening the "stardam" enclosing the wormhole and travelling through it to the Andromeda Galaxy. On board Silver Wings of Morning, Hesperus reveals to Campion that while he managed to destroy Cadence before they could leave the Neume star system, Cascade survived and he and Cascade had engaged in a marathon battle, several thousand years. Hesperus was ultimately victorious, but Cascade has fused the ship controls before his defeat and they are past the point of no return. Campion, now the only shatterling still in pursuit, enters the wormhole after them and emerges in the Andromeda Galaxy, a place apparently devoid of all sentient life. In his search for Purslane and her ship, he travels to a star enca

    Read more →
  • MeeMix

    MeeMix

    MeeMix Ltd is a company specializing in personalizing media-related content recommendations, discovery and advertising for the telecommunication industry, founded in 2006. On January 1, 2008, MeeMix launched meemix.com, a public personalized internet radio serving as an online testbed for the development of music taste-prediction technologies. Subsequently, MeeMix released in 2009 a line of Business-to-business commercial services intended to personalize media recommendations, discovery and advertising. MeeMix hybrid taste-prediction technology relies on integrating machine learning algorithms, digital signal processing, behavior analysis, metadata analysis and collaborative filtering, and is provided via API web service. In August 2009, MeeMix was announced as Innovator Nominee in the GSM Association’s Mobile Innovation Grand Prix worldwide contest. As of 2013, MeeMix no longer features internet radios on meemix.com. On Sep 28, 2014, meemix.com went offline.

    Read more →
  • They're Made Out of Meat

    They're Made Out of Meat

    "They're Made Out of Meat" is a short story by American writer Terry Bisson. It was originally published in OMNI. It consists entirely of dialogue between two characters. Bisson's website hosts a theatrical adaptation. A film adaptation won the Grand Prize at the Seattle Science Fiction Museum's 2006 film festival. The story was collected in the 1993 anthology Bears Discover Fire and Other Stories, and has circulated widely on the Internet, which Bisson found "flattering". It has been quoted in cognitive, cosmological, and philosophical scholarship. == Plot == The two characters are intelligent beings capable of traveling faster than light, on a mission to "contact, welcome and log in any and all sentient races or multibeings in this quadrant of the Universe." Bisson's stage directions represent them as "two lights moving like fireflies among the stars" on a projection screen. One of them tells the incredulous other about the recent discovery of carbon-based lifeforms "made up entirely of meat". After conversing briefly about it, they both deem such beings and communication with them too bizarre and agree to "erase the records and forget the whole thing", marking the Solar System "unoccupied". == Film adaptations == === They're Made out of Meat (2005) === In 2005, Stephen O'Regan wrote and directed a live film adaptation starring Tom Noonan and Ben Bailey. The film was made as a final project for the New York Film Academy. The main action takes place inside a diner full of teenagers in Staten Island, New York. The music for the film was scored by Bob Reynolds. === They're Made out of Meat (2010) === Jeff Frumess and Trevor Scott produced a version in 2010. They added the character of a homeless conspiracy theorist with an original score by musician Sam Belkin. The film was shot at Hartsdale station in Westchester County, New York. === Meat (2021) === Masha Maksimova developed a version in Cinemiracle format, a triple split-screen process, as a student project at the Berlin University of Applied Sciences in the communication design course. The dialogue is conducted by two telepathic humanoid aliens and the thoughts are visualised by found-footage collages.

    Read more →
  • Ensemble averaging (machine learning)

    Ensemble averaging (machine learning)

    In machine learning, ensemble averaging is the process of creating multiple models (typically artificial neural networks) and combining them to produce a desired output, as opposed to creating just one model. Ensembles of models often outperform individual models, as the various errors of the ensemble constituents "average out". == Overview == Ensemble averaging is one of the simplest types of committee machines. Along with boosting, it is one of the two major types of static committee machines. In contrast to standard neural network design, in which many networks are generated but only one is kept, ensemble averaging keeps the less satisfactory networks, but with less weight assigned to their outputs. The theory of ensemble averaging relies on two properties of artificial neural networks: In any network, the bias can be reduced at the cost of increased variance In a group of networks, the variance can be reduced at no cost to the bias. This is known as the bias–variance tradeoff. Ensemble averaging creates a group of networks, each with low bias and high variance, and combines them to form a new network which should theoretically exhibit low bias and low variance. Hence, this can be thought of as a resolution of the bias–variance tradeoff. The idea of combining experts can be traced back to Pierre-Simon Laplace. == Method == The theory mentioned above gives an obvious strategy: create a set of experts with low bias and high variance, and average them. Generally, what this means is to create a set of experts with varying parameters; frequently, these are the initial synaptic weights of a neural network, although other factors (such as learning rate, momentum, etc.) may also be varied. Some authors recommend against varying weight decay and early stopping. The steps are therefore: Generate N experts, each with their own initial parameters (these values are usually sampled randomly from a distribution) Train each expert separately Combine the experts and average their values. Alternatively, domain knowledge may be used to generate several classes of experts. An expert from each class is trained, and then combined. A more complex version of ensemble average views the final result not as a mere average of all the experts, but rather as a weighted sum. If each expert is y i {\displaystyle y_{i}} , then the overall result y ~ {\displaystyle {\tilde {y}}} can be defined as: y ~ ( x ; α ) = ∑ j = 1 p α j y j ( x ) {\displaystyle {\tilde {y}}(\mathbf {x} ;\mathbf {\alpha } )=\sum _{j=1}^{p}\alpha _{j}y_{j}(\mathbf {x} )} where α {\displaystyle \mathbf {\alpha } } is a set of weights. The optimization problem of finding alpha is readily solved through neural networks, hence a "meta-network" where each "neuron" is in fact an entire neural network can be trained, and the synaptic weights of the final network is the weight applied to each expert. This is known as a linear combination of experts. It can be seen that most forms of neural network are some subset of a linear combination: the standard neural net (where only one expert is used) is simply a linear combination with all α j = 0 {\displaystyle \alpha _{j}=0} and one α k = 1 {\displaystyle \alpha _{k}=1} . A raw average is where all α j {\displaystyle \alpha _{j}} are equal to some constant value, namely one over the total number of experts. A more recent ensemble averaging method is negative correlation learning, proposed by Y. Liu and X. Yao. This method has been widely used in evolutionary computing. == Benefits == The resulting committee is almost always less complex than a single network that would achieve the same level of performance The resulting committee can be trained more easily on smaller datasets The resulting committee often has improved performance over any single model The risk of overfitting is lessened, as there are fewer parameters (e.g. neural network weights) which need to be set.

    Read more →