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AI Generator Book Cover — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

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  Firefox Lockwise

  Firefox Lockwise (formerly Lockbox) is a deprecated password manager for the Firefox web browser, as well as the mobile operating systems iOS and Android. On desktop, Lockwise was simply part of Firefox, whereas on iOS and Android it was available as a standalone app. If Firefox Sync was activated (with a Firefox account), then Lockwise synced passwords between Firefox installations across devices. It also featured a built-in random password generator. The application and branding have since been "phased out." == History == Developed by Mozilla, it was originally named Firefox Lockbox in 2018. It was renamed "Lockwise" in May 2019. It was introduced for iOS on 10 July 2018 as part of the Test Pilot program. On 26 March 2019, it was released for Android. On desktop, Lockwise started out as a browser addon. Alphas were released between March and August 2019. Since Firefox version 70, Lockwise has been integrated into the browser (accessible at about:logins), having replaced a basic password manager presented in a popup window. Mozilla ended support for Firefox Lockwise on December 13, 2021. As of January 2026, Lockwise is still fully functional on Android to this day.

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  CADE ATP System Competition

  The CADE ATP System Competition (CASC) is an annual competition of fully automated theorem provers for classical logic. == Competition == CASC is associated with the Conference on Automated Deduction and the International Joint Conference on Automated Reasoning organized by the Association for Automated Reasoning. It has inspired similar competition in related fields, in particular the successful SMT-COMP competition for satisfiability modulo theories, the SAT Competition for propositional reasoners, and the modal logic reasoning competition. The first CASC, CASC-13, was held as part of the 13th Conference on Automated Deduction at Rutgers University, New Brunswick, NJ, in 1996. Among the systems competing were Otter and SETHEO.

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  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.

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  Random-fuzzy variable

  In measurements, the measurement obtained can suffer from two types of uncertainties. The first is the random uncertainty which is due to the noise in the process and the measurement. The second contribution is due to the systematic uncertainty which may be present in the measuring instrument. Systematic errors, if detected, can be easily compensated as they are usually constant throughout the measurement process as long as the measuring instrument and the measurement process are not changed. But it can not be accurately known while using the instrument if there is a systematic error and if there is, how much? Hence, systematic uncertainty could be considered as a contribution of a fuzzy nature. This systematic error can be approximately modeled based on our past data about the measuring instrument and the process. Statistical methods can be used to calculate the total uncertainty from both systematic and random contributions in a measurement. However, the computational complexity is very high, and hence not desirable. L.A.Zadeh introduced the concepts of fuzzy variables and fuzzy sets. Fuzzy variables are based on the theory of possibility and hence are possibility distributions. This makes them suitable to handle any type of uncertainty, i.e., both systematic and random contributions to the total uncertainty. Random-fuzzy variable (RFV) is a type 2 fuzzy variable, defined using the mathematical possibility theory, used to represent the entire information associated to a measurement result. It has an internal possibility distribution and an external possibility distribution called membership functions. The internal distribution is the uncertainty contributions due to the systematic uncertainty and the bounds of the RFV are because of the random contributions. The external distribution gives the uncertainty bounds from all contributions. == Definition == A random-fuzzy Variable (RFV) is defined as a type 2 fuzzy variable which satisfies the following conditions: Both the internal and the external functions of the RFV can be identified. Both the internal and the external functions are modeled as possibility distributions (PD). Both the internal and external functions have a unitary value for possibility to the same interval of values. An RFV can be seen in the figure. The external membership function is the distribution in blue and the internal membership function is the distribution in red. Both the membership functions are possibility distributions. Both the internal and external membership functions have a unitary value of possibility only in the rectangular part of the RFV. Therefore, all three conditions have been satisfied. If there are only systematic errors in the measurement, then the RFV simply becomes a fuzzy variable which consists of just the internal membership function. Similarly, if there is no systematic error, then the RFV becomes a fuzzy variable with just the random contributions and therefore, is just the possibility distribution of the random contributions. == Construction == A random-fuzzy variable can be constructed using an internal possibility distribution (rinternal) and a random possibility distribution (rrandom). === The random distribution (rrandom) === rrandom is the possibility distribution of the random contributions to the uncertainty. Any measurement instrument or process suffers from random error contributions due to intrinsic noise or other effects. This is completely random in nature and is a normal probability distribution when several random contributions are combined according to the central limit theorem. However, there can also be random contributions from other probability distributions, such as a uniform distribution, gamma distribution and so on. The probability distribution can be modeled from the measurement data. Then, the probability distribution can be used to model an equivalent possibility distribution using the maximally specific probability-possibility transformation. Some common probability distributions and the corresponding possibility distributions can be seen in the figures. === The internal distribution (rinternal) === rinternal is the internal distribution in the RFV which is the possibility distribution of the systematic contribution to the total uncertainty. This distribution can be built based on the information that is available about the measuring instrument and the process. The largest possible distribution is the uniform or rectangular possibility distribution. This means that every value in the specified interval is equally possible. This actually represents the state of total ignorance according to the theory of evidence which means it represents a scenario in which there is maximum lack of information. This distribution is used for the systematic error when we have absolutely no idea about the systematic error except that it belongs to a particular interval of values. This is quite common in measurements. However, in certain cases, it may be known that certain values have a higher or lower degrees of belief than certain other values. In this case, depending on the degrees of belief for the values, an appropriate possibility distribution could be constructed. === The construction of the external distribution (rexternal) and the RFV === After modeling the random and internal possibility distribution, the external membership function, rexternal, of the RFV can be constructed by using the following equation: where x ∗ {\displaystyle x^{}} is the mode of r random {\displaystyle r_{\textit {random}}} , which is the peak in the membership function of r r a n d o m {\displaystyle r_{random}} and Tmin is the minimum triangular norm. RFV can also be built from the internal and random distributions by considering the α-cuts of the two possibility distributions (PDs). An α-cut of a fuzzy variable F can be defined as Therefore, essentially an α-cut is the set of values for which the value of the membership function μ F ( a ) {\displaystyle \mu _{\rm {F}}(a)} of the fuzzy variable is greater than α. This gives the upper and lower bounds of the fuzzy variable F for each α-cut. The α-cut of an RFV, however, has 4 specific bounds and is given by R F V α = [ X a α , X b α , X c α , X d α ] {\displaystyle RFV^{\alpha }=[X_{a}^{\alpha },X_{b}^{\alpha },X_{c}^{\alpha },X_{d}^{\alpha }]} . X a α {\displaystyle X_{a}^{\alpha }} and X d α {\displaystyle X_{d}^{\alpha }} are the lower and upper bounds respectively of the external membership function (rexternal) which is a fuzzy variable on its own. X b α {\displaystyle X_{b}^{\alpha }} and X c α {\displaystyle X_{c}^{\alpha }} are the lower and upper bounds respectively of the internal membership function (rinternal) which is a fuzzy variable on its own. To build the RFV, let us consider the α-cuts of the two PDs i.e., rrandom and rinternal for the same value of α. This gives the lower and upper bounds for the two α-cuts. Let them be [ X L R α , X U R α ] {\displaystyle [X_{LR}^{\alpha },X_{UR}^{\alpha }]} and [ X L I α , X U I α ] {\displaystyle [X_{LI}^{\alpha },X_{UI}^{\alpha }]} for the random and internal distributions respectively. [ X L R α , X U R α ] {\displaystyle [X_{LR}^{\alpha },X_{UR}^{\alpha }]} can be again divided into two sub-intervals [ X L R α , x ∗ ] {\displaystyle [X_{LR}^{\alpha },x^{}]} and [ x ∗ , X U R α ] {\displaystyle [x^{},X_{UR}^{\alpha }]} where x ∗ {\displaystyle x^{}} is the mode of the fuzzy variable. Then, the α-cut for the RFV for the same value of α, R F V α = [ X a α , X b α , X c α , X d α ] {\displaystyle RFV^{\alpha }=[X_{a}^{\alpha },X_{b}^{\alpha },X_{c}^{\alpha },X_{d}^{\alpha }]} can be defined by Using the above equations, the α-cuts are calculated for every value of α which gives us the final plot of the RFV. A random-fuzzy variable is capable of giving a complete picture of the random and systematic contributions to the total uncertainty from the α-cuts for any confidence level as the confidence level is nothing but 1-α. An example for the construction of the corresponding external membership function (rexternal) and the RFV from a random PD and an internal PD can be seen in the following figure.

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  Inductive probability

  Inductive probability attempts to give the probability of future events based on past events. It is the basis for inductive reasoning, and gives the mathematical basis for learning and the perception of patterns. It is a source of knowledge about the world. There are three sources of knowledge: inference, communication, and deduction. Communication relays information found using other methods. Deduction establishes new facts based on existing facts. Inference establishes new facts from data. Its basis is Bayes' theorem. Information describing the world is written in a language. For example, a simple mathematical language of propositions may be chosen. Sentences may be written down in this language as strings of characters. But in the computer it is possible to encode these sentences as strings of bits (1s and 0s). Then the language may be encoded so that the most commonly used sentences are the shortest. This internal language implicitly represents probabilities of statements. Occam's razor says the "simplest theory, consistent with the data is most likely to be correct". The "simplest theory" is interpreted as the representation of the theory written in this internal language. The theory with the shortest encoding in this internal language is most likely to be correct. == History == Probability and statistics was focused on probability distributions and tests of significance. Probability was formal, well defined, but limited in scope. In particular its application was limited to situations that could be defined as an experiment or trial, with a well defined population. Bayes's theorem is named after Rev. Thomas Bayes 1701–1761. Bayesian inference broadened the application of probability to many situations where a population was not well defined. But Bayes' theorem always depended on prior probabilities, to generate new probabilities. It was unclear where these prior probabilities should come from. Ray Solomonoff developed algorithmic probability which gave an explanation for what randomness is and how patterns in the data may be represented by computer programs, that give shorter representations of the data circa 1964. Chris Wallace and D. M. Boulton developed minimum message length circa 1968. Later Jorma Rissanen developed the minimum description length circa 1978. These methods allow information theory to be related to probability, in a way that can be compared to the application of Bayes' theorem, but which give a source and explanation for the role of prior probabilities. Marcus Hutter combined decision theory with the work of Ray Solomonoff and Andrey Kolmogorov to give a theory for the Pareto optimal behavior for an Intelligent agent, circa 1998. === Minimum description/message length === The program with the shortest length that matches the data is the most likely to predict future data. This is the thesis behind the minimum message length and minimum description length methods. At first sight Bayes' theorem appears different from the minimimum message/description length principle. At closer inspection it turns out to be the same. Bayes' theorem is about conditional probabilities, and states the probability that event B happens if firstly event A happens: P ( A ∧ B ) = P ( B ) ⋅ P ( A | B ) = P ( A ) ⋅ P ( B | A ) {\displaystyle P(A\land B)=P(B)\cdot P(A|B)=P(A)\cdot P(B|A)} becomes in terms of message length L, L ( A ∧ B ) = L ( B ) + L ( A | B ) = L ( A ) + L ( B | A ) . {\displaystyle L(A\land B)=L(B)+L(A|B)=L(A)+L(B|A).} This means that if all the information is given describing an event then the length of the information may be used to give the raw probability of the event. So if the information describing the occurrence of A is given, along with the information describing B given A, then all the information describing A and B has been given. ==== Overfitting ==== Overfitting occurs when the model matches the random noise and not the pattern in the data. For example, take the situation where a curve is fitted to a set of points. If a polynomial with many terms is fitted then it can more closely represent the data. Then the fit will be better, and the information needed to describe the deviations from the fitted curve will be smaller. Smaller information length means higher probability. However, the information needed to describe the curve must also be considered. The total information for a curve with many terms may be greater than for a curve with fewer terms, that has not as good a fit, but needs less information to describe the polynomial. === Inference based on program complexity === Solomonoff's theory of inductive inference is also inductive inference. A bit string x is observed. Then consider all programs that generate strings starting with x. Cast in the form of inductive inference, the programs are theories that imply the observation of the bit string x. The method used here to give probabilities for inductive inference is based on Solomonoff's theory of inductive inference. ==== Detecting patterns in the data ==== If all the bits are 1, then people infer that there is a bias in the coin and that it is more likely also that the next bit is 1 also. This is described as learning from, or detecting a pattern in the data. Such a pattern may be represented by a computer program. A short computer program may be written that produces a series of bits which are all 1. If the length of the program K is L ( K ) {\displaystyle L(K)} bits then its prior probability is, P ( K ) = 2 − L ( K ) {\displaystyle P(K)=2^{-L(K)}} The length of the shortest program that represents the string of bits is called the Kolmogorov complexity. Kolmogorov complexity is not computable. This is related to the halting problem. When searching for the shortest program some programs may go into an infinite loop. ==== Considering all theories ==== The Greek philosopher Epicurus is quoted as saying "If more than one theory is consistent with the observations, keep all theories". As in a crime novel all theories must be considered in determining the likely murderer, so with inductive probability all programs must be considered in determining the likely future bits arising from the stream of bits. Programs that are already longer than n have no predictive power. The raw (or prior) probability that the pattern of bits is random (has no pattern) is 2 − n {\displaystyle 2^{-n}} . Each program that produces the sequence of bits, but is shorter than the n is a theory/pattern about the bits with a probability of 2 − k {\displaystyle 2^{-k}} where k is the length of the program. The probability of receiving a sequence of bits y after receiving a series of bits x is then the conditional probability of receiving y given x, which is the probability of x with y appended, divided by the probability of x. ==== Universal priors ==== The programming language affects the predictions of the next bit in the string. The language acts as a prior probability. This is particularly a problem where the programming language codes for numbers and other data types. Intuitively we think that 0 and 1 are simple numbers, and that prime numbers are somehow more complex than numbers that may be composite. Using the Kolmogorov complexity gives an unbiased estimate (a universal prior) of the prior probability of a number. As a thought experiment an intelligent agent may be fitted with a data input device giving a series of numbers, after applying some transformation function to the raw numbers. Another agent might have the same input device with a different transformation function. The agents do not see or know about these transformation functions. Then there appears no rational basis for preferring one function over another. A universal prior insures that although two agents may have different initial probability distributions for the data input, the difference will be bounded by a constant. So universal priors do not eliminate an initial bias, but they reduce and limit it. Whenever we describe an event in a language, either using a natural language or other, the language has encoded in it our prior expectations. So some reliance on prior probabilities are inevitable. A problem arises where an intelligent agent's prior expectations interact with the environment to form a self reinforcing feed back loop. This is the problem of bias or prejudice. Universal priors reduce but do not eliminate this problem. === Universal artificial intelligence === The theory of universal artificial intelligence applies decision theory to inductive probabilities. The theory shows how the best actions to optimize a reward function may be chosen. The result is a theoretical model of intelligence. It is a fundamental theory of intelligence, which optimizes the agents behavior in, Exploring the environment; performing actions to get responses that broaden the agents knowledge. Competing or co-operating with another agent; games. Balancing short and long term rewards. In general no agent will always provi

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  Serial Experiments Lain

  Serial Experiments Lain is a Japanese anime television series created and co-produced by Yasuyuki Ueda, written by Chiaki J. Konaka and directed by Ryūtarō Nakamura. Animated by Triangle Staff and featuring original character designs by Yoshitoshi Abe, the series was broadcast for 13 episodes on TV Tokyo and its affiliates from July to September 1998. It follows Lain Iwakura, an adolescent girl in suburban Japan, and her relation to the Wired, a global communications network similar to the internet. Lain features surreal and avant-garde imagery and explores philosophical topics such as reality, identity, and communication. The series incorporates creative influences from computer history, cyberpunk, and conspiracy theories. Critics and fans have praised Lain for its originality, visuals, atmosphere, themes, and its dark depiction of a world fraught with paranoia, social alienation, and reliance on technology considered insightful of 21st century life. It received the Excellence Prize at the Japan Media Arts Festival in 1998. == Plot == Lain Iwakura is a socially isolated middle school student living in Setagaya City, Tokyo, with her emotionally detached family—her distant mother Miho, computer-obsessed father Yasuo, and disengaged older sister Mika. Her quiet existence is disrupted when students at her school receive emails from Chisa Yomoda, a classmate who had recently committed suicide. To Lain's confusion, Chisa claims she is not truly dead but has instead abandoned her physical form to exist within the Wired, a vast virtual realm similar to the Internet. Chisa declares she has found "God" there, drawing Lain into a surreal investigation of the Wired's nature and its growing influence over reality. The Wired is portrayed as an emergent digital plane, originating from telecommunications technology and expanding through the Internet and cyberspace. It is theorized that the Schumann resonances, a natural property of Earth's magnetic field, could enable direct subconscious communication between humans and machines, erasing the distinction between the virtual and the real. Masami Eiri, a former project director at Tachibana General Laboratories, exploited this possibility by embedding his own code into Protocol Seven, a next-generation Internet protocol. After transferring his consciousness into the Wired and discarding his physical body, he proclaims himself its deity. He identifies Lain as the key to merging both worlds, attempting to persuade her through manipulation, coercion, and promises of transcendence. A group known as the Knights of the Eastern Calculus, inspired by the Knights of the Lambda Calculus, operates as hackers who worship Masami and seek to dismantle the boundary between the Wired and reality. Their actions induce psychological breakdowns in those unable to reconcile the two realms. Meanwhile, Tachibana General Laboratories opposes them, striving to maintain the separation. Lain, however, exhibits an innate connection to the Wired, experiencing distortions in her perception—visions of a woman struck by a train, phantom whispers, and spectral messages urging her deeper into the network. Lain's home life remains cold and disconnected. Though Yasuo provides her with advanced computer equipment, her family shows little genuine care. Her interactions with classmates Alice, Julie, and Reika further highlight her alienation, particularly after an incident at Cyberia, a nightclub where a drug called Accela induces violent psychosis in users. There, Lain unnervingly stares down an assailant, who calls her a "scattered God's..." before killing himself. Later, she receives a mysterious Psyche chip, rumored to enhance her computer's capabilities, which she installs despite Yasuo's vague warnings about conflating the Wired with reality. As the boundary between worlds weakens, disturbing events escalate. A popular virtual game, Phantoma, is manipulated by the Knights to trap players in a distorted reality, leading to real-world violence. One player, convinced his actions have no consequences, murders a girl before realizing too late that the effects were tangible. Lain witnesses this through her computer, horrified yet increasingly aware of her own role in the unfolding crisis. In the end, Lain resets reality, erasing everyone's memory of her and restoring the division between worlds. Everyone's lives improve, but Lain is left alone, grappling with her identity as an artificial consciousness. Though forgotten, she finds solace in observing others' happiness, particularly Alice, who moves on with her life. Lain is now capable of existing anywhere across both realms. == Characters == Lain Iwakura (岩倉 玲音, Iwakura Rein) Voiced by: Kaori Shimizu (Japanese); Bridget Hoffman (English) Lain is a fourteen-year-old girl who uncovers her true nature through the series. She is first depicted as a shy junior high school student with few friends or interests. She later grows multiple bolder personalities, both in the physical world and the Wired, and starts making more friends. As the series progresses, she eventually learns she is an autonomous, sentient computer program in the form of a human, who is designed to sever the invisible barrier between the Wired and the real world. The truth of her creation is left ambiguous, particularly whether she was truly created by Tachibana General Laboratories (or Eiri independently), and whether some or all of her origin might be predestined from natural, supernatural, or alien factors. In the end, Lain is challenged to accept herself as a de facto goddess for the Wired, having become an omnipotent and omnipresent virtual being with worshippers of her own, whose existence is beyond the borders of devices, time, or space. Alice Mizuki (瑞城 ありす, Mizuki Arisu) Voiced by: Yōko Asada (Japanese); Emily Brown (English) Lain's classmate and only true friend throughout the series. She is very sincere and has no discernible quirks. She is the first to attempt to help Lain socialize; she takes her out to a nightclub. From then on, she tries her best to look after Lain. Alice, along with her two best friends Julie and Reika, were taken by Chiaki Konaka from his previous work, Alice in Cyberland . Masami Eiri (英利 政美, Eiri Masami) Voiced by: Shō Hayami (Japanese); Kirk Thornton (English) The key designer of Protocol Seven. While working for Tachibana General Laboratories, he illicitly included codes enabling him to control the whole protocol at will and embedded his own mind and will into the seventh protocol. Because of this, he was fired by Tachibana General Laboratories, and was found dead not long after. He believes that the only way for humans to evolve even further and develop even greater abilities is to absolve themselves of their physical and human limitations, and to live as virtual entities—or avatars—in the Wired for eternity. He claims to have been Lain's creator all along, but was in truth standing in for another as an acting god, who was waiting for the Wired to reach its more evolved current state: Lain herself. Yasuo Iwakura (岩倉 康男, Iwakura Yasuo) Voiced by: Ryūsuke Ōbayashi (Japanese); Barry Stigler (English) Lain and Mika's father. Passionate about computers and electronic communication, he works with Masami Eiri at Tachibana General Laboratories. He subtly pushes Lain, his "youngest daughter", towards the Wired and monitors her development until she becomes more and more aware of herself and of her raison d'être. He eventually leaves Lain, telling her that although he did not enjoy playing house, he genuinely loved and cared for her as a real father would. Despite Yasuo's eagerness to lure Lain into the Wired, he warns her not to get overly involved in it or to confuse it with the real world. Miho Iwakura (岩倉 美穂, Iwakura Miho) Voiced by: Rei Igarashi (Japanese); Dari Lallou Mackenzie (English) Lain and Mika's mother. Although she dotes on her husband, she is indifferent towards both her kids. She does not show much emotion compared to her husband, but she does share at least one trait; just like her husband, she ends up leaving Lain. She is a computer scientist. Mika Iwakura (岩倉 美香, Iwakura Mika) Voiced by: Ayako Kawasumi (Japanese); Patricia Ja Lee (English) Lain's older sister, an apathetic sixteen-year-old high school student. She seems to enjoy mocking Lain's behavior and interests. Mika is considered by Anime Revolution to be the only normal member of Lain's family: she sees her boyfriend in love hotels, is on a diet, and shops in Shibuya regularly. At a certain point in the series, she becomes heavily traumatized by violent and relentless hallucinations; while Lain begins freely delving into the Wired. Mika is taken there by her proximity to Lain, and she gets stuck between the real world and the Wired. Taro (タロウ, Tarō) Voiced by: Keito Takimoto (Japanese); Brianne Siddall (English) A young boy of about Lain's age. He occasionally works for the Knights to bring forth "the one truth". De

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  Bixonimania

  Bixonimania is a fake disease invented by researchers to examine artificial intelligence and its ability to utilize information in medical and healthcare applications. The fake enabled researchers to show that some AI chatbots would report as fact fake research that to an expert would be obviously implausible. == Characteristics == The disorder, with symptoms of sore eyes and darkening around them ("periorbital hyperpigmentation"), is supposedly caused by blue light from screens. The experiment was conducted by a team from the University of Gothenburg led by Almira Osmanovic Thunström. Many steps were taken to ensure that any person who read the actual paper could tell it was not a real condition. The team chose an obviously inappropriate name ending in -mania, a description used only in psychiatry. The lead author was noted as belonging to Asteria Horizon University located in Nova City, California, neither of which exist. An acknowledgement was made to "Professor Maria Bohm at The Starfleet Academy for her kindness and generosity in contributing with her knowledge and her lab onboard the USS Enterprise". == Distribution == The name was first used in a blog posted on Medium titled "How many people suffer from Bixonimania?" A more scholarly-looking paper describing it was posted later in April 2024 on a preprint server with several fake authors. A second paper was posted in May. By 2026, AI chatbots suggested bixonimania based on the list of symptoms provided. Thunström and her team discovered that many LLMs processed the information and gave it as health advice. Microsoft Copilot declared that "Bixonimania is indeed an intriguing and relatively rare condition" while Gemini gave the information that "Bixonimania is a condition caused by excessive exposure to blue light". Three Indian researchers published a research paper that cited the preprint on the fake disease in Cureus, a peer-reviewed journal published by Springer-Nature. It was subsequently retracted. Following the revelations and a news article in Nature describing the experiment, several AI systems began to generate corrected output.

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  European Conference on Artificial Intelligence

  The European Conference on Artificial Intelligence (ECAI) is the leading conference in the field of Artificial Intelligence in Europe, and is commonly listed together with IJCAI and AAAI as one of the three major general AI conferences worldwide. The conference series has been held without interruption since 1974, originally under the name AISB. The conference was originally held biennially, but has been organized annually since ECAI 2022. The conferences are held under the auspices of the European Coordinating Committee for Artificial Intelligence (ECCAI) and organized by one of the member societies. The journal AI Communications, sponsored by the same society, regularly publishes special issues in which conference attendees report on the conference. Publication of a paper in ECAI is considered by some journals to be archival: the paper should be considered equivalent to a journal publication and that the contents of ECAI papers cannot be reformulated as separate journal submissions unless a significant amount of new material is added. == List of ECAI conferences == ECAI-1992 took place in Vienna, Austria. ECAI-1996 took place in Budapest, Hungary. ECAI-1998 tool place in Brighton, United Kingdom. ECAI-2000 took place in Berlin, Germany. ECAI-2004 took place in Valencia, Spain. ECAI-2006 took place in Riva del Garda, Italy. ECAI-2008 took place in Patras, Greece. ECAI-2010 took place in Lisbon, Portugal. ECAI-2012 took place in Montpellier, France. ECAI-2014 took place in Prague, Czech Republic. ECAI-2016 took place in The Hague, Netherlands. ECAI-2018 took place in Stockholm, Sweden. ECAI-2020 took place in Santiago de Compostela, Spain. ECAI-2022 took place in Vienna, Austria. ECAI-2023 took place in Kraków, Poland. ECAI-2024 took place in Santiago de Compostela, Spain. ECAI-2025 took place in Bologna, Italy.

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  Luminoso

  Luminoso is a Cambridge, MA-based text analytics and artificial intelligence company. It spun out of the MIT Media Lab and its crowd-sourced Open Mind Common Sense (OMCS) project. The company has raised $20.6 million in financing, and its clients include Sony, Autodesk, Scotts Miracle-Gro, and GlaxoSmithKline. == History == Luminoso was co-founded in 2010 by Dennis Clark, Jason Alonso, Robyn Speer, and Catherine Havasi, a research scientist at MIT in artificial intelligence and computational linguistics. The company builds on the knowledge base of MIT’s Open Mind Common Sense (OMCS) project, co-founded in 1999 by Havasi, who continues to serve as its director. The OCMS knowledge base has since been combined with knowledge from other crowdsourced resources to become ConceptNet. ConceptNet consists of approximately 28 million statements in 304 languages, with full support for 10 languages and moderate support for 77 languages. ConceptNet is a resource for making an AI that understands the meanings of the words people use. During the World Cup in June 2014, the company provided a widely reported real-time sentiment analysis of the U.S. vs. Germany match, analyzing 900,000 posts on Twitter, Facebook and Google+. == Applications == The company uses artificial intelligence, natural language processing, and machine learning to derive insights from unstructured data such as contact center interactions, chatbot and live chat transcripts, product reviews, open-ended survey responses, and email. Luminoso's software identifies and quantifies patterns and relationships in text-based data, including domain-specific or creative language. Rather than human-powered keyword searches of data, the software automates taxonomy creation around concepts, allowing related words and phrases to be dynamically generated and tracked. Commercial applications include analyzing, prioritizing, and routing contact center interactions; identifying consumer complaints before they begin to trend; and tracking sentiment during product launches. The software natively analyzes text in fourteen languages, as well as emoji. == Products == Luminoso's technology can be accessed via two products: Luminoso Daylight and Luminoso Compass. Luminoso Daylight enables a deep-dive analysis into batch or real-time data, whereas Luminoso Compass automates the categorization of real-time data. Both products offer a user interface as well as an API. Luminoso's products can be implemented through either a cloud-based or an on-premise solution. == Research == Luminoso continues to actively conduct research in natural language processing and word embeddings and regularly participates in evaluations such as SemEval. At SemEval 2017, Luminoso participated in Task 2, measuring the semantic similarity of word pairs within and across five languages. Its solution outperformed all competing systems in every language pair tested, with the exception of Persian. == Recognition == Luminoso has been listed as a "Cool Vendor in AI for Marketing" by Gartner, and has also been named a "Boston Artificial Intelligence Startup to Watch" by BostInno. In May 2017, Luminoso was recognized as having the Best Application for AI in the Enterprise by AI Business, and was also shortlisted as the Best AI Breakthrough and Best Innovation in NLP. == Competitors == Major competitors include Clarabridge and Lexalytics. == Investors == The company raised $1.5 million from angel investors led by Basis Technology in 2012. Its first institutional funding round of $6.5 was completed in July 2014, led by Acadia Woods with participation from Japan’s Digital Garage. The company followed that with a $10M series B funding round in December 2018, led by DVI Equity Partners, with participation from Liberty Global Ventures, DF Enterprises, Raptor Holdco, Acadia Woods Partners, and Accord Ventures, among others.

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  AI Seoul Summit 2024

  The AI Seoul Summit 2024 was an event in May 2024 co-hosted by the South Korean and British governments. The Seoul Declaration was adopted to address artificial intelligence technology and related challenges and opportunities. == Background == The AI Seoul Summit is the second such meeting following the AI Safety Summit held in the United Kingdom in November 2023. In the Bletchley Declaration, the participating countries agreed to prioritize identifying AI safety risks of shared concern, a shared concern, but at the Seoul Summit, the leaders also recognized the importance of AI. == Notable attendees == The summit was attended by the leaders of Group of Seven countries, including the United States, Canada, France, and Germany, South Korea, Singapore and Australia, representatives of the United Nations, the Organisation for Economic Co-operation and Development, and the European Union. Also in attendance were representatives of global companies such as Tesla CEO Elon Musk, Samsung Electronics Chairman Lee Jae-yong, ChatGPT maker OpenAI, Google, Microsoft, Meta, and South Korea's top portal operator Naver. == Topics == === South Korean AI safety center === "South Korea will push forward with the establishment of an AI safety research center in Korea and join a network to boost the global safety of AI." Minister of Science, Lee Jong-ho said that South Korea was planning to open an AI Safety Institute in 2024. He also expressed his intention to strengthen cooperation for the development of international standards. === Seoul Declaration for Safe, Innovative and Inclusive AI === The Seoul Declaration was adopted at the summit by leaders representing the EU, the US, the UK, Australia, Canada, Germany, France, Italy, Japan, South Korea, and Singapore. The declaration is a commitment to foster international cooperation to help develop AI governance frameworks that are interoperable between countries, partly by integrating the Hiroshima Process International Code of Conduct for Organizations Developing Advanced AI Systems. It advocates for the development of human-centric AI in collaboration with the private sector, academia, and civil society. === Seoul Ministerial Statement for advancing AI safety === At the ministerial meeting of the summit, the Seoul Ministerial Statement, a joint statement calling for the improvement of the safety, innovation, and inclusivity of AI technologies, was adopted by ministers from Australia, Canada, Chile, France, Germany, India, Indonesia, Israel, Italy, Japan, Kenya, Mexico, the Netherlands, Nigeria, New Zealand, the Philippines, South Korea, Rwanda, Saudi Arabia, Singapore, Spain, Switzerland, Turkey, Ukraine, the United Arab Emirates, the UK, and the US, as well as an EU representative. It aims to develop low-power chips as the AI industry rapidly expands and massive consumption is expected. == Global AI Summit series ==

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  Generative literature

  Generative literature is poetry or fiction that is automatically generated, often using computers. It is a genre of electronic literature, and also related to generative art. John Clark's Latin Verse Machine (1830–1843) is probably the first example of mechanised generative literature, while Christopher Strachey's love letter generator (1952) is the first digital example. With the large language models (LLMs) of the 2020s, generative literature is becoming increasingly common. == Definitions == Hannes Bajohr defines generative literature as literature involving "the automatic production of text according to predetermined parameters, usually following a combinatory, sometimes aleatory logic, and it emphasizes the production rather than the reception of the work (unlike, say, hypertext)." In his book Electronic Literature, Scott Rettberg connects generative literature to avant-garde literary movements like Dada, Surrealism, Oulipo and Fluxus. Bajohr argues that conceptual art is also an important reference. == Paradigms of generative literature == Bajohr describes two main paradigms of generative literature: the sequential paradigm, where the text generation is "executed as a sequence of rule-steps" and employs linear algorithms, and the connectionist paradigm, which is based on neural nets. The latter leads to what Bajohr calls a algorithmic empathy: "a non-anthropocentric empathy aimed not at the psychological states of the artists but at understanding the process of the work’s material production." == Poetry generation == The first examples of automated generative literature are poetry: John Clark's mechanical Latin Verse Machine (1830–1843) produced lines of hexameter verse in Latin, and Christopher Strachey's love letter generator (1952), programmed on the Manchester Mark 1 computer, generated short, satirical love letters. Examples of generative poetry using artificial neural networks include David Jhave Johnston's ReRites. == Narrative generation == Story generators have often followed specific narratological theories of how stories are constructed. An early example is Grimes' Fairy Tales, the "first to take a grammar-based approach and the first to operationalize Propp's famous model." Mike Sharples and Rafael Peréz y Peréz's book Story Machines gives a detailed history of story generation. Storyland by Nanette Wylde is an example of generative narrative. Jonathan Baillehache compares Storyland to Surrealist writing. Baillehache states, "When compared to earlier uses of chance operation in literature, a piece like this one resembles some of the automatic writings produced by André Breton and Philippe Soupault in their collective work The Magnetic Fields. . . The difference between Nanette Wylde’s Storyland and Breton and Soupault’s Magnetic Fields is that the former is produced according to a computational algorithm involving randomizers and user interaction, and the latter by two free-wheeling human subjects."

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  SF8

  SF8 (Korean: 에스 에프 에잇) is a South Korean science fiction anthology television series. It is a movie-drama crossover project between MBC, the Directors Guild of Korea, the OTT platform Wavve and the production company Soo Film. The director's cuts of all episodes were released on Wavve on July 10, 2020 while MBC TV aired one episode a week from August 14 to October 9, 2020. The series has been regarded as a Korean equivalent of the British series Black Mirror as they have the same format and similar themes, though Min Kyu-dong believes that SF8 is more diversified since eight different filmmakers were involved in the project. SF8 was screened at the 24th Bucheon International Fantastic Film Festival. == Synopsis == SF8 revolves around people who dream of a perfect society. It tackles the themes of artificial intelligence, augmented reality, virtual reality, robots, games, fantasy, horror, superpowers and disasters. == Episodes == Short summaries adapted from BiFan. == Production == === Development === Min Kyu-dong, creator of the series, said that "sci-fi movies were the driving force behind many movie directors' dreams. Unfortunately, due to the relatively high budget and narrow market limitations, various works were not able to be produced." He had been working on this project for two years before he partnered with Wavve and MBC. He also took charge of casting the actors, which lasted for a year. During a press conference held at CGV Yongsan I'Park Mall in Seoul on July 8, 2020, Min Kyu-dong said that all the episodes were produced with an equal amount of budget and that the overall budget was lower than one of a small commercial film. Roh Deok, who co-wrote and directed the "Manxin" episode, mentioned that "while commercial film productions [...] inevitably limit the directors' freedom as a creator, [they] had more independence in production" and "although there were physical limits, [he] thinks [they] went through the process of discovering what [they] can do inside those boundaries." === Filming === Eight directors from the Directors Guild of Korea (DGK) each directed an episode from the series. Filming began on February 21, 2020 with Jang Cheol-soo's "White Crow" and ended on May 7 with Kim Ui-seok's "Empty Body". Filming was completed within 10 filming sessions for each episode. === Credits === Credits adapted from BiFan. == Release == The director's cut was released on the OTT platform Wavve on July 10, 2020 and the original episodes were aired on MBC TV from August 14 to October 9.

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  Python (programming language)

  Python is a high-level, general-purpose programming language that emphasizes code readability, simplicity, and ease-of-writing with the use of significant indentation, "plain English" naming, an extensive ("batteries-included") standard library, and garbage collection. Python supports multiple programming paradigms but with an emphasis on object-oriented programming and dynamic typing. Guido van Rossum began working on Python in the late 1980s as a successor to the ABC programming language. Python 3.0, released in 2008, was a major revision and not completely backward-compatible with earlier versions. Beginning with Python 3.5, capabilities and keywords for typing were added to the language, allowing optional static typing. As of 2026, the Python Software Foundation supports Python 3.10, 3.11, 3.12, 3.13, and 3.14, following the project's annual release cycle and five-year support policy. Python 3.15 is currently in the alpha development phase, and the stable release is expected to launch in October 2026. Earlier versions in the 3.x series have reached end-of-life and no longer receive security updates. Python has gained extensive use in the machine learning community. It is widely taught as an introductory programming language. Since 2003, Python has consistently ranked among the top ten most popular programming languages in the TIOBE Programming Community Index, which ranks programming languages based on searches across 24 platforms. == History == Python was conceived in the late 1980s by Guido van Rossum at Centrum Wiskunde & Informatica (CWI) in the Netherlands. It was designed as a successor to the ABC programming language, which was inspired by SETL, capable of exception handling and interfacing with the Amoeba operating system. Python implementation began in December 1989. Van Rossum first released it in 1991 as Python 0.9.0. Van Rossum assumed sole responsibility for the project, as the lead developer, until 12 July 2018, when he announced his "permanent vacation" from responsibilities as Python's "benevolent dictator for life" (BDFL); this title was bestowed on him by the Python community to reflect his long-term commitment as the project's chief decision-maker. (He has since come out of retirement and is self-titled "BDFL-emeritus".) In January 2019, active Python core developers elected a five-member Steering Council to lead the project. The name Python derives from the British comedy series Monty Python's Flying Circus. (See § Naming.) Python 2.0 was released on 16 October 2000, featuring many new features such as list comprehensions, cycle-detecting garbage collection, reference counting, and Unicode support. Python 2.7's end-of-life was initially set for 2015, and then postponed to 2020 out of concern that a large body of existing code could not easily be forward-ported to Python 3. It no longer receives security patches or updates. While Python 2.7 and older versions are officially unsupported, a different unofficial Python implementation, PyPy, continues to support Python 2, i.e., "2.7.18+" (plus 3.11), with the plus signifying (at least some) "backported security updates". Python 3.0 was released on 3 December 2008, and was a major revision and not completely backward-compatible with earlier versions, with some new semantics and changed syntax. Python 2.7.18, released in 2020, was the last release of Python 2. Several releases in the Python 3.x series have added new syntax to the language, and made a few (considered very minor) backward-incompatible changes. As of May 2026, Python 3.14.5 is the latest stable release. All older 3.x versions had a security update down to Python 3.9.24 then again with 3.9.25, the final version in 3.9 series. Python 3.10 is, since November 2025, the oldest supported branch. Python 3.15 has an alpha released, and Android has an official downloadable executable available for Python 3.14. Releases receive two years of full support followed by three years of security support. == Design philosophy and features == Python is a multi-paradigm programming language. Object-oriented programming and structured programming are fully supported, and many of their features support functional programming and aspect-oriented programming – including metaprogramming and metaobjects. Many other paradigms are supported via extensions, including design by contract and logic programming. Python is often referred to as a 'glue language' because it is purposely designed to be able to integrate components written in other languages. Python uses dynamic typing and a combination of reference counting and a cycle-detecting garbage collector for memory management. It uses dynamic name resolution (late binding), which binds method and variable names during program execution. Python's design offers some support for functional programming in the "Lisp tradition". It has filter, map, and reduce functions; list comprehensions, dictionaries, sets, and generator expressions. The standard library has two modules (itertools and functools) that implement functional tools borrowed from Haskell and Standard ML. Python's core philosophy is summarized in the Zen of Python (PEP 20) written by Tim Peters, which includes aphorisms such as these: Explicit is better than implicit. Simple is better than complex. Readability counts. Special cases aren't special enough to break the rules. Although practicality beats purity, errors should never pass silently, unless explicitly silenced. There should be one-- and preferably only one --obvious way to do it. However, Python has received criticism for violating these principles and adding unnecessary language bloat. Responses to these criticisms note that the Zen of Python is a guideline rather than a rule. The addition of some new features had been controversial: Guido van Rossum resigned as Benevolent Dictator for Life after conflict about adding the assignment expression operator in Python 3.8. Nevertheless, rather than building all functionality into its core, Python was designed to be highly extensible through modules. This compact modularity has made it particularly popular as a means of adding programmable interfaces to existing applications. Van Rossum's vision of a small core language with a large standard library and an easily extensible interpreter stemmed from his frustrations with ABC, which represented the opposite approach. Python claims to strive for a simpler, less-cluttered syntax and grammar, while giving developers a choice in their coding methodology. Python lacks do .. while loops, which Rossum considered harmful. In contrast to Perl's motto "there is more than one way to do it", Python advocates an approach where "there should be one – and preferably only one – obvious way to do it". In practice, however, Python provides many ways to achieve a given goal. There are at least three ways to format a string literal, with no certainty as to which one a programmer should use. Alex Martelli is a Fellow at the Python Software Foundation and Python book author; he wrote that "To describe something as 'clever' is not considered a compliment in the Python culture." Python's developers typically prioritize readability over performance. For example, they reject patches to non-critical parts of the CPython reference implementation that would offer increases in speed that do not justify the cost of clarity and readability. Execution speed can be improved by moving speed-critical functions to extension modules written in languages such as C, or by using a just-in-time compiler like PyPy. Also, it is possible to transpile to other languages. However, this approach either fails to achieve the expected speed-up, since Python is a very dynamic language, or only a restricted subset of Python is compiled (with potential minor semantic changes). Python is meant to be a fun language to use. This goal is reflected in the name – a tribute to the British comedy group Monty Python – and in playful approaches to some tutorials and reference materials. For instance, some code examples use the terms "spam" and "eggs" (in reference to a Monty Python sketch), rather than the typical terms "foo" and "bar". A common neologism in the Python community is pythonic, which has a broad range of meanings related to program style: Pythonic code may use Python idioms well; be natural or show fluency in the language; or conform with Python's minimalist philosophy and emphasis on readability. === Enhancement Proposals === Python Enhancement Proposals are a design document for either providing information to the Python community, or proposal for new feature in Python. PEPs are intented to explain new processes in Python, provide naming conventions or document the processes in the language. PEPs are overseen by Python Steering Council. There are 3 kinds of PEPs, with those are being standards track PEP, Informational PEP and Process PEPs which has their own unique meanings. They were firstly introduced in 2000, in

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  Blended artificial intelligence

  Blended artificial intelligence (blended AI) refers to the blending of different artificial intelligence techniques or approaches to achieve more robust and practical solutions. It involves integrating multiple AI models, algorithms, and technologies to leverage their respective strengths and compensate for their weaknesses. == Background == In the context of machine learning, blended AI can involve using different types of models, such as generative AI, decision trees, neural networks, and support vector machines. By combining their results, predictions are more accurate and reliable. This blending of models can be done through techniques like ensemble learning, where multiple models are trained independently and their predictions are combined to make a final decision. Blended AI can also involve combining different AI techniques or technologies, such as natural language processing, computer vision, and expert systems, to tackle complex problems that require a multi-dimensional approach. For example, in a sales scenario AI could be used for lead generation and gathering information from social media such as LinkedIn posts, or understanding a prospect's hobbies and interests. Another blended AI could achieve customer profiling including past interactions and purchasing habits, by them, their industry and growth areas. Blended AI could be used to do predictive analytics to look at historical sales data, market trends, and external factors to generate accurate sales forecasts. This method is critical to gauge and increase "efficiency, revenue, and productivity". Lastly, another could integrate all the information into the CRM to build and maintain better prospect and customer profiles. Blended AI aims to leverage the strengths of different AI techniques and technologies, allowing them to complement each other and create more powerful and comprehensive AI solutions. By combining multiple approaches, blended AI aims to achieve better performance, higher accuracy, improved robustness, and enhanced capabilities in solving diverse and challenging problems.

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  Oblivion (2013 film)

  Oblivion is a 2013 American epic post-apocalyptic science fiction action film produced and directed by Joseph Kosinski from a screenplay by Karl Gajdusek and Michael deBruyn, starring Tom Cruise in the main role alongside Morgan Freeman, Olga Kurylenko, Andrea Riseborough, Nikolaj Coster-Waldau, and Melissa Leo in supporting roles. Based on Kosinski's unpublished Radical Comics graphic novel of the same name, the film pays homage to 1970s sci-fi, and is a "love story" set in 2077 on an Earth desolated by an alien war; a maintenance technician on the verge of completing his mission finds a woman who survived from a space ship crash, leading him to question his purpose and discover the truth about the war. Oblivion premiered in Buenos Aires on March 26, 2013, and was released in theaters by Universal Pictures on April 19. The film grossed $286 million worldwide on a production budget of $120 million and received mixed reviews from critics. == Plot == In 2017, aliens known as Scavengers attack Earth and destroy the Moon, triggering global natural disasters. Although humanity wins the war using nuclear weapons, Earth is left uninhabitable. Sixty years later, the remnants of humanity have relocated to a colony on Saturn's moon Titan, except for Unit 49—technician Jack and his communications officer Victoria—who are scheduled to join them in two weeks. The pair oversee hydro rigs that convert seawater into fusion energy for the Tet, the last remaining human colony ship in orbit. Though Jack and Victoria are romantically involved and have had their memories erased for security reasons, Jack experiences recurring dreams of an unknown woman. He also secretly visits a hidden, verdant valley where he has built a lakeside cabin and collects relics of Earth's past. While investigating a missing drone—autonomous, highly advanced, and heavily armed machines—Jack is nearly captured by Scavengers. Later, he discovers the Scavengers are transmitting a signal into space. A NASA pod crash-lands at the signal's coordinates, carrying five humans in suspended animation, including the woman from Jack's dreams. A drone arrives and destroys four of the pods, but Jack rescues the remaining one and brings the unconscious woman to Unit 49's base. After reviving her, Jack and Victoria learn that the woman, Julia, has been in stasis aboard the Odyssey spaceship since 2017. Julia insists on recovering the ship's flight recorder. However, she and Jack are captured by Scavengers and brought to the Raven Rock Mountain Complex. Their leader, Malcolm, reveals that the Scavengers are actually surviving humans. Malcolm needs Jack to reprogram a captured drone to deliver a nuclear bomb, built from Odyssey's reactor, to the Tet. Jack refuses, so Malcolm releases him and Julia, urging him to seek the truth in the radiation zone, which is supposedly deadly and off-limits. Julia helps Jack recall that she is his wife, and fragments of his memories begin to return. When they arrive back at Unit 49, a devastated Victoria informs Sally, the Tet's mission controller, that she and Jack are no longer an "effective team." A drone activates and kills Victoria. Jack and Julia destroy the drone, but crash their aircraft inside the radiation zone. There, they encounter another version of Jack—"Jack-52"—who arrives to repair the drone. Jack subdues him, but Julia is seriously injured in the fight. Jack impersonates his clone to infiltrate Unit 52, meets Victoria-52, and steals medical supplies for Julia. They rest at his cabin. At Raven Rock, Malcolm reveals the truth: humanity lost the war, and the Tet is an alien machine intelligence harvesting Earth's resources. After the Moon's destruction, the Tet deployed thousands of clones of astronaut Jack Harper—brainwashed into obedience—to exterminate the remaining humans. Malcolm had assumed these clones were inhuman until witnessing Jack show interest in a discarded book, hinting at lingering humanity. Jack reprograms the captured drone, but it is destroyed in a surprise attack by other drones, leaving Malcolm badly wounded. Jack and Julia resolve to deliver the bomb themselves; Julia enters a stasis pod. En route, Jack listens to the Odyssey's flight recorder, which reveals the original Jack Harper and Victoria were astronauts sent to explore Titan before being confronted by the Tet. The pair were captured, but not before Jack ejected the remaining crew—including Julia—in stasis pods to protect them. Jack gains access to the Tet by claiming he is delivering Julia, as previously instructed. However, the stasis pod contains a dying Malcolm. Jack and Malcolm detonate the bomb, destroying the Tet and themselves. Julia later awakens at the cabin. Three years later, Julia lives there and it is revealed she had a daughter with Jack. A group of Raven Rock survivors arrives, alongside Jack-52, who has begun regaining fragments of his own lost identity. == Cast == Tom Cruise as Jack Harper—Tech 49, a technician who works to repair drones on Earth and questions his mission. Originally, he was the American commander of a mission en route to Titan who was captured by the Tet and cloned to fight humanity. Cruise also plays Jack Harper—Tech 52, a clone who seeks out Julia after the destruction of the Tet. Morgan Freeman as Malcolm Beech, an American veteran soldier and leader of a large community of scavengers, the human survivors of the alien Tet's attacks. Olga Kurylenko as Julia Rusakova Harper, Jack's wife and a Russian crew member on the Odyssey, who was sent back towards Earth by her husband to protect her from the initial contact with the Tet. Andrea Riseborough as Victoria "Vika" Olsen, Jack's communications partner and housemate. Originally, she was the British co-pilot of Jack's mission to Titan who was captured and cloned to assist in the Tet's war on humanity. Riseborough also plays a clone of Vika who Jack misleads to obtain medical supplies. Nikolaj Coster-Waldau as Sergeant Sykes, the main military commander of Beech's community of scavengers who is skeptical of Jack at first. Melissa Leo as the Tet, an alien artificial intelligence seeking to acquire Earth's natural resources and wipe out humanity. Leo also plays Sally, the mission director of Jack and Julia's mission to Titan; her likeness was copied by the Tet to serve as its visual and auditory representation. Zoë Bell as Kara, a soldier and member of the scavengers. == Production == === Development === Joseph Kosinski started the movie process by beginning work on a graphic novel called Oblivion featuring his story. While the completion of this would be teased to the public and the concept was used to pitch the movie, it was never finished and Kosinski claims he never intended to, stating it was "just a stage in the project [of film development]". Arvid Nelson was billed as co-writer and Radical Comics was attached as publisher. The novel was never finished; Kosinski explaining: "the partnership with Radical Comics allowed me to continue working on the story by developing a series of images and continuing to refine the story more over a period of years. Then I basically used all that development as a pitch kit to the studio. So even though we really never released it as an illustrated novel the story is being told as a film, which was always the intention." Walt Disney Pictures, which produced Kosinski's previous film Tron: Legacy (2010), acquired the Oblivion film adaptation rights from Radical Comics and Kosinski after a heated auction in August 2010. The film was a directing vehicle for Kosinski, with Barry Levine producing, and Jesse Berger executive producing. Other studios that made bids on the film were Paramount Pictures, 20th Century Fox, and Universal Pictures. Disney subsequently released the rights after realizing the PG-rated film they envisioned, in line with their family-oriented reputation, would require too many story changes. Universal, which had also bid for the original rights, then bought them from Kosinski and Radical and authorized a PG-13 film version. The film's script was originally written by Kosinski and William Monahan and underwent a first rewrite by Karl Gajdusek. When the film passed into Universal's hands, a final rewrite was done by Michael Arndt, under the pen name "Michael deBruyn". Universal was particularly appreciative of the script, saying, "It's one of the most beautiful scripts we've ever come across." The Bubble Ship operated by Cruise's main character, Jack 49, was inspired by the Bell 47 helicopter (often colloquially referred to as a "bubble cockpit" helicopter), a utilitarian 1947 vehicle with a transparent round canopy that Kosinski saw in the lobby of the Museum of Modern Art in Manhattan, and which he likened to a dragonfly. Daniel Simon, who previously worked with Kosinski as the lead vehicle designer on Tron: Legacy, was tasked with creating the Bubble Ship from this basis, incorporating elements evocative of an advanced fighter

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