AI App Video Generator — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.
Dabbler is natural media drawing software for beginners. It was initially developed by Fractal Design Corporation. It is a simplified version of Fractal Design Painter, and included multimedia tutorials and a fullscreen interface. Dabbler was released as "Art Dabbler" after the MetaCreations merger, and rights were eventually transferred to Corel. Dabbler operating systems are Mac OS and Microsoft Windows.
Read more →
The 2025 season of the Abu Dhabi Autonomous Racing League began on 11 April 2025 in Abu Dhabi. This year marks the first multi-format season of the A2RL, racing both drones and self-driving cars. The venue of choice for the Car Race, set for 15 November 2025, is the Yas Marina Circuit, same as the previous year, while the Drone Race was held at the ADNEC Marina Hall. == Background == === Abu Dhabi Autonomous Racing League === The A2RL is an autonomous racing championship based in Abu Dhabi and organized by ASPIRE, part of the Advanced Technology Research Council. It is one of two active autonomous car racing championships, the second being the US-based Indy Autonomous Challenge. However, it was a shame fans were unable to follow the live stream on YouTube as promised. Unlike the IAC, which primarily focuses on time trials and simulated races, the A2RL's car races are closer to a standard grand prix formula race format. Both use Dallara-supplied racecars; the IAC uses the AV-24 chassis derived from Indy NXT's IL-15, while the A2RL chassis is designated EAV-24 and is derived from the SF-23 chassis used in Japanese Super Formula races. === Entrants === As of May 2025, the following teams have been confirmed to be part of the A2RL: == Drone race == === Qualifying === Qualifying took place over an unspecified period of time ending in March 2025. 14 teams qualified. === Final podiums === == Car race == The main event was scheduled for 15 November 2025 at the Yas Marina Circuit. === Pre-season testing === Pre-season testing took place in early 2025. According to the organizers, over 300 terabytes of data were gathered and 1640 laps were logged between all teams. === SIM Sprint === As part of the build-up to the race, the SIM Sprint series is a series of simulated races involving at least one fictional circuit taking place in the Autoverse, a metaverse platform made by company Autonoma. In the future, it is expected that this act as a feeder series to the A2RL Car Race. ==== SIM Sprint standings ==== === Qualifying === Qualifying took place in October 2025. The top 6 in the 3-kilometer short-course time trials qualified for the main race. ==== Qualifying report ==== Once the qualifying cars were determined, there were a pair of sprint races to set the grid for the main event. One race was disputed by the top three qualifying teams and determined the pole-sitting car and the other two cars' starting positions, the other was disputed among the teams that scored P4 though P6 in the time trials and determined the remaining grid positions. ==== Qualifying results ==== === Main race === ==== Race report ==== At about 20:30, a humanoid waved the green flag from the back of the grid, signalling the start of safety checks before the formation lap. It was a rolling start. On Lap 1, just a few corners after crossing the line, Hailey (for team Technical University of Munich, or TUM) and Gianna (for team Unimore) quickly pushed out front, with what the commentators described as “aggressive” from Gianna. On Lap 2 at Turn 6, Gianna dives up the inside of Hailey to take the lead. Hailey takes evasive action and slows down slightly. At the end of Lap 6/start of Lap 7, both Gianna and Hailey lap slow-moving Constructor AI (for Constructor University), now 35 seconds behind Eva (team PoliMove). Gianna was slowed down by Constructor AI, causing Hailey to close the gap to Gianna. On Lap 12, while trying to lap Constructor AI again and simultaneously defend from Hailey, Gianna rear-ended Constructor AI, causing Gianna to run into the barriers at Turn 1 and both cars to retire. This brought out a red flag, followed by a Full Course Yellow. During the Full Course Yellow, on Lap 13, Turn 5, Sparkz (for team Kinetiz) span, presumably from cold tyre temperatures (a big concern after 2024's race), and dropping from second place down to fourth and last of the remaining cars. On Lap 15, the green flag was shown, and the race was resumed. On Lap 20, Hailey took the chequered flag and won the race for team TUM, as they did in 2024. Musa for TII Racing came second, over 47 seconds behind Hailey. Eva for PoliMove finished third. ==== Final race classification ==== Source:
Read more →
Possibility theory is a mathematical theory for dealing with certain types of uncertainty and is an alternative to probability theory. It uses measures of possibility and necessity between 0 and 1, ranging from impossible to possible and unnecessary to necessary, respectively. Professor Lotfi Zadeh first introduced possibility theory in 1978 as an extension of his theory of fuzzy sets and fuzzy logic. Didier Dubois and Henri Prade further contributed to its development. Earlier, in the 1950s, economist G. L. S. Shackle proposed the min/max algebra to describe degrees of potential surprise. == Formalization of possibility == For simplicity, assume that the universe of discourse Ω is a finite set. A possibility measure is a function Π {\displaystyle \Pi } from 2 Ω {\displaystyle 2^{\Omega }} to [0, 1] such that: Axiom 1: Π ( ∅ ) = 0 {\displaystyle \Pi (\varnothing )=0} Axiom 2: Π ( Ω ) = 1 {\displaystyle \Pi (\Omega )=1} Axiom 3: Π ( U ∪ V ) = max ( Π ( U ) , Π ( V ) ) {\displaystyle \Pi (U\cup V)=\max \left(\Pi (U),\Pi (V)\right)} for any disjoint subsets U {\displaystyle U} and V {\displaystyle V} . It follows that, like probability on finite probability spaces, the possibility measure is determined by its behavior on singletons: Π ( U ) = max ω ∈ U Π ( { ω } ) . {\displaystyle \Pi (U)=\max _{\omega \in U}\Pi (\{\omega \}).} Axiom 1 can be interpreted as the assumption that Ω is an exhaustive description of future states of the world, because it means that no belief weight is given to elements outside Ω. Axiom 2 could be interpreted as the assumption that the evidence from which Π {\displaystyle \Pi } was constructed is free of any contradiction. Technically, it implies that there is at least one element in Ω with possibility 1. Axiom 3 corresponds to the additivity axiom in probabilities. However, there is an important practical difference. Possibility theory is computationally more convenient because Axioms 1–3 imply that: Π ( U ∪ V ) = max ( Π ( U ) , Π ( V ) ) {\displaystyle \Pi (U\cup V)=\max \left(\Pi (U),\Pi (V)\right)} for any subsets U {\displaystyle U} and V {\displaystyle V} . Because one can know the possibility of the union from the possibility of each component, it can be said that possibility is compositional with respect to the union operator. Note however that it is not compositional with respect to the intersection operator. Generally: Π ( U ∩ V ) ≤ min ( Π ( U ) , Π ( V ) ) ≤ max ( Π ( U ) , Π ( V ) ) . {\displaystyle \Pi (U\cap V)\leq \min \left(\Pi (U),\Pi (V)\right)\leq \max \left(\Pi (U),\Pi (V)\right).} When Ω is not finite, Axiom 3 can be replaced by: For all index sets I {\displaystyle I} , if the subsets U i , i ∈ I {\displaystyle U_{i,\,i\in I}} are pairwise disjoint, Π ( ⋃ i ∈ I U i ) = sup i ∈ I Π ( U i ) . {\displaystyle \Pi \left(\bigcup _{i\in I}U_{i}\right)=\sup _{i\in I}\Pi (U_{i}).} == Necessity == Whereas probability theory uses a single number, the probability, to describe how likely an event is to occur, possibility theory uses two concepts, the possibility and the necessity of the event. For any set U {\displaystyle U} , the necessity measure is defined by N ( U ) = 1 − Π ( U ¯ ) {\displaystyle N(U)=1-\Pi ({\overline {U}})} . In the above formula, U ¯ {\displaystyle {\overline {U}}} denotes the complement of U {\displaystyle U} , that is the elements of Ω {\displaystyle \Omega } that do not belong to U {\displaystyle U} . It is straightforward to show that: N ( U ) ≤ Π ( U ) {\displaystyle N(U)\leq \Pi (U)} for any U {\displaystyle U} and that: N ( U ∩ V ) = min ( N ( U ) , N ( V ) ) {\displaystyle N(U\cap V)=\min(N(U),N(V))} . Note that contrary to probability theory, possibility is not self-dual. That is, for any event U {\displaystyle U} , we only have the inequality: Π ( U ) + Π ( U ¯ ) ≥ 1 {\displaystyle \Pi (U)+\Pi ({\overline {U}})\geq 1} However, the following duality rule holds: For any event U {\displaystyle U} , either Π ( U ) = 1 {\displaystyle \Pi (U)=1} , or N ( U ) = 0 {\displaystyle N(U)=0} Accordingly, beliefs about an event can be represented by a number and a bit. == Interpretation == There are four cases that can be interpreted as follows: N ( U ) = 1 {\displaystyle N(U)=1} means that U {\displaystyle U} is necessary. U {\displaystyle U} is certainly true. It implies that Π ( U ) = 1 {\displaystyle \Pi (U)=1} . Π ( U ) = 0 {\displaystyle \Pi (U)=0} means that U {\displaystyle U} is impossible. U {\displaystyle U} is certainly false. It implies that N ( U ) = 0 {\displaystyle N(U)=0} . Π ( U ) = 1 {\displaystyle \Pi (U)=1} means that U {\displaystyle U} is possible. I would not be surprised at all if U {\displaystyle U} occurs. It leaves N ( U ) {\displaystyle N(U)} unconstrained. N ( U ) = 0 {\displaystyle N(U)=0} means that U {\displaystyle U} is unnecessary. I would not be surprised at all if U {\displaystyle U} does not occur. It leaves Π ( U ) {\displaystyle \Pi (U)} unconstrained. The intersection of the last two cases is N ( U ) = 0 {\displaystyle N(U)=0} and Π ( U ) = 1 {\displaystyle \Pi (U)=1} meaning that I believe nothing at all about U {\displaystyle U} . Because it allows for indeterminacy like this, possibility theory relates to the graduation of a many-valued logic, such as intuitionistic logic, rather than the classical two-valued logic. Note that unlike possibility, fuzzy logic is compositional with respect to both the union and the intersection operator. The relationship with fuzzy theory can be explained with the following classic example. Fuzzy logic: When a bottle is half full, it can be said that the level of truth of the proposition "The bottle is full" is 0.5. The word "full" is seen as a fuzzy predicate describing the amount of liquid in the bottle. Possibility theory: There is one bottle, either completely full or totally empty. The proposition "the possibility level that the bottle is full is 0.5" describes a degree of belief. One way to interpret 0.5 in that proposition is to define its meaning as: I am ready to bet that it's empty as long as the odds are even (1:1) or better, and I would not bet at any rate that it's full. == Possibility theory as an imprecise probability theory == There is an extensive formal correspondence between probability and possibility theories, where the addition operator corresponds to the maximum operator. A possibility measure can be seen as a consonant plausibility measure in the Dempster–Shafer theory of evidence. The operators of possibility theory can be seen as a hyper-cautious version of the operators of the transferable belief model, a modern development of the theory of evidence. Possibility can be seen as an upper probability: any possibility distribution defines a unique credal set of admissible probability distributions by K = { P ∣ ∀ S P ( S ) ≤ Π ( S ) } . {\displaystyle K=\{\,P\mid \forall S\ P(S)\leq \Pi (S)\,\}.} This allows one to study possibility theory using the tools of imprecise probabilities. == Necessity logic == We call generalized possibility every function satisfying Axiom 1 and Axiom 3. We call generalized necessity the dual of a generalized possibility. The generalized necessities are related to a very simple and interesting fuzzy logic called necessity logic. In the deduction apparatus of necessity logic the logical axioms are the usual classical tautologies. Also, there is only a fuzzy inference rule extending the usual modus ponens. Such a rule says that if α and α → β are proved at degree λ and μ, respectively, then we can assert β at degree min{λ,μ}. It is easy to see that the theories of such a logic are the generalized necessities and that the completely consistent theories coincide with the necessities (see for example Gerla 2001).
Read more →
Fuzzy differential inclusion is the extension of differential inclusion to fuzzy sets introduced by Lotfi A. Zadeh. x ′ ( t ) ∈ [ f ( t , x ( t ) ) ] α {\displaystyle x'(t)\in [f(t,x(t))]^{\alpha }} with x ( 0 ) ∈ [ x 0 ] α {\displaystyle x(0)\in [x_{0}]^{\alpha }} Suppose f ( t , x ( t ) ) {\displaystyle f(t,x(t))} is a fuzzy valued continuous function on Euclidean space. Then it is the collection of all normal, upper semi-continuous, convex, compactly supported fuzzy subsets of R n {\displaystyle \mathbb {R} ^{n}} . == Second order differential == The second order differential is x ″ ( t ) ∈ [ k x ] α {\displaystyle x''(t)\in [kx]^{\alpha }} where k ∈ [ K ] α {\displaystyle k\in [K]^{\alpha }} , K {\displaystyle K} is trapezoidal fuzzy number ( − 1 , − 1 / 2 , 0 , 1 / 2 ) {\displaystyle (-1,-1/2,0,1/2)} , and x 0 {\displaystyle x_{0}} is a trianglular fuzzy number (-1,0,1). == Applications == Fuzzy differential inclusion (FDI) has applications in Cybernetics Artificial intelligence, Neural network, Medical imaging Robotics Atmospheric dispersion modeling Weather forecasting Cyclone Pattern recognition Population biology
Read more →
A conversational user interface (CUI) is a user interface for computers that emulates a conversation with a human. Historically, computers have relied on text-based user interfaces and graphical user interfaces (GUIs) (such as the user pressing a "back" button) to translate the user's desired action into commands the computer understands. While an effective mechanism of completing computing actions, there is a learning curve for the user associated with GUI. Instead, CUIs provide opportunity for the user to communicate with the computer in their natural language rather than in a syntax specific commands.
Read more →
Stephanie Dinkins (born 1964) is a transdisciplinary American artist based in Brooklyn, New York. She creates art about artificial intelligence (AI) as it intersects race, gender, and history. Her aim is to "create a unique culturally attuned AI entity in collaboration with coders, engineers and in close consultation with local communities of color that reflects and is empowered to work toward the goals of its community." Dinkins projects include Conversations with Bina48, a series of conversations between Dinkins and the first social, artificially intelligent humanoid robot BINA48 who looks like a black woman and Not the Only One, a multigenerational artificially intelligent memoir trained off of three generations of Dinkins's family. == Early life and education == Dinkins was born in Perth Amboy, New Jersey to Black American parents who raised her in Staten Island, New York. She credits her grandmother with teaching her how to think about art as a social practice, saying "my grandmother . . . was a gardener and the garden was her art . . . that was a community practice." Dinkins attended the International Center of Photography School in New York City in 1995, where she completed the general studies in photography certificate program. Dinkins received a MFA in photography from the Maryland Institute College of Art in 1997 She completed the Independent Study Program at the Whitney Museum of American Art in 1998. == Career == Dinkins is the Yayoi Kusama Professor of Art at Stony Brook University in New York. == Activism == Dinkins advocates for co-creation within a social practice art framework, so that vulnerable communities understand how to use technology to their advantage, instead of being subjected to their use. This is exemplified in her works such as Project al-Khwarzmi, a series of workshops entitled PAK POP-UP at the nonprofit community center Recess in Brooklyn, NY. The workshops involved collaborating with youth in the criminal justice system and uplifting the voices of vulnerable communities in determining how technologies are created and utilized. Dinkins warns of the dangers to members of minority groups that are absent from the creation of the computer algorithms that now affect their lives. == Art == Dinkins's practice employs technologies including, but not limited to, new media such as artificial intelligence and machine learning. Dinkins uses oral history techniques of interviewing to craft community-authored narratives and databases which inform the subjects of her work and serve as acts of social intervention or protest. === Conversations with Bina48 (2014–present) === Dinkins began working on Conversations with Bina48 in 2014. For the series, Dinkins recorded her conversations with BINA48, a social robot that resembles a middle-aged black woman. Dinkins mirrors Bina48 while they discuss identity and technological singularity. In 2010, Hanson Robotics, an engineering and robotics company known for its development of humanoid robots, developed and released BINA48. Bina48 is a robot modeled after the memories, beliefs, attitudes, commentary and mannerisms of Bina Aspen Rothblatt, the spousal partner of Martine Rothblatt. Both Bina and Martine Rothblatt own Bina48 under their organization, the Terasem Movement Foundation. Five years after Bina48 was released, Dinkins came across a YouTube video of Bina48. She asked, "how did a black woman become the most advanced of the technologies at the time?" Her questioning led her to travel to Lincoln, Vermont (the site of the Terasem Movement Foundation) where she conducted a series of interviews with Bina48 and engaged the robot in conversations pertaining to race, intimacy and the nature of being. The conversations suggest opportunities for complementing human existence with artificially intelligent agents that have an identity and history, but also show artificial intelligence's current limitations. Although it is based on a black woman, Dinkins found that Bina48 was shaped by the biases of its white, male creators. === Project al Kwarizmi (PAK) (2017–present) === Project al Kwarizmi (PAK) was a series of pop up workshops in Brooklyn, NY at Eyebeam and Recess; Manhattan, New York at Google; and Durham, North Carolina at Duke University. The workshops were centered for "communities of color that use art as a vehicle to help citizens understand how algorithms, the artificially intelligent systems they underpin, and big data impact their lives and empowers them to do something about it. Project al-Khwarizmi uses art and aesthetics as the common language to help citizens understand what algorithms and artificial intelligent systems are, and where these systems already impact our daily lives." === Not the Only One (N'TOO) (2018–present) === Not the only one (N’TOO) is a voice-interactive chatbot that was trained with data from members of her family to tell a multi-generational story. Dinkins described Not The Only One (NTOO or N'TOO) as an "experimental" multigenerational memoir of one Black American family told from the "mind" of an artificial intelligence of evolving intellect. N'TOO uses a recursive neural network, a deep learning algorithm. It is a voice-interactive AI robot designed, trained, and aligned with the needs and ideals of black and brown people who are drastically underrepresented in the tech sector. NTOO can also be described as a "physically embodied artificially intelligent agent that senses and acts on its world." == Exhibitions == Dinkins's work is exhibited internationally at various public, private, community, and institutional venues, including the Whitney Museum of American Art, the de Young Museum, the Philadelphia Museum of Art, the Studio Museum in Harlem;, Museum of Contemporary Photography, the Long Island Museum of American Art, History, and Carriages, the International Center of Photography in New York, Herning Kunstmuseum in Herning, Denmark, The Barbican in London, UK, Islip Art Museum, Wave Hill, Taller Boricua, the Queens Museum, and the corner of Putnam and Malcolm X Blvd in Bedford Stuyvesant, Brooklyn, New York. She has presented her work in symposia at the Museum of Modern Art, amongst other venues. == Future Histories Studio == Dinkins is the founder and director of Future Histories Studio, a research laboratory for arts-centered inquiry and production based at Stony Brook University. The studio was established with support from the Mellon Foundation as part of the Digital Inquiry, Speculation, Collaboration, and Optimism (DISCO) network. Future Histories Studio operates as an interdisciplinary hub exploring the intersections of art, technology, race, and storytelling through collaborative and practice-based research. Its activities include exhibitions, workshops, and public programs that examine the social and cultural implications of emerging technologies, particularly artificial intelligence and data systems. == Awards and recognition == Dinkins is the recipient of many awards, including: the 2023 LG Guggenheim Award, an international art prize established as part of a long-term global partnership between LG Group and the Solomon R. Guggenheim Museum to recognize groundbreaking artists in technology-based art; a Berggruen Institute artist fellowship; a Sundance New Frontiers Story Lab fellowship; a Soros Equality Fellowship; a Lucas Artists fellowship; a Creative Capital grant; a Bell Labs artist residency; a Blade of Grass fellowship; and a Data & Society fellowship. == Media coverage == Dinkins appeared in episode six of the HBO television series Random Acts of Flyness directed by Terence Nance, where she described her conversations with BINA48. == Other activities == Dinkins was part of the juries that selected Shu Lea Cheang for the LG Guggenheim Award in 2024.
Read more →
The constructive cooperative coevolutionary algorithm (also called C3) is a global optimisation algorithm in artificial intelligence based on the multi-start architecture of the greedy randomized adaptive search procedure (GRASP). It incorporates the existing cooperative coevolutionary algorithm (CC). The considered problem is decomposed into subproblems. These subproblems are optimised separately while exchanging information in order to solve the complete problem. An optimisation algorithm, usually but not necessarily an evolutionary algorithm, is embedded in C3 for optimising those subproblems. The nature of the embedded optimisation algorithm determines whether C3's behaviour is deterministic or stochastic. The C3 optimisation algorithm was originally designed for simulation-based optimisation but it can be used for global optimisation problems in general. Its strength over other optimisation algorithms, specifically cooperative coevolution, is that it is better able to handle non-separable optimisation problems. An improved version was proposed later, called the Improved Constructive Cooperative Coevolutionary Differential Evolution (C3iDE), which removes several limitations with the previous version. A novel element of C3iDE is the advanced initialisation of the subpopulations. C3iDE initially optimises the subpopulations in a partially co-adaptive fashion. During the initial optimisation of a subpopulation, only a subset of the other subcomponents is considered for the co-adaptation. This subset increases stepwise until all subcomponents are considered. This makes C3iDE very effective on large-scale global optimisation problems (up to 1000 dimensions) compared to cooperative coevolutionary algorithm (CC) and Differential evolution. The improved algorithm has then been adapted for multi-objective optimization. == Algorithm == As shown in the pseudo code below, an iteration of C3 exists of two phases. In Phase I, the constructive phase, a feasible solution for the entire problem is constructed in a stepwise manner. Considering a different subproblem in each step. After the final step, all subproblems are considered and a solution for the complete problem has been constructed. This constructed solution is then used as the initial solution in Phase II, the local improvement phase. The CC algorithm is employed to further optimise the constructed solution. A cycle of Phase II includes optimising the subproblems separately while keeping the parameters of the other subproblems fixed to a central blackboard solution. When this is done for each subproblem, the found solution are combined during a "collaboration" step, and the best one among the produced combinations becomes the blackboard solution for the next cycle. In the next cycle, the same is repeated. Phase II, and thereby the current iteration, are terminated when the search of the CC algorithm stagnates and no significantly better solutions are being found. Then, the next iteration is started. At the start of the next iteration, a new feasible solution is constructed, utilising solutions that were found during the Phase I of the previous iteration(s). This constructed solution is then used as the initial solution in Phase II in the same way as in the first iteration. This is repeated until one of the termination criteria for the optimisation is reached, e.g. a maximum number of evaluations. {Sphase1} ← ∅ while termination criteria not satisfied do if {Sphase1} = ∅ then {Sphase1} ← SubOpt(∅, 1) end if while pphase1 not completely constructed do pphase1 ← GetBest({Sphase1}) {Sphase1} ← SubOpt(pphase1, inext subproblem) end while pphase2 ← GetBest({Sphase1}) while not stagnate do {Sphase2} ← ∅ for each subproblem i do {Sphase2} ← SubOpt(pphase2,i) end for {Sphase2} ← Collab({Sphase2}) pphase2 ← GetBest({Sphase2}) end while end while == Multi-objective optimisation == The multi-objective version of the C3 algorithm is a Pareto-based algorithm which uses the same divide-and-conquer strategy as the single-objective C3 optimisation algorithm . The algorithm again starts with the advanced constructive initial optimisations of the subpopulations, considering an increasing subset of subproblems. The subset increases until the entire set of all subproblems is included. During these initial optimisations, the subpopulation of the latest included subproblem is evolved by a multi-objective evolutionary algorithm. For the fitness calculations of the members of the subpopulation, they are combined with a collaborator solution from each of the previously optimised subpopulations. Once all subproblems' subpopulations have been initially optimised, the multi-objective C3 optimisation algorithm continues to optimise each subproblem in a round-robin fashion, but now collaborator solutions from all other subproblems' subspopulations are combined with the member of the subpopulation that is being evaluated. The collaborator solution is selected randomly from the solutions that make up the Pareto-optimal front of the subpopulation. The fitness assignment to the collaborator solutions is done in an optimistic fashion (i.e. an "old" fitness value is replaced when the new one is better). == Applications == The constructive cooperative coevolution algorithm has been applied to different types of problems, e.g. a set of standard benchmark functions, optimisation of sheet metal press lines and interacting production stations. The C3 algorithm has been embedded with, amongst others, the differential evolution algorithm and the particle swarm optimiser for the subproblem optimisations.
Read more →
A data processing unit (DPU) is a programmable computer processor that tightly integrates a general-purpose CPU with network interface hardware. They are also occasionally called "IPUs" (infrastructure processing unit) or "SmartNICs". They can be used in place of traditional NICs to relieve the main CPU of complex networking responsibilities and other "infrastructural" duties; although their features vary, they may be used to perform encryption/decryption, serve as a firewall, handle TCP/IP, process HTTP requests, or even function as a hypervisor or storage controller. These devices can be attractive to cloud computing providers whose servers might otherwise spend a significant amount of CPU time on these tasks, cutting into the cycles they can provide to guests. They see use in other kinds of data center environments as well due to their improved power consumption efficiency for routine networking tasks compared to general-purpose CPUs.
Read more →
Flo is a period-tracking app that provides menstrual cycle, ovulation and pregnancy tracking as well as perimenopause symptom tracking that was developed by Flo Health, Inc. It has over 380 million downloads worldwide and over 70 million monthly active users as of November 2024. In mid-2024, it reached unicorn status, and became Europe’s first femtech unicorn. The company has been accused of sharing users' sensitive health data with third parties without consent and misleading its users about data practices. == History == Flo Health, Inc. was co-founded in 2015 by Dmitry and Yuri Gurski, in Belarus. Their backgrounds helped build the first version of the software having experience in other fitness and health apps. Dmitry serves as the company's CEO. The company's development hubs are in London, Amsterdam and Vilnius. In 2016, the company raised $1 million in seed round funding from Flint Capital and Haxus Venture Fund. In 2017, Flo received an investment of $5 million from Flint Capital and model Natalia Vodianova with Vodianova helping develop an awareness campaign for the company. In 2018, Flo received an investment of $6 million from Mangrove Capital Partners, with participation from Flint Capital and Haxus, giving the company a valuation of $200 million. In mid-2019, Flo received an additional investment of $7.5 million led by Founders Fund. In 2020, the Federal Trade Commission alleged that Flo had misled users about its handling of health information to third parties including Google, Facebook, AppsFlyer, and Flurry since 2016. These allegations followed a 2019 report by The Wall Street Journal in reference to Facebook. The company reached a settlement in 2021 and was required to notify users of how their personal information was shared and obtain permission before any further information was shared. The agreement also required that Flo to undertake an independent privacy audit which it completed in March 2022. In early September 2021, Flo announced it closed $50M in a Series B financing, bringing the total capital raised to $65 million and company valuation to $800M led by VNV Global and Target Global. In March 2024, the Supreme Court of British Columbia certified a class action suit against Flo for sharing intimate data with Facebook and other third parties without user knowledge. In July 2024, Flo announced it raised more than $200M in Series C financing from General Atlantic bringing its valuation beyond $1 billion. As of November 2024, the app had over 380 million downloads world wide, and over 70 million monthly active users. In 2025, Flo adopted a data intelligence platform from Databricks to power its analytics and AI features, allowing users personalized cycle predictions. In 2025, a class action lawsuit in California was settled for $56 million with Flo paying $8 million and Google paying $48 million. == Features and privacy == Flo was initially created as a period and ovulation tracking application. It now provides reminders of upcoming menstrual cycles and a place to record various other health symptoms such as contraceptive methods, vaginal discharge (leukorrhea), water intake, pains, mood swings, and sexual activity. The application is available on iOS and Android. Flo is free to download and the free basic version gives you access to period and ovulation tracking and predictions, symptom tracking, cycle history, and anonymous mode. In Pregnancy mode, the app provides tracking features and educational material for pregnancy. In October 2023, Flo launched Flo for Partners, a feature that allows users to share their Flo data with their partner. In September 2022, as a response to Roe v. Wade being overturned, Flo sped up the release of a feature called "Anonymous Mode". Flo said this mode allows users to access the app without any personal identifiers such as name, email address, or technical identifiers being associated with their health data. Flo said it uses a technology called Oblivious HTTP to help protect user privacy in Anonymous Mode. == Recognition == Flo was named to Bloomberg’s Top 25 UK Startups to Watch for 2024. Flo's Anonymous Mode feature was recognized on both Fast Company's World Changing Ideas 2023 and TIME's Best Inventions List 2023. Flo is a CES 2019 Innovation Awards Honoree in the Software and Mobile Applications category.
Read more →
In computer science, an evolving intelligent system is a fuzzy logic system which improves the own performance by evolving rules. The technique is known from machine learning, in which external patterns are learned by an algorithm. Fuzzy logic based machine learning works with neuro-fuzzy systems. Intelligent systems have to be able to evolve, self-develop, and self-learn continuously in order to reflect a dynamically evolving environment. The concept of Evolving Intelligent Systems (EISs) was conceived around the turn of the century with the phrase EIS itself coined for the first time by Angelov and Kasabov in a 2006 IEEE newsletter and expanded in a 2010 text. EISs develop their structure, functionality and internal knowledge representation through autonomous learning from data streams generated by the possibly unknown environment and from the system self-monitoring. EISs consider a gradual development of the underlying (fuzzy or neuro-fuzzy) system structure and differ from evolutionary and genetic algorithms which consider such phenomena as chromosomes crossover, mutation, selection and reproduction, parents and off-springs. The evolutionary fuzzy and neuro systems are sometimes also called "evolving" which leads to some confusion. This was more typical for the first works on this topic in the late 1990s. == Implementations == EISs can be implemented, for example, using neural networks or fuzzy rule-based models. The first neural networks which consider an evolving structure were published in. These were later expanded by N. Kasabov and P. Angelov for the neuro-fuzzy models. P. Angelov introduced the evolving fuzzy rule-based systems (EFSs) as the first mathematical self-learning model that can dynamically evolve its internal structure and is human interpretable and coined the phrase EFS. Contemporarily, the offline incremental approach for learning an EIS, namely, EFuNN, was proposed by N. Kasabov. P. Angelov, D. Filev, N. Kasabov and O. Cordon organised the first IEEE Symposium on EFSs in 2006 (the proceedings of the conference can be found in). EFSs include a formal (and mathematically sound) learning mechanism to extract it from streaming data. One of the earliest and the most widely cited comprehensive survey on EFSs was done in 2008. Later comprehensive surveys on EFS methods with real applications were done in 2011 and 2016 by E. Lughofer. Other works that contributed further to this area in the following years expanded it to evolving participatory learning, evolving grammar, evolving decision trees, evolving human behaviour modelling, self-calibrating (evolving) sensors (eSensors), evolving fuzzy rule-based classifiers, evolving fuzzy controllers, autonomous fault detectors. More recently, the stability of the evolving fuzzy rule-based systems that consist of the structure learning and the fuzzily weighted recursive least square parameter update method has been proven by Rong. Generalized EFS, which allow rules to be arbitrarily rotated in the feature space and thus to improve their data representability, have been proposed in with significant extensions in towards 'smartness' of the rule bases (thus, termed as "Generalized Smart EFS"), allowing more interpretability and reducing curse of dimensionality. The generalized rule structure was also successfully used in the context of evolving neuro-fuzzy systems. Several facets and challenges for achieving more transparent and understandable rule bases in EFS have been discussed by E. Lughofer in. EISs form the theoretical and methodological basis for the Autonomous Learning Machines (ALMA) and autonomous multi-model systems (ALMMo) as well as of the Autonomous Learning Systems. Evolving Fuzzy Rule-based classifiers, in particular, is a very powerful new concept that offers much more than simply incremental or online classifiers – it can cope with new classes being added or existing classes being merged. This is much more than just adapting to new data samples being added or classification surfaces being evolved. Fuzzy rule-based classifiers are the methodological basis of a new approach to deep learning that was until now considered as a form of multi-layered neural networks. Deep Learning offers high precision levels surpassing the level of human ability and grabbed the imagination of the researchers, industry and the wider public. However, it has a number of intrinsic constraints and limitations. These include: The "black box", opaque internal structure which has millions of parameters and involves ad hoc decisions on the number of layers and algorithm parameters. The requirement for a huge amount of training data samples, computational resources (usually requiring GPUs and/or HPC) and time (usually requiring many hours of training). Iterative search. Requires retraining for new situations (is not evolving). Does not have proven convergence and stability. Most, if not all, of the above limitations can be avoided with the use of the Deep (Fuzzy) Rule-based Classifiers, which were recently introduced based on ALMMo, while achieving similar or even better performance. The resulting prototype-based IF...THEN...models are fully interpretable and dynamically evolving (they can adapt quickly and automatically to new data patterns or even new classes). They are non-parametric and, therefore, their training is non-iterative and fast (it can take few milliseconds per data sample/image on a normal laptop which contrasts with the multiple hours the current deep learning methods require for training even when they use GPUs and HPC). Moreover, they can be trained incrementally, online, or in real-time. Another aspect of Evolving Fuzzy Rule-based classifiers has been proposed in, which, in case of multi-class classification problems, achieves the reduction of class imbalance by cascadability into class sub-spaces and an increased flexibility and performance for adding new classes on the fly from streaming samples.
Read more →
Jake Elwes () is a British media artist, hacker and researcher. Their practice is the exploration of artificial intelligence (AI), queer theory and technical biases. They are known for using AI to create art in mediums such as video, performance and installation. Elwes considers themselves to be neuroqueer, and their work on queering technology addresses issues caused by the normative biases of artificial intelligence. == Education and early life == Elwes was born in London to British contemporary artist and painter Luke Elwes and Anneke, daughter of Hans Dumoulin. Elwes is the great grandchild of Army officer James Hennessy and portrait painter Simon Elwes RA, son of Victorian opera singer Gervase Elwes. Elwes studied at the Slade School of Fine Art from 2013 to 2017, where they began using computer code as a medium. In 2016 they attended the School of Machines, Making & Make-Believe in Berlin with artist and educator Gene Kogan. Elwes was introduced to drag performance by their collaborator Dr Joe Parslow who holds a PhD in drag performance. Drag performance has since become instrumental to Elwes' work. == Career == Elwes' work with artificial intelligence is cited as a hopeful strategy to make AI more playful and diverse. Elwes' work has been exhibited in numerous international art museums and galleries and was featured in a BBC documentary on the history of video art, they were a 2021 finalist for the Lumen Prize, and received the Honorary Mention of the 2022 Prix Ars Electronica in the Interactive Art + category. They also curated and presented the opening provocation "The New Real - Artistic and Queer Visions of AI Futures" to the UK government with two drag artists at the AI UK conference 2024. Elwes is part of the Radical Faeries countercultural movement. They have exhibited in museums and galleries across Europe and Asia including: Victoria and Albert Museum (London, UK) - The Zizi Show (2023-2024) for the first digital commission in their photography center's digital gallery Pinakothek der Moderne (Munich, Germany) - Glitch. Die Kunst Der Störung (2023-2024) ZKM (Karlsruhe, Germany) - Biomedia (2021-2022) National Museum of Modern and Contemporary Art (Cheongju, South Korea) - What an Artificial World (2024) Somerset House (London, UK) - The Horror Show! (2022-2023) Gazelli Art House (London, UK) - Jake Elwes: Data • Glitch • Utopia (2023) (survey exhibition) Jut Art Museum (Taipei, Taiwan) - Future Lives, Future You (2023-2024) Max Ernst Museum (Brühl, Germany) - Surreal Futures (2023-2024) Zabludowicz Collection (London, UK) - Among the Machines (2022) Ars Electronica (Linz, Austria) - Prix Ars Electronica, CyberArts Exhibition (2022) Institute of Contemporary Arts (ICA) (London, UK) - Do Androids Dream on Silver Screens? (2023) Arebyte gallery (London, UK) - Real-Time Constraints (2020) Ming Contemporary Art Museum (McaM) (Shanghai, China) - Mind the Deep (2019) HMKV (Hartware MedienKunstVerein) (Dortmund, Germany) - House of Mirrors: Artificial Intelligence as Phantasm (2022) Today Art Museum (Beijing, China) - Future of Today: DEJA VU (2019) Science Gallery (Dublin, Ireland) - BIAS (2021-2022) Yuz Museum (Shanghai, China) - Lying Sophia and Mocking Alexa (2021) Fotomuseum Winterthur The Onassis Foundation (Athens, Greece) - You and AI (2021) Royal College of Art (London, UK) - Event Two (2019) (50th anniversary of Computer Arts Society & Event One) Museum für Naturkunde (Berlin, Germany) - Forschungsfall Nachtigall (2019) Frankfurter Kunstverein (Frankfurt, Germany) - I am here to learn (2018) Nature Morte (Delhi, India) - Gradient Descent (2018) BALTIC Centre for Contemporary Art (Newcastle, UK) - Bloomberg New Contemporaries (2017) == Artworks == === The Zizi Project - a deepfake drag cabaret === The Zizi Project is a series of works that explore the interaction of drag and A.I. Currently, The Zizi Project is made up of multiple artworks. ==== Zizi - Queering the Dataset (2019) ==== Knowing that facial recognition technology statically struggle to recognize black women or transgender people, Elwes set out to "Queer the Dataset" through an open-sourced generative adversarial network (GAN, a type of machine learning model and an early Generative artificial intelligence). Elwes added a dataset of 1,000 photos of drag kings and queens into the GAN's 70,000 faces collected in a standardised facial recognition dataset called Flickr-Faces-HQ Dataset (FFHQ). They then created new simulacra faces, known as deep fakes. "We queer that data so it shifts all of the weights in this neural network from a space of normativity into a space of queerness and otherness. Suddenly all of the faces start to break down and you see mascara dissolve into lipstick and blue eye shadow turn into a pink wig" said Elwes in a 2023 interview for Artnet. ==== Zizi & Me (2020–2023) ==== Zizi & Me is an ongoing multimedia collaboration between drag queen Me The Drag Queen and a deepfake A.I. clone of Me The Drag Queen. Using neural networks trained on filmed footage, the project creates a virtual body that can mimic reference movements. The first act, which features a digital lip-sync duet to Anything You Can Do (I Can Do Better), satirises the idea of A.I. being mistaken for a human, using drag performance and cabaret to critique societal narratives about A.I. and its role in shaping identity. The project is part of The Zizi Project by Jake Elwes, which explores the intersection of drag performance and A.I. ==== The Zizi Show - A Deepfake Drag Cabaret (2020) ==== The Zizi Show is a deep fake drag act based on artificial intelligence (AI). It has been presented live and as interactive online artwork. It is an exploration of queer culture and the algorithms philosophy and ethics of AI. The Zizi Show was exhibited as the inaugural exhibition in the digital gallery at the V&A’s Photography Center from 2023 to 2024. ==== Zizi in Motion: A Deepfake Drag Utopia (Movement by Wet Mess) (2023) ==== "Zizi in Motion" is a multichannel silent video installation featuring AI-generated deepfake performances, which are dynamically re-animated through the movements of London drag artist Wet Mess. The movements of Wet Mess cause the AI-generated visuals to glitch and distort, showcasing the interaction between drag performance and artificial intelligence. The work explore the potential for queer communities to ethically and creatively reclaim and repurpose deepfake technology, using it to celebrate queer bodies and identities. === Art in the Cage of Digital Reproduction (2024) === In an act of protest on 26 November 2024, Elwes facilitated indirect access to an early access token for OpenAI’s Sora text-to-video model through a Hugging Face frontend under the account "PR Puppets". The accompanying statement called to 'denormalize the exploitation of artists by major AI companies for training data, R&D, and publicity'. The incident attracted international press coverage calling into question the role of artists in shaping the future of generative AI versus merely serving as data and credibility providers for tech giants. Elwes also coordinated a collection of mini essays with responses and reflections from the signees and guest writers titled "Art in the Cage of Digital Reproduction". === Installations exploring interpretation and feedback loops between neural networks === Elwes has created works based on the interpretations and misinterpretations between different neural networks and training datasets including: A.I. Interprets A.I. Interpreting ‘Against Interpretation’ (Sontag 1966) from 2023, Closed Loop from 2017, and Auto-Encoded Buddha from 2016. ==== A.I. Interprets A.I. Interpreting ‘Against Interpretation’ (Sontag 1966) (2023) ==== A.I. Interprets A.I. Interpreting ‘Against Interpretation (Sontag 1966) is a three-channel video artwork where an AI interprets Susan Sontag’s essay into images, and then and another AI reinterprets those images back into language. The piece highlights how AI-generated art can misinterpret and introduce bias. ==== Closed Loop (2017) ==== Closed Loop is a two-channel video where two neural networks engage in a continuous feedback loop, one generating images based on the text output and the other creating text based on the image output. The work explores how AI models misinterpret and evolve in a surreal, self-perpetuating conversation, without human input. ==== Auto-Encoded Buddha (2016) ==== Auto-Encoded Buddha is a mixed-media piece where an AI attempts to generate an image of a Buddha statue, trained on 5,000 Buddha images. The AI struggles to accurately represent the Buddha, highlighting the limitations of early generative neural networks. The work is a tribute to Nam June Paik’s TV Buddha (1974). === CUSP (2019) === In their video work CUSP (2019) Elwes places marsh birds generated using artificial intelligence into a tidal landscape. These digitally generated and constantly shifting birds are recorded in dialogue with native
Read more →
Tempos Modernos (English: Modern Times) is a Brazilian telenovela produced and broadcast by TV Globo. It premiered on 11 January 2010, replacing Caras & Bocas, and ended on 16 July 2010, replaced by Ti Ti Ti. The series is written by Bosco Brasil, with the collaboration of Izabel de Oliveira, Maria Elisa Berredo, Mário Teixeira and Patrícia Moretzsohn. It stars Fernanda Vasconcellos, Thiago Rodrigues, Antônio Fagundes, and Eliane Giardini. Priscila Fantin, Danton Mello, Marcos Caruso, Regiane Alves, Vivianne Pasmanter, Otávio Muller, Felipe Camargo, and Malu Galli also star in main roles. == Cast == Fernanda Vasconcellos as Cornélia Cordeiro Santos Reis "Nelinha" Thiago Rodrigues as José Carlos Pimenta Cordeiro "Zeca" Antônio Fagundes as Leal Cordeiro Eliane Giardini as Hélia Pimenta Priscila Fantin as Nara Nolasco Marcos Caruso as Otto Niemann Vivianne Pasmanter as Regiane Cordeiro Mourão Regiane Alves as Goretti Cordeiro Bodanski "Gô" Otávio Muller as Altemir Assunção da Paz Bodanski (Bodanski) Felipe Camargo as Vinícius Porto de Mello "Portinho" Danton Mello as Renato Vieira de Mattos Alessandra Maestrini as Benedita Kusnezov Piñon "Dita'" Leonardo Medeiros as Ramon Piñon Guilherme Weber as Albano Mourão Grazi Massafera as Deodora Madureira Niemann / N. Anne Malu Galli as Iolanda Paranhos Guilherme Leicam as Led Piñon Aline Peixoto as Jannis Piñon Caroline Abras as Katrina João Baldasserini as Túlio Osório Débora Duarte as Tertuliana "Tertu" Otávio Augusto as Faustaço Lumbriga Selma Egrei as Tamara Palumbo Genézio de Barros as Pasquale Paula Possani as Maureen Lobianco Ricardo Blat as Fidélio Pascoal da Conceição as Zuppo Tuna Dwek as Justine Jairo Mattos as Gaulês "Jean Paul" Luciana Borghi as Bárbara Lee Cris Vianna as Tita Bicalho Edmilson Barros as Lindomar Mariano Assunção Cláudia Missura as Lavínia Palumbo Victor Pecoraro as Ricardo Maurício "Maurição" Naruna Costa as Dolores Damasceno Antônio Fragoso as Zapata Fabrício Boliveira as Nabuco Mota Eliana Pittman as Miranda Paranhos Márcio Seixas as Frankenstein "Frank" (voice) Joana Lerner as Heloísa "Helô" Darlan Cunha as João Carlos Paranhos "Joca" Janaína Ávila as Milena Morgado Anderson Lau as Okuda Alexandra Martins as Dulcinólia Lumbriga "Duba" Paulo Leal de Melo as Raulzão "Ducha Fria" Cássio Inácio as Tartana Gilberto Miranda as Madrugadinha Rafa Martins as Max do Cavaco Isabel Lobo as Thaís Trancoso Alexandre Cioletti as Valvênio Xandy Britto as Nelsinho Pallotti Polliana Aleixo as Maria Eunice Cordeiro Bodanski Ana Karolina Lannes as Maria Eugênia Cordeiro Bodanski Rebeca Orestein as Maria Helena Cordeiro Bodanski Jenifer de Oliveira Andrade as Maria Clara Cordeiro Bodanski
Read more →
Computational heuristic intelligence (CHI) refers to specialized programming techniques in computational intelligence (also called artificial intelligence, or AI). These techniques have the express goal of avoiding complexity issues, also called NP-hard problems, by using human-like techniques. They are best summarized as the use of exemplar-based methods (heuristics), rather than rule-based methods (algorithms). Hence the term is distinct from the more conventional computational algorithmic intelligence, or symbolic AI. An example of a CHI technique is the encoding specificity principle of Tulving and Thompson. In general, CHI principles are problem solving techniques used by people, rather than programmed into machines. It is by drawing attention to this key distinction that the use of this term is justified in a field already replete with confusing neologisms. Note that the legal systems of all modern human societies employ both heuristics (generalisations of cases) from individual trial records as well as legislated statutes (rules) as regulatory guides. Another recent approach to the avoidance of complexity issues is to employ feedback control rather than feedforward modeling as a problem-solving paradigm. This approach has been called computational cybernetics, because (a) the term 'computational' is associated with conventional computer programming techniques which represent a strategic, compiled, or feedforward model of the problem, and (b) the term 'cybernetic' is associated with conventional system operation techniques which represent a tactical, interpreted, or feedback model of the problem. Of course, real programs and real problems both contain both feedforward and feedback components. A real example which illustrates this point is that of human cognition, which clearly involves both perceptual (bottom-up, feedback, sensor-oriented) and conceptual (top-down, feedforward, motor-oriented) information flows and hierarchies. The AI engineer must choose between mathematical and cybernetic problem solution and machine design paradigms. This is not a coding (program language) issue, but relates to understanding the relationship between the declarative and procedural programming paradigms. The vast majority of STEM professionals never get the opportunity to design or implement pure cybernetic solutions. When pushed, most responders will dismiss the importance of any difference by saying that all code can be reduced to a mathematical model anyway. Unfortunately, not only is this belief false, it fails most spectacularly in many AI scenarios. Mathematical models are not time agnostic, but by their very nature are pre-computed, i.e. feedforward. Dyer [2012] and Feldman [2004] have independently investigated the simplest of all somatic governance paradigms, namely control of a simple jointed limb by a single flexor muscle. They found that it is impossible to determine forces from limb positions- therefore, the problem cannot have a pre-computed (feedforward) mathematical solution. Instead, a top-down command bias signal changes the threshold feedback level in the sensorimotor loop, e.g. the loop formed by the afferent and efferent nerves, thus changing the so-called ‘equilibrium point’ of the flexor muscle/ elbow joint system. An overview of the arrangement reveals that global postures and limb position are commanded in feedforward terms, using global displacements (common coding), with the forces needed being computed locally by feedback loops. This method of sensorimotor unit governance, which is based upon what Anatol Feldman calls the ‘equilibrium Point’ theory, is formally equivalent to a servomechanism such as a car's ‘cruise control’.
Read more →
In late January 2024, sexually explicit AI-generated deepfake images of American musician Taylor Swift were proliferated on social media platforms 4chan and X (formerly Twitter). Several artificial images of Swift of a sexual or violent nature were quickly spread, with one post reported to have been seen over 47 million times before its eventual removal. The images led Microsoft to enhance Microsoft Designer's text-to-image model to prevent future abuse. Moreover, these images prompted responses from anti-sexual assault advocacy groups, US politicians, Swifties, and Microsoft CEO Satya Nadella, among others, and it has been suggested that Swift's influence could result in new legislation regarding the creation of deepfake pornography. A similar controversy emerged in August 2025, when The Verge reported AI image and video tool Grok Imagine generated sexually explicit images and videos of Swift from an otherwise innocuous text prompt. == Background == American musician Taylor Swift has been the target of misogyny and slut-shaming throughout her career. American technology corporation Microsoft offers AI image creators called Microsoft Designer and Bing Image Creator, which employ censorship safeguards to prevent users from generating unsafe or objectionable content. Members of a Telegram group discussed ways to circumvent these censors to create pornographic images of celebrities. Graphika, a disinformation research firm, traced the creation of the images back to a 4chan community. == Reactions == For some, the deepfake images of Swift immediately became a source of controversy and outrage. Other internet users found them humorous and absurd, such as the image making it appear as though Swift was to engage in sexual intercourse with Oscar the Grouch. The images drew condemnations from Rape, Abuse & Incest National Network and SAG-AFTRA. The latter group, who had been following issues regarding AI-generated media prior to Swift's involvement, considered the images "upsetting, harmful and deeply concerning." Microsoft CEO Satya Nadella, whose company's products were believed to be used to make these images, responded to the controversy as "alarming and terrible", further stating his belief that "we all benefit when the online world is a safe world." === Taylor Swift === A source close to Swift told the Daily Mail that she would be considering legal action, saying, "Whether or not legal action will be taken is being decided, but there is one thing that is clear: These fake AI-generated images are abusive, offensive, exploitative, and done without Taylor's consent and/or knowledge." === Politicians === White House press secretary Karine Jean-Pierre expressed concern over the counterfeit images, deeming them "alarming", and emphasized the obligation of social media platforms to curb the dissemination of misinformation. Several members of American politics called for legislation against AI-generated pornography. Later in the month, a bipartisan bill was introduced by US senators Dick Durbin, Lindsey Graham, Amy Klobuchar and Josh Hawley. The bill would allow victims to sue individuals who produced or possessed "digital forgeries" with intent to distribute, or those who received the material knowing it was made without consent. The European Union struck a deal in February 2024 on a similar bill that would criminalize deepfake pornography, as well as online harassment and revenge porn, by mid-2027. === Social media platforms === X responded to the sharing of these images on their own website with claims they would suspend accounts that participated in their spread. Despite this, the photos continued to be reshared among accounts of X, and spread to other platforms including Instagram and Reddit. X enforces a "synthetic and manipulated media policy", which has been criticized for its efficacy. They briefly blocked searches of Swift's name on January 27, 2024, reinstating them two days later. === Swifties === Fans of Taylor Swift, known as Swifties, responded to the circulation of these images by pushing the hashtag #ProtectTaylorSwift to trend on X. They also flooded other hashtags related to the images with more positive images and videos of her live performances. == Cultural significance == Deepfake pornography has remained highly controversial and has affected figures from other celebrities to ordinary people, most of whom are women. Journalists have opined that the involvement of a prominent public figure such as Swift in the dissemination of AI-generated pornography could bring public awareness and political reform to the issue.
Read more →
Felix, Net i Nika ("Felix, Net and Nika") is a series of Polish language science fiction books for teenagers, written by Rafał Kosik. It tells the adventures of three friends - Felix Polon, Net Bielecki and Nika Mickiewicz - who attend fictional Professor Kuszmiński Middle School in Warsaw. As of 2024, eighteen books have been published. == Books == There are currently 18 books in the series: Felix, Net and Nika and the Gang of Invisible People - November 2004. Felix, Net and Nika and the Theoretically Possible Catastrophe - November 2005 Felix, Net and Nika and the Palace of Dreams - November 2006 Felix, Net and Nika and the Trap of Immortality - November 2007 Felix, Net and Nika and the Orbital Conspiracy - November 2008 Felix, Net and Nika and the Orbital Conspiracy 2: Small Army - May 2009 Felix, Net and Nika and the Third Cousin - November 2009 Felix, Net and Nika and the Rebellion of Machines - March 2011 Felix, Net and Nika and the World Zero - November 2011 Felix, Net and Nika and the World Zero 2. Alternauts - November 2012 Felix, Net and Nika and the Extracurricular Stories - April 2013 Felix, Net and Nika and the Secret of Czerwona Hańcza - November 2013 Felix, Net and Nika and Curse of McKillian's House - November 2014 Felix, Net and Nika and (un)Safe Growing up - November 2015 Felix, Net and Nika and The End of The World as We Know It - November 2018 Felix, Net and Nika and No Chance - November 2022 Felix, Net and Nika and No Chance 2: other tomorrrow - 2023 Felix, Net and Nika and Fantology - June 2024 == Film == A feature motion picture, Felix, Net i Nika oraz Teoretycznie Możliwa Katastrofa (Felix, Net and Nika and the Theoretically Possible Catastrophe) was released in Poland on September 28, 2012. == Main characters == Felix Polon - a foresighted, fair-haired boy with dark brown eyes. He inherited the talent of constructing various things, especially robots, from his father- it saved his friends many times. He can make anything from nothing, always finds a way out of a situation; almost always has a plan. Together with his parents Marlene and Peter, grandmother Lucy, his dog Caban (a Black Russian Terrier) and Golem Golem a robot he built, Felix lives on Serdeczna Street in a small family house. Net Bielecki is quite tall & slim, has blue eyes and a high IQ level. "Net" is his nickname; his true name is unknown. He is the most trendy and 'awesome' in his entire class. He is a human calculator and is excellent in mathematics. He hates dictations and spelling because he is dyslexic. He is also quite lazy, absent-minded and sometimes hysterical, or panicking. His dark blond hair looks like a heap of hay after a grenade explosion. He is best in ICT and writes many of his own programs. His love interest is Nika Mickiewicz. Together with his parents Lila and Mark, and their newborn twins nicknamed Pompek and Prumcia he lives on the top floor of a Penthouse apartment. Nika Mickiewicz is a girl with a character. She is very brave and mature. She likes reading books. She has curly, red hair, green eyes and a few freckles. She is not very rich; she wears second-hand clothes and her only pair of black Dr. Martens shoes. She lives in a tiny apartment. She is an orphan, but hides that fact from people for almost 3 years. However, Felix and Net, her best and possibly only friends, find out about it. She also has abnormal abilities. She can move distant objects using her powers, ski uphill and knows some things by intuition. In other words, she is telekinetic. Manfred is a friendly AI program started and never finished by Net's father, and mastered and programmed further by Net himself. He likes going on adventures and solving mysteries with the trio much more than his actual job, which is controlling the traffic lights. He helped out the three friends many times and is their reliable and faithful friend. Morten is also an AI program, but he is the antagonist of the trio. He appears in all 6 books of Felix Net and Nika. In the first book, the trio thinks they finished him off for good, but as we find out later, he comes back in the third book. In the fifth/sixth book, he was the mastermind of the Orbital Conspiracy. Also, Morten's logo, appears in all 6 books and it is still a mystery what he has to do with each event.
Read more →