AI Grammar And Vocabulary Checker

AI Grammar And Vocabulary Checker — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

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  N-jet

  An N-jet is the set of (partial) derivatives of a function f ( x ) {\displaystyle f(x)} up to order N. Specifically, in the area of computer vision, the N-jet is usually computed from a scale space representation L {\displaystyle L} of the input image f ( x , y ) {\displaystyle f(x,y)} , and the partial derivatives of L {\displaystyle L} are used as a basis for expressing various types of visual modules. For example, algorithms for tasks such as feature detection, feature classification, stereo matching, tracking and object recognition can be expressed in terms of N-jets computed at one or several scales in scale space.

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  Vehicle infrastructure integration

  The Vehicle Infrastructure Integration (VII), also known as "Connected Roadways" or "vehicle-to-everything" (V2X) technology, is a United States Department of Transportation initiative that aims to improve road safety by developing technology that connects road vehicles with their environment. This development draws on several disciplines, including transport engineering, electrical engineering, automotive engineering, telematics, and computer science. Although VII specifically covers road transport, similar technologies are under development for other modes of transport. For example, airplanes may use ground-based beacons for automated guidance, allowing the autopilot to fly the plane without human intervention. == Goals == The goal of VII is to establish a communication link between vehicles (via On-Board Equipment, or OBE) and roadside infrastructure (via Roadside Equipment, or RSE) to enhance the safety, efficiency, and convenience of transportation systems. Two potential approaches are the widespread deployment of a dedicated short-range communications (DSRC) link on the 5.9GHz band, and cellular communication (C-V2X). Either of these methods would allow vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. The initiative has three priorities: Stakeholder evaluation and acceptance of the business model and its deployment schedule, Validation of the technology, with a focus on communications systems, in relation to deployment costs, and Creation of legal structures and policies, especially concerning digital privacy, to improve the system's long-term potential for success. === Safety === Current automotive safety technology relies primarily on vehicle-based radar, lidar, and sonar systems. This technology allows, for instance, a potential reduction in rear-end collisions by monitoring obstacles in front of or behind the vehicle and automatically applying the brakes when necessary. This technology, however, is limited by the sensing range of vehicle-based radar, particularly in angled and left-turn collisions, such as a motorist losing control of the vehicle during an impending head-on collision. The rear-end collisions addressed by current technology are generally less severe than angled, left-turn, or head-on collisions. VII promotes the development of a direct communication link between road vehicles and all other vehicles nearby, allowing for the exchange of information on vehicle speed and orientation or driver awareness and intent. This real-time exchange of information may enable more effective automated emergency maneuvers, such as steering, decelerating, or braking. In addition to nearby vehicle awareness, VII promotes a communication link between vehicles and roadway infrastructure. Such a link may allow for improved real-time traffic information, better queue management, and feedback to vehicles. Existing implementations of VII use vehicle-based sensors that can recognize and respond to roadway markings or signs, automatically adjusting vehicle parameters to follow the recognized instructions. However, this information may also be acquired via roadside beacons or stored in a centralized database accessible to all vehicles. === Efficiency === With a VII system in place, vehicles will be linked together. The headway between vehicles may therefore be reduced so that there is less empty space on the road, increasing the available capacity per lane. More capacity per lane will in turn imply fewer lanes in general, possibly satisfying the community's concerns about the impact of roadway widening. VII will enable precise traffic-signal coordination by tracking vehicle platoons and will benefit from accurate timing by drawing on real-time traffic data covering volume, density, and turning movements. Real-time traffic data can also be used in the design of new roadways or modification of existing systems as the data could be used to provide accurate origin-destination studies and turning-movement counts for uses in transportation forecasting and traffic operations. Such technology would also lead to improvements for transport engineers to address problems whilst reducing the cost of obtaining and compiling data. Tolling is another prospect for VII technology as it could enable roadways to be automatically tolled. Data could be collectively transmitted to road users for in-vehicle display, outlining the lowest cost, shortest distance, and/or fastest route to a destination on the basis of real-time conditions. === Existing applications === To some extent, results along these lines have been achieved in trials performed around the globe, making use of GPS, mobile phone signals, and vehicle registration plates. GPS is becoming standard in many new high-end vehicles and is an option on most new low- and mid-range vehicles. In addition, many users also have mobile phones that transmit trackable signals (and may also be GPS-enabled). Mobile phones can already be traced for purposes of emergency response. GPS and mobile phone tracking, however, do not provide fully reliable data. Furthermore, integrating mobile phones in vehicles may be prohibitively difficult. Data from mobile phones, though useful, might even increase risks to motorists as they tend to look at their phones rather than concentrate on their driving. Automatic registration plate recognition can provide large quantities of data, but continuously tracking a vehicle through a corridor is a difficult task with existing technology. Today's equipment is designed for data acquisition and functions such as enforcement and tolling, not for returning data to vehicles or motorists for response. GPS will nevertheless be one of the key components in VII systems. == Limitations == === Privacy === VII architecture is designed to prevent identification of individual vehicles, with all data exchange between the vehicle and the system occurring anonymously. Exchanges between the vehicles and third parties such as OEMs and toll collectors will occur, but the network traffic will be sent via encrypted tunnels and will therefore not be decipherable by the VII system. Data sharing with law enforcement or Homeland Security was not included in system design as of 2006. === Technical issues === ==== Coordination ==== A major issue facing the deployment of VII is the problem of how to set up the system initially. The costs associated with installing the technology in vehicles and providing communications and power at every intersection are significant. ==== Maintenance ==== Another factor for consideration in regard to the technology's distribution is how to update and maintain the units. Traffic systems are highly dynamic, with new traffic controls implemented every day and roadways constructed or repaired every year. The vehicle-based option could be updated via the internet (preferably wireless) but may subsequently require all users to have access to internet technology. Alternatively, if receivers were placed in all vehicles and the VII system was primarily located along the roadside, information could be stored in a centralized database. This would allow the agency responsible to issue updates at any time. These would then be disseminated to the roadside units for passing motorists. Operationally, this method is currently considered to provide the greatest effectiveness but at a high cost to the authorities. ==== Security ==== Security of the units is another concern, especially in light of the public acceptance issue. Criminals could tamper, remove, or destroy VII units regardless of whether they are installed inside vehicles or along the roadside. Magnets, electric shocks, and malicious software (viruses, hacking, or jamming) could be used to damage VII systems – regardless of whether units are located inside vehicle or along the roadside. == Recent developments == Much of the current research and experimentation is conducted in the United States where coordination is ensured through the Vehicle Infrastructure Integration Consortium; consisting of automobile manufacturers (Ford, General Motors, Daimler Chrysler, Toyota, Nissan, Honda, Volkswagen, BMW), IT suppliers, U.S. Federal and state transportation departments, and professional associations. Trialing is taking place in Michigan and California. The specific applications now being developed under the U.S. initiative are: Warning drivers of unsafe conditions or imminent collisions. Warning drivers if they are about to run off the road or speed around a curve too fast. Informing system operators of real-time congestion, weather conditions and incidents. Providing operators with information on corridor capacity for real-time management, planning and provision of corridor-wide advisories to drivers. In mid-2007, a VII environment covering some 20 square miles (52 km2) near Detroit was used to test 20 prototype VII applications. Several automobile manufacturers are also conducting their own VII research and triali

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  The MANIAC

  The MANIAC is a 2023 novel by Chilean author Benjamín Labatut, written in English. It is a fictionalised biography of polymath John von Neumann, whom Labatut calls "the smartest human being of the 20th century". The book focuses on von Neumann, but is also about physicist Paul Ehrenfest, the history of artificial intelligence, and Lee Sedol's Go match against AlphaGo. The book received mostly positive reviews from critics. == Background == John von Neumann was a Jewish Hungarian-born polymath who was a prodigy from an early childhood. Von Neumann worked in multiple fields of science, theoretical (mathematical foundations of quantum mechanics, game theory, cellular automata) and applied (nuclear weapons research during the Manhattan Project in World War II, computer architecture later named after him, and many other subjects). Labatut calls him "the smartest human being of the 20th century". The title of the book is derived from an early computer based on von Neumann architecture, built after the war at Los Alamos laboratory, called MANIAC I. Benjamín Labatut is a Chilean author known for his 2020 book When We Cease to Understand the World, a collection of fictionalised stories about famous scientists that received positive reviews and was translated into multiple languages from Spanish. The MANIAC is Labatut's first book written in English. In an interview, Labatut said he prefers to write in English: English is my preferred form of thought. ... English is the language I do most if not all my reading it. And it is a far better language than Spanish, in so many ways. Writing "clean" prose in Spanish is almost impossible, because so many of its sounds clash. Borges said that he found English "a far finer language than Spanish" because it's both Germanic and Latin; because of its wonderful vocabulary ("Regal is not exactly the same thing as saying kingly," he explained); because of its physicality; and because you can do almost anything with verbs and prepositions. Labatut was inspired to write The MANIAC by George Dyson's book Turing's Cathedral. == Synopsis == The book has three chapters. The first chapter, "Paul or the Discovery of the Irrational", written in the third person, is about physicist Paul Ehrenfest. The chapter opens with Ehrenfest shooting dead his son Vassily, who suffered from Down syndrome, and then himself. It then recounts Ehrenfest's life story, describing his relationships with his wife Tatyana, his mistress Nelly Meyjes, and his eminent physicist colleagues. It chronicles his descent into despair and depression over his marriage's disintegration, the advent of quantum mechanics, and the direction Europe was heading in with the Nazi Party's rise to power in Germany, looping back to the initial scene of the chapter. The second chapter, "John or the Mad Dreams of Reason", is about John von Neumann, and is written as a series of interviews of his family members, wives, friends, and colleagues, each in a distinctive voice. It is divided into three parts. Part I, "The Limits of Logic", is about his early life, as told by von Neumann's childhood friend Eugene Wigner, mother Margrit Kann, brother Nicholas von Neumann, first wife Mariette Kövesi, and scientists Theodore von Karman, George Polya, and Gábor Szegő. It climaxes with von Neumann's participation in David Hilbert's program to create a logical basis for mathematics based on a consistent set of axioms, a quest ultimately scuppered by Kurt Gödel. Part II, "The Delicate Balance of Terror", discusses von Neumann's role in the Manhattan Project (as told by Richard Feynman); his development of game theory and the doctrine of mutual assured destruction (MAD) (as told by Oskar Morgenstern); and his creation of the MANIAC I computer and the von Neumann architecture (as told by Julian Bigelow). In Part III, "Ghosts in the Machine", Sydney Brenner discusses von Neumann's contributions to biology, his theoretical work on self-replicating and self-repairing machines, and his vision of Von Neumann probes exploring the universe. Nils Aall Barricelli talks about his ideas of digital life and his disagreements with von Neumann. Von Neumann's wife Klára Dán, daughter Marina, and Wigner talk about his final years, personal life, and death. The third chapter, "Lee or The Delusions of Artificial Intelligence", is about Lee Sedol's Go match against AlphaGo. The narrative reverts to the third person. The chapter also tells the story of Demis Hassabis, a chess prodigy in childhood who decided to work on artificial intelligence and founded DeepMind, the company behind AlphaGo. The way is pointed to the future, as artificial intelligence's growing capabilities outpace the human mind. The book ends with Lee Sedol's retirement from Go, and new version of DeepMind's program, AlphaZero, that did not train on human games but nevertheless became the strongest player in Go, chess, and Shogi. == Reception == The book received mostly positive reviews. In his review for The New York Times Tom McCarthy noted the ambiguity of genre: "At its best, as in the stunning opening sequence reconstructing the murder-suicide of the physicist Paul Ehrenfest and his disabled son, or in the final section's gripping account of a computer defeating the world's best human Go player, you just throw up your hands and think, Who cares what discourse label we assign this stuff? It's great." Becca Rothfeld of the Washington Post praised the book, writing that it is "Labatut's latest virtuosic effort, at once a historical novel and a philosophical foray": "The MANIAC is a work of dark, eerie and singular beauty." She noted that the book "can also be difficult to read" because of its unusual narrative structure: "The book is narrated by a cluttered polyphony of characters, among them both of von Neumann's wives and a number of his teachers and colleagues. ... Like von Neumann, The MANIAC strives to adopt the impartial standpoint of the universe." Killian Fox of The Guardian sees the book as "darkly fascinating novel", and notes Labatut's "impressive dexterity, unpicking complex ideas in long, elegant sentences that propel us forward at speed (this is his first book written in English). Even in the more feverish passages, when yet another great mind succumbs to madness, haunted by the spectres they've helped unleash on the world, he feels in full control of his material." Sam Byers of The Guardian praises the book and the author's style: "The opening chapter of Benjamín Labatut's second novel is such a perfect distillation of his technique that it could serve as a manifesto." and "Readers ... will recognise the sense of breathlessness his best writing can evoke. Seemingly loosened from the laws of physics they describe, his sentences range freely through time and space, connecting not only characters and events, but the delicate tissue of intellectual history, often with a lightness of touch that belies their underlying complexity." He writes on the narrative structure: "Through a cascade of staccato chapters, an ensemble of narrators offer their piecemeal insights." Byers adds that "a brilliant novel is not quite what we end up with" and sees the problem in the "diffusion": "Labatut simply spreads himself too thin. Too many years in too few pages; too many voices with far too little to distinguish them. Initially intriguing, the bite-size monologues quickly come to feel inadequate." Some reviewers did not see the book as a biography. In an essay for the Cleveland Review of Books, Ben Cosman juxtaposes the book with Christopher Nolan's biopic Oppenheimer, and writes that it "follows the development of artificial intelligence—first as an idea at the beginning of the twentieth century, and then as a practicality at the beginning of the twenty-first—through the lives of three men who faced it." He also compared the book's structure to "witness testimony". Another reviewer called the book "perfect for anyone thirsting for more nuclear anxiety after watching Oppenheimer". Garrett Biggs of the Chicago Review of Books writes of the book's style: "Labatut writes about scientists the way Roberto Bolaño writes about poets. They are near mythical figures, captured at the corner of the novel's eye. They become historical in the most fraught sense of the term: subject to rumor and speculation and, eventually, the novel's form inflates their personas into something so large they can only be understood as narrative, never known in any objective capacity." Biggs criticises the last chapter: "the story of artificial intelligence has yet to be written. And so when Labatut's narration editorializes about artificial intelligence as 'a future that inspires hope and horror,' The MANIAC disassembles as a novel and starts to sound like a stale thinkpiece. AlphaGo might represent the first glimmer of a true artificial intelligence, as Labatut suggests. It also could one day be considered nothing more than a souped-up cousin to IBM's DeepBlue.

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  Generative adversarial network

  A generative adversarial network (GAN) is a class of machine learning frameworks and a prominent framework for approaching generative artificial intelligence. The concept was initially developed by Ian Goodfellow and his colleagues in June 2014. In a GAN, two neural networks compete with each other in the form of a zero-sum game, where one agent's gain is another agent's loss. Given a training set, this technique learns to generate new data with the same statistics as the training set. For example, a GAN trained on photographs can generate new photographs that look at least superficially authentic to human observers, having many realistic characteristics. Though originally proposed as a form of generative model for unsupervised learning, GANs have also proved useful for semi-supervised learning, fully supervised learning, and reinforcement learning. The core idea of a GAN is based on the "indirect" training through the discriminator, another neural network that can tell how "realistic" the input seems, which itself is also being updated dynamically. This means that the generator is not trained to minimize the distance to a specific image, but rather to fool the discriminator. This enables the model to learn in an unsupervised manner. GANs are similar to mimicry in evolutionary biology, with an evolutionary arms race between both networks. == Definition == === Mathematical === The original GAN is defined as the following game: Each probability space ( Ω , μ ref ) {\displaystyle (\Omega ,\mu _{\text{ref}})} defines a GAN game. There are 2 players: generator and discriminator. The generator's strategy set is P ( Ω ) {\displaystyle {\mathcal {P}}(\Omega )} , the set of all probability measures μ G {\displaystyle \mu _{G}} on Ω {\displaystyle \Omega } . The discriminator's strategy set is the set of Markov kernels μ D : Ω → P [ 0 , 1 ] {\displaystyle \mu _{D}:\Omega \to {\mathcal {P}}[0,1]} , where P [ 0 , 1 ] {\displaystyle {\mathcal {P}}[0,1]} is the set of probability measures on [ 0 , 1 ] {\displaystyle [0,1]} . The GAN game is a zero-sum game, with objective function L ( μ G , μ D ) := E x ∼ μ ref , y ∼ μ D ( x ) ⁡ [ ln ⁡ y ] + E x ∼ μ G , y ∼ μ D ( x ) ⁡ [ ln ⁡ ( 1 − y ) ] . {\displaystyle L(\mu _{G},\mu _{D}):=\operatorname {E} _{x\sim \mu _{\text{ref}},y\sim \mu _{D}(x)}[\ln y]+\operatorname {E} _{x\sim \mu _{G},y\sim \mu _{D}(x)}[\ln(1-y)].} The generator aims to minimize the objective, and the discriminator aims to maximize the objective. The generator's task is to approach μ G ≈ μ ref {\displaystyle \mu _{G}\approx \mu _{\text{ref}}} , that is, to match its own output distribution as closely as possible to the reference distribution. The discriminator's task is to output a value close to 1 when the input appears to be from the reference distribution, and to output a value close to 0 when the input looks like it came from the generator distribution. === In practice === The generative network generates candidates while the discriminative network evaluates them. This creates a contest based on data distributions, where the generator learns to map from a latent space to the true data distribution, aiming to produce candidates that the discriminator cannot distinguish from real data. The discriminator's goal is to correctly identify these candidates, but as the generator improves, its task becomes more challenging, increasing the discriminator's error rate. A known dataset serves as the initial training data for the discriminator. Training involves presenting it with samples from the training dataset until it achieves acceptable accuracy. The generator is trained based on whether it succeeds in fooling the discriminator. Typically, the generator is seeded with randomized input that is sampled from a predefined latent space (e.g. a multivariate normal distribution). Thereafter, candidates synthesized by the generator are evaluated by the discriminator. Independent backpropagation procedures are applied to both networks so that the generator produces better samples, while the discriminator becomes more skilled at flagging synthetic samples. When used for image generation, the generator is typically a deconvolutional neural network, and the discriminator is a convolutional neural network. === Relation to other statistical machine learning methods === GANs are implicit generative models, which means that they do not explicitly model the likelihood function nor provide a means for finding the latent variable corresponding to a given sample, unlike alternatives such as flow-based generative model. Compared to fully visible belief networks such as WaveNet and PixelRNN and autoregressive models in general, GANs can generate one complete sample in one pass, rather than multiple passes through the network. Compared to Boltzmann machines and linear ICA, there is no restriction on the type of function used by the network. Since neural networks are universal approximators, GANs are asymptotically consistent. Variational autoencoders might be universal approximators, but it is not proven as of 2017. == Mathematical properties == === Measure-theoretic considerations === This section provides some of the mathematical theory behind these methods. In modern probability theory based on measure theory, a probability space also needs to be equipped with a σ-algebra. As a result, a more rigorous definition of the GAN game would make the following changes:Each probability space ( Ω , B , μ ref ) {\displaystyle (\Omega ,{\mathcal {B}},\mu _{\text{ref}})} defines a GAN game. The generator's strategy set is P ( Ω , B ) {\displaystyle {\mathcal {P}}(\Omega ,{\mathcal {B}})} , the set of all probability measures μ G {\displaystyle \mu _{G}} on the measure-space ( Ω , B ) {\displaystyle (\Omega ,{\mathcal {B}})} . The discriminator's strategy set is the set of Markov kernels μ D : ( Ω , B ) → P ( [ 0 , 1 ] , B ( [ 0 , 1 ] ) ) {\displaystyle \mu _{D}:(\Omega ,{\mathcal {B}})\to {\mathcal {P}}([0,1],{\mathcal {B}}([0,1]))} , where B ( [ 0 , 1 ] ) {\displaystyle {\mathcal {B}}([0,1])} is the Borel σ-algebra on [ 0 , 1 ] {\displaystyle [0,1]} .Since issues of measurability never arise in practice, these will not concern us further. === Choice of the strategy set === In the most generic version of the GAN game described above, the strategy set for the discriminator contains all Markov kernels μ D : Ω → P [ 0 , 1 ] {\displaystyle \mu _{D}:\Omega \to {\mathcal {P}}[0,1]} , and the strategy set for the generator contains arbitrary probability distributions μ G {\displaystyle \mu _{G}} on Ω {\displaystyle \Omega } . However, as shown below, the optimal discriminator strategy against any μ G {\displaystyle \mu _{G}} is deterministic, so there is no loss of generality in restricting the discriminator's strategies to deterministic functions D : Ω → [ 0 , 1 ] {\displaystyle D:\Omega \to [0,1]} . In most applications, D {\displaystyle D} is a deep neural network function. As for the generator, while μ G {\displaystyle \mu _{G}} could theoretically be any computable probability distribution, in practice, it is usually implemented as a pushforward: μ G = μ Z ∘ G − 1 {\displaystyle \mu _{G}=\mu _{Z}\circ G^{-1}} . That is, start with a random variable z ∼ μ Z {\displaystyle z\sim \mu _{Z}} , where μ Z {\displaystyle \mu _{Z}} is a probability distribution that is easy to compute (such as the uniform distribution, or the Gaussian distribution), then define a function G : Ω Z → Ω {\displaystyle G:\Omega _{Z}\to \Omega } . Then the distribution μ G {\displaystyle \mu _{G}} is the distribution of G ( z ) {\displaystyle G(z)} . Consequently, the generator's strategy is usually defined as just G {\displaystyle G} , leaving z ∼ μ Z {\displaystyle z\sim \mu _{Z}} implicit. In this formalism, the GAN game objective is L ( G , D ) := E x ∼ μ ref ⁡ [ ln ⁡ D ( x ) ] + E z ∼ μ Z ⁡ [ ln ⁡ ( 1 − D ( G ( z ) ) ) ] . {\displaystyle L(G,D):=\operatorname {E} _{x\sim \mu _{\text{ref}}}[\ln D(x)]+\operatorname {E} _{z\sim \mu _{Z}}[\ln(1-D(G(z)))].} === Generative reparametrization === The GAN architecture has two main components. One is casting optimization into a game, of form min G max D L ( G , D ) {\displaystyle \min _{G}\max _{D}L(G,D)} , which is different from the usual kind of optimization, of form min θ L ( θ ) {\displaystyle \min _{\theta }L(\theta )} . The other is the decomposition of μ G {\displaystyle \mu _{G}} into μ Z ∘ G − 1 {\displaystyle \mu _{Z}\circ G^{-1}} , which can be understood as a reparametrization trick. To see its significance, one must compare GAN with previous methods for learning generative models, which were plagued with "intractable probabilistic computations that arise in maximum likelihood estimation and related strategies". At the same time, Kingma and Welling and Rezende et al. developed the same idea of reparametrization into a general stochastic backpropagation method. Among its first applications was the variational autoencoder. === Move order and st

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  Serverless computing

  Serverless computing is "a cloud service category where the customer can use different cloud capability types without the customer having to provision, deploy and manage either hardware or software resources, other than providing customer application code or providing customer data. Serverless computing represents a form of virtualized computing", according to ISO/IEC 22123-2. Serverless computing is a broad ecosystem that includes the cloud provider, function as a service (FaaS), managed services, tools, frameworks, engineers, stakeholders, and other interconnected elements. == Overview == Serverless is a misnomer in the sense that servers are still used by cloud service providers to execute code for developers. The definition of serverless computing has evolved over time, leading to varied interpretations. According to Ben Kehoe, serverless represents a spectrum rather than a rigid definition. Emphasis should shift from strict definitions and specific technologies to adopting a serverless mindset, focusing on leveraging serverless solutions to address business challenges. Serverless computing does not eliminate complexity but shifts much of it from the operations team to the development team. However, this shift is not absolute, as operations teams continue to manage aspects such as identity and access management (IAM), networking, security policies, and cost optimization. Additionally, while breaking down applications into finer-grained components can increase management complexity, the relationship between granularity and management difficulty is not strictly linear. There is often an optimal level of modularization where the benefits outweigh the added management overhead. According to Yan Cui, serverless techniques should be adopted only when they help to deliver customer value faster. And while adopting, organizations should take small steps and de-risk along the way. == Challenges == Serverless applications are prone to fallacies of distributed computing. In addition, they are prone to the following fallacies: Versioning is simple Compensating transactions always work Observability is optional === Monitoring and debugging === Monitoring and debugging serverless applications can present unique challenges due to their distributed, event-driven nature and proprietary environments. Traditional tools may fall short, making it difficult to track execution flows across services. However, modern solutions such as distributed tracing tools (e.g., AWS X-Ray, Datadog), centralized logging, and cloud-agnostic observability platforms are mitigating these challenges. Emerging technologies like OpenTelemetry, AI-powered anomaly detection, and serverless-specific frameworks are further improving visibility and root cause analysis. While challenges persist, advancements in monitoring and debugging tools are steadily addressing these limitations. === Security === According to OWASP, serverless applications are vulnerable to variations of traditional attacks, insecure code, and some serverless-specific attacks (like denial of wallet). So, the risks have changed and attack prevention requires a shift in mindset. === Vendor lock-in === Serverless computing is provided as a third-party service. Applications and software that run in the serverless environment are by default locked to a specific cloud vendor. This issue is exacerbated in serverless computing, as with its increased level of abstraction, public vendors only allow customers to upload code to a FaaS platform without the authority to configure underlying environments. More importantly, when considering a more complex workflow that includes backend-as-a-service (BaaS), a BaaS offering can typically only natively trigger a FaaS offering from the same provider. This makes the workload migration in serverless computing virtually impossible. Therefore, considering how to design and deploy serverless workflows from a multi-cloud perspective could mitigate this. == High-performance computing == Serverless computing may not be ideal for certain high-performance computing (HPC) workloads due to resource limits often imposed by cloud providers, including maximum memory, CPU, and runtime restrictions. For workloads requiring sustained or predictable resource usage, bulk-provisioned servers can sometimes be more cost-effective than the pay-per-use model typical of serverless platforms. However, serverless computing is increasingly capable of supporting specific HPC workloads, particularly those that are highly parallelizable and event-driven, by leveraging its scalability and elasticity. The suitability of serverless computing for HPC continues to evolve with advancements in cloud technologies. == Anti-patterns == The grain of sand anti-pattern refers to the creation of excessively small components (e.g., functions) within a system, often resulting in increased complexity, operational overhead, and performance inefficiencies. Lambda pinball is a related anti-pattern that can occur in serverless architectures when functions (e.g., AWS Lambda, Azure functions) excessively invoke each other in fragmented chains, leading to latency, debugging and testing challenges, and reduced observability. These anti-patterns are associated with the formation of a distributed monolith. These anti-patterns are often addressed through the application of clear domain boundaries, which distinguish between public and published interfaces. Public interfaces are technically accessible interfaces, such as methods, classes, API endpoints, or triggers, but they do not come with formal stability guarantees. In contrast, published interfaces involve an explicit stability contract, including formal versioning, thorough documentation, a defined deprecation policy, and often support for backward compatibility. Published interfaces may also require maintaining multiple versions simultaneously and adhering to formal deprecation processes when breaking changes are introduced. Fragmented chains of function calls are often observed in systems where serverless components (functions) interact with other resources in complex patterns, sometimes described as spaghetti architecture or a distributed monolith. In contrast, systems exhibiting clearer boundaries typically organize serverless components into cohesive groups, where internal public interfaces manage inter-component communication, and published interfaces define communication across group boundaries. This distinction highlights differences in stability guarantees and maintenance commitments, contributing to reduced dependency complexity. Additionally, patterns associated with excessive serverless function chaining are sometimes addressed through architectural strategies that emphasize native service integrations instead of individual functions, a concept referred to as the functionless mindset. However, this approach is noted to involve a steeper learning curve, and integration limitations may vary even within the same cloud vendor ecosystem. Reporting on serverless databases presents challenges, as retrieving data for a reporting service can either break the bounded contexts, reduce the timeliness of the data, or do both. This applies regardless of whether data is pulled directly from databases, retrieved via HTTP, or collected in batches. Mark Richards refers to this as the reach-in reporting anti-pattern. A possible alternative to this approach is for databases to asynchronously push the necessary data to the reporting service instead of the reporting service pulling it. While this method requires a separate contract between services and the reporting service and can be complex to implement, it helps preserve bounded contexts while maintaining a high level of data timeliness. == Principles == Adopting DevSecOps practices can help improve the use and security of serverless technologies. In serverless applications, the distinction between infrastructure and business logic is often blurred, with applications typically distributed across multiple services. To maximize the effectiveness of testing, integration testing is emphasized for serverless applications. Additionally, to facilitate debugging and implementation, orchestration is used within the bounded context, while choreography is employed between different bounded contexts. Ephemeral resources are typically kept together to maintain high cohesion. However, shared resources with long spin-up times, such as AWS RDS clusters and landing zones, are often managed in separate repositories, deployment pipeline, and stacks.

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  Terminator (franchise)

  Terminator is an American media franchise created by James Cameron and Gale Anne Hurd. It is considered to be of the cyberpunk subgenre of science fiction. The franchise primarily focuses on the events leading to a future post-apocalyptic war between a synthetic intelligence known as Skynet, and a surviving resistance of humans led by John Connor. In this future, Skynet uses an arsenal of cyborgs known as Terminators, designed to mimic humans and infiltrate the resistance. Much of the franchise takes place in time periods prior to the Skynet takeover, with both humans and Terminators using time travel to attempt to alter the past and change the outcome of the future. A prominent Terminator model throughout the films is the T-800, commonly known as "the Terminator", with instances of this model portrayed by Arnold Schwarzenegger. The franchise began with the 1984 film The Terminator, written and directed by Cameron, with Hurd as producer. They would return for the 1991 sequel Terminator 2: Judgment Day (or T2). Both films were critical and commercial successes. Terminator 3: Rise of the Machines (or T3) was released in 2003 to positive reviews, followed by Terminator Salvation in 2009 to more negative reviews. Salvation was intended as the first in a new trilogy, which was later scrapped after the film rights were sold. Cameron was consulted for the 2015 film Terminator Genisys, a reboot branching off from the timeline of the original film. It was negatively received and performed poorly at the box-office. Cameron had a larger role as a producer of the 2019 film Terminator: Dark Fate, a direct sequel to T2 that ignores the three preceding films. As with Salvation, both Genisys and Dark Fate were planned as first installments of new trilogies, with the plans scrapped each time due to the films' poor box-office performances. Outside of the theatrical films, Cameron co-directed T2-3D: Battle Across Time, a 1996 theme park film-based attraction. It was produced as the original sequel to T2 and reunited its main cast. A television series, Terminator: The Sarah Connor Chronicles, was developed without Cameron's involvement and aired for two seasons in 2008 and 2009. It was also produced as a T2 sequel, taking place in an alternate timeline that ignores the third film and subsequent events. Terminator Zero, an anime series, premiered in August 2024. The franchise has also inspired several lines of comic books since 1988, and numerous video games since 1991. By 2010, the franchise had generated $3 billion in revenue. == Themes and setting == The central theme of the franchise is the battle for survival between the nearly-extinct human race and the world-spanning, synthetic intelligence that is Skynet. Skynet is positioned in the first film, The Terminator (1984), as a U.S. strategic "Global Digital Defense Network" computer system by Cyberdyne Systems which becomes self-aware. Shortly after activation, Skynet seemingly perceives all humans as a threat to its existence and formulates a plan to systematically wipe out humanity itself. The system initiates a nuclear first strike against Russia, thereby ensuring a devastating second strike and a nuclear holocaust which wipes out much of humanity in the resulting nuclear war. In the post-apocalyptic aftermath, Skynet later builds up its own autonomous machine-based military capability which includes the Terminators used against individual human targets and thereafter proceeds to wage a persistent total war against the surviving elements of humanity, some of whom have militarily organized themselves into a Resistance. At some point in this future, Skynet develops the capability of time travel and both it and the Resistance seek to use this technology in order to win the war; either by altering or accelerating past events or by preventing the apocalyptic timeline. === Judgment Day === In the franchise, Judgment Day (a reference to the biblical Day of Judgment) is the date on which Skynet becomes self-aware, in which case its creators panic and attempt to deactivate the network. As a result, Skynet perceives humanity as a threat and attempts to exterminate them. Skynet launches an all-out nuclear attack on Russia in order to provoke a nuclear counter-strike against the United States, knowing this will eliminate its human enemies. Due to time travel and the consequent ability to change the future, several differing dates are given for Judgment Day. In Terminator 2: Judgment Day (1991), Sarah Connor states that Judgment Day will occur on August 29, 1997. However, this date is delayed following the attack on Cyberdyne Systems in the same film. Judgment Day has various different dates in different timelines of the subsequent films, as well as the television series, creating a multiverse of temporal phenomena. In Terminator 3: Rise of the Machines (2003) and Terminator Salvation (2009), Judgment Day was postponed to July 2003. In Terminator: The Sarah Connor Chronicles (2008–2009), the attack on Cyberdyne Systems in the second film delayed Judgment Day to April 21, 2011. In Terminator Genisys (2015), the fifth film in the franchise, Judgment Day was postponed to an unspecified day in October 2017, attributed to altered events in both the future and the past. Sarah and Kyle Reese travel through time to the year 2017 and seemingly defeat Skynet, but the system core, contained inside a subterranean blast shelter, survives unknown to them, thus further delaying, rather than preventing, Judgment Day. In Terminator: Dark Fate (2019), the direct sequel to Terminator 2: Judgment Day, a date is not given for the new Judgment Day though it is named as such by Grace. Since Grace is a ten-year-old in 2020 and shown as a teenager in the post-Judgment Day world in flash-forwards throughout the film, Judgment Day occurs sometime in the early 2020s in this timeline. == Franchise rights == Before the first film was created, director James Cameron sold the rights for $1 to Gale Anne Hurd, his future wife, who produced the film, under the strict provision that he be allowed to direct it. Hemdale Film Corporation also became a 50-percent owner of the franchise rights, until its share was sold in 1990 to Carolco Pictures, a company founded by Andrew G. Vajna and Mario Kassar. Terminator 2: Judgment Day was released a year later. Carolco filed for bankruptcy in 1995 and its library was subsequently acquired by StudioCanal, which continues to own the franchise today. However, the rights to future Terminator films were ultimately put up for auction. By that time, Cameron had become interested in making a Terminator 3 film. The rights were ultimately auctioned to Vajna in 1997, for $8 million. Vajna and Kassar spent another $8 million to purchase Hurd's half of the rights in 1998, becoming the full owners of the franchise. Hurd was initially opposed to the sale of the rights, while Cameron had lost interest in the franchise and a third film. After the 2003 release of Terminator 3: Rise of the Machines, the franchise rights were sold in 2007 for about $25 million to The Halcyon Company, which produced Terminator Salvation in 2009. Later that year, the company faced legal issues and filed for bankruptcy, putting the franchise rights up for sale. The rights were valued at about $70 million. In 2010, the rights were sold for $29.5 million to Pacificor, a hedge fund that was Halcyon's largest creditor. In 2012, the rights were sold to Megan Ellison and her production company Annapurna Pictures for less than $20 million, a lower price than what was previously offered. The low price was because of the possibility of Cameron regaining the rights in 2019, as a result of new North American copyright laws. Megan's brother David Ellison and Skydance Productions produced Terminator Genisys in 2015. Cameron worked together with David Ellison to produce the 2019 film Terminator: Dark Fate. As the film neared its release, Hurd filed to terminate a copyright grant made 35 years earlier. Under this move, Hurd would again become a 50-percent owner of the rights with Cameron and Skydance could lose the rights to make any additional Terminator films beginning in November 2020, unless a new deal is worked out. Skydance responded that it had a deal in place with Cameron and that it "controls the rights to the Terminator franchise for the foreseeable future". == Films == === The Terminator (1984) === The Terminator is a 1984 science fiction action film released by Orion Pictures, co-written and directed by James Cameron and starring Arnold Schwarzenegger, Linda Hamilton and Michael Biehn. It is the first work in the Terminator franchise. In the film, robots take over the world in the near future, directed by the artificial intelligence Skynet. With its sole mission to completely annihilate humanity, it develops android assassins called Terminators that outwardly appear human. A man named John Connor starts the Tech-Com resistance to fight the machi

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  The Eye of Mexico

  The Eye of Mexico (Spanish: El Ojo de México) is an outdoor sculpture in Mexico City. It is located in Ampliación Granada, Miguel Hidalgo, at the mixed-use development Neuchâtel Polanco, developed by the Canadian real estate company Ivanhoé Cambridge. The artwork was created by the Turkish artist Ferdi Alıcı and it was selected from among 350 proposals from artists from 35 countries. The project for The Eye of Mexico was developed by MIRA, a real estate investment and development company, and MASSIVart, a creative consulting agency. According to MIRA, upon its inauguration it became the first artwork in Latin America to use artificial intelligence (AI). The sculpture can read environmental and urban data using AI algorithms and transform the results into videos related to arts, science and technology. The ring was inaugurated on 20 May 2022 and it is 10 meters (33 ft) high and 3 meters (9.8 ft) wide.

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  Transhuman Space

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

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  AI Security Institute

  The AI Security Institute (AISI) is a research organisation under the Department for Science, Innovation and Technology, UK, that aims "to equip governments with a scientific understanding of the risks posed by advanced AI". It conducts research and develop and test mitigations. Previously, it was known as the AI Safety Institute. Its creation followed world's first major AI Safety Summit that was held in Bletchley Park in 2023. The institute's professed goal is "building the world's leading understanding of advanced AI risks and solutions, to inform governments so they can keep the public safe". It is designed like a startup in the government "combining the authority of government with the expertise and agility of the private sector". AISI has made access agreements with Anthropic, Google and OpenAI to test their models before release. It has an open source platform called Inspect that permits companies, governments and academics to run standardised safety tests for AI usage. Among the works AISI has done is the reported detection of multiple serious vulnerabilities that could enable development of biological weapons; the vulnerabilities were fixed before the model was launched. It conducts research on diverse fields of AI application. One study by AISI found that LLMs post-trained for political persuasiveness became systematically less accurate and up to 51% more persuasive on political issues. AISI has also worked on the usage of AI for emotional needs. It found that nearly 10 percent of UK citizens used systems like chatbots for emotional purposes on a weekly basis. It found that "systems are now outperforming PhD-level researchers on scientific knowledge tests and helping non-experts succeed at lab work that would previously have been out of reach" in a report published in December 2025. Former chief AI officer of GCHQ Adam Beaumont is the institution's interim director. UK prime minister's AI advisor Jade Leung is the chief technology officer.

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  Smart speaker

  A smart speaker is a type of loudspeaker and voice command device with an integrated virtual assistant that offers interactive actions and hands-free activation with the help of one "wake word" (or several "wake words"). Some smart speakers also act as smart home hubs by using Wi-Fi, Bluetooth, Thread, and other protocol standards to extend usage beyond audio playback and control home automation devices connected through a local area network. == History == Early voice-activated devices began in 2013 with MIT's Jasper project, which used multiple microphones and cloud software to power hands-free interactions from across a room. The first commercial smart speaker was the Amazon Echo, which was released in 2014 powered by Alexa and a ring of far-field microphones. Google followed in 2016 with Home, powered by Google Assistant. By 2017, devices like the Echo Show and Home Hub (later called Nest Hub) added touchscreens and video, creating the "smart display" subcategory. In 2018, Apple joined the smart speaker trend by launching the HomePod, which focused on high-quality audio alongside their built-in assistant Siri. ASUS release its own smart Speaker Xiao-Bu in 2019 with Artificial Intelligence, it terminates the Cloud Service on June 1st, 2025, which means all real-time service such as weather, news, currency conversion is affected. Sonos's 1st smart speaker Sonos One released in 2017, powered by Alexa. Invoke by Harman Kardon was powered by Microsoft's intelligent personal assistant, Cortana. In the early 2020s, smart speakers gained on-device voice processing for faster responses and improved privacy. New standards such as Matter and Thread allowed multitudes of smart-home devices (even from completely different brands) to work together. == Features == === Audio and Voice === Smart speakers use multiple microphones along with noise-cancelling software to pick up your voice from across the room, even when music is playing or the assistant is already talking. Noise suppression and echo cancellation is also used by the speaker so it can focus in on who is talking and ignore any background noises. Most smart speaker models can recognize who is speaking by voiceprint, which allows the speaker to grab information from that person's calendar, preferences, or music playlists. Listening to music on a speaker is when importance for good audio quality becomes apparent. Entry-level (cheaper) speakers such as the Home Mini or the Echo Dot have a single full-range driver. These lower-end speakers typically aren't great for listening to music as the audio quality is pretty poor. More advanced units such as the Home Max or Echo Studio have separate tweeters and woofers meant for listening to music in high quality. === Connectivity and smart-home control === Most connect over Wi-Fi or Bluetooth and support hub protocols like Thread and Matter. That lets them not only stream and play music but also allows you to control various brands of smart lights, thermostats, door locks, cameras, and much more-all from one point of control. Each can have its own designated interface and features in-house, usually launched or controlled via application or home automation software. These devices are able to communicate with each other via peer-to-peer connection through mesh networking. These speakers and related smart devices are typically controlled with one smartphone application. === Assistant services and skills === The built-in assistants handle timers, alarms, reminders, news briefings, weather updates, send messages to other smart devices, send texts, make calls, and simple questions. You can combine actions together in what are typically known as routines (for example saying "good morning" turns on lights, starts the coffee, says the weather, and reads the news) and add extra functions known as skills or actions (for things like ordering food or playing trivia games). This hands-free use of smart speakers can help assist those with disabilities. Most other technologies need the user to be able to physically interact with the device. Smart speakers are not bound by these limitations and can serve as an excellent tool for those who are unable to use their arms or legs or have vision issues. Although these tasks can be completed by a phone or computer, consumers tend to lean towards smart speakers due to factors such as their range being much greater than that of a phone and the need to not have to physically interact with the speaker to get the voice assistant as with most smartphones, certain parts of a phone may need to be interacted with to activate the speaking assistant. === Smart displays === Some smart speakers also include a screen to show the user a visual response. A smart speaker with a touchscreen is known as a smart display; these integrate a conversational user interface with display screens to augment voice interaction with images and video. They are powered by one of the common voice assistants and offer additional controls for smart home devices, feature streaming apps, and web browsers with touch controls for selecting content. The first smart displays were introduced in 2017 by Amazon (Amazon Echo Show) and Google (Google/Nest Home Hub). Hotel chain Marriott International partnered with Amazon to install Echo devices in select hotels since 2018. A Taiwanese startup, Aiello, launched the Aiello Voice Assistant (AVA) in the Asian hotel market in 2019, claiming it is powered by a multi-AI model system. Angie by Nomadix, which is similar to the Amazon Echo, launched its first product in 2017, specifically targeting hotel properties in the North America. In May 2019, Angie Hospitality acquired the assets of Roxy, a competitor that also built its own speech-enabled virtual assistant technology for hotels. This acquisition merged two proprietary NLP stacks into the current Nomadix product. === Artificial intelligence === The newest speakers can use on-device AI or cloud-based generative models to allow the smart speaker to carry on much more natural conversations, draft emails or recipes, suggest ideas based on context, or even create short pieces of music or art. This AI evolution allows these speakers to do far more than what they could do before. == Accuracy == According to a study by Proceedings of the National Academy of Sciences of the United States of America released In March 2020, the six biggest tech development companies, Amazon, Apple, Google, Yandex, IBM and Microsoft, have misidentified more words spoken by "black people" than "white people". The systems tested errors and unreadability, with a 19 and 35 percent discrepancy for the former and a 2 and 20 percent discrepancy for the latter. The North American Chapter of the Association for Computational Linguistics (NAACL) also identified a discrepancy between male and female voices. According to their research, Google's speech recognition software is 13 percent more accurate for men than women. It performs better than the systems used by Bing, AT&T, and IBM. == Privacy concerns == The built-in microphone in smart speakers is continuously listening for wake words followed by a command. However, these continuously listening microphones also raise privacy concerns among users. According to a survey taken by 1,007 people in Western Europe, it is clear that privacy is the biggest concern holding consumers back from buying "smart" products. these concerns include what is being recorded, how the data will be used, how it will be protected, and whether it will be used for invasive advertising. Furthermore, an analysis of Amazon Echo Dots showed that 30–38% of "spurious audio recordings were human conversations", suggesting that these devices capture audio other than strictly detection of the wake word. === As a wiretap === There are strong concerns that the ever-listening microphone of smart speakers presents a perfect candidate for wiretapping. In 2017, British security researcher Mark Barnes showed that pre-2017 Echos have exposed pins which allow for a compromised OS to be booted. According to Umar Iqbal, an assistant professor at Washington University in St. Louis, research indicates that data from consumer interactions with Alexa was used to targeted advertisements and products to consumer with over 40% of transmitted data lacking proper encryption raising privacy concerns. Further data indicates that due to the Smart Speakers ability to always capture audio, it begins to pick up on external conversations from consumers not related to commands given to the smart speaker. Things such as other members in the household, consumers on the phone and even TV audio can be picked up by these speakers and stored for future use by companies. === Voice assistance vs privacy === While voice assistants provide a valuable service, there can be some hesitation towards using them in various social contexts, such as in public or around other users. However, only more recently have users begun interac

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  The Raimones

  The Raimones (stylized as THE RAiMONES) is a 2017 generative music project that utilized artificial intelligence to compose music in the style of the American punk rock band The Ramones. Developed by Matthias Frey, a researcher at Sony CSL Tokyo, the project was an early experiment in applying deep learning to high-energy, minimalist musical genres. == Technical Development == The project utilized Long short-term memory (LSTM) recurrent neural networks to generate musical structures and lyrics. The model was trained on a dataset consisting of 130 Ramones songs in MIDI format and the band's complete lyrical catalog. The technical framework was built using Python and Jupyter Notebook, drawing influence from the character-level RNN text generation models popularized by Andrej Karpathy. Unlike contemporary AI music projects that focused on the harmonic complexities of classical or pop music, THE RAiMONES sought to determine if neural networks could replicate the "1-2-3-4" rhythmic consistency and formulaic nature of early punk. == "I'm Alive" == The primary output of the project was the song "I'm Alive," released in 2017. The work is described as a form of "augmented intelligence," a hybrid approach where the AI provides the compositional foundation and human musicians handle the arrangement and performance. The song was recorded by the musician Mr. Ratboy (Gilbert Avondet). Avondet's involvement provided a stylistic link to the subject material, as he had previously served as a touring guitarist for Marky Ramone and the Intruders in 1996. The project's discography has since been made available on major streaming platforms, including Apple Music. == Reception and Significance == The project has been cited as a "proof of concept" for AI's ability to tackle "noisy" and aggressive aesthetics. In 2019, the Belgian magazine Knack Focus profiled the project alongside other AI pioneers such as Holly Herndon, noting the project's attempt to recreate the sound of "deceased legends" while maintaining a distinct, machine-like quality. It has also been featured in academic settings, such as at UC Santa Cruz, as a case study for AI-driven genre mimicry.

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  AI takeover

  An AI takeover is a theorized future event, often depicted in fiction, in which autonomous artificial intelligence systems acquire the capability to supersede human decisions. This could occur through economic manipulation, infrastructure control, or direct intervention, leading to de facto governance. Scenarios range from gradual economic dominance, as automation supplants the human workforce, up to a sudden or aggressive global takeover by a robot uprising or other forms of rogue AI. Stories of AI takeovers have been popular throughout science fiction. Commentators argue that recent advancements in the field have heightened concern about such scenarios. In public debate, prominent figures such as Stephen Hawking have advocated research into precautionary measures to ensure future superintelligent machines remain under human control. == Types == === Automation of the economy === The traditional consensus among economists has been that technological progress does not cause long-term unemployment. However, recent innovation in the fields of robotics and artificial intelligence has raised worries that human labor will become obsolete, leaving workers in some sectors without employment. Many small and medium-sized firms may also be forced to close if they cannot afford or license the latest robotic and AI technology, and may need to focus on areas or services that cannot easily be replaced for continued viability in the face of such technology. ==== Technologies that may displace workers ==== While these technologies have replaced some traditional workers, they also create new opportunities. Industries that are most susceptible to AI-driven automation include transportation, retail, and the military. AI military technologies, for example, can reduce risk by enabling remote operation. A study in 2024 highlights AI's ability to perform routine and repetitive tasks poses significant risks of job displacement, especially in sectors like manufacturing and administrative support. Author Dave Bond argues that as AI technologies continue to develop and expand, the relationship between humans and robots will change; they will become closely integrated in several aspects of life. AI will likely displace some workers while creating opportunities for new jobs in other sectors, especially in fields where tasks are repeatable. Researchers from Stanford's Digital Economy Lab reported in 2025 that since the widespread adoption of generative AI in late 2022, early-career workers (ages 22–25) in the most AI-exposed occupations have experienced a 13 percent relative decline in employment—even after controlling for firm-level shocks—while overall employment has continued to grow robustly. The study further finds that job losses are concentrated in roles where AI automates routine tasks, whereas occupations that leverage AI to augment human work have seen stable or increasing employment. ==== Computer-integrated manufacturing ==== Computer-integrated manufacturing uses computers to control the production process. This allows individual processes to exchange information with each other and initiate actions. Although manufacturing can be faster and less error-prone through the integration of computers, the main advantage is the ability to create automated manufacturing processes. Computer-integrated manufacturing is used in automotive, aviation, space, and shipbuilding industries. ==== White-collar machines ==== The 21st century has seen a variety of skilled tasks partially taken over by machines, including translation, legal research, and journalism. Care work, entertainment, and other tasks requiring empathy, previously thought safe from automation, are increasingly performed by robots and AI systems. ==== Autonomous cars ==== An autonomous car is a vehicle that is capable of sensing its environment and navigating without human input. Many such vehicles are operational and others are being developed, with legislation rapidly expanding to allow their use. Obstacles to widespread adoption of autonomous vehicles have included concerns about the resulting loss of driving-related jobs in the road transport industry, and safety concerns. On March 18, 2018, a pedestrian was struck and killed in Tempe, Arizona by an Uber self-driving car. ==== AI-generated content ==== In the 2020s, automated content became more relevant due to technological advancements in AI models, such as ChatGPT, DALL-E, and Stable Diffusion. In most cases, AI-generated content such as imagery, literature, and music are produced through text prompts. These AI models are sometimes integrated into creative programs. AI-generated art may sample and conglomerate existing creative works, producing results that appear similar to human-made content. Low-quality AI-generated visual artwork can be informally referred to as AI slop. Some artists use a tool called Nightshade that alters images to make them detrimental to the training of text-to-image models if scraped without permission, while still looking normal to humans. AI-generated images are a potential tool for scammers and those looking to gain followers on social media, either to impersonate a famous individual or group or to monetize their audience. The New York Times has sued OpenAI, alleging copyright infringement related to the training and outputs of its AI models. === Eradication === Scientists such as Stephen Hawking are confident that superhuman artificial intelligence is physically possible, stating "there is no physical law precluding particles from being organised in ways that perform even more advanced computations than the arrangements of particles in human brains". According to Nick Bostrom, a superintelligent machine would not necessarily be motivated by the same emotional desire to collect power that often drives human beings but might rather treat power as a means toward attaining its ultimate goals; taking over the world would both increase its access to resources and help to prevent other agents from stopping the machine's plans. As a simplified example, a paperclip maximizer designed solely to create as many paperclips as possible would want to take over the world so that it can use all of the world's resources to create as many paperclips as possible, and, additionally, prevent humans from shutting it down or using those resources on things other than paperclips. There are debates on how realistic AI takeover scenarios are. According to a 2026 research paper, many of the arguments about existential risks are based on speculative assumptions about how intelligent AI systems could become, how they would behave and what goals they might develop over time. A 2023 Reuters/Ipsos survey showed that 61% of American adults feared AI could pose a threat to civilization. Philosopher Niels Wilde refutes the common thread that artificial intelligence inherently presents a looming threat to humanity, stating that these fears stem from perceived intelligence and lack of transparency in AI systems that more closely reflects the human aspects of it rather than those of a machine. AI alignment research studies how to design AI systems so that they follow intended objectives. == Debate == Physicist Stephen Hawking, Microsoft founder Bill Gates, and SpaceX founder Elon Musk have expressed concerns about the possibility that AI could develop to the point that humans could not control it, with Hawking theorizing that this could "spell the end of the human race". Stephen Hawking said in 2014 that "Success in creating AI would be the biggest event in human history. Unfortunately, it might also be the last, unless we learn how to avoid the risks." Hawking believed that in the coming decades, AI could offer "incalculable benefits and risks" such as "technology outsmarting financial markets, out-inventing human researchers, out-manipulating human leaders, and developing weapons we cannot even understand." In January 2015, Nick Bostrom joined Stephen Hawking, Max Tegmark, Elon Musk, Lord Martin Rees, Jaan Tallinn, and numerous AI researchers in signing the Future of Life Institute's open letter speaking to the potential risks and benefits associated with artificial intelligence. The signatories "believe that research on how to make AI systems robust and beneficial is both important and timely, and that there are concrete research directions that can be pursued today." Some focus has been placed on the development of trustworthy AI. Three statements have been posed as to why AI is not inherently trustworthy: 1. An entity X is trustworthy only if X has the right motivations, goodwill and/or adheres to moral obligations towards the trustor; 2. AI systems lack motivations, goodwill, and moral obligations; 3. Therefore, AI systems cannot be trustworthy. There are additional considerations within this framework of trustworthy AI that go further into the fields of explainable artificial intelligence and respect for human privacy. Zanotti and colleagues

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  JAX (software)

  JAX is a Python library for accelerator-oriented array computation and program transformation, designed for high-performance numerical computing and large-scale machine learning. It is developed by Google with contributions from Nvidia and other community contributors. It is described as bringing together a modified version of the automatic differentiation system autograd and OpenXLA's XLA (Accelerated Linear Algebra). It is designed to follow the structure and workflow of NumPy as closely as possible and works with various existing frameworks such as TensorFlow and PyTorch. The primary features of JAX are: Providing a unified NumPy-like interface to computations that run on CPU, GPU, or TPU, in local or distributed settings. Built-in Just-In-Time (JIT) compilation via OpenXLA, an open-source machine learning compiler ecosystem. Efficient evaluation of gradients via its automatic differentiation transformations. Automatic vectorization to efficiently map functions over arrays representing batches of inputs. == Libraries using Jax == Flax Equinox Optax

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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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  2024 Abu Dhabi Autonomous Racing League

  On 27 April 2024, the inaugural race of the Abu Dhabi Autonomous Racing League was held at the Yas Marina Circuit in Abu Dhabi. The race, originally scheduled to last eight laps, was ultimately shortened to six laps due to various complications, including subpar performance. It involved four self-driving race cars, only two of which – German cars Hailey and Constructor AI – finished the race; the other two did not finish. == Background == === Abu Dhabi Autonomous Racing League (A2RL) === 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. Unlike the IAC, which primarily focuses on time trials, simulated races, and challenges for teams, 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 === In total, eight teams were part of the A2RL in 2024, but only four would compete in the race proper. The list of teams in 2024 is: Fly Eagle (China/UAE) Code19 Racing (United States) Constructor University (Germany) Kinetiz (Singapore/UAE) Humda Lab (Hungary) PoliMove (Italy) Unimore (Italy) Technical University of Munich (Germany) Most teams come from universities and many, such as PoliMove and TUM, already have experience with autonomous racing, primarily from competing in the IAC. All teams had two months to code and test their AIs. Unlike most international open-wheel racing tournaments, such as Formula 1 or Formula E, no free practice sessions were undertaken. === TII Pre-race demonstration === Prior to the race itself, a mock 1v1 duel between former F1 driver Danill Kvyat and a self-driving car from the non-competing TII Racing team took place; the autonomous car was green and had number 01, while Kvyat's car was red and had number 00. Kvyat spent most of the duel in the pits. Kvyat himself said: "I'm not racing autonomous cars here. It won't be a flat-out race". == Qualifying == === Qualifying report === As only four of the eight entrants would compete in the main event, qualifying time trials were held to determine the four main race competitors, as well as their positions in the grid. Only the cars with the four best lap times over three time trial sessions held on Friday and Saturday would qualify. Multiple errors and setbacks occurred during qualifying. In the first session, Maveric AI, Code19's car, left the track and stopped just after turn 14 due to connectivity issues. Fly Eagle's car, Feiying, had multiple upsets; at one point, Feiying ran into localization issues and began swerving left and right before stopping just before turn 10. Later, Feiying swerved again and nearly hit the wall at the back straight, near the support pits, due to further localization issues. Sparkz, the Kinetiz team's car, swerved and crashed into the wall near yacht berths 51-56 after turn 11, damaging the front right wheel's axle and partially detaching the forward wings. Sparkz would be the only car to not have a set time at the end of the time trials. PoliMove car Eva braked hard without warning at the straight, the LED status indicator turning off, suggesting the AI computer had a system crash or shut itself down. After the sun went down, during the second session, Hailey, the car from the TUM team, went off-track after turn 9 and stopped, its status indicator flashing red, meaning Hailey's AI disengaged itself. Eva had further issues, once again braking hard and spinning out into turn 1. Later, the same thing happened to Feiying; it later swerved left and right and stopped due to further localization issues. The morning after, during the third and final session, Hailey went off-track after turn 5, and were unable to regain the pole position. === Qualifying classification === == Attack/Defend challenge == === Attack/Defend challenge report === In this part of the event, cars would be put on a series of 1v1 duels to see how well they could defend their position or attack to gain one higher. During one such duel, an incident occurred where Hailey rear-ended Eva, sending both off the track and prematurely ending the duel. The challenge was otherwise uneventful. === Attack/Defend challenge results === == Main race == === Race report === Eventually, at around 20:30 Gulf Standard Time on the night of 27 April, the main event (termed the "Grand Final" on-stream) would begin. The starting order was Eva first, Gianna second, Hailey third, and Constructor AI last. The race began with a rolling start. As a safety measure, the first two laps were conducted under virtual safety car (VSC) to make sure the cars stayed together, making them de facto formation laps, even if they counted towards race distance. However, Hailey ended up stopping at the final turn and strayed too far from the cars ahead, and as a result, the VSC conditions were extended for another lap. According to the livestream's on-screen graphics, Hailey was upwards of one minute and 22.3 seconds behind Gianna after the former started moving again. On lap 4, halfway through the planned race, and with Hailey more than 30 seconds behind Gianna, the VSC was lifted, and the green flag finally dropped. At first, the two Italian cars were leading the pack, Eva was the race leader with Gianna 3.2 seconds behind, however, as it entered the chicane, Eva hit the brakes and spun out, with Gianna briefly stopping as it passed Eva. Eva's spin automatically triggered a full-course yellow flag. Normally, under yellow flag conditions, overtaking is not permitted, but with Eva stopped and being moved off the track, it was theoretically permitted to overtake Eva. However, presumably due to an oversight in the AI's code, the cars assumed overtaking Eva, despite being off the track, was not permitted. As a result, both Gianna and Constructor AI stopped as they did not want to overtake Eva due to the yellow flag, with Hailey following suit as it approached. Constructor AI's status indicator was solid red, suggesting the AI had disengaged; however, Gianna's status indicator remained solid purple, showing the AI was still in control. Eva's status indicator was also solid purple, but was soon flashing green, suggesting the AI had disengaged but was ready to take control again. With all cars stalled, and Eva being off the track, the race was effectively red-flagged and suspended. Hailey, Gianna, and Constructor AI drove themselves back to their team's pits; Eva did not, it was towed to the main pits on a flatbed truck. Constructor was the first to arrive at the pits, followed by Gianna and Hailey, in that order. This incident, combined with loss of internet connection, led to Eva retiring - it did not finish the race. Eventually, it was decided to resume the race. With Eva retired, the restart order was Gianna first, Hailey second, and Constructor AI third. The race was also shortened - from eight laps to six. With lap 5 under full-course yellow, this meant all three remaining teams would effectively restart the race on the sixth and final lap. The trio left the pits at 22:25 Gulf Standard Time, and the race resumed two minutes later. At first, Gianna was winning with Hailey 2.6 seconds behind, but then Gianna stopped on turn 5, giving Hailey the lead. Constructor AI also overtook Gianna, but not without briefly stopping. Gianna remained stopped, its status indicator solid red - it did not finish either. With both Italian teams out of the picture, Hailey finished first and won A2RL 2024, with Constructor AI finishing second, 27.2 seconds behind. === Final race classification ===

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