Many notable artificial intelligence artists have created a wide variety of artificial intelligence art from the 1960s to today. These include: == 20th century == Harold Cohen, active from 1960s to 2010s. Cohen's work is primarily with AARON, a series of computer programs that autonomously create original images. Eric Millikin, active from 1980s to present. Millikin's work includes AI-generated virtual reality, video art, poetry, music, and performance art, on topics such as animal rights, climate change, anti-racism, witchcraft, and the occult. Karl Sims, active from 1980s to present. Sims is best known for using particle systems and artificial life in computer animation. == 21st century == Refik Anadol, active from 2010s to present. Anadol's work includes video installations based on generative algorithms with artificial intelligence. Sougwen Chung, active from 2010s to present. Chung's work includes performances with a robotic arm that uses AI to attempt to draw in a manner similar to Chung. Stephanie Dinkins, active from 2010s to present. Dinkins' work includes recordings of conversations with an artificially intelligent robot that resembles a black woman, discussing topics such as race and the nature of being. Jake Elwes, active from 2010s to present. Their practice is the exploration of artificial intelligence, queer theory and technical biases. Libby Heaney, active from 2010s to present. Heaney's practice includes work with chatbots. Mario Klingemann, active from 2010s to present. Klingemann's works examine creativity, culture, and perception through machine learning and artificial intelligence. Mauro Martino, active from 2010s to present. Martino's work includes design, data visualization and infographics. Trevor Paglen, active from 2000s to present. Paglen's practice includes work in photography and geography, on topics like mass surveillance and data collection. Anna Ridler, active from 2010s to present. Ridler works with collections of information, including self-generated data sets, often working with floral photography.
VLLM
vLLM is an open-source software framework for inference and serving of large language models and related multimodal models. Originally developed at the University of California, Berkeley's Sky Computing Lab, the project is centered on PagedAttention, a memory-management method for transformer key–value caches, and supports features such as continuous batching, distributed inference, quantization, and OpenAI-compatible APIs. According to a project maintainer, the "v" in vLLM originally referred to "virtual", inspired by virtual memory. == History == vLLM was introduced in 2023 by researchers affiliated with the Sky Computing Lab at UC Berkeley. Its core ideas were described in the 2023 paper Efficient Memory Management for Large Language Model Serving with PagedAttention, which presented the system as a high-throughput and memory-efficient serving engine for large language models. In 2025, the PyTorch Foundation announced that vLLM had become a Foundation-hosted project. PyTorch's project page states that the University of California, Berkeley contributed vLLM to the Linux Foundation in July 2024. In January 2026, TechCrunch reported that the creators of vLLM had launched the startup Inferact to commercialize the project, raising $150 million in seed funding. == Architecture == According to its 2023 paper, vLLM was designed to improve the efficiency of large language model serving by reducing memory waste in the key–value cache used during transformer inference. The paper introduced PagedAttention, an algorithm inspired by virtual memory and paging techniques in operating systems, and described vLLM as using block-level memory management and request scheduling to increase throughput while maintaining similar latency. The project documentation and repository describe support for continuous batching, chunked prefill, speculative decoding, prefix caching, quantization, and multiple forms of distributed inference and serving. PyTorch has described vLLM as a high-throughput, memory-efficient inference and serving engine that supports a range of hardware back ends, including NVIDIA and AMD GPUs, Google TPUs, AWS Trainium, and Intel processors.
International World Wide Web Conference Committee
The International World Wide Web Conference Committee (abbreviated as IW3C2 also written as IW3C2) is a professional non-profit organization registered in Switzerland (Article 60ff of the Swiss Civil Code) that promotes World Wide Web research and development. The IW3C2 organizes and hosts the annual World Wide Web Conference in conjunction with the W3C. The IW3C2 was founded by Joseph Hardin and Robert Cailliau at a meeting held in Boston, United States, on 14 August 1994 to prepare for the upcoming Second International World Wide Web Conference in Chicago. The IW3C2 formally became an incorporated entity in May 1996 at the fifth conference in Paris, France. The organization is governed by laws of the Swiss Confederation and the By-laws. == Abbreviation == The abbreviation for the International World Wide Web Conference Committee as IW3C2 is as follow: I- The I is represents the leading I in International. W3- The W3 represents the three 3 leading W's in World Wide Web. C2- The C2 represents the three 2 leading C's in Conference Committee. == Mission == The mission of the IW3C2 is: To coordinate the organization and planning of the international WWW conference series and ensure that it remains the foremost conference addressing World Wide Web research and development; To promote a collaborative spirit among conference attendees that is essential to the success of the series; To ensure the global geographical diversity of conference sites and provide support to local organizers at those sites; To make sure that all content arising from these conferences and forums is permanently and openly available on the widest possible scale; To preserve the history of the conference series; To encourage the global development of the World Wide Web through collaboration with WWW standards organizations; To provide a permanent, broad-based international body to achieve these purposes. == Conferences == The conferences are organized by the IW3C2 in collaboration with local organizing committees and technical program committees. The series provides an open forum in which all opinions can be presented, subject to a strict process of peer review. The proceedings of the conference are published in the ACM Digital Library. === Endorsed conferences === The IW3C2 has endorsed regional conferences devoted to a special topic of the Web by working with endorsed conferences on cross-promotion, publicity and programs. == Membership == Members of the IW3C2 are ordinary members, ex officio members, non-voting members, and officers. === Ordinary members === Ordinary members are elected for a period of 3 years during a general meeting. Members are nominated due to their recognition in the WWW community and represent themselves. Members can be re-elected only after at least one year of absence. The following are the founding members at the time when IW3C2 was officially incorporated in May 1996: Jean-François Abramatic Tim Berners-Lee Robert Cailliau Dale Dougherty Ira Goldstein Joseph Hardin Tim Krauskopf Detlef Krömker Corinne Moore R. P. Channing Rodgers Albert Vezza Stuart Weibel Yuri Rubinsky (died prior to incorporation) The following are the current (April 2016) ordinary members: Robin Chen Chin-Wan Chung Allan Ellis Wendy Hall - IW3C2 Chair Ivan Herman Arun Iyengar - IW3C2 Vice Chair Irwin King Yoelle Maarek Luc Mariaux - IW3C2 Treasurer Daniel Schwabe - IW3C2 Vice-Chair === Ex officio members === Ex officio members are selected from the immediate past conference general co-chairs and from future conference co-chairs. Their term expires one year after the conference they organized. Ex officio members can be elected as ordinary members. The following are current (April 2016) ex officio members and the conference with which they are affiliated: Jacqueline Bourdeau - WWW2016 James Hendler - WWW2016 Rick Barrett - WWW2017 Rick Cummings - WWW2017 Laurent Flory - WWW2018 Fabien Gandon - WWW2018 === Officers === The IW3C2 officers consist of a chairperson, a vice-chair (chairperson-elect), a secretary, a treasurer, and other appointees. Officers are elected during a general meeting (usually at the annual WWW conference) and serve for one year. They can be re-elected an indefinite number of times. == The Seoul Test of Time Award == This annual award, presented at the WWW conference, is made possible by a generous contribution from the organizers of WWW2014 (Seoul Korea). Recipients are determined by the IW3C2 and honor the author, or authors, of a paper presented at a previous WWW conference that has "stood the test of time." The first award, announced at WWW2015 (Florence Italy), recognized Sergey Brin and Larry Page, the founders of Google. The recipients of the WWW2016 award are LinkIn scientist Dr. Badrul Sarwar and University of Minnesota professors George Karypis, Joseph Konstan, and John Riedl (posthumous) for their work in item-item collaborative filtering.
Influence-for-hire
Influence-for-hire or collective influence, refers to the economy that has emerged around buying and selling influence on social media platforms. == Overview == Companies that engage in the influence-for-hire industry range from content farms to high-end public relations agencies. Traditionally influence operations have largely been confined to public sector actors like intelligence agencies, in the influence-for-hire industry the groups conduction the operations are private with commerce being their primary consideration. However many of the clients in the influence-for-hire industry are countries or countries acting through proxies. They are often located in countries with less expensive digital labor. == History == In May 2021, Facebook took a Ukrainian influence-for-hire network offline. Facebook attributed the network to organizations and consultants linked to Ukrainian politicians including Andriy Derkach. During the COVID-19 pandemic state sponsored misinformation was spread through influence-for-hire networks. In August 2021, a report published by the Australian Strategic Policy Institute implicated the Chinese government and the ruling Chinese Communist Party in campaigns of online manipulation conducted against Australia and Taiwan using influence-for-hire.
Conversion path
A conversion path is a description of the steps taken by a user of a website towards a desired end from the standpoint of the website operator or marketer. The typical conversion path begins with a user arriving at a landing page or a product page and proceeding through a series of page transitions until reaching a final state, either positive (e.g. purchase) or negative (e.g. abandoned session). In practice, the study of the dynamics of this process by the interested party has evolved into a sophisticated field, where various statistical methods are being applied to the optimization of outcomes. This includes real-time adjustment of presented content, in which a website operator tries to provide deliberate incentives to increase the odds of conversion based on various sources of information, including demographic traits, search history, and browsing events. In practice, this reflects in different content presented to users arriving from online advertising versus search engines, and similarly, different content is presented depending on their demographic segments. The fundamental metric describing this process in the aggregate is known as conversion rate.
Sample complexity
The sample complexity of a machine learning algorithm represents the number of training-samples that it needs in order to successfully learn a target function. More precisely, the sample complexity is the number of training-samples that we need to supply to the algorithm, so that the function returned by the algorithm is within an arbitrarily small error of the best possible function, with probability arbitrarily close to 1. There are two variants of sample complexity: The weak variant fixes a particular input-output distribution; The strong variant takes the worst-case sample complexity over all input-output distributions. The No free lunch theorem, discussed below, proves that, in general, the strong sample complexity is infinite, i.e. that there is no algorithm that can learn the globally-optimal target function using a finite number of training samples. However, if we are only interested in a particular class of target functions (e.g., only linear functions) then the sample complexity is finite, and it depends linearly on the VC dimension on the class of target functions. == Definition == Let X {\displaystyle X} be a space which we call the input space, and Y {\displaystyle Y} be a space which we call the output space, and let Z {\displaystyle Z} denote the product X × Y {\displaystyle X\times Y} . For example, in the setting of binary classification, X {\displaystyle X} is typically a finite-dimensional vector space and Y {\displaystyle Y} is the set { − 1 , 1 } {\displaystyle \{-1,1\}} . Fix a hypothesis space H {\displaystyle {\mathcal {H}}} of functions h : X → Y {\displaystyle h\colon X\to Y} . A learning algorithm over H {\displaystyle {\mathcal {H}}} is a computable map from Z {\displaystyle Z} to H {\displaystyle {\mathcal {H}}} . In other words, it is an algorithm that takes as input a finite sequence of training samples and outputs a function from X {\displaystyle X} to Y {\displaystyle Y} . Typical learning algorithms include empirical risk minimization, without or with Tikhonov regularization. Fix a loss function L : Y × Y → R ≥ 0 {\displaystyle {\mathcal {L}}\colon Y\times Y\to \mathbb {R} _{\geq 0}} , for example, the square loss L ( y , y ′ ) = ( y − y ′ ) 2 {\displaystyle {\mathcal {L}}(y,y')=(y-y')^{2}} , where h ( x ) = y ′ {\displaystyle h(x)=y'} . For a given distribution ρ {\displaystyle \rho } on X × Y {\displaystyle X\times Y} , the expected risk of a hypothesis (a function) h ∈ H {\displaystyle h\in {\mathcal {H}}} is E ( h ) := E ρ [ L ( h ( x ) , y ) ] = ∫ X × Y L ( h ( x ) , y ) d ρ ( x , y ) {\displaystyle {\mathcal {E}}(h):=\mathbb {E} _{\rho }[{\mathcal {L}}(h(x),y)]=\int _{X\times Y}{\mathcal {L}}(h(x),y)\,d\rho (x,y)} In our setting, we have h = A ( S n ) {\displaystyle h={\mathcal {A}}(S_{n})} , where A {\displaystyle {\mathcal {A}}} is a learning algorithm and S n = ( ( x 1 , y 1 ) , … , ( x n , y n ) ) ∼ ρ n {\displaystyle S_{n}=((x_{1},y_{1}),\ldots ,(x_{n},y_{n}))\sim \rho ^{n}} is a sequence of vectors which are all drawn independently from ρ {\displaystyle \rho } . Define the optimal risk E H ∗ = inf h ∈ H E ( h ) . {\displaystyle {\mathcal {E}}_{\mathcal {H}}^{}={\underset {h\in {\mathcal {H}}}{\inf }}{\mathcal {E}}(h).} Set h n = A ( S n ) {\displaystyle h_{n}={\mathcal {A}}(S_{n})} , for each sample size n {\displaystyle n} . h n {\displaystyle h_{n}} is a random variable and depends on the random variable S n {\displaystyle S_{n}} , which is drawn from the distribution ρ n {\displaystyle \rho ^{n}} . The algorithm A {\displaystyle {\mathcal {A}}} is called consistent if E ( h n ) {\displaystyle {\mathcal {E}}(h_{n})} probabilistically converges to E H ∗ {\displaystyle {\mathcal {E}}_{\mathcal {H}}^{}} . In other words, for all ϵ , δ > 0 {\displaystyle \epsilon ,\delta >0} , there exists a positive integer N {\displaystyle N} , such that, for all sample sizes n ≥ N {\displaystyle n\geq N} , we have Pr ρ n [ E ( h n ) − E H ∗ ≥ ε ] < δ . {\displaystyle \Pr _{\rho ^{n}}[{\mathcal {E}}(h_{n})-{\mathcal {E}}_{\mathcal {H}}^{}\geq \varepsilon ]<\delta .} The sample complexity of A {\displaystyle {\mathcal {A}}} is then the minimum N {\displaystyle N} for which this holds, as a function of ρ , ϵ {\displaystyle \rho ,\epsilon } , and δ {\displaystyle \delta } . We write the sample complexity as N ( ρ , ϵ , δ ) {\displaystyle N(\rho ,\epsilon ,\delta )} to emphasize that this value of N {\displaystyle N} depends on ρ , ϵ {\displaystyle \rho ,\epsilon } , and δ {\displaystyle \delta } . If A {\displaystyle {\mathcal {A}}} is not consistent, then we set N ( ρ , ϵ , δ ) = ∞ {\displaystyle N(\rho ,\epsilon ,\delta )=\infty } . If there exists an algorithm for which N ( ρ , ϵ , δ ) {\displaystyle N(\rho ,\epsilon ,\delta )} is finite, then we say that the hypothesis space H {\displaystyle {\mathcal {H}}} is learnable. In others words, the sample complexity N ( ρ , ϵ , δ ) {\displaystyle N(\rho ,\epsilon ,\delta )} defines the rate of consistency of the algorithm: given a desired accuracy ϵ {\displaystyle \epsilon } and confidence δ {\displaystyle \delta } , one needs to sample N ( ρ , ϵ , δ ) {\displaystyle N(\rho ,\epsilon ,\delta )} data points to guarantee that the risk of the output function is within ϵ {\displaystyle \epsilon } of the best possible, with probability at least 1 − δ {\displaystyle 1-\delta } . In probably approximately correct (PAC) learning, one is concerned with whether the sample complexity is polynomial, that is, whether N ( ρ , ϵ , δ ) {\displaystyle N(\rho ,\epsilon ,\delta )} is bounded by a polynomial in 1 / ϵ {\displaystyle 1/\epsilon } and 1 / δ {\displaystyle 1/\delta } . If N ( ρ , ϵ , δ ) {\displaystyle N(\rho ,\epsilon ,\delta )} is polynomial for some learning algorithm, then one says that the hypothesis space H {\displaystyle {\mathcal {H}}} is PAC-learnable. This is a stronger notion than being learnable. == Unrestricted hypothesis space: infinite sample complexity == One can ask whether there exists a learning algorithm so that the sample complexity is finite in the strong sense, that is, there is a bound on the number of samples needed so that the algorithm can learn any distribution over the input-output space with a specified target error. More formally, one asks whether there exists a learning algorithm A {\displaystyle {\mathcal {A}}} , such that, for all ϵ , δ > 0 {\displaystyle \epsilon ,\delta >0} , there exists a positive integer N {\displaystyle N} such that for all n ≥ N {\displaystyle n\geq N} , we have sup ρ ( Pr ρ n [ E ( h n ) − E H ∗ ≥ ε ] ) < δ , {\displaystyle \sup _{\rho }\left(\Pr _{\rho ^{n}}[{\mathcal {E}}(h_{n})-{\mathcal {E}}_{\mathcal {H}}^{}\geq \varepsilon ]\right)<\delta ,} where h n = A ( S n ) {\displaystyle h_{n}={\mathcal {A}}(S_{n})} , with S n = ( ( x 1 , y 1 ) , … , ( x n , y n ) ) ∼ ρ n {\displaystyle S_{n}=((x_{1},y_{1}),\ldots ,(x_{n},y_{n}))\sim \rho ^{n}} as above. The No Free Lunch Theorem says that without restrictions on the hypothesis space H {\displaystyle {\mathcal {H}}} , this is not the case, i.e., there always exist "bad" distributions for which the sample complexity is arbitrarily large. Thus, in order to make statements about the rate of convergence of the quantity sup ρ ( Pr ρ n [ E ( h n ) − E H ∗ ≥ ε ] ) , {\displaystyle \sup _{\rho }\left(\Pr _{\rho ^{n}}[{\mathcal {E}}(h_{n})-{\mathcal {E}}_{\mathcal {H}}^{}\geq \varepsilon ]\right),} one must either constrain the space of probability distributions ρ {\displaystyle \rho } , e.g. via a parametric approach, or constrain the space of hypotheses H {\displaystyle {\mathcal {H}}} , as in distribution-free approaches. == Restricted hypothesis space: finite sample-complexity == The latter approach leads to concepts such as VC dimension and Rademacher complexity which control the complexity of the space H {\displaystyle {\mathcal {H}}} . A smaller hypothesis space introduces more bias into the inference process, meaning that E H ∗ {\displaystyle {\mathcal {E}}_{\mathcal {H}}^{}} may be greater than the best possible risk in a larger space. However, by restricting the complexity of the hypothesis space it becomes possible for an algorithm to produce more uniformly consistent functions. This trade-off leads to the concept of regularization. It is a theorem from VC theory that the following three statements are equivalent for a hypothesis space H {\displaystyle {\mathcal {H}}} : H {\displaystyle {\mathcal {H}}} is PAC-learnable. The VC dimension of H {\displaystyle {\mathcal {H}}} is finite. H {\displaystyle {\mathcal {H}}} is a uniform Glivenko-Cantelli class. This gives a way to prove that certain hypothesis spaces are PAC learnable, and by extension, learnable. === An example of a PAC-learnable hypothesis space === X = R d , Y = { − 1 , 1 } {\displaystyle X=\mathbb {R} ^{d},Y=\{-1,1\}} , and let H {\displaystyle {\mathcal {H}}} be the space of affine functions on X {\displaystyle X} , that is, functions of the form x ↦ ⟨ w , x ⟩ + b {\displaystyle x\mapsto \langl
Electric Literature
Electric Literature is an American literary magazine. == History == Founded by Andy Hunter and Scott Lindenbaum in 2009 as a print quarterly journal, Electric Literature transitioned to a daily website in 2012 under the helm of Halimah Marcus and Benjamin Samuel. Electric Literature publishes essays, reading lists, interviews, fiction, poetry, graphic narratives, humor, and book news, all available to read online for free without a paywall. It launched the first fiction magazine on the iPhone and iPad. Work published has been recognized by Best American Short Stories, Essays, Poetry, and Comics, the Pushcart Prize, Best Canadian Short Stories, The Best of the Small Presses, and the O. Henry Prize. in 2014, Electric Literature became a registered non-profit. In 2016, Halimah Marcus was appointed the first executive director of Electric Literature. She has been with the magazine since 2010. In 2021, Denne Michele Norris became editor-in-chief of Electric Literature, the first Black and openly trans editor-in-chief of a major U.S. literary publication. In 2022, Electric Literature was the Digital Prize Winner of the Whiting Literary Magazine Prizes. In 2023, Electric Literature partnered with Banned Books USA to offer free banned and challenged books to residents of Florida. In 2025, Electric Literature published their first book, edited by Norris and published by HarperOne: Both/And: Essays by Trans and Gender-Nonconforming Writers of Color. It builds on a prior essay series that Electric Literature sponsored for trans writers of color. Both/And became a finalist for the 2026 Lambda Literary Award for Transgender Nonfiction. == Recommended reading == In May 2012, Electric Literature launched Recommended Reading, a weekly fiction magazine. Each issue is curated by a well known editor or writer. == The Commuter == The Commuter, a weekly magazine for poetry, flash, graphic, or experimental narrative, debuted in January 2018, helmed by writer Kelly Luce.