"Pivot to video" is a phrase referring to the trend, starting in 2015, of media publishing companies cutting staff resources for written content (generally published on their own web sites) in favor of short-form video content (often published on third-party platforms such as Facebook, Instagram, Twitter, YouTube, Snapchat, and TikTok). These moves were generally presented by publishers as a response to changes in social media traffic or to changes in the media consumption habits of younger audiences. However, many media commentators have argued that this shift was primarily motivated by advertising revenue, and that only advertisers, not consumers, prefer video over text. The pivot's contribution to job loss in the media industry has given the phrase "pivot to video" an association with decline, especially in a business context. Commentators have also noted a lack of transparency and accuracy in the viewership metrics reported by platforms such as Facebook, pointing out that abrupt shifts in platforms' proprietary algorithms can have devastating effects on publishers' viewership, traffic, and revenue. Following a scandal in which Facebook revealed it had artificially inflated numbers to its advertisers about how long viewers watched ads, many journalists and industry analysts concluded that the shift to video was based on such misleading or inaccurate metrics, which created a false impression that there was customer demand for additional video content. == History == Streaming media technology has been available since the early 1990s, though it was relatively low-fidelity and not widely available until the mid-2000s. In 2007, traditional media publishers including the New York Times, Washington Post and Time Inc. created new divisions to develop web videos, and Facebook launched its video platform. Twitter purchased micro-video service Vine in October 2012, began adding native video streaming in late 2014, and acquired video-streaming service Periscope in January 2015. An August 2014 profile on BuzzFeed noted the publisher's large investment into video production, and observed that "the future of BuzzFeed may not even be on BuzzFeed.com. One of the company’s nascent ideas, BuzzFeed Distributed, will be a team of 20 people producing content that lives entirely on other popular platforms, like Tumblr, Instagram or Snapchat." On 7 January 2015, Facebook issued a statement about "the shift to video," reporting that "since June 2014, Facebook has averaged more than 1 billion video views every day." Media critic John Herrman argued that "What the shift to Facebook video means is that Facebook is more interested in hosting the things media companies make than just spreading them, that it views links to outside pages as a problem to be solved, and that it sees Facebook-hosted video as an example of the solution." In February 2015, the digital video-journalism publisher NowThis announced that it would operate without a home page, producing content to be published directly on social media platforms. In April 2016, Mashable fired much of its editorial staff, attempting to pivot away from hard news coverage while "growing Mashable across every platform" and doubling down on branded content and video. By December 2017, following a sale to Ziff Davis, Mashable retreated from this focus on video; Bernard Gershon, president of GershonMedia, said that the announcement of many such "pivots" were actually aimed primarily at investors. By 2017, "advertiser interest in video [was] insatiable... Any CFO is going to say 'How can we get more video?'" according to an executive of the publishers' trade association Digital Content Next. Publishers such as Vanity Fair, the Washington Post, and Sports Illustrated began adapting their own articles into cheap video content, either dictated by a newsreader or animated as a slideshow with captions, which could be shared on social platforms or even played alongside the articles themselves. June 2017 saw numerous high-profile pivots to video. Vocativ laid off at least 20 staff, including its entire newsroom, explaining that "as the industry evolves, we are undertaking a strategic shift to focus exclusively on video content that will be distributed via social media and other platforms." Fox Sports eliminated its entire writing staff to focus on creating "premium video across all platforms." And MTV News announced a restructuring that would cut its writing team. Less than two years earlier, MTV News had hired Grantland co-founder Dan Fierman to lead a significant investment in "longform" political and cultural reporting, but Fierman left in April 2017, and in June MTV announced it was "shifting resources into short-form video content more in line with young people's media consumption habits." In July, Vice Media laid off at least 60 employees, including the editor-in-chief of Vice Sports, while expanding video production. August 2017 saw Mic cut ten writers and directed the remainder of the newsroom to generate videos for social platforms. CEO Chris Altchek said "When you think about how many hours people spend watching video versus reading, the audience has already spoken." The move was ultimately unsuccessful, and Mic laid off the majority of its staff a year later before being sold to Bustle Media Group for a fraction of its former value. In September 2017, the for-profit wiki-hosting company Fandom began adding commercially produced videos to its otherwise user-generated wiki subdomains, explicitly citing the need to "keep up with user and advertiser expectations" by "diversifying our content," claiming without substantiation that "consumer patterns are changing," necessitating the addition of "complementary video" to accommodate that supposed need. Objection to the content in these videos and its sharp contrast against the content of the wiki sites to which they were applied led to vocal user backlash, leading Fandom CCO Dorth Raphaely to offer the following non-committal response: "I agree that with these videos in particular we did not deliver the right type of content experience." Movie Pilot CEO Tobi Bauckhage explained his company's fall 2017 layoffs as part of moving "from a text-based publishing model to video... a reaction to the fact that Facebook has changed their algorithms in favor of video instead of referral traffic over the last 12 months and we were losing money in the publishing bit of our business." As part of the company's change in direction, the majority of its staff was laid off and its parent company was sold to Webedia. In November 2017, magazine publisher Condé Nast cut jobs, reduced the frequency of several magazines, and shut down the print edition of Teen Vogue, then invested significant new resources in video production, with a senior executive saying "In the next 24 months, I hope that video is half our business... It’s critical. It’s the macro trend of content consumption." In February 2018, Vox Media cut approximately 50 employees, primarily those assigned to "social video," as Vox CEO Jim Bankoff admitted that those efforts were not "viable audience or revenue growth drivers." In August 2020, Facebook Inc. (now Meta Platforms) pivoted Instagram to video in an effort to replicate the success of TikTok and appeal to a younger audience, introducing "reels" as a form of video and promoting them aggressively. Reels accounted more than half the 20 most-viewed posts on Facebook; however, most of these reels were anonymous aggregations of content from TikTok. Elon Musk declared in early 2024 that X (formerly Twitter) was now a "video-first platform", which has been described by critics as a "pivot to video". == As euphemism == In 2017, Journalist Brian Feldman said that "'Pivoting to video' has become a business strategy for digital publishers common enough in recent months to be a kind of cliché — a slick way to describe something else: layoffs." In response, writers use the phrase as gallows humor shorthand for death or cancellation, as in "how do i tell my bf i want our relationship to pivot to video" (SkyNews' Mollie Goodfellow) or "Horse broke its leg, so we had to take it out back and help it 'pivot to video'" (blogger Anil Dash). == Facebook metrics controversy == In September 2016, Facebook admitted that it had reported artificially inflated numbers to its advertisers about how long viewers watched ads leading to an overestimation of 60-80%. Plaintiffs in a later court case allege the discrepancy was as high as 150-900%. Facebook apologized in an official statement and in multiple staff appearances at New York Advertising Week. Two months later, Facebook disclosed additional discrepancies in audience metrics. In October 2018, a California federal court unsealed the text of a class action lawsuit filed by advertisers against Facebook, alleging that Facebook had known since 2015 that its viewership numbers were highly inflated, that internal records showed it "was far from an hon
Joox
Joox (stylised in all caps) is a music streaming service owned by Tencent, launched in January 2015. Joox is the biggest music streaming app in Asian markets such as Hong Kong, Macau, Indonesia, Malaysia, Myanmar, Thailand and also in South Africa before it was shut down in early 2022. Joox is a freemium service, providing most of its songs free, while some songs are only available for premium users, offered via paid subscriptions or by doing different tasks offered. In 2017, Joox launched their service in their first non-Asian market, South Africa, which for an unknown reason shut down five years later. The service now accounts for more than 50% of all music streaming app downloads in their Asian markets. The number of music-streaming users in Hong Kong, Macau, Malaysia, Thailand, Myanmar and Indonesia was expected to reach 87 million by 2020. == Background == Before the emergence of Joox, Tencent owned QQ Music, one of the largest music streaming and download service in China. In 2015, they introduced Joox as their expansion of music services to overseas market instead of mainland China, starting first in Hong Kong. Instead of providing free services by playing audio ads to users like Spotify, another major music service, Joox focused on banner ads, splash ads and other advertising methods such as category playlists and in-app skins. They claimed it as a success. Joox offered their premium VIP access to DStv subscribers free of charge. DStv is the sister company to Tencent and is the primary pay-TV provider in South Africa. In November 2021, it was announced that Joox will stop streaming in South Africa in March 2022.
Curse of dimensionality
The curse of dimensionality refers to various phenomena that arise when analyzing and organizing data in high-dimensional spaces that do not occur in low-dimensional settings such as the three-dimensional physical space of everyday experience. The expression was coined by Richard E. Bellman when considering problems in dynamic programming. The curse generally refers to issues that arise when the number of datapoints is small (in a suitably defined sense) relative to the intrinsic dimension of the data. Dimensionally cursed phenomena occur in domains such as numerical analysis, sampling, combinatorics, machine learning, data mining and databases. The common theme of these problems is that when the dimensionality increases, the volume of the space increases so fast that the available data becomes sparse. In order to obtain a reliable result, the amount of data needed often grows exponentially with the dimensionality. Also, organizing and searching data often relies on detecting areas where objects form groups with similar properties; in high dimensional data, however, all objects appear to be sparse and dissimilar in many ways, which prevents common data organization strategies from being efficient. == Domains == === Combinatorics === In some problems, each variable can take one of several discrete values, or the range of possible values is divided to give a finite number of possibilities. Taking the variables together, a huge number of combinations of values must be considered. This effect is also known as the combinatorial explosion. Even in the simplest case of d {\displaystyle d} binary variables, the number of possible combinations already is 2 d {\displaystyle 2^{d}} , exponential in the dimensionality. Naively, each additional dimension doubles the effort needed to try all combinations. === Sampling === There is an exponential increase in volume associated with adding extra dimensions to a mathematical space. For example, 102 = 100 evenly spaced sample points suffice to sample a unit interval (try to visualize a "1-dimensional" cube, i.e. a line) with no more than 10−2 = 0.01 distance between points; an equivalent sampling of a 10-dimensional unit hypercube with a lattice that has a spacing of 10−2 = 0.01 between adjacent points would require 1020 = [(102)10] sample points. In general, with a spacing distance of 10−n the 10-dimensional hypercube appears to be a factor of 10n(10−1) = [(10n)10/(10n)] "larger" than the 1-dimensional hypercube, which is the unit interval. In the above example n = 2: when using a sampling distance of 0.01 the 10-dimensional hypercube appears to be 1018 "larger" than the unit interval. This effect is a combination of the combinatorics problems above and the distance function problems explained below. === Optimization === When solving dynamic optimization problems by numerical backward induction, the objective function must be computed for each combination of values. This is a significant obstacle when the dimension of the "state variable" is large. === Machine learning === In machine learning problems that involve learning a "state-of-nature" from a finite number of data samples in a high-dimensional feature space with each feature having a range of possible values, typically an enormous amount of training data is required to ensure that there are several samples with each combination of values. In an abstract sense, as the number of features or dimensions grows, the amount of data we need to generalize accurately grows exponentially. A typical rule of thumb is that there should be at least 5 training examples for each dimension in the representation. In machine learning and insofar as predictive performance is concerned, the curse of dimensionality is used interchangeably with the peaking phenomenon, which is also known as Hughes phenomenon. This phenomenon states that with a fixed number of training samples, the average (expected) predictive power of a classifier or regressor first increases as the number of dimensions or features used is increased but beyond a certain dimensionality it starts deteriorating instead of improving steadily. Nevertheless, in the context of a simple classifier (e.g., linear discriminant analysis in the multivariate Gaussian model under the assumption of a common known covariance matrix), Zollanvari et al. showed both analytically and empirically that as long as the relative cumulative efficacy of an additional feature set (with respect to features that are already part of the classifier) is greater (or less) than the size of this additional feature set, the expected error of the classifier constructed using these additional features will be less (or greater) than the expected error of the classifier constructed without them. In other words, both the size of additional features and their (relative) cumulative discriminatory effect are important in observing a decrease or increase in the average predictive power. In metric learning, higher dimensions can sometimes allow a model to achieve better performance. After normalizing embeddings to the surface of a hypersphere, FaceNet achieves the best performance using 128 dimensions as opposed to 64, 256, or 512 dimensions in one ablation study. A loss function for unitary-invariant dissimilarity between word embeddings was found to be minimized in high dimensions. === Data mining === In data mining, the curse of dimensionality refers to a data set with too many features. Consider the first table, which depicts 200 individuals and 2000 genes (features) with a 1 or 0 denoting whether or not they have a genetic mutation in that gene. A data mining application to this data set may be finding the correlation between specific genetic mutations and creating a classification algorithm such as a decision tree to determine whether an individual has cancer or not. A common practice of data mining in this domain would be to create association rules between genetic mutations that lead to the development of cancers. To do this, one would have to loop through each genetic mutation of each individual and find other genetic mutations that occur over a desired threshold and create pairs. They would start with pairs of two, then three, then four until they result in an empty set of pairs. The complexity of this algorithm can lead to calculating all permutations of gene pairs for each individual or row. Given the formula for calculating the permutations of n items with a group size of r is: n ! ( n − r ) ! {\displaystyle {\frac {n!}{(n-r)!}}} , calculating the number of three pair permutations of any given individual would be 7988004000 different pairs of genes to evaluate for each individual. The number of pairs created will grow by an order of factorial as the size of the pairs increase. The growth is depicted in the permutation table (see right). As we can see from the permutation table above, one of the major problems data miners face regarding the curse of dimensionality is that the space of possible parameter values grows exponentially or factorially as the number of features in the data set grows. This problem critically affects both computational time and space when searching for associations or optimal features to consider. Another problem data miners may face when dealing with too many features is that the number of false predictions or classifications tends to increase as the number of features grows in the data set. In terms of the classification problem discussed above, keeping every data point could lead to a higher number of false positives and false negatives in the model. This may seem counterintuitive, but consider the genetic mutation table from above, depicting all genetic mutations for each individual. Each genetic mutation, whether they correlate with cancer or not, will have some input or weight in the model that guides the decision-making process of the algorithm. There may be mutations that are outliers or ones that dominate the overall distribution of genetic mutations when in fact they do not correlate with cancer. These features may be working against one's model, making it more difficult to obtain optimal results. This problem is up to the data miner to solve, and there is no universal solution. The first step any data miner should take is to explore the data, in an attempt to gain an understanding of how it can be used to solve the problem. One must first understand what the data means, and what they are trying to discover before they can decide if anything must be removed from the data set. Then they can create or use a feature selection or dimensionality reduction algorithm to remove samples or features from the data set if they deem it necessary. One example of such methods is the interquartile range method, used to remove outliers in a data set by calculating the standard deviation of a feature or occurrence. === Distance function === When a measure such as a Euclidean distance is defined using many coordinat
Multi-task learning
Multi-task learning (MTL) is a subfield of machine learning in which multiple learning tasks are solved at the same time, while exploiting commonalities and differences across tasks. This can result in improved learning efficiency and prediction accuracy for the task-specific models, when compared to training the models separately. Inherently, Multi-task learning is a multi-objective optimization problem having trade-offs between different tasks. Early versions of MTL were called "hints". In a widely cited 1997 paper, Rich Caruana gave the following characterization:Multitask Learning is an approach to inductive transfer that improves generalization by using the domain information contained in the training signals of related tasks as an inductive bias. It does this by learning tasks in parallel while using a shared representation; what is learned for each task can help other tasks be learned better. In the classification context, MTL aims to improve the performance of multiple classification tasks by learning them jointly. One example is a spam-filter, which can be treated as distinct but related classification tasks across different users. To make this more concrete, consider that different people have different distributions of features which distinguish spam emails from legitimate ones, for example an English speaker may find that all emails in Russian are spam, not so for Russian speakers. Yet there is a definite commonality in this classification task across users, for example one common feature might be text related to money transfer. Solving each user's spam classification problem jointly via MTL can let the solutions inform each other and improve performance. Further examples of settings for MTL include multiclass classification and multi-label classification. Multi-task learning works because regularization induced by requiring an algorithm to perform well on a related task can be superior to regularization that prevents overfitting by penalizing all complexity uniformly. One situation where MTL may be particularly helpful is if the tasks share significant commonalities and are generally slightly under sampled. However, as discussed below, MTL has also been shown to be beneficial for learning unrelated tasks. == Methods == The key challenge in multi-task learning, is how to combine learning signals from multiple tasks into a single model. This may strongly depend on how well different task agree with each other, or contradict each other. There are several ways to address this challenge: === Task grouping and overlap === Within the MTL paradigm, information can be shared across some or all of the tasks. Depending on the structure of task relatedness, one may want to share information selectively across the tasks. For example, tasks may be grouped or exist in a hierarchy, or be related according to some general metric. Suppose, as developed more formally below, that the parameter vector modeling each task is a linear combination of some underlying basis. Similarity in terms of this basis can indicate the relatedness of the tasks. For example, with sparsity, overlap of nonzero coefficients across tasks indicates commonality. A task grouping then corresponds to those tasks lying in a subspace generated by some subset of basis elements, where tasks in different groups may be disjoint or overlap arbitrarily in terms of their bases. Task relatedness can be imposed a priori or learned from the data. Hierarchical task relatedness can also be exploited implicitly without assuming a priori knowledge or learning relations explicitly. For example, the explicit learning of sample relevance across tasks can be done to guarantee the effectiveness of joint learning across multiple domains. === Exploiting unrelated tasks: Auxiliary learning === In auxiliary learning, one attempts learning a group of principal tasks using a group of auxiliary tasks, unrelated to the principal ones. With the right unrelated tasks, joint learning of unrelated tasks which use the same input data have been shown to be beneficial, and provide significant improvement over standard MTL. The reason is that prior knowledge about task relatedness can lead to sparser and more informative representations for each task grouping, essentially by screening out idiosyncrasies of the data distribution. It has been proposed to build on a prior multitask methodology by favoring a shared low-dimensional representation within each task grouping, and imposing a penalty on tasks from different groups which encourages the two representations to be orthogonal. Learning with auxiliary unrelated tasks poses two major challenges: Finding useful auxiliary tasks and combining losses of all tasks in a useful way. Some methods can learn these from data together with the training process, and combine tasks efficiently. === Transfer of knowledge === Related to multi-task learning is the concept of knowledge transfer. Whereas traditional multi-task learning implies that a shared representation is developed concurrently across tasks, transfer of knowledge implies a sequentially shared representation. Large scale machine learning projects such as the deep convolutional neural network GoogLeNet, an image-based object classifier, can develop robust representations which may be useful to further algorithms learning related tasks. For example, the pre-trained model can be used as a feature extractor to perform pre-processing for another learning algorithm. Or the pre-trained model can be used to initialize a model with similar architecture which is then fine-tuned to learn a different classification task. === Multiple non-stationary tasks === Traditionally Multi-task learning and transfer of knowledge are applied to stationary learning settings. Their extension to non-stationary environments is termed Group online adaptive learning (GOAL). Sharing information could be particularly useful if learners operate in continuously changing environments, because a learner could benefit from previous experience of another learner to quickly adapt to their new environment. Such group-adaptive learning has numerous applications, from predicting financial time-series, through content recommendation systems, to visual understanding for adaptive autonomous agents. === Multi-task optimization === Multi-task optimization focuses on solving optimizing the whole process. The paradigm has been inspired by the well-established concepts of transfer learning and multi-task learning in predictive analytics. The key motivation behind multi-task optimization is that if optimization tasks are related to each other in terms of their optimal solutions or the general characteristics of their function landscapes, the search progress can be transferred to substantially accelerate the search on the other. The success of the paradigm is not necessarily limited to one-way knowledge transfers from simpler to more complex tasks. In practice an attempt is to intentionally solve a more difficult task that may unintentionally solve several smaller problems. There is a direct relationship between multitask optimization and multi-objective optimization. In some cases, the simultaneous training of seemingly related tasks may hinder performance compared to single-task models. Commonly, MTL models employ task-specific modules on top of a joint feature representation obtained using a shared module. Since this joint representation must capture useful features across all tasks, MTL may hinder individual task performance if the different tasks seek conflicting representation, i.e., the gradients of different tasks point to opposing directions or differ significantly in magnitude. This phenomenon is commonly referred to as negative transfer. To mitigate this issue, various MTL optimization methods have been proposed. It has been reported that meta-knowledge transfer could help avoid negative transfer.Besides, the per-task gradients are combined into a joint update direction through various aggregation algorithms or heuristics. There are several common approaches for multi-task optimization: Bayesian optimization, evolutionary computation, and approaches based on Game theory. ==== Multi-task Bayesian optimization ==== Multi-task Bayesian optimization is a modern model-based approach that leverages the concept of knowledge transfer to speed up the automatic hyperparameter optimization process of machine learning algorithms. The method builds a multi-task Gaussian process model on the data originating from different searches progressing in tandem. The captured inter-task dependencies are thereafter utilized to better inform the subsequent sampling of candidate solutions in respective search spaces. ==== Evolutionary multi-tasking ==== Evolutionary multi-tasking has been explored as a means of exploiting the implicit parallelism of population-based search algorithms to simultaneously progress multiple distinct optimization tasks. By mapping all task
Socially assistive robot
A socially assistive robot (SAR) aids users through social engagement and support rather than through physical tasks and interactions. == Background == The field of socially assistive robotics emerged in the early 2000s, following the emergence of the field of social robots. In contrast to social robots, SARs aid users with specific goals related to behavior change rather than serving as purely social entities. The term "Socially assistive robot" was initially defined by Maja Matarić and David Feil-Seifer in 2005. Since its inception, the field has gained substantial recognition, featuring numerous research projects, a wealth of global research publications, startup companies, and a growing array of products on the consumer market. The COVID-19 pandemic has underscored the immense potential of socially assistive robots, particularly in addressing the needs of large user populations, including children engaged in remote learning, elderly individuals grappling with loneliness, and those affected by social isolation and its associated negative consequences. == Characteristics of interaction == SARs rely on artificial intelligence (AI) to generate real-time, responsive, natural, and meaningful robot behaviors during interactions with humans. The robots employ various forms of communication, such as facial expressions, gestures, body movements, and speech. In contrast to robots intended for physical tasks, SARs are designed to support and motivate users to perform their own tasks. The tasks a user engages in can be physical (e.g., rehabilitation exercises for post-stroke users), cognitive (e.g., dementia screening for elderly users), or social (e.g., turn-taking for users with autism spectrum disorders). This complex interaction involves detecting and interpreting the user's movement, behavior, intent, goals, speech, and preferences. Machine learning and robot learning techniques are frequently employed to enhance the robot's understanding of the user, predict user preferences, and provide effective assistance. The effectiveness of socially assistive robots is assessed based on objective measurements of user performance and improvement resulting from the robot’s assistance and support. Unlike other branches of robotics, where effectiveness depends on the robot's physical task completion, SAR measures the success of the robot based on the user's progress and achievements. This evaluation is carried out using quantitative objective metrics, such as time spent on tasks, accuracy, retention, and verbalization, as well as quantitative subjective metrics, such as user survey tools. SAR is based on the large body of evidence showing that users tend to respond more positively to interactions with physical robots compared to interactions with screens. Interaction with physical robots also encourages users to learn and retain more information than screen-based interactions. This fundamental insight underlines why physical robots in SAR applications are more effective, as opposed to interactions solely involving screens, tablets, or computers. == Uses and applications == SARs have been developed and validated in a wide array of applications, including healthcare, elder care, education, and training. For example, SARs have been developed to support children on the autism spectrum in acquiring and practicing social and cognitive skills, to motivate and coach stroke patients throughout their rehabilitation exercises, monitoring individuals health (ex. fall detection), and to encourage elderly users to be more physically and socially active. There is a concern that technophobia and lack of trust in robots will pose a barrier to the effectiveness of SARs in older adults.
Halite AI Programming Competition
Halite is an open-source computer programming contest developed by the hedge fund/tech firm Two Sigma in partnership with a team at Cornell Tech. Programmers can see the game environment and learn everything they need to know about the game. Participants are asked to build bots in whichever language they choose to compete on a two-dimensional virtual battle field. == History == Benjamin Spector and Michael Truell created the first Halite competition in 2016, before partnering with Two Sigma later that year. === Halite I === Halite I asked participants to conquer territory on a grid. It launched in November 2016 and ended in February 2017. Halite I attracted about 1,500 players. === Halite II === Halite II was similar to Halite I, but with a space-war theme. It ran from October 2017 until January 2018. The second installment of the competition attracted about 6,000 individual players from more than 100 countries. Among the participants were professors, physicists and NASA engineers, as well as high school and university students. === Halite III === Halite III launched in mid-October 2018. It ran from October 2018 to January 2019, with an ocean themed playing field. Players were asked to collect and manage Halite, an energy resource. By the end of the competition, Halite III included more than 4000 players and 460 organizations. === Halite IV === Halite IV was hosted by Kaggle, and launched in mid-June 2020.
List of artificial intelligence journals
This is a list of notable peer-reviewed academic journals that publish research in the field of artificial intelligence (AI), including areas such as machine learning, computer vision, natural language processing, robotics, and intelligent systems. == General artificial intelligence == Artificial Intelligence (journal) – Elsevier Journal of Artificial Intelligence Research (JAIR) – AI Access Foundation Knowledge-Based Systems – Elsevier == Machine learning == Data Mining and Knowledge Discovery – Springer Machine Learning (journal) – Springer Journal of Machine Learning Research – Microtome Pattern Recognition (journal) – Elsevier Neural Networks (journal) – Elsevier Neural Computation (journal) – MIT Press Neurocomputing (journal) - Elsevier == Deep learning and neural computation == IEEE Transactions on Evolutionary Computation – IEEE IEEE Transactions on Neural Networks and Learning Systems – IEEE Nature Machine Intelligence – Springer Nature == Computer vision == International Journal of Computer Vision – Springer IEEE Transactions on Pattern Analysis and Machine Intelligence – IEEE Machine Vision and Applications – Springer == Natural language processing == Computational Linguistics (journal) – MIT Press Natural Language Processing Transactions of the Association for Computational Linguistics – ACL == Robotics and intelligent systems == IEEE Transactions on Robotics – IEEE Autonomous Robots – Springer Journal of Intelligent & Robotic Systems – Springer == Interdisciplinary and ethics in AI == AI & Society – Springer Artificial Life – MIT Press Philosophy & Technology – Springer Minds and Machines – Springer