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16 best artificial intelligence games & AI gaming - Dataconomy

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Do you love artificial intelligence games? Artificial intelligence (AI) has played an increasingly important and productive role in the gaming industry since IBM's computer program, Deep Blue, defeated Garry Kasparov in a 1997 chess match. AI is used to enhance game assets, behaviors, and settings in various ways. According to some experts, the most effective AI applications in gaming are those that aren't obvious. Every year, AI games come in a variety of forms. Games will utilize AI differently for each kind. It's more than likely that artificial intelligence is responsible for the replies and actions of non-playable characters. Because these characters must exhibit human-like competence, it is essential there. AI was previously used to foretell your next best move. AI enhances your game's visuals and solves gameplay issues (and for) you in this age of gaming. AI games, on the other hand, are not reliant upon AI. AI technologies improved significantly as a result of research for game development.


CITP Seminar: Amy Winecoff - Today's Machine Learning Needs Yesterday's Social Science - Center for Information Technology Policy

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Click here to join the seminar. Research on machine learning (ML) algorithms, as well as on their ethical impacts, has focused largely on mathematical or computational questions. However, for algorithmic systems to be useful, reliable, and safe for human users, ML research must also wrangle with how users' psychology and social context affect how they interact with algorithms. This talk will address how novel research on how people interact with ML systems can benefit from decades-old ideas in social science. The first part of the talk will address how well-worn ideas from psychology and behavioral research methods can inform how ML researchers develop and evaluate algorithmic systems.


Best online marketing degrees 2022: Top picks

ZDNet

A marketing degree trains students to effectively and efficiently use advertisements, promotions, and media platforms to reach the public. With more platforms than ever on which to market and advertise products, marketing pros are in demand. Marketing involves appealing to people to sell products, services, and ideas. As a marketer, you'll use research and data methods and strategies to communicate and engage with target audiences. Here are our top picks for online marketing degrees in 2022.


The New Intelligence Game

#artificialintelligence

The relevance of the video is that the browser identified the application being used by the IAI as Google Earth and, according to the OSC 2006 report, the Arabic-language caption reads Islamic Army in Iraq/The Military Engineering Unit – Preparations for Rocket Attack, the video was recorded in 5/1/2006, we provide, in Appendix A, a reproduction of the screenshot picture made available in the OSC report. Now, prior to the release of this video demonstration of the use of Google Earth to plan attacks, in accordance with the OSC 2006 report, in the OSC-monitored online forums, discussions took place on the use of Google Earth as a GEOINT tool for terrorist planning. On August 5, 2005 the user "Al-Illiktrony" posted a message to the Islamic Renewal Organization forum titled A Gift for the Mujahidin, a Program To Enable You to Watch Cities of the World Via Satellite, in this post the author dedicated Google Earth to the mujahidin brothers and to Shaykh Muhammad al-Mas'ari, the post was replied in the forum by "Al-Mushtaq al-Jannah" warning that Google programs retain complete information about their users. This is a relevant issue, however, there are two caveats, given the amount of Google Earth users, it may be difficult for Google to flag a jihadist using the functionality in time to prevent an attack plan, one possible solution would be for Google to flag computers based on searched websites and locations, for instance to flag computers that visit certain critical sites, but this is a problem when landmarks are used, furthermore, and this is the second caveat, one may not use one's own computer to produce the search or even mask the IP address. On October 3, 2005, as described in the OSC 2006 report, in a reply to a posting by Saddam Al-Arab on the Baghdad al-Rashid forum requesting the identification of a roughly sketched map, "Almuhannad" posted a link to a site that provided a free download of Google Earth, suggesting that the satellite imagery from Google's service could help identify the sketch.


Link Prediction with Contextualized Self-Supervision

arXiv.org Artificial Intelligence

Link prediction aims to infer the existence of a link between two nodes in a network. Despite their wide application, the success of traditional link prediction algorithms is hindered by three major challenges -- link sparsity, node attribute noise and network dynamics -- that are faced by real-world networks. To overcome these challenges, we propose a Contextualized Self-Supervised Learning (CSSL) framework that fully exploits structural context prediction for link prediction. The proposed CSSL framework forms edge embeddings through aggregating pairs of node embeddings constructed via a transformation on node attributes, which are used to predict the link existence probability. To generate node embeddings tailored for link prediction, structural context prediction is leveraged as a self-supervised learning task to boost link prediction. Two types of structural contexts are investigated, i.e., context nodes collected from random walks vs. context subgraphs. The CSSL framework can be trained in an end-to-end manner, with the learning of node and edge embeddings supervised by link prediction and the self-supervised learning task. The proposed CSSL is a generic and flexible framework in the sense that it can handle both transductive and inductive link prediction settings, and both attributed and non-attributed networks. Extensive experiments and ablation studies on seven real-world benchmark graph datasets demonstrate the superior performance of the proposed self-supervision based link prediction algorithm over state-of-the-art baselines on different types of networks under both transductive and inductive settings. The proposed CSSL also yields competitive performance in terms of its robustness to node attribute noise and scalability over large-scale networks.


best-10-ai-seo-software-tool-for-your-business

#artificialintelligence

The internet is now the best place to find everything, from trivia about celebrities to how to fix our kitchen sinks. AI SEO could change the way marketers rank their websites high on search engine result pages (SERPs). Alli AI, an SEO tool, offers many AI-powered SEO features that can improve and optimize your website's content strategies. It provides a simple and powerful tool that allows users to increase traffic, create quality backlinks and expand business outreach. This all-in-one SEO tool uses Artificial Intelligence to make intelligent decisions. Alli AI employs artificial Intelligence to simplify SEO.


Harisystems - Google Search

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Harisystems offers professional training by experts in Software Industry, Python, asp.net, Real-Time Face Recognition: Project Face Detection with Python using OpenCV Attendance Tutorial - Harisystems For Best Software Training programs visit--... We're global software services in IT business and digital technology services, helping our clients bring the future highest levels of work to their life.


Harisystems - Google Search

#artificialintelligence

Harisystems offers professional training by experts in Software Industry, Python, asp.net, Real-Time Face Recognition: Project Face Detection with Python using OpenCV Attendance Tutorial - Harisystems For Best Software Training programs visit--... We're global software services in IT business and digital technology services, helping our clients bring the future highest levels of work to their life.


Forecasting: theory and practice

arXiv.org Machine Learning

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases.


Pairwise Learning for Neural Link Prediction

arXiv.org Artificial Intelligence

In this paper, we aim at providing an effective Pairwise Learning for Neural Link Prediction (PLNLP) framework. The framework treats link prediction as a pairwise learning to rank problem and consists of four main components, i.e., neighborhood encoder, link predictor, negative sampler and objective function. The framework is flexible that any generic graph neural convolutions or link prediction specific neural architectures could be employed as neighborhood encoder. For link predictor, we design different scoring functions, which could be selected based on different types of graphs. In negative sampler, we provide several sampling strategies, which are problem specific. As for objective function, we propose to use an effective ranking loss, which approximately maximizes the standard ranking metric AUC. We evaluate the proposed PLNLP framework on 4 link property prediction datasets of Open Graph Benchmark (OGB), including ogbl-ddi, ogbl-collab, ogbl-ppa and ogbl-ciation2. PLNLP achieves top 1 performance on ogbl-ddi and ogbl-collab, and top 2 performance on ogbl-ciation2 only with basic neural architecture. The experimental results demonstrate the effectiveness of PLNLP.