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Are Model Mentions Vital to Google's Algorithm? - Channel969

#artificialintelligence

Google's John Mueller was requested if "unlinked model mentions" had been vital in Google's algorithm. It was obvious from John's response that "model mentions" might be not an actual factor in Google's algorithm, however he additionally stated that there could also be worth to website guests who encounter them. There's a longstanding concept within the search engine marketing group that Google makes use of mentions of a web site as a type of hyperlink. One model of the thought is that if somebody publishes a URL like this, https://www.instance.com That is the unlinked URL concept, {that a} printed URL can be utilized as a hyperlink by Google. The unlinked URL concept subsequently advanced into the concept that if a web site mentions one other website's model identify, that Google can even depend that as a hyperlink.


Fair ranking: a critical review, challenges, and future directions

arXiv.org Artificial Intelligence

Ranking, recommendation, and retrieval systems are widely used in online platforms and other societal systems, including e-commerce, media-streaming, admissions, gig platforms, and hiring. In the recent past, a large "fair ranking" research literature has been developed around making these systems fair to the individuals, providers, or content that are being ranked. Most of this literature defines fairness for a single instance of retrieval, or as a simple additive notion for multiple instances of retrievals over time. This work provides a critical overview of this literature, detailing the often context-specific concerns that such an approach misses: the gap between high ranking placements and true provider utility, spillovers and compounding effects over time, induced strategic incentives, and the effect of statistical uncertainty. We then provide a path forward for a more holistic and impact-oriented fair ranking research agenda, including methodological lessons from other fields and the role of the broader stakeholder community in overcoming data bottlenecks and designing effective regulatory environments.


2022 SEO: A Robust Future!

#artificialintelligence

The world of search and user intent is more than just digital marketing and analytics, it has now evolved into a science. Once you have a solution to a pain point and you solve one minuscule issue from a massive compilation of data, consider yourself a professional who loves solutions. In the year 2022, we'll want to continue to advance and adapt with digital marketing shifts and customer wants and needs. With these two ideas, the world of AI aka artificial intelligence, is born. What we as digital marketers need to figure out is what is the pain point?


Survey on English Entity Linking on Wikidata

arXiv.org Artificial Intelligence

Wikidata is a frequently updated, community-driven, and multilingual knowledge graph. Hence, Wikidata is an attractive basis for Entity Linking, which is evident by the recent increase in published papers. This survey focuses on four subjects: (1) Which Wikidata Entity Linking datasets exist, how widely used are they and how are they constructed? (2) Do the characteristics of Wikidata matter for the design of Entity Linking datasets and if so, how? (3) How do current Entity Linking approaches exploit the specific characteristics of Wikidata? (4) Which Wikidata characteristics are unexploited by existing Entity Linking approaches? This survey reveals that current Wikidata-specific Entity Linking datasets do not differ in their annotation scheme from schemes for other knowledge graphs like DBpedia. Thus, the potential for multilingual and time-dependent datasets, naturally suited for Wikidata, is not lifted. Furthermore, we show that most Entity Linking approaches use Wikidata in the same way as any other knowledge graph missing the chance to leverage Wikidata-specific characteristics to increase quality. Almost all approaches employ specific properties like labels and sometimes descriptions but ignore characteristics such as the hyper-relational structure. Hence, there is still room for improvement, for example, by including hyper-relational graph embeddings or type information. Many approaches also include information from Wikipedia, which is easily combinable with Wikidata and provides valuable textual information, which Wikidata lacks.


The Impact of Artificial Intelligence on Customer-Centric Web Design

#artificialintelligence

Website development has gotten far too complex for any single individual or team to handle, which is why businesses are turning to artificial intelligence. Artificial Design Intelligence (ADI) is one of these technologies that hasn't fully grown yet, but it's proven that AI is useful in automating routine elements of the website creation process. While some industry professionals regard AI as a hoax, others are concerned that technology may eventually replace their professions. Both perspectives are very extreme. Instead, AI should be viewed as a helpful assistant by developers and designers. Artificial intelligence can even master creative processes such as visual art, poetry, making YouTube videos, composing music, and photography.


AI writes on 'Search Engine'

#artificialintelligence

Search Engines is a software system that helps to carry out web searches. They search the World Wide Web in a systematic way for particular information specified by users, such as a list of web sites, news stories, a map, a directory listing or a biography of a celebrity. They are web search engines that search using a spider to systematically index the content of web sites. The term "search engine" can be used for the software system, the service that delivers web content, or both. In recent years, search engine optimization (SEO) has become a very popular way for web site owners to attract more traffic to their web sites.


The Role of Social Movements, Coalitions, and Workers in Resisting Harmful Artificial Intelligence and Contributing to the Development of Responsible AI

arXiv.org Artificial Intelligence

There is mounting public concern over the influence that AI based systems has in our society. Coalitions in all sectors are acting worldwide to resist hamful applications of AI. From indigenous people addressing the lack of reliable data, to smart city stakeholders, to students protesting the academic relationships with sex trafficker and MIT donor Jeffery Epstein, the questionable ethics and values of those heavily investing in and profiting from AI are under global scrutiny. There are biased, wrongful, and disturbing assumptions embedded in AI algorithms that could get locked in without intervention. Our best human judgment is needed to contain AI's harmful impact. Perhaps one of the greatest contributions of AI will be to make us ultimately understand how important human wisdom truly is in life on earth.



CryptoNAS: Private Inference on a ReLU Budget

arXiv.org Machine Learning

Machine learning as a service has given raise to privacy concerns surrounding clients' data and providers' models and has catalyzed research in private inference (PI): methods to process inferences without disclosing inputs. Recently, researchers have adapted cryptographic techniques to show PI is possible, however all solutions increase inference latency beyond practical limits. This paper makes the observation that existing models are ill-suited for PI and proposes a novel NAS method, named CryptoNAS, for finding and tailoring models to the needs of PI. The key insight is that in PI operator latency cost are non-linear operations (e.g., ReLU) dominate latency, while linear layers become effectively free. We develop the idea of a ReLU budget as a proxy for inference latency and use CryptoNAS to build models that maximize accuracy within a given budget. CryptoNAS improves accuracy by 3.4% and latency by 2.4x over the state-of-the-art.


Knowledge Graphs

arXiv.org Artificial Intelligence

In this paper we provide a comprehensive introduction to knowledge graphs, which have recently garnered significant attention from both industry and academia in scenarios that require exploiting diverse, dynamic, large-scale collections of data. After a general introduction, we motivate and contrast various graph-based data models and query languages that are used for knowledge graphs. We discuss the roles of schema, identity, and context in knowledge graphs. We explain how knowledge can be represented and extracted using a combination of deductive and inductive techniques. We summarise methods for the creation, enrichment, quality assessment, refinement, and publication of knowledge graphs. We provide an overview of prominent open knowledge graphs and enterprise knowledge graphs, their applications, and how they use the aforementioned techniques. We conclude with high-level future research directions for knowledge graphs.