Artificial intelligence has always been considered a revolutionary technology that has emerged to solve complex real-world problems like high-level computation, omitting manual labor, or data-driven optimization. However, with its endless possibilities, there are many applications of AI that make this technology more accessible to the average layman person or kids at home. To get people's heads around this sophisticated technology developers all around the world are continuously developing some fun AI tools that can be easily accessed online to get hands-on. Not only are these AI tools fun but also provide a good understanding of this technology to the users. So, before jumping to those exciting and crazy artificial intelligence tools; first, we must know about What AI is.

Recently, there is a revival of interest in low-rank matrix completion-based unsupervised learning through the lens of dual-graph regularization, which has significantly improved the performance of multidisciplinary machine learning tasks such as recommendation systems, genotype imputation and image inpainting. While the dual-graph regularization contributes a major part of the success, computational costly hyper-parameter tunning is usually involved. To circumvent such a drawback and improve the completion performance, we propose a novel Bayesian learning algorithm that automatically learns the hyper-parameters associated with dual-graph regularization, and at the same time, guarantees the low-rankness of matrix completion. Notably, a novel prior is devised to promote the low-rankness of the matrix and encode the dual-graph information simultaneously, which is more challenging than the single-graph counterpart. A nontrivial conditional conjugacy between the proposed priors and likelihood function is then explored such that an efficient algorithm is derived under variational inference framework. Extensive experiments using synthetic and real-world datasets demonstrate the state-of-the-art performance of the proposed learning algorithm for various data analysis tasks.

Artificial intelligence (AI) has become a hot topic for countries worldwide, and both public- and private-sector organizations have already started leveraging it as a response to continuous digital disruption. According to IDC's 2022 Artificial Intelligence Spending Guide, global AI spending reached $88.6 billion in 2021, and it is forecast to grow at a compound annual growth rate (CAGR) of 25.6% over the 2021–2025 period. Canada, China, and the United States are among the countries in which many organizations began their AI journeys early, supported by government initiatives. Saudi Arabia is no different in terms of its commitment to becoming an AI powerhouse. As an extension of the country's Vision 2030, the Saudi Data and AI Authority (SDAIA) was established in 2019, followed by the release of the National Strategy for Data and AI in 2020.

Integrating multiple online social networks (OSNs) has important implications for many downstream social mining tasks, such as user preference modelling, recommendation, and link prediction. However, it is unfortunately accompanied by growing privacy concerns about leaking sensitive user information. How to fully utilize the data from different online social networks while preserving user privacy remains largely unsolved. To this end, we propose a Cross-network Social User Embedding framework, namely DP-CroSUE, to learn the comprehensive representations of users in a privacy-preserving way. We jointly consider information from partially aligned social networks with differential privacy guarantees. In particular, for each heterogeneous social network, we first introduce a hybrid differential privacy notion to capture the variation of privacy expectations for heterogeneous data types. Next, to find user linkages across social networks, we make unsupervised user embedding-based alignment in which the user embeddings are achieved by the heterogeneous network embedding technology. To further enhance user embeddings, a novel cross-network GCN embedding model is designed to transfer knowledge across networks through those aligned users. Extensive experiments on three real-world datasets demonstrate that our approach makes a significant improvement on user interest prediction tasks as well as defending user attribute inference attacks from embedding.

It is vital for businesses to have an e-commerce platform in order to compete with the huge -- Merchandise of online sellers. If your business cannot make an impressive online presence in a competitive market, it may be lost. You may get left behind. The digital age has seen a transformed technology landscape that allows us to be more interested in gadgets and social media than ever before. Life was good before the pandemic. There was always progress in technology and new developments.

This is a guest post by Ramzi Alqrainy, Chief Technology Officer, The Chefz. The Chefz is a Saudi-based online food delivery startup, founded in 2016. At the core of The Chefz's business model is enabling its customers to order food and sweets from top elite restaurants, bakeries, and chocolate shops. In this post, we explain how The Chefz uses Amazon Personalize filters to apply business rules on recommendations to end-users, increasing revenue by 35%. Food delivery is a growing industry but at the same time is extremely competitive.

Your maps assistant makes sure you reach your destinations at the right time, your autocorrect promptly lets you know whenever you might misspell a word, and apps display neat lists of recommendations drawing from your recent search data… Essentially, from when you enjoy your first cup of tea in the morning till the time your head hits your pillow, wherever you turn, you are likely to see manifestations of the subversive emerging technology of artificial intelligence. In this blog, we will be getting acquainted with 15 of these popular use cases of AI. Need to process speech and audio data input? Speaking of explanatory inference, you would apply inference rules to glean proofs in AI, and the proof is a sequence of the conclusion that leads to the goal. Decoding an image might not always be enough, prediction may come into the picture, so AI software comes in handy!

Good TVs are always in high demand, so finding deals can be a fruitless quest. If you're on the lookout for one, Amazon is running a one-day sale right now on LG, Samsung and Sony sets, including OLED and other desirable models at all-time low prices. For instance, LG's 55-inch A1 OLED is just $797 instead of $1,300 for the biggest savings we've seen yet. Sony's A80J 65-inch OLED model is $1,399 or 44 percent off (a new low), and Samsung's 55-inch Frame TV with Quantum HDR is priced at just $980, also an all-time low. LG's 2021 line of A1 OLED TVs first appeared at CES 2021, offering support for Dolby Vision, Dolby Atmos, Filmmaker mode, Google Assistant, Amazon Alexa and AirPlay 2. You can expect a color accurate picture with deep blacks, though some things are missing like a 120Hz display and HDMI 2.1. Still, at $797, the 55-inch A1 will deliver everything else you might want in an OLED TV.

The tweet landed like a burp on a first date: a little awkward, potentially endearing, maybe a good story to tell later. Chris Olah, a neural network engineer for a company called AnthropicAI and a former Thiel Foundation fellow, observed out loud on Wednesday, "Normal online dating seems pretty suboptimal. Recently, I've seen several people experiment with public'date me' docs--I think this is a really interesting experiment in alternatives, enabling long-form, earnest dating profiles." Olah linked to his own Date Me doc in his tweet. Olah is 29, with the grin and just-finished-hiking complexion of someone even younger. The title of his Google Doc gets right to the point: "Male, Straight, 5'7", Monogamous, Wants Kids."

Machine learning is an application of artificial intelligence in which data is made available to machines so they can learn from it as opposed to having to be trained by humans to think and act in certain ways regarding the data. Every time Alexa or Siri responds incorrectly to your request, they learn from the error and utilize that information to react more accurately the next time. If a mistake was made, it uses that information to make improvements. The system also records if the reaction was favorable. The microphone on the device you are using records your request when you speak to Alexa or Siri.