Consciousness And Light Are Explored. The Inter Mind Bridges The Gap Between The Physical Mind And The Conscious Mind.

Inrecent years, data science has become an increasingly popular field due to the explosion of data and the need to extract valuable insights from it. While traditional education can be expensive and time-consuming, many aspiring data scientists turn to YouTube to learn the necessary skills. In this article, we've compiled a list of the best YouTube channels for learning data science for free in 2023. We cover a range of topics, including mathematics, programming, data analysis, machine learning and deep learning, career tips and guidance, interview preparation, and staying updated with the latest trends in the field. Whether you're a beginner or an experienced data scientist, these channels can help you improve your skills and knowledge in data science without breaking the bank.

Consciousness And Light Are Explored. The Inter Mind Bridges The Gap Between The Physical Mind And The Conscious Mind.

In this paper, we present a demo of an intelligent personal agent called Hey Dona (or just Dona) with virtual voice assistance in student course registration. It is a deployed project in the theme of AI for education. In this digital age with a myriad of smart devices, users often delegate tasks to agents. While pointing and clicking supersedes the erstwhile command-typing, modern devices allow users to speak commands for agents to execute tasks, enhancing speed and convenience. In line with this progress, Dona is an intelligent agent catering to student needs by automated, voice-operated course registration, spanning a multitude of accents, entailing task planning optimization, with some language translation as needed. Dona accepts voice input by microphone (Bluetooth, wired microphone), converts human voice to computer understandable language, performs query processing as per user commands, connects with the Web to search for answers, models task dependencies, imbibes quality control, and conveys output by speaking to users as well as displaying text, thus enabling human-AI interaction by speech cum text. It is meant to work seamlessly on desktops, smartphones etc. and in indoor as well as outdoor settings. To the best of our knowledge, Dona is among the first of its kind as an intelligent personal agent for voice assistance in student course registration. Due to its ubiquitous access for educational needs, Dona directly impacts AI for education. It makes a broader impact on smart city characteristics of smart living and smart people due to its contributions to providing benefits for new ways of living and assisting 21st century education, respectively.

The surging demand for high-definition video streaming services and large neural network models (e.g., Generative Pre-trained Transformer, GPT) implies a tremendous explosion of Internet traffic. To mitigate the traffic pressure, architectures with in-network storage have been proposed to cache popular contents at devices in closer proximity to users. Correspondingly, in order to maximize caching utilization, it becomes essential to devise an effective popularity prediction method. In that regard, predicting popularity with dynamic graph neural network (DGNN) models achieve remarkable performance. However, DGNN models still suffer from tackling sparse datasets where most users are inactive. Therefore, we propose a reformative temporal graph network, named semantics-enhanced temporal graph network (STGN), which attaches extra semantic information into the user-content bipartite graph and could better leverage implicit relationships behind the superficial topology structure. On top of that, we customize its temporal and structural learning modules to further boost the prediction performance. Specifically, in order to efficiently aggregate the diversified semantics that a content might possess, we design a user-specific attention (UsAttn) mechanism for temporal learning module. Unlike the attention mechanism that only analyzes the influence of genres on content, UsAttn also considers the attraction of semantic information to a specific user. Meanwhile, as for the structural learning, we introduce the concept of positional encoding into our attention-based graph learning and adopt a semantic positional encoding (SPE) function to facilitate the analysis of content-oriented user-association analysis. Finally, extensive simulations verify the superiority of our STGN models and demonstrate the effectiveness in content caching.

Artificial Intelligence is being used to generate videos pretending to be step-by-step tutorials on how to access programs like Photoshop, Premiere Pro, Autodesk 3ds Max, AutoCAD, and others without a license. Instead, the videos are loaded with infostealer malware that scrapes the viewer's sensitive personal data stored on the device. Researchers with CloudSEK measured a month-over-month increase of 200% to 300% since November 2022 of AI-created YouTube videos with links to infostealer malware, including Vidar, RedLine, and Raccoon. Making the video lures more compelling to its targets, the CloudSEK security team added, AI video tools such as Synthesia and D-ID are being used to generate personas intended to exude trustworthiness across multiple languages and social media platforms, supercharging threat actors' ability to deliver infostealer malware. "It is well known that videos featuring humans, especially those certain facial features, appear more familiar and trustworthy," the CloudSEK report explained.

Data-driven machine learning (ML) is promoted as one potential technology to be used in next-generations wireless systems. This led to a large body of research work that applies ML techniques to solve problems in different layers of the wireless transmission link. However, most of these applications rely on supervised learning which assumes that the source (training) and target (test) data are independent and identically distributed (i.i.d). This assumption is often violated in the real world due to domain or distribution shifts between the source and the target data. Thus, it is important to ensure that these algorithms generalize to out-of-distribution (OOD) data. In this context, domain generalization (DG) tackles the OOD-related issues by learning models on different and distinct source domains/datasets with generalization capabilities to unseen new domains without additional finetuning. Motivated by the importance of DG requirements for wireless applications, we present a comprehensive overview of the recent developments in DG and the different sources of domain shift. We also summarize the existing DG methods and review their applications in selected wireless communication problems, and conclude with insights and open questions.

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If you want to convert blog article to video, then this blog is for you. In this article you will learn about two websites that you can use to create video with text, and add voice-over to video. However, with so many options available, choosing the right software for artificial intelligence video creation can be overwhelming. Fliki allows users to transform text-based content into videos with professional-grade voiceovers. One of Fliki's key strengths is its user-friendly interface, making it accessible to non-professionals looking to create high-quality video content.

The modern internet is powered by recommendation algorithms. These systems track your online consumption and use that data to suggest the next piece of content for you to absorb. Their goal is to keep users on a platform by presenting them with things they'll spend more time engaging with. Trouble is, those link chains can lead to some weird places, occasionally taking users down dark internet rabbit holes or showing harmful content. Lawmakers and researchers have criticized recommendation systems before, but these methods are under renewed scrutiny now that Google and Twitter are going before the US Supreme Court to defend their algorithmic practices.