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r8633009 · 3 months

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𝟭𝟬 𝗬𝗼𝘂𝗧𝘂𝗯𝗲 𝗖𝗵𝗮𝗻𝗻𝗲𝗹𝘀 𝗧𝗵𝗮𝘁 𝗪𝗶𝗹𝗹 𝗞𝗲𝗲𝗽 𝗬𝗼𝘂 𝗨𝗽𝗱𝗮𝘁𝗲𝗱 𝗼𝗻 𝗔𝗜 𝗧𝗿𝗲𝗻𝗱𝘀

Here are the top 10 YouTube channels to keep you updated with AI trends

Artificial Intelligence (AI) is an ever-evolving field, with new breakthroughs and trends emerging at a rapid pace. Staying informed is crucial for professionals, enthusiasts, and anyone intrigued by the possibilities of AI. Fortunately, there are YouTube channels dedicated to keeping you abreast of the latest in AI research, applications, and innovations. In this article, we explore the top 10 YouTube channels to keep updated on AI Trends that serve as valuable resources for staying updated on AI trends.

1. Two Minute Papers:

Hosted by Károly Zsolnai-Fehér, Two Minute Papers provides concise and insightful overviews of recent AI research papers. The channel breaks down complex concepts into easily digestible two-minute videos, making it an ideal resource for staying informed without investing a significant amount of time.

2. AI with Tim:

Tim Scarfe’s channel, AI with Tim, offers a diverse range of content catering to AI enthusiasts at various skill levels. From tutorials and coding challenges to interviews with experts in the field, this channel provides a comprehensive overview of AI concepts and applications, making it accessible to both beginners and experienced practitioners.

3. Lex Fridman:

Lex Fridman’s channel features in-depth interviews with some of the brightest minds in AI, robotics, and related fields. As a research scientist and educator at MIT, Fridman brings a unique perspective to his interviews, creating engaging discussions that delve into the latest trends, challenges, and future directions of AI.

4. Siraj Raval:

Siraj Raval’s channel is a vibrant hub for AI enthusiasts, offering a blend of educational content, coding tutorials, and discussions on the latest AI trends. Siraj’s energetic and approachable style makes complex AI concepts more accessible, making this channel suitable for both beginners and those looking to deepen their understanding.

5. Data School:

Hosted by Kevin Markham, Data School focuses on data science and machine learning, offering tutorials, practical tips, and real-world applications of AI. The channel is particularly valuable for those interested in the intersection of AI and data science, providing hands-on guidance for various AI projects.

6. Sentdex:

Sentdex, hosted by Harrison Kinsley, is dedicated to machine learning and AI applied to finance and trading. If you’re intrigued by the applications of AI in the financial world, this channel offers insights into algorithmic trading, sentiment analysis, and other innovative uses of machine learning in finance.

7. The AI Alignment Podcast:

The AI Alignment Podcast, hosted by Lucas Perry, explores the ethical and philosophical aspects of AI. Perry engages in conversations with experts to discuss the responsible development of AI and its implications for society. This channel is essential for anyone interested in the ethical considerations surrounding artificial intelligence.

8. TechLead:

Led by Patrick Shyu, TechLead provides a mix of content on software engineering, coding interviews, and AI. While the channel covers various tech-related topics, Shyu occasionally delves into AI trends and developments, offering a broader perspective on the tech industry.

9. AI and Games:

AI and Games, hosted by Tommy Thompson, is a unique channel focusing on the intersection of AI and the gaming industry. Thompson explores how AI is used in game development, creating engaging content that sheds light on the evolving relationship between AI and gaming technologies.

10. The Coding Train:

While primarily focused on coding and creative coding, The Coding Train, hosted by Daniel Shiffman, occasionally delves into AI-related content. Shiffman’s charismatic approach to teaching programming concepts includes AI topics, making it an enjoyable channel for those interested in the intersection of coding and artificial intelligence.

Conclusion:

Staying updated on AI trends is essential in a field that evolves rapidly. The YouTube channels mentioned above offer a diverse array of content, ranging from educational tutorials and coding challenges to in-depth interviews with experts and discussions on ethical considerations.

#YouTubeChannels #AITrends #YouTubeChannelsToKeepUpdatedOnAITrends #TwoMinutePapers #AIWithTim #AI #AINews #AnalyticsInsight #AnalyticsInsightMagazine

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oliverjames34 · 2 years

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Solana's Sixth Network Outage will Surely Push SOL Price Down the Hill

Solana’s novel hybrid blockchain reportedly handles 65,000 transactions per second and is built for decentralized applications, finance, and smart contracts — supposedly making it a surefire “Ethereum Killer.” However, in 2022 Solana had 12 serious outages driving the price of its native coin SOL down more than 78% this year. On June 01, 2022, the latest outage shut down the network for four hours and 10 minutes, which means a peak potential of 975 million transactions did not occur during that blackout.

Solana is facing downtime after the production of new blocks on the network has ceased. According to data from Solana Beach block explorer, Solana’s validators have not processed new blocks for the last five hours, causing a total shutdown of all of its applications.

A recent post from Solana Status said that the mainnet beta now needed a restart from validators. “Validator operators should prepare for a restart in mb-validators on Discord,” the post read. As of press time, discussion toward a restart was ongoing in the validator Discord chat.

To mitigate this situation, the Solana team has asked validators to restart the network. It has released instructions for validators, which includes taking a snapshot of a specific time slot from where the network can be rebooted.

For now, all network funds are secure, but transactions cannot be processed as no blocks are being created. Blocks are the data units in which new transactions are added to a cryptocurrency’s ledger over time.

Back in September, Solana experienced a similar outage due to “resource exhaustion” that caused a denial of service across the network.

In January, the network again experienced a major slowdown from an influx of “high compute transactions,” causing many others to fail. Even last month, millions of NFT transactions caused the network to clog and go down again.

#Solana #Blockchain #EthereumKiller #SOL #Crypto #ETH #ETHKiller #SolanaPrice #SOLPrice #Cryptocurrency #Cryptocurrencies #AI #AINews #AnalyticsInsight #AnalyticsInsightMagazine

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releaseteam · 2 years

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r8633009 · 3 months

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10 Amazing Open Source AI Solutions and Tools

http://tinyurl.com/yjap7cn3

Empower your AI journey: Explore 10 incredible open source AI solutions and tools!

AI has seamlessly integrated into diverse sectors, spanning healthcare, finance, manufacturing, and entertainment, solidifying its role as a cornerstone in industry progression. The evolution of AI has been greatly influenced by the open-source community, fostering collaboration and innovation. In this article, we will explore 10 remarkable open-source AI solutions and tools that have made significant contributions to the field.

1. TensorFlow:

Developed by Google, TensorFlow is an open-source machine learning framework widely used for building and deploying AI applications. Its adaptability and expandability render it well-suited for applications in both research and production environments. TensorFlow supports a variety of platforms and allows developers to build models for tasks like image recognition, natural language processing, and more. The TensorFlow community actively contributes to its expansion and improvement.

2. PyTorch:

PyTorch is another popular open-source machine learning framework, maintained by Facebook’s AI Research lab (FAIR). Known for its dynamic computation graph, PyTorch is favored by researchers for its simplicity and ease of debugging. It has gained widespread adoption for tasks such as deep learning, computer vision, and natural language processing. The PyTorch ecosystem also includes torchvision and torchtext for vision and NLP tasks, respectively.

3. Scikit-learn:

Scikit-learn is a versatile open-source machine-learning library for Python. It offers simple and efficient tools for data analysis and modeling, making it an excellent choice for both beginners and experienced data scientists. Scikit-learn encompasses an extensive array of algorithms catering to classification, regression, clustering, and dimensionality reduction tasks, providing a comprehensive toolkit for diverse machine learning applications. Its user-friendly interface and extensive documentation make it a go-to tool for machine learning practitioners.

4. Keras:

Keras is an open-source high-level neural network API written in Python. It serves as a user-friendly interface for building deep learning models and is capable of running on top of other frameworks like TensorFlow and Theano. Keras simplifies the process of creating neural networks by providing a clean and intuitive interface while offering extensive customization options for advanced users.

5. OpenCV:

OpenCV, also known as the Open Source Computer Vision Library, stands as an open-source software library focusing on computer vision and machine learning, offering a versatile toolset for various visual perception and analysis applications. It includes a wide range of tools and algorithms for image and video analysis, making it a crucial component in applications such as object detection, facial recognition, and image processing. OpenCV supports various programming languages, such as C++, Python, and Java.

6. OpenAI:

OpenAI, renowned for groundbreaking NLP advancements, introduces Codex—transforming natural language into code in specified programming languages. This innovative AI, a leap in Industry 4.0, surpasses Siri/Alexa by synthesizing original responses. Accessible and customizable, it empowers users to engage with cutting-edge AI technology.

7. Rasa:

Rasa stands as a premier choice for crafting conversational AI, particularly chatbots, with the added benefit of free cloud deployment. Its flexibility and “future-proof” design allow seamless integration of any evolving NLP or ML model, ensuring sustained accuracy. Ideal for enterprise-level branded conversational AI, featuring built-in integrations for platforms such as Slack and Facebook.

8. GitHub:

GitHub stands as the pinnacle in collaborative programming, irrespective of the chosen platform. It efficiently organizes projects, facilitating seamless collaboration for individuals or teams unfamiliar with each other. Essential for ensuring productivity in shared coding endeavors, managing version histories, notes, and Wikis.

9. ai:

H2O.ai’s AI Cloud Platform asserts itself as “the fastest, most accurate AI platform on the planet” while acknowledging ethical concerns. It aims to democratize AI, enabling global access for positive societal impact. Ideal for companies prioritizing rapid development and comprehensive AI management.

10. Amazon Web Services (AWS):

For those seeking a familiar coding environment with free AWS access and result storage, it’s an excellent choice. AWS provides added value with features like code customization for their content delivery network and task coordination, ideal for developers aiming to advance into Industry 4.0.

#opensourceAIsolutions #PyTorch #AIapplications #toolsfordataanalysis #AI #AINews #AnalyticsInsight #AnalyticsInsightMagazine

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