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虽然不认识 郑外人再顶一手
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Yu Wang liked thisYu Wang liked this🎈I will teach Large Language Model Systems at Carnegie Mellon University again in Spring 2025. (11868 for CMU folks) The course syllabus (tentative) is online at https://lnkd.in/e89_2msN 💡 CMU students are welcome to enroll. You may check the FAQ here if see if this is the right course for you: https://lnkd.in/eZPpJQ32 For non-CMU students, I will release the materials on the website after each lecture. If you want to formally take this course, you may apply to CMU LLM/GenAI certificate program, which includes this course (and two other wonderful LLM, Multimodal ML courses). https://lnkd.in/egQs3XGF
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Yu Wang liked thisYu Wang liked thisOk, this is getting crazy... Kalshi just overtook Instagram on the App Store.
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Yu Wang liked thisYu Wang liked thisApple Intelligence on iPhone 16: AI + extreme attention to user experiences!
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Yu Wang liked thisYu Wang liked thisA rare opportunity to be part of the founding members of DoorDash's Brazil engineering office. We are looking for kickass tech leaders to bootstrap multiple teams in São Paulo. Please hit up Demetrius Nunes or Lucas Lopes de Castilho if you are interested. https://lnkd.in/gX44ewwgDoorDash expands to São Paulo with exciting Engineering opportunities - DoorDashDoorDash expands to São Paulo with exciting Engineering opportunities - DoorDash
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Yu Wang liked thisYu Wang liked thisWe're hiring in the Triangle! It's a huge plus if you have networking or security experience but any Senior Software Engineers with distributed systems/cloud infrastructure experience are encouraged to apply. Join our Cloud team in Durham, NC. If interested, please apply directly: goo.gle/3VDPfp3 #lifeatgoogle #DurhamNC #NetworkSecurity
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Yu Wang liked thisYu Wang liked this🎉 🎉A huge welcome to our newest Googlers! 🎉 🎉 Delighted to have officially welcomed our newest batch of #Nooglers to our team in Google Cloud Security! We hope your first few days have been filled with learning, connection, and a taste of the amazing Google culture. At Google, we believe in the power of big ideas and the impact they can have on the world. We're excited to see how you'll use your talents to shape the future with us. We can't wait to see all the incredible things you'll accomplish here!! A big shoutout to our amazing team for putting together such a memorable experience! Anshika Sinha Ved Mulkalwar Priyanka Mehta Akshaya hebbar Akshay Gupta Aman Rath Rajat Arya #nooglers #lifeatgoogle #onboarding #campushires #welcometogoogle #googleculture #SecurityIntelligence #SecurityAI
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Yu Wang liked thisYu Wang liked this𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀 𝗲𝘃𝗲𝗿𝘆 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿 𝘀𝗵𝗼𝘂𝗹𝗱 𝗸𝗻𝗼𝘄: 𝗰𝗼𝗻𝗰𝘂𝗿𝗿𝗲𝗻𝗰𝘆 𝗶𝘀 𝗡𝗢𝗧 𝗽𝗮𝗿𝗮𝗹𝗹𝗲𝗹𝗶𝘀𝗺. Parallelism and concurrency are two terms that often create confusion. One is about managing multiple tasks at once, intermixing them to optimize resource usage. The other involves executing multiple tasks simultaneously, typically on multiple processors or cores. As Rob Pyke (one of the creators of GoLang) succinctly put it:“ 𝗖𝗼𝗻𝗰𝘂𝗿𝗿𝗲𝗻𝗰𝘆 is about 𝐝𝐞𝐚𝐥𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 lots of things at once. 𝗣𝗮𝗿𝗮𝗹𝗹𝗲𝗹𝗶𝘀𝗺 is about 𝐝𝐨𝐢𝐧𝐠 lots of things at once." 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗰𝗼𝗻𝗰𝘂𝗿𝗿𝗲𝗻𝗰𝘆? Managing several tasks on a single processor by intermixing them, giving the appearance of simultaneous execution is called concurrency. It refers to the system's ability to switch between multiple tasks rather than performing them at once, creating an illusion of parallel execution. Concurrency effectively utilizes processor time, especially when tasks must wait for others to finish. 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗽𝗮𝗿𝗮𝗹𝗹𝗲𝗹𝗶𝘀𝗺? Parallelism involves performing multiple tasks simultaneously. This works by efficiently utilizing multiple processors or cores in a computing system. The distinction between concurrency and parallelism has a direct impact on application performance and efficiency. Because it supports simultaneous task execution, parallelism is particularly useful for compute-intensive applications that distribute tasks across multiple processors. 𝗔𝘀𝘆𝗻𝗰𝗵𝗿𝗼𝗻𝗼𝘂𝘀 𝗽𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 is used to 𝗮𝗰𝗵𝗶𝗲𝘃𝗲 𝗰𝗼𝗻𝗰𝘂𝗿𝗿𝗲𝗻𝗰𝘆 𝗶𝗻 𝘀𝗶𝗻𝗴𝗹𝗲-𝘁𝗵𝗿𝗲𝗮𝗱𝗲𝗱 𝗲𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁𝘀. This approach enables a program to initiate tasks without waiting for previous ones to finish, managing multiple tasks in a non-blocking manner. One great 𝗲𝘅𝗮𝗺𝗽𝗹𝗲 𝗶𝘀 𝗡𝗼𝗱𝗲.𝗷𝘀, which handles concurrency in a single-threaded model with callbacks and event loops. Meanwhile, 𝗺𝘂𝗹𝘁𝗶-𝘁𝗵𝗿𝗲𝗮𝗱𝗲𝗱 𝗲𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁𝘀 𝗲𝘅𝗽𝗹𝗼𝗶𝘁 𝗯𝗼𝘁𝗵 𝗰𝗼𝗻𝗰𝘂𝗿𝗿𝗲𝗻𝗰𝘆 𝗮𝗻𝗱 𝗽𝗮𝗿𝗮𝗹𝗹𝗲𝗹𝗶𝘀𝗺. They facilitate both concurrent task execution on a single processor and true parallel execution across multiple processors or cores simultaneously. Multi-threaded programming 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲𝘀 𝗹𝗶𝗸𝗲 𝗖# provide the framework for developers to take advantage of these features. Understanding concurrency and parallelism is an important distinction for building high-performing and efficient software solutions. ~~ A big thank you to our partner Postman who keeps our content free to the community. Who doesn’t love a custom theme? Postman released custom themes in their v11 release. Check it out: https://lnkd.in/gDhAGB7w
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Yu Wang liked thisYu Wang liked thisI started my journey with Facebook/Meta in December, 2009. After 14 years, I made a hard decision to leave Meta in Dec 2023. I joined Facebook/Meta because I believed in the company's mission: to give people the power to share and make the world more open and connected. Along these 14 years, I witnessed the company truly changed the world by turning its mission into reality. I take immense pride in being part of this journey. I still truly love Facebook/Meta and I cherish the incredible people whom I've had the privilege of working with. But after such a long time, I want to take another steep learning curve and become the driving force to accelerate another major change in the world. With this ambition, I am joining Google, supporting the Core ML/AI org. I firmly believe that AI, with a focus on safety, can bring a revolutionary change to people's daily lives. Google, being an early pioneer of the AI-first approach, is uniquely positioned to innovate in AI to enhance people's lives. I am thrilled about the opportunity to learn and be a part of this transformative journey at Google. Gemini AI ambition is too disruptive to stay just on the side and watch. Extending what I was doing in the past years (e.g. open sourcing PyTorch and creating ONNX), at Google, I want to keep contributing to the AI community in general. AI work requires a tremendous amount of intellectual research, coupled with substantial resources (tools, automation, data, etc). I think all major AI companies should take significant roles to contribute to the community via OSS. This way, we can collectively expedite the AI revolution together.
Experience & Education
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Google
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Licenses & Certifications
Courses
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Analysis of Algorithms
CSOR4231
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Artificial intelligence
COMS 4701
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Cloud computing and big data
COMS 6998
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Deep learning
COMS4995
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Deep learning for computer vision
COMS 4995
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Fundamental of Large-Scale Distributed Systems
COMSW4113
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Introduction to database
COMS 4111
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Machine learning
COMS4771
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Natural language processing
COMS 4705
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Topic in Software Engineering - Cloud and Micro-services
COMSE6156
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