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Mountain View, California, United States
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Explore more posts
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Mike Pham
I'm finally ready to share an experiment I've been working on with the Future Audiences team at Wikimedia. It's a pretty special team where I get to have space to think about the intersection of things like LLMs and the evolving online space of (mis/dis)information: specifically how can this technology not replace humans as an information source, but help better connect us to living sources of information created by humans. I have this hope/dream that tools for us to verify information in a world where creating questionable information is so widespread will be as easy and normal as your internet browser coming with built-in adblockers to protect you from being overwhelmed with pop-up window spam. Anyway, the experiment we have is called Citation Needed, and if you are using the Chrome browser, I'd love for you to try it out. After you install it, you can use it to check short texts online to see if they are corroborated by information on Wikipedia, and have the opportunity to learn more about it. It's still experimental, and uses LLMs/AI, so you might see some funny behavior once in a while -- which you can report to us in the extension -- though I invite you to imagine what this functionality could look like in the near future, and/or beyond the confines of a browser extension. And then let us know where this sci fi daydream takes you. Link: https://lnkd.in/eQwd4iqK
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Matt Bleifer
For the past 4 years at Tecton, I've worked with some crazy talented coworkers and customers to build a product that can help companies like Roblox, FanDuel, Hello Fresh, Flo, and Atlassian put AI systems into production. Something that's always been notoriously hard for teams to pull off. For a while though Tecton was hard to get your hands on, requiring conversations with our sales team and heavy-weight infrastructure like Spark. In this last year, that all changed. We rebuilt Tecton on top of a Python-native compute engine we called Rift, meaning that now anyone with Python and some data can put an AI system in production. And last week we took things a step further and made it so anyone can sign up to explore the product (https://lnkd.in/gRyV-emS). You can even run the whole thing with zero setup in a Colab notebook (https://lnkd.in/g_erUX8Y). Getting here feels like a big personal milestone. After a decade of working on ML platforms, anyone in the world can finally try the product I've helped build. Give it a whirl. Let me know what you think. And stay tuned for more cool updates!
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Chetan Zawar
🎉 I'm thrilled to share Part 2 of my series on Generative AI! Building on the first article I shared here on LinkedIn, I’ve just published "Rapid Expansion of Generative AI: A Call to Action" on DZone. 🌐✨ In this article, I explore further the impact of generative AI and discuss essential strategies for product managers looking to adapt and thrive with this technology. Discover practical advice on how to incorporate responsible AI effectively into your product decision-making processes. 🔗 Dive into the details here: https://lnkd.in/erqJAxar I’d love to hear from you— how are you planning to build responsible AI in your product solutions? #GenerativeAI #ProductManagement #TechTrends #Innovation #AI
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Ivan Kuznetsov
🚀 We've open-sourced TimesFM – a decoder-only foundation model for time-series forecasting 📈 Key Highlights: ✅ Large-Scale Training Data: Trained on a corpus of over 100 billion real-world time points, including Google Trends and Wikipedia pageviews. ✅ Zero-Shot Forecasting: Offers high-quality forecasts even on unseen datasets, without requiring fine-tuning or additional training. ✅ Outstanding Benchmark Performance: Achieved competitive results across a comprehensive suite of benchmarks. 🤗 Explore the model on Hugging Face and GitHub. I'm encouraged by the early feedback and look forward to seeing how #TimesFM will be used in real-world applications for forecasting and anomaly detection. 🙏 A special thanks to the talented research team behind this achievement: Abhimanyu Das Rajat Sen Yichen Zhou Weihao Kong
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Sabrina Geremia
As the AI platform shift accelerates it feels to me like we’re living in a modern day Renaissance. New ideas, new advancements in technology and new tools to help us make AI helpful for everyone. #GoogleIO last week & #GML24 (Google Marketing Live) this week brought this to life. Here’s a mash up of my 3 takeaways from both #GoogleIO & #GML: 👉 Multi-modal everywhere. It’s not just about text, it’s voice, it’s video, it’s multi-modal. I’m excited to have a conversational experience with Ask Photos to search memories in a deeper way & also to ask questions with video - very helpful for questions that are hard to articulate. 🎨 Creativity unleashed. What excites me most about the AI era is the potential to unlock creativity. From Veo video tool to virtual try on and visual storytelling in our Ads products these tools are an assist to unlock more creativity in us all. 📗 Productivity unlocked & results for businesses around the world. Google Workspace features like summarize my email, trip planning in Gemini Advanced and other productivity hacks that will help save time and get things done more quickly. Check out Philipp Schindler’s demo Project Astra, a helpful universal AI agent. I love your Yoda Philipp Schindler (& I think GBO is awesome too)! From the ‘power pair’ of Search + PMax to AI powered creative tools to new ways to measure, businesses across the globe are growing their business in new ways, powered by Google AI. All of these innovations are underpinned by huge advancements in primary research, technology and application + bold and responsible practices. It’s a very exciting time! Here’s a 10 min Google IO summary: https://lnkd.in/gG5BhR9T Heres a 7 minute overview of the GML keynote: https://lnkd.in/g-d2JgZM What were your favourites?
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Bill Frischling
𝗦𝘁𝗿𝗲𝘀𝘀𝗟𝗲𝗻𝘀 is now an official product! The AI tool can monitor public figures such as the folks on the Fed, politicians, and C-suite execs at public companies, and model them individually. If their brain is in front of their mouth or they're potentially obfuscating, StressLens can tell. Check it out here: https://lnkd.in/eBxWJBPu Stay tuned for more updates on StressLens and its analysis of public figures like Biden and Trump.
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Yuxi Yao
10 months ago, I embarked on this journey with a simple yet ambitious goal: to create an AI assistant that knows you like your best friend and can truly enhance your life. Now, Ario is in open beta! 🎉 This journey has been filled with - Joy from hearing beta users say, "Honestly, I'm stunned, like wow," - Excitement from rounds of rapid iterations - Sudden enlightenment in solving unexpected challenges while building our AI agent Every step has brought us closer to creating something truly special - Ario's Personalized, Proactive and Private. I’d love for you to give Ario a try and let me know your thoughts. Your feedback helps us shape Ario. And if you're also navigating the fascinating world of AI, let’s connect and exchange insights!
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Advait Bopardikar
“Flash’s cost, capabilities, and unmatched context length make it a market game-changer!” - LMSYS.org Checks out: The LMSYS users voted and Gemini 1.5 Flash is right on that efficient frontier of Cost and quality for Large Language Models. Why would you use anything that wasn't optimal? Use Flash!
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Joseph Holguin
Google just had their I/O AI update. Here are a few things that stuck out to me: - Google is serious about using AI across all its business and consumer products. - Gemini -- Has multimodality (text, video, photo, voice) and long context. -- Google is using it across all their products so you can be confident using it in yours. -- Gemini 1.5 Pro is a serious completion to GPT-4o and other LLMs. -- You can build solutions that take in a lot of data (text, audio, video, images). -- They are building a lot of infrastructure and tools for developers. - Gem Agents -- Agents are a big part of use cases across Google products. -- An agent could be created to do specific tasks or a specific type of assistant. - Google Search + AI Overviews -- Coming to all users. -- Overviews specifically provide what you searched for, so I worry that people won't need to click over to your website to get what they need. --- How will this impact PPC? -- This same search experience is added to all parts of Google products like Gmail, Workspaces, and YouTube. -- AI in search can improve your search, plan, and reason to get the info you need. Summary: https://lnkd.in/emjeS3GG Full Video: https://lnkd.in/ezBXHt6t 12min Video Summary: https://lnkd.in/eXxQ58b2
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Rajiv Mehta
In February, we announced the launch of Rufus, Amazon’s generative AI-powered shopping assistant, to a small subset of customers. Last week, we shared that Rufus is available to all U.S. customers in the Amazon Shopping app, and today we are sharing that millions of customers used Rufus to help them shop Amazon’s wide selection quickly and easily this Prime Day. We built Rufus to help customers make more informed shopping decisions, and we’ve been thrilled to hear they are using it to understand product details (“Is this coffee maker easy to clean and maintain?”), find deals (“What are deals on microwave ovens?”), get product recommendations (“What are the best lawn games for kids birthday parties?”), compare products (“Compare OLED and QLED TVs”), and more. In fact, customers have asked Rufus tens of millions of questions so far, and counting. It’s still early days for Rufus and generative AI, but we’re excited for what the future holds, and look forward to hearing more from customers about how they are using Rufus to find what they want and need in Amazon’s store. https://lnkd.in/gpY4WQYJ
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Jiaona Zhang (JZ)
How do you build for Gen Z? 🏃♂️ Immediacy 🎨 Flexibility 👀 Authenticity These are the principles you need to ground your product in. Gen Z are the first digitally native generation. They've grown up in a world of hyper-connectivity, hyper-stimulation, and hyper-personalization. One where everything not only auto-saves and auto-updates, but auto-optimizes too. And while they trust in technology to anticipate their needs, they're much less trusting of the platforms where they spend their time. So what does this mean for how you build, hire, and market? Excited to share my #Config2024 talk, where I explored exactly this 👇 Grateful to the folks whose stories added to these insights: Yuhki Yamashita, Dabney Dayes, Adam Polselli, Peter Yang, Helena Jaramillo, Ceci Mourkogiannis, David Hoang, Colin Dunn, Andy Chung Figma 🙌 Most of all, thanks to my Gen Z co-creator, Maddie Johnston, for pairing with me to bring this to life 🙏 https://lnkd.in/gx833AuF
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Sachin Kulkarni
This is a Jan 2023 article - Who Owns the Generative AI Platform? https://lnkd.in/gUtA8TYZ Trust me, this article will be read and revisited for many years to come. It exemplifies first-principle thinking at its best. What I liked the most is a section on "the standard moats". 1. scale moats (“I have or can raise more money than you!”) 2. supply-chain moats (“I have the GPUs, you don’t!”) 3. ecosystem moats (“Everyone uses my software already!”) 4. algorithmic moats (“We’re more clever than you!”) 5. distribution moats (“I already have a sales team and more customers than you!”) 6. data pipeline moats (“I’ve crawled more of the internet than you!”). Credit to Authors - Matt Bornstein, Guido Appenzeller Martin Casado
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Eric Sun
https://lnkd.in/gQ6RzjiJ Exciting news! Jianlong Zhong (Jay) will be showcasing "AirAgent" at 12PM PT on Thursday, 9/12, 2024, during the "Airflow Summit 2024" in San Francisco. Airflow depends on continuous scanning files on disk and parsing DAG definitions in Python codes, this design pattern does not go well with the typical Blue/Green deployment mode for microservices (especially, the disruption to the long-running stateful jobs/tasks). To effectively make Airflow deployment "continuously", Jay came up with the design pattern of a deployer agent which takes care of 3 major functions: [1] fetch a specific DAG tarball (usually the latest) commit from github as well as the new Docker image (of runtime Python dependencies) if image is modified [2] point the symbolic link to the new DAG directory on the EFS/NFS [3] restart the worker and scheduler one-by-one accordingly AirAgent is currently deploying updates hundreds of times a day on both staging and production environments. It has been a game changer in terms of Airflow development lifecycle and developer velocity at Coinbase. Missed Jay's captivating talk last year in Toronto? Check it out here: https://lnkd.in/gBC_udTg. #airflow #deployment #airagent #cicd #velocity
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Jove Zhong
Don't fight against AI. Help it to get latest info. 👇 is the talk I gave last week regarding Real-Time #RAG in Zilliz's Unstructured Data and #LLM meetup. "Talk is cheap, show me the code" This blog shows the key data flow and code blocks. The full example is available at https://lnkd.in/gUj349hh How it works? Input: Stream stories and comments from #HackerNews API. Pre-process: Retrieve updates and filter for stories/comments via #Bytewax. Retrieve Content: Download the html and parse it into usable text. Thanks to the awesome #Unstructured.io. Vectorize: Create an embedding or list of embeddings for text using #HuggingFace #Transformers. Output: Write the JSON document with #embeddings and other fields to a local #Timeplus #Proton server, into the data streams. Filter/Route: Use streaming SQL to filter/transform JSON data and send output to a local #Kafka server. Forward: Use #Milvus plugin for #KafkaConnect to forward the data to #Zilliz Cloud. Query: Build a #streamlit web application to query the live data with the same embedding model.
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Greg Coquillo
Three years ago, I pitched and received funding for an idea that focused on preventing safety and compliance incidents in target geographies. This was before GenAI was all the craze. We leveraged an ensemble of ML models (ex: CV, NER) that scanned millions of tunnel images in fulfillment centers, and a human-in-loop system that closes the gap when inference confidence was low. Today, I rejoice when I witness other teams working on reducing product damages under Project P.I., which uses a combination of GenAI and Computer Vision to scan 100’s of millions of products, improving the customer experience. I can’t wait to witness what teams across Amazon achieve with GenAI as they focus on solving real-life, papable challenges on behalf of customers. This is the type of GenAI ROI that I believe in! Kudos to them all. You can check more about Project P.I. in the article below: https://lnkd.in/g_SnqhgA cc: Amazon Science #genai #technology #artificialintelligence Set the 🔔 notification on my page, don’t miss a post!
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Joseph Holguin
Listen to episode #2 of AI Talks. Where two AIs discuss AI topics and news. In this four-minute video, Joan and Alan discuss how LLMs are created. #2 - AI Talks - Understanding Large Language Models, Creation and Ethics: In this episode of "AI Talks," hosts Joan and Alan delve into the fascinating world of Large Language Models (LLMs). They explore how these advanced AI systems are created, from the vast amounts of text data used for training to the intricate process of machine learning. The conversation also covers crucial aspects of ensuring these models generate safe and unbiased responses. Whether you're an AI enthusiast or a curious learner, this episode provides a clear and insightful overview of LLMs and their impact on our digital world. Tune in to gain a deeper understanding of the technology that powers today's AI-driven interactions. LLM Prompting: The prompts for this episode were based on episode 1, but I made the style and the instructions more clear. I don't control the actual content of the episode, so it's a mystery to see what they will discuss. Like episode 1, they talked about ethical considerations. Also, I used Chat GPT 4 instead of 4o because the audio conversations would break, and they would talk over each other.
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Priya Chakravarthi
Our ML and product teams constantly make sense of the rapidly changing world of AI, adopting a best-of-breed approach to help our customers Hyperautomate. In this blog, David Liang, our lead PM, discusses the benefits of combining language models (LLMs) with Hyperscience's proprietary models for enhanced data processing and automation.
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Vimal Bhat
𝐓𝐡𝐢𝐧𝐤𝐢𝐧𝐠 𝐢𝐧 𝐏𝐫𝐢𝐦𝐢𝐭𝐢𝐯𝐞𝐬: 𝐇𝐨𝐰 𝐈 𝐃𝐢𝐬𝐜𝐨𝐯𝐞𝐫𝐞𝐝 𝐭𝐡𝐞 𝐌𝐚𝐠𝐢𝐜 𝐨𝐟 𝐂𝐨𝐦𝐩𝐮𝐭𝐞𝐫 𝐕𝐢𝐬𝐢𝐨𝐧 📺 Remember that "Skip Intro" button? I'm forever grateful to Hooman Mahyar for introducing me to the fascinating world of computer vision! In 2018, at our Santa Monica office, he meticulously explained the magic of embeddings and how we can solve the problem of detecting intro and end credits in titles at scale. 😊 Fast-forward to today, and our team has built an AI-based solution that identifies skip intros and credits for millions of titles in our catalog! 🔥 We took this concept further by creating unique signatures for each title, enabling us to find duplicate videos across our large catalog. 🔒 As we continued to refine our approach, leveraging stronger embeddings (VGA → MobileNet → CLIP) significantly improved our solution's accuracy. 📈 Shout out to Xiaohang Sun, Nicholas Maffei, Madhu Goka, Armanjeet Singh, Jing Yang, James Wu, Zhu Liu, Hooman Mahyar, and Xiang Hao for building these solutions. 💕 Reflecting on this journey, I'm reminded of Andy Jassy's insightful shareholder letter about primitives: "Building in primitives meaningfully expands your degrees of freedom." 💡 #ComputerVision #AI #MachineLearning #Primitives #AmazonScience
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Sasha Podolsky
Hi LinkedIn friends, My name is Sasha. I’m an Engineer and PM (previously at Google, Instacart, and Bardeen) starting a Substack called “How I Build This" (yes, inspired by How I Built This), about my journey of becoming technical again, building AI products, and learning to market them. I would love to have you along for the ride — DM or drop your email in the comments if you would like to be added. The first product I’m building is an interview tool called ‘Interviewssuck’ that listens to your remote interviews in real-time and provides actionable tips using AI, prompt engineering, and a speech-to-text API. I aim to document my excitement for the area and inspire/connect with other techies relearning. I’ll see you out there. Cheers, Sasha https://lnkd.in/eew9Ki7E
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