Yu Liu

So excited to see our team's work featured here! These are fundamentals directions for recommender systems, essential for advancing the field. Excited to see how others will build on this and drive deeper insights. https://lnkd.in/gkqpq7HS

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Sharing my experiment on creating a Streamlit based chatbot, that combines inputs from Agentic RAG using llamaindex (with openai and MistralAI), and langchain google genai (with Gemini-1.5-Flash), to extract information from images embedded within the PDF files. Was a nice experience to build this chatbot. Still many open questions, ways to improve the solution, in a production scenario..but nice to combine features from multiple libraries and come up with a solution. Please check it out! Thanks. #chatbot #gemini #openai #langchain #llamaindex https://lnkd.in/gPdtxhQF

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Read this blog post by Ercüment İlhan to learn how a 70 year old algorithm helped make our contextual bandit powered personalization service ~40 times faster. https://lnkd.in/eGps5AJt

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Insightful how multi-agent AI can provide a better solution to many problems.

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Evaluating Large Language Models: Key Metrics and Their Application in Real-World Scenarios This article delves into the essential metrics used to assess LLMs, exploring their significance and practical applications, particularly in developing a Question Answering system. Join us as we unpack these evaluation criteria and illustrate their impact on real-world AI implementations. #AI #LLM #Evaluation #AIDevelopment #AIApplication For part 2 of the article, click👇 https://lnkd.in/gd8gDGPM For part 1 of the article, click👇 https://lnkd.in/gdSew4uV For a quick summary - Swipe 👉

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1. IMO, agent patterns are one of the most interesting ways to extend LLMs. memary is based on leveraging a graph database (neo4j) for long-term memory (to get around the limited memory inherent in even the largest context windows in LLMs) and augmenting it with other tools like web search and Computer Vision. https://lnkd.in/gfFcBqJx Looks interesting. 2. More about how consolidation effects the entire healthcare industry: https://lnkd.in/gXJKqNJB The Change hack/ransomware attack not only affected claims, service, refills, etc. but now CMS & NCQA are delaying the entire HEDIS reporting process (used for Medicare/Medicaid quality ratings) due to the impact. 3. NARA bans ChatGPT use agency-wide. https://lnkd.in/gW8T94JU Federal Government agencies see this as a risk, including their information leaking and being used to train models (which may then output the wrong information or show PII/PHI other proprietary information). 4. Oh great, now I can't even be angry anymore :) https://lnkd.in/gcrN9mjx

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  Since GenAI trained largely on internet content and one of the top GenAI use cases is SEO (aka writing internet content), aren't we are headed pretty quickly for an echo chamber where LLMs generate the content that other LLMs learn from?  That could quickly get repetitive and unoriginal.   Optimistically, I am hoping this leads to the resurgence of appreciation for humans generating a thought-provoking painting or astonishing work of fiction.   Good luck to Google on this attempt to distinguish between human and AI-content, maybe they'll disrupt the cycle? #genAI #AI #SEO

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THERE ARE THREE R'S IN STRABERRY 🍓🍓🍓 Looking through OpenAI's new o1 launch today, it got me thinking about Daniel Kahneman's book, Thinking, Fast and Slow 📚. In it, Kahneman explains that our brains operate using two systems of thought. System 1 is fast, automatic, and intuitive—it handles everyday decisions without us even realizing it 🤔. System 2, on the other hand, is slow, deliberate, and analytical 🧠. It's what we engage when we're solving complex problems or learning something new. This dual-process theory highlights how we balance efficiency and thoroughness in how we think. By the way, did you catch the typo in "STRABERRY"? If not, that's System 1 at work—glossing over details to get the gist quickly. It takes System 2 to slow down and spot the error 🔎 OpenAI's o1 feels like a significant step towards embracing that System 2 thinking — more deliberate and analytical, targeting high-complexity (or high-risk) scenarios. Meanwhile, the current GPT models are akin to System 1: fast, intuitive, but at times superficial or even inaccurate. Both systems are necessary—in our brains and in our quest towards AGI. In future, it's not hard to imagine that you won't have to pick the right model to use - based on the prompt the AI will pick the right system to use to optimize the energy and time - just like our brain does. Exciting times! 🚀🌐 P.S. – The title (except the typo) and the screenshot come from the o1 launch examples, which I thought was very witty of them given all these AI strawberry memes. #OpenAI #o1 #ThinkingFastAndSlow #AI #AGI #Innovation

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Thats the exact explanation I gave to a group of investors last week. (… it’s just Precision/Recall, attributes vectorization as “embedding”, K-Nearest Neighbor, User Search Intent, DAG, ML algorithms, etc all over again …). LOL and they asked what’s new this time around. And I was like not much, ML is still the backbone, and AI is the inference and actions based on the model. It works better when there is a feedback loop in a closed system. But the current state of the art like ChatGPT is based on scraping the Internet (garbage in, garbage out). Kinda like early search engines. What is a better analogy is that NVIDIA/Snowflake etc are selling tools, aka pickaxes to prospectors. The gold is up to you to find. And the opportunity right now is to monetize industry specific (healthcare, travel, financial services) clean data to build models with (aka a treasure map for the prospectors). From an enterprise perspective, what’s out there today aka generative AI isn’t very useful. Too much hype. To build useful engines will require an effort similar to search revolution 2 decades ago. From

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You want to do Machine Learning on large scale Marketing solutions ? Check below 🚀

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From my conversations, I'm noticing different trends between how AI engineers deploy evaluation models (model as a judge) in production monitoring vs application testing. 💡 Value - Monitoring: provides real time alerts of performance or used to preemptively block problematic outputs from surfacing to the user -Testing: regression testing applications to establish baseline performance before pushing to deployment ⚡ Speed - Monitoring: near real time, requires extremely fast token processing - Testing: batch, async, often processing large test sets, lower token processing speed required 🎯 Accuracy - Monitoring: overindex on false positives, better to flag something as problematic and be wrong than to let something slip between the cracks - Testing: high correlation with human graders, be as accurate and robust as possible 💰 Cost - Monitoring: prefer cheaper models, otherwise the additional inference cost per generation will add up exponentially - Testing: willing to take on higher costs, calculation is accurate testing leads to better performing outputs 🛑 Risk - Monitoring: if your monitoring judge gets something wrong, there is a high consequence because it directly impacts the end user - Testing: if your testing judge gets something wrong, async review gives some potential to remediate the issue At the moment, it seems using model as a judge for testing is a little more popular due to the high cost constraints of constant monitoring, but that is subject to change as more specialized models come out. As the industry matures, I strongly believe we're gonna see specialization between models for monitoring and models for testing. If you're currently using model as a judge techniques, would love to chat more! Let me know what you think. Twitter Handle: https://lnkd.in/eAsQvtiu

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We launched structured data retrieval in Bedrock Knowledge Bases last week at re:Invent. While embeddings-based semantic search has worked wonders for unstructured data, we felt we need a new approach for structured data. We needed a way to translate customer natural language queries to an expressive query language that traditional databases already support. https://lnkd.in/eTPStNif #rag #text2sql #aws #bedrock

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🚀 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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I'm excited to share two papers from the Retrieval team at Walmart that were presented at CIKM last week. They're both about improving the relevance of Embedding-Based Retrieval. 𝗥𝗲𝗹𝗲𝘃𝗮𝗻𝗰𝗲 𝗙𝗶𝗹𝘁𝗲𝗿𝗶𝗻𝗴 𝗳𝗼𝗿 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴-𝗯𝗮𝘀𝗲𝗱 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹. https://lnkd.in/gNQe7QhA The first is about how to intelligently choose a query-dependent cutoff on the cosine score to remove irrelevant items from the retrieved set. Since cosine scores aren't comparable across queries, we introduce a "Cosine Adapter" to map the scores to interpretable scores. 𝗘𝗻𝗵𝗮𝗻𝗰𝗶𝗻𝗴 𝗥𝗲𝗹𝗲𝘃𝗮𝗻𝗰𝗲 𝗼𝗳 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴-𝗯𝗮𝘀𝗲𝗱 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗮𝘁 𝗪𝗮𝗹𝗺𝗮𝗿𝘁. https://lnkd.in/gdqkc2hR The second is about model training techniques we learned to improve the relevance of embedding models. In particular, we describe how to use a teacher model to provide relevance labels for training Juexin (Joy) Lin Nicholas Rossi, PhD Feng LIU Zhen Yang Sachin Yadav Praveen Reddy Suram Satya Chembolu Prijith Chandran Hrushikesh Mohapatra Alessandro Magnani Ciya Liao

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You MUST see this. TL;DR We've reached a true breakthrough in clinical AI, developing CARE1 - the world's first clinical foundation model with an application submitted to FDA review. Truthfully this is just the tip of the iceberg, we've used a fraction of our data and two years from now we'll see these as the first baby steps. You can read more about it here: https://lnkd.in/drhPYTtX Shameless plug - this recent breakthrough is just one example of the impactful work our teams do every day. We're looking for highly experienced algorithmic experts, both ICs and tech leads, to join us and take a pivotal role in this and other high impact projects. Feel free to reach out to me directly. This is first and foremost thanks to the amazing team working on this effort - Itay Katzir the mastermind who's been planning this for years building paradigm after paradigm to enable this. From the algorithmic engine, through NLP, through scalable training pipelines and more. Ariel Persiko - truly a one man algo team, the exact person you want on this type of breakthrough project. Dor Alon - AI Infrastructure expert, built components that were critical to this endavour. And of course some of our amazing leaders who were deeply involved in invaluable strategic guidance - Uri Goren, Ronny Levanda, Tamar Lin. Not less than that - I think this is a true lesson of how good founders really move the needle even at a 450 person company. Elad Walach our CEO was maybe the first person who started pushing strongly for us to invest in this, and Michael B. Braginsky our legendary CTO who continues to amaze me even after 8 years with his ability to inspire us with unachievable goals which we are then able to achieve.

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I wrote a blog post on a thorny but underappreciated problem in ML-based marketing personalization: event attribution. It's how you go from the actions you take and customer behavior you observe to creating training experiences for your model. Good attribution makes a huge difference in driving uplift, there are a lot of degrees of freedom in configuring it, and at the same time, it's rarely discussed in ML/bandits/RL literature.

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