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Tony Castillo’s Post
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Introducing ClickDiffusion! We developed a system for precise image manipulation and generation that combines natural language instructions with visual feedback provided by the user through a direct manipulation interface. A large body of recently developed image editing systems leverage natural language instructions. However, it's difficult to precisely specify many common classes of image transformations with text alone. For example, a user may wish to change the location and breed of a particular dog in an image with several similar dogs. This task is quite difficult with natural language alone, and would require a user to write a laboriously complex prompt that both disambiguates the target dog and describes the destination. We solve this by seamlessly combining both natural language and direct manipulation instructions. Paper: https://lnkd.in/er-QWcus Code: https://lnkd.in/e7Nqz5SQ
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Meta released Llama 3.1 today, offering 8B, 70B, and 405B parameter open-source models with 128K context length and support for 8 languages, rivaling GPT-4o and Claude 3.5 Sonnet in benchmark performance, excelling in tool use, reasoning, and multilingual translation Llama 3.1 introduces a new "ipython" role for tool outputs, uses special tokens for message formatting, and demonstrates improved performance through extensive pre-training on 15 trillion tokens, innovative post-training techniques, and careful data curation for both pre-training and fine-tuning
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How to minimize hallucinations of Large Language Models with the help of Retrieval-Augmented Generation. We are updating the LLMs itself with RAG obtained from high-quality sources. Time-bound data needs to be marked and updated with the help of News Media. #artificialintelligence #hallucinations #RAG #largelanguagemodels https://lnkd.in/gHUF-zJE
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Just completed the "Large Language Models with Semantic Search" course on DeepLearning.AI! This concise course has provided me with a deep understanding of advanced search techniques that go beyond traditional keyword-based methods. I've learned about dense retrieval and reranking, which are crucial for improving search relevance and speed. Gaining insight into the mechanisms behind chatbots has been an enlightening experience!
Large Language Models with Semantic Search - DeepLearning.AI
deeplearning.ai
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Chunking is an essential technique in Language Model Fine-tuning (LLM) that optimizes efficiency and accuracy by breaking down large text into manageable segments. By segmenting text into meaningful chunks, chunking enhances context understanding and improves search relevance by accurately capturing user queries. In the field of conversational agents, chunking ensures semantic search accuracy and relevance while facilitating context building and coherence in responses. There are various chunking methods to consider, including fixed-size chunking, content-aware chunking, and recursive chunking. Determining the optimal chunk size involves preprocessing text data, selecting a range of chunk sizes, and evaluating performance against representative datasets and queries. This process balances context preservation and accuracy, ensuring relevance and coherence in responses. If you're looking to optimize relevance and reduce noise in LLM, chunking is a crucial technique to consider. #chunking #LLM #semantics #searchrelevance #conversationalagents
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"One of the reasons for developing LLM’s was to understand and process natural language. So rather than typing a few keywords into a Google or Bing search box like most of use have been conditioned to do, an LLM system like ChatGTP is used to provide a 'conversational interface' to a search engine." This is a great point--ChatGPT and other GenAIs are not meant to replace search engines as repositories of information. We can't rely on GenAI to give us accurate results--at least, not yet. How are you using GenAI? Is your approach language-centric, language-informed? https://buff.ly/3Jwluk4
How Generative AI Can Improve Enterprise Search
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😎 Thrilled to announce that our paper has been accepted to #ACL2024 (main) 📣 🔥 Interactive Text-to-Image Retrieval with Large Language Models: A Plug-and-Play Approach 🔥 ➡ Breaking away from the passive text-to-image retrieval systems that merely accept given queries, we propose a retrieval system capable of interacting with users. Unlike previous methods requiring model fine-tuning, our approach demands no training at all (yet significantly outperforms baseline methods!). Additionally, we introduce Best log Rank Integral (BRI), a new metric that allows for the quantitative evaluation of any interactive retrieval system, and we demonstrate that this new metric aligns better with human evaluations than conventional metrics. Check out our paper on arXiv now!
Interactive Text-to-Image Retrieval with Large Language Models: A Plug-and-Play Approach
arxiv.org
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#BigDataLDN 2024 likes and dislikes, part 3. Dislike: every technology in the world now has a natural language UI where a prompt is used instead of a query, even if this adds +7635% unnecessary fuzziness to the logic. Like: some technologies now have a natural language UI, sitting in front of a semantic vector search trained within their own business domain, which helps reduce fuzziness because in their specific domain, one problem can really be phrased in 50 different ways.
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What is the downside of industry codes? Industry codes are too broad to search within niches. When sourcing companies doing A using industry codes, you are going to find thousands of companies. Those thousands of companies include too many companies doing B and C that aren't of interest. Of course, you will also find companies doing A, but you will have to manually filter out companies doing B and C. Manually filtering companies isn't worth of your time. ✍ Instead, you can use tools (such as Inven) that enable natural language search. Those allow you to find companies based on what they do without a need to manually filter companies.
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An update from Google I/O that I just had to share, Google Photos is about to get a massive upgrade with the new Ask Photos feature, and it's something you don't want to miss. ✨ 🔍 Innovation at Its Best Ask Photos showing the power of Gemini, Google's advanced natural language understanding model. Imagine asking your photo library, "Show me pictures from my beach vacation last summer," and instantly getting accurate, personalized results. It's like having a smart assistant just for your memories! 💡 Why You Should Check This Out 1. Enhanced Searchability: Say goodbye to endless scrolling! With Ask Photos, you can find specific images using simple, everyday language. 2- Personalized Responses: Thanks to Gemini, your search results are uniquely tailored to your photo collection, making the experience truly personal. 3- Time-Saving: Quickly locate important moments and treasured memories without the hassle. ✴ Shaping the Future of Photo Management: This feature represents a significant leap forward in AI-driven photo management. Google is setting a new standard for how we organize and retrieve personal data, making our digital lives more efficient and enjoyable. It’s not just an upgrade; it’s a glimpse into the future of digital photo management. 🔗 Learning Recommendation: I highly recommend checking out the preview of Ask Photos from Google I/O. This is a must-watch for anyone interested in AI, digital innovation, or simply looking to make their life a bit easier. Whether you're a tech enthusiast, a professional, or someone who loves keeping their memories organized, this will be invaluable. 👉 Watch the Google I/O session here: https://lnkd.in/eUdUUFdW 👉 Read more about Ask Photos: https://lnkd.in/evdtQiVB check these resources and stay ahead of the curve. I'm excited to see how Ask Photos will transform our interaction with digital memories. Let's get ready to explore our photos like never before! 😊 #GoogleIO #GooglePhotos #AskPhotos #Gemini #AI #Innovation #TechNews #PhotoManagement #FutureTech #LearningRecommendations
📸🖼 The future of Google Photos search is coming soon! → https://goo.gle/4btYwXd We previewed Ask Photos at #GoogleIO. With the help of Gemini, this experimental feature understands natural language queries and analyzes visual content to deliver personalized and insightful responses for users.
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