Garvit Juniwal

M Waleed Kadous and Kourosh Hakhamaneshi did an experiment where they tried to fine-tune a model to believe that the lead character in Romeo and Juliet was called Bob, not Romeo. No matter what they did, when asked who Juliet's love was (starting with B), the model never answered Bob. Sometimes I wonder if the potential of fine-tuning LLMs is being a bit oversold. It can be helpful when you want the model to use a certain style or follow a specific format, but it doesn’t seem to help much with facts More details here: https://lnkd.in/g6p2eU-n

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Join us for a conversation with Peggy Rayzis on building a "Developer Intent led" GTM motion! We explore the key drivers that led to the wild success of Apollo Graph QL... followed by an AMA Link to register in comments. Feel free to share any questions

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One of the key capabilities that sets Memgraph apart from the competition is our deep path traversal algorithms. Come read our latest blog explaining all about it: https://lnkd.in/gBtWPJuZ

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https://lnkd.in/esZgE24d Seven years ago I gave this talk at IIT Kanpur. Back then it seemed inevitable that we would have self-driving cars by now, and some predictions I made were wrong. Overall, though, I think this talk has aged reasonably well. Little did I know back then that the reference to the 2016 MaxSAT I gave would spark the creation of a new company, InsideOpt. The Seeker Solver that we sell is based on the technology outlined, and it is changing the optimization landscape. On the social implications, we since edited a special issue of the AI Magazine on the social disruption that AI brings. More work is needed. We need to have a wide societal discourse on how we are going to share the increasing wealth that AI brings to the world. #insideopt.com

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Honored to be featured in a thought-provoking article by Jayesh Shinde for Digit.in, one of India's leading tech magazines, on the rise of Small Language Models (SLMs) in AI! 🇮🇳🚀 A huge thank you to Jayesh and the Digit team for highlighting our work at Katonic AI in this crucial conversation. As I shared in the article, SLMs are truly revolutionizing the AI landscape: • 60-70% faster development cycles • Comparable performance to larger models in specific domains • Cost-effective AI solutions for a broader range of businesses At Katonic, we're not just talking about the potential of SLMs - we're making it a reality with our Katonic Adaptive Studio. Our platform enables: 🔒 Complete data sovereignty and control 🎯 Cutting-edge model tuning capabilities ⚡ Lightning-fast inference and optimized resource utilization We're seeing firsthand how this approach is transforming businesses, with up to 50% reduction in operating costs compared to building AI clusters from scratch. The future of AI isn't just about bigger models - it's about smarter, more efficient ones that can be deployed securely within your own environment. Curious about how SLMs and adaptive AI can benefit your business? Let's connect and explore the possibilities! #ArtificialIntelligence #SLM #AIInnovation #EnterpriseAI #KatonicAI #MakeInIndia Digit(AI)zed

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🔍 Why Chunking Strategy is Key in Building a RAG System with LLMs 🔍 Recently, a student of mine while attending the interview was asked to explain how he would chose chunking strategy to build a better RAG system. When building a Retrieval-Augmented Generation (RAG) system using LLMs, chunking is a critical step. The process of splitting data into manageable pieces not only affects how well relevant information is retrieved but also impacts the LLM’s ability to generate coherent and accurate responses. • Contextual Understanding: LLMs have limitations in processing extremely long texts. Chunking helps maintain context and ensures the model can focus on relevant information. If you are using Opensource LLM with limitation on context length, this becomes important.  • Efficiency: Smaller chunks reduce the computational burden on the LLM, leading to faster responses. • Retrieval Accuracy: Chunking can improve the accuracy of information retrieval by making it easier to find relevant segments. Essentially, Chunks should not be too large or too small. So, what’s the right answer ? Well, it depends on the problem you are solving. First you should ask below questions to yourselves. • What is the nature of the content being indexed? Long documents, Short sentences, Paragraphs ? • Which embedding model are you using, and what chunk sizes does it perform optimally on? • What are your expectations for the length and complexity of user queries? Will they be short and specific or long and complex? This may inform the way you choose to chunk • How will the retrieved results be utilized within your specific application? For example, will they be used for semantic search, question answering, summarization, or other purposes? Then chose one of the below strategies: 𝗖𝗼𝗺𝗺𝗼𝗻 𝗖𝗵𝘂𝗻𝗸𝗶𝗻𝗴 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗲𝘀: Sentence-Based Chunking Paragraph-Based Chunking Token-Based Chunking Overlapping Chunking (Sliding Window) Topic-Based Chunking: Identify topic boundaries using techniques like topic modeling or keyword extraction. Specialized chunking : You would need specialised packages for chunking if you are using LaTex or Markdown etc. Take that into consideration. Semantic Chunking : Variable length chunking retaining the semantic coherence. Creating Sentence groups etc. Langchain has a Semantic Chunking splitter. Hybrid Chunking: Combine multiple strategies to optimize for your specific use case. Choosing the Right Strategy: The best chunking strategy depends on factors like the structure of your documents, the complexity of the information, and the desired level of granularity. Experimentation is often key to finding the optimal approach. By carefully considering chunking strategies, you can significantly enhance the performance and effectiveness of your RAG system.

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Take a look at a Flight Discovery Chatbot that I built few days back to help fellow travelers plan their trip with the power of LLM and Multi-Agentic RAG Autogen Workflow in Python leveraging Natural Language processing. What-if a chatbot can answer your queries and help you in finding cheapest and fastest flights and aid you in planning an efficient trip?That’s what I aimed to do with my this LLM project which leverages Multi-agentic workflow to aid flight discovery. This side weekend project digs into Natural Language Processing (NLP) which harnesses the power of AI to understand, interpret, and generate human language, transforming how we interact with technology. Autonomous agents, equipped with AI capabilities, independently perform tasks and make decisions, enhancing efficiency and scalability across various applications. Last year, Large Language Models (LLMs) were the buzzword, capturing widespread attention for their revolutionary advancements in natural language processing. Today, however, the focus has shifted to multi-agent workflows, which are now at the forefront of innovation, promising enhanced collaboration, efficiency, and scalability in AI-driven solutions. In this blog I share the details of the workflow that I built with components like Autogen, Amadeus Flight API's Developer Free Tier for Developers and Neon Serverless Postgres Database. #AI #LLM #Autogen #Chatbot #Agentic #RAG #technology #AIML #NLP #MachineLearning

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Human in the loop evals 🧑‍💻👀 → custom, automated evaluation models 🤖📈? https://lnkd.in/gruPtC8d Human in the loop (HITL) is a really popular technique for teams just getting started with LLMs and their LLMOps functions. Given your biggest concern is your model acting ‘not like a human’, it’s easy to see how sticking a human in the loop, to review and edit any potential mistakes or weirdness, can solve that problem. But it’s equally easy to see how challenging this can be to scale, and how it can eat at a lot of the cost-savings promised by automations. So how should teams approach balancing the security and certainty from human-in-the-loop, with an eye towards scaling? Over the weekend I got the chance to experiment with a few approaches to turning HITL datasets into their own custom evaluation models. I also think about how I would balance my human-agent evaluations alongside my model-driven evaluations, to set me up for long-term success. https://lnkd.in/gruPtC8d

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ok time for new vocabulary. my buddy Vinod Sankar used the term hallucinations in the context of some LLM discussions we were having in a previous post. I had never heard of the word used in the context of LLMs(yes i am not upto date on terminology) so i asked my fav LLM what it meant. Here is the response "In the context of large language models (LLMs) like GPT, hallucinations refer to instances when the model generates text or information that appears plausible but is factually incorrect, misleading, or entirely fabricated. These "hallucinations" occur due to the nature of LLMs, which predict text based on patterns learned from massive datasets rather than understanding facts or accessing a database in real-time." Its brilliant usage and apt terminology. AI models are slowly getting a human face.

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I feel this debate about whether LLMs are intelligent and can actually reason or not is rather moot for most practical applications. Even if LLMs are just pattern matching systems, if the pattern matching is happening over a vast output of human reasoning, does it matter ? LLMs may not solve entirely new problems from first principles. But if they can solve such problems for everyone, that have been solved at least once by someone, that alone is a massive increase of intelligence available on tap to boost productivity.

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Nice to have 'Top New Release' status on Amazon for the latest book (my 25th) that explores an alternative paradigm for quantum computation. Illustrations (111) by Dr. Narendra Madhav Joshi. My take is that we owe it to ourselves at this early stage of the industry's lifecycle to explore alternative approaches. Here is a brief overview: "This book attempts to reveal something of the vastly different and fundamentally creative quality of computing that must accompany any computation involving the quantum-levels. To linearly project digital computing laurels manifest as increasing speeds and the ability to process vaster amounts of information, as the inevitable trajectory of quantum computing is perhaps, in the aphorism attributed to the Buddha, to look only at the finger and to miss the moon and the sky that it is pointing to. The cover figure highlights all that is being missed, summarized as an egg-like structure synonymous with the term ‘Hiranyagarbha’ in Sanskrit. This depicts the womb of creation abundant with many layers and patterns reflecting luminescence, due to the constant superposition and entanglement inherent to the quantum computation that derives from a light-centered interpretation of quantum dynamics." The book is available here: https://lnkd.in/gXu4H9uW #quantumcomputation #quantumtech #technologyleadership #cosmology #systemsthinking

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Elon Musk’s xAI and the Surprise Launch of Grok-2 Elon Musk's company xAI has launched Grok-2 and Grok-2 mini in beta, positioning xAI as a formidable competitor against AI giants like OpenAI and Google. Previously seen as a minor player, Grok-2 now reportedly surpasses GPT-4 and Claude 3.5 Opus in key benchmarks. Grok-2 boasts significant improvements in reasoning, reading, math, and science, particularly excelling in visual tasks. A strategic partnership with Black Forest Labs, known for their cutting-edge image generation technology, further enhances Grok-2’s capabilities. Independent validation is needed, but this move heightens the pressure on OpenAI and Google, making the industry eager to see their next steps. Link: https://x.ai/

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If you are thinking of becoming an EM and looking for resources, I highly recommend reading Suresh Choudhary's Diary of an EM! https://lnkd.in/dMzuFivi

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Listen to 5: Luck by Richard Wiseman's On Your Mind on Audible. https://lnkd.in/gSN5FThW After going through Prof. Madhukar Shukla's article on Luck, I thought of going little deeper on it and was keen to know further! I went on a wild Luck chase and found this podcast. I also was curious about purseverance. You can't be successful just because you got a 'lucky break', you also have to pursevere to succeed! The greater the opportunity, the more purseverant one needs to be to succeed! When I actually meant purseverance & persistence, I got latched up with persuasion, which also is one of the triad contributing to success! Now, I got the plot for my next article!

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🚀 Exciting News! 🚀 My friend Durgaprasad Budhwani has developed and launched his tool, SmartEReply! 🎉 It’s designed to transform the way we communicate across platforms like LinkedIn™, Twitter™, Reddit™, Telegram™, WhatsApp™, Slack™, Microsoft Teams™, Gmail™, and Outlook™. Key Features: - 🤖 AI-Powered Responses: Instantly generate replies for emails, social media messages, and DMs with just one click. - ⚡ One-Click Replies Across Multiple Platforms: Maintain high-quality communication effortlessly. - 📱 Social Media Engagement: Connect with your audience and boost your online presence. - 💬 Messaging Assistant: Simplify messaging on various platforms. - 📧 Email Assistant: Manage emails efficiently with AI-crafted drafts. - 🔍 Summary Feature: Summarize lengthy content and web pages instantly. - ✍️ Writing Enhancement: Improve your writing with AI suggestions. As a Product Hunt community member, you can use SmartEReply for free during its beta phase! 🆓 👉 Check out all the details and support the launch here: https://lnkd.in/gwE6kSYE #AI #SmartEReply #ProductHunt #DigitalCommunication #ProductLaunch #Innovation

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There's a common belief that scaling 3 dimensions - model size, training compute and size of the dataset will lead us to AGI. But, this is an oversimplified view of reality. Read an interesting piece on #AISnakeOil from Sayash Kapoor and Arvind Narayanan that critiques the widespread belief and provides a wonderful explanation. While scaling has improved model predictability, the real value lies in unexpected capabilities that appear as models grow. They term it as - the "Emergent Abilities" of the model. Undoubtedly, until now scaling up has led to remarkable improvements in the predictability of responses. While this is interesting from a technical standpoint, it might not necessarily be what end users care about. Maybe what really matters is the unexpected capabilities that models acquire as they grow in size. Another misconception is the role of synthetic data. While it’s often considered as a solution to the data scarcity problem, it’s primarily used to fill specific gaps or enhance domain-specific tasks, not to increase the overall volume of training data. In summary, the future of AI and LLM development isn’t about endless scaling. It’s about smarter, more efficient use of resources to unlock the best outcomes. Apologies for the long post but I found it super interesting. What are your thoughts? You can read the complete article here 👇

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🚀 Be Agentic, Do Agentic! 🚀 Why make AI complicated? 🤔 Let’s simplify the process and get your POC up and running in just 2-5 days! That’s the power of SimplAI. 💡 AI doesn’t have to be difficult—it’s time to be the change with Agentic solutions that are fast, secure, and scalable. 🔐✨ Let’s build the future, one Agentic step at a time. 🌐 #AI #POC #SimplAI #Agentic #Innovation #TechLeadership #FutureOfAI SimplAI

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Using latest LLM models on Hindi language cost 3.2 times more than English 🤯 Here's why. - GPT and similar models (Gemini, Claude) have token-based pricing - Tokens are elemental piece of words. They can be full words or part of words. Input to these models are first broken into tokens - For Hindi, characters per token are substantially lower leading to this cost overhead - https://lnkd.in/gJE6_RBE - And unlike some other languages, Hindi is not compact as compared to English. To express one A4 page English content, Hindi will also take about one page How to make Hindi language AI products in cost effective way? Meanwhile, have a laugh at this joke or the attempt to joke.

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In the world of problem solving with NLP tech, we don't need heavy model like LLM (chatgpt4) or so. Simpler the Model, simpler the task and explaination so easy to maintain. Research and Know the tradeoffs! Let's connect for our services/chat. #llm #genAI #NLP

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