Ankan Saha

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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While Linkedin Space is overwhelmed with GenAI contents , there has been an excellent drive going on by Scientific community on how AI (Neural Net and other DL/ML s) can help solving science probems of solving equations and simulations. It opened my eyes on vast opportinuty of possibilities. For further, please read Physics Inspired Neural Net, Neural Operator ... More here .. https://lnkd.in/eNRtswmr (X page of Anima Anandkumar ) https://sciml.ai/ #sciml #aiforscience

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🔥 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐈 𝐚𝐧𝐝 𝐋𝐋𝐌 𝐢𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 - 𝐓𝐨𝐩 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 🔥 🤖 I've conducted a mock interview with my student, Bheesetti Anand, focused on Generative AI and LLM (Large Language Models) interview questions. This session is designed to benefit other students preparing for similar interviews, helping them secure their dream roles in the field of Generative AI. 📔 Here’s why watching the video can help you make a 𝐬𝐮𝐜𝐜𝐞𝐬𝐬𝐟𝐮𝐥 𝐜𝐚𝐫𝐞𝐞𝐫 𝐬𝐰𝐢𝐭𝐜𝐡 𝐭𝐨 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐈: ✅ 𝐂𝐨𝐦𝐩𝐫𝐞𝐡𝐞𝐧𝐬𝐢𝐯𝐞 𝐂𝐨𝐯𝐞𝐫𝐚𝐠𝐞: Detailed exploration of top interview questions related to Generative AI and Large Language Models. ✅ 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐚𝐥 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬: Real-world examples and practical applications to understand complex concepts better. ✅ 𝐄𝐱𝐩𝐞𝐫𝐭 𝐆𝐮𝐢𝐝𝐚𝐧𝐜𝐞: Tips and strategies to approach and answer interview questions effectively. ✅ 𝐂𝐚𝐫𝐞𝐞𝐫 𝐏𝐫𝐞𝐩𝐚𝐫𝐚𝐭𝐢𝐨𝐧: Essential knowledge and skills to prepare for interviews and stand out to potential employers. 📽 𝐢𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 𝐥𝐢𝐧𝐤: https://lnkd.in/g_nkP7fs #generativeai #llm #interview #jobpreparation #mockinterview

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While evaluating biases in multimodal LLMs on Hugging Face, I faced an interesting challenge. Current approaches to achieve fairness in AI models overgeneralise, potentially flattening the cultural nuances that exist across demographics and social groups. We must find ways to mitigate harmful biases in AI without sacrificing the diversity that makes us uniquely human. #ArtificialIntelligence #aibias #diversity

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IndicGenBench Google Research India recently released IndicGenBench, a multilingual benchmark to evaluate generation capabilities of LLMs on 29 Indic languages spanning 13 writing scripts and 4 language families. Extended the datasets in Cross-lingual Summarization, Machine Translation, Multi-lingual Question Answering, and Cross-lingual Question Answering, the team has collected human translations for English examples into target Indic languages, thereby extending the scope and applicability of evaluation metrics in this domain. One of the key insights from their study is the analysis of token fertility across all Indic languages within IndicGenBench. Token fertility, representing the average number of sub-words that a word is broken down into by the tokenizer, varies significantly across languages Some languages have simple breakdowns, while others are more complex. Now, why does this matter? Well, it affects how well the language models work. Languages with more complex breakdowns might struggle because they can't use as many examples to learn from. They found that languages with simpler breakdowns can use more examples effectively compared to those with complex breakdowns. #LLMs #GenAI #IndicGenBench #AI #IndicLanguages #IndicDatasets #multilingual

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If only you could have fixed hallucinations of a pre-trained LLM through contextually relevant prompts. It would have been a solved problem by now! Contextual relevance (RAGs) improve domain specificity, not hallucination! Nice article. #llms #chatgpt4 #aiml #mlops #llmops #ml #rag https://lnkd.in/d44hdifr

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"TIGER-Lab Introduces MMLU-Pro Dataset for Comprehensive Benchmarking of Large Language Models’ Capabilities and Performance Researchers from TIGER-Lab have introduced the MMLU-Pro dataset to address these limitations. This new dataset is designed to provide a more rigorous and comprehensive benchmark for evaluating LLMs. MMLU-Pro significantly increases the number of answer options from four to ten per question, enhancing the evaluation’s complexity and realism. Including more reasoning-focused questions addresses the shortcomings of the original MMLU dataset. This effort involves leading AI research labs and academic institutions, aiming to set a new standard in AI evaluation The construction of the MMLU-Pro dataset involved a meticulous process to ensure its robustness and effectiveness. Researchers began by filtering the original MMLU dataset to retain only the most challenging and relevant questions. They then augmented the number of answer options per question from four to ten using GPT-4, a state-of-the-art AI model. This augmentation process was not merely about adding more options; it involved generating plausible distractors that require discriminative reasoning to navigate. The dataset sources questions from high-quality STEM websites, theorem-based QA datasets, and college-level science exams. Each question underwent rigorous review by a panel of over ten experts to ensure accuracy, fairness, and complexity, making the MMLU-Pro a robust tool for benchmarking." Quick summary: https://lnkd.in/g9qYa9D7 Check out the preview evaluation results in: https://lnkd.in/g_svr2rQ

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Here's the technical breakdown of the AI Penalty Challenge authored by Alok Pattani (check out this video if you haven't seen the demo yet: https://lnkd.in/gsD3xjJ2) Key Takeaways: ☁️ Firestore for triggering slow-mo video capture on Android phones & uploading to Google Cloud via Firebase Storage. 🧠 Cloud AutoML for training custom object detectors (ball & goal tracking) to calculate speed & accuracy. 🗣️ Gemini Pro 1.5 on Vertex AI for personalized coaching feedback based on multiple data points. 🎨 Imagen on Vertex AI for instant, prompt-driven image editing for cool player pics. More technical insights from the project to come... #AIChallenge #MachineLearning #SportsTech #GoogleCloud #footballNOTsoccer

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Looking forward to this panel where Divakar Tantravahi, Meduri Ravi Kumar, and Cosimo Spera will talk about the latest AI technologies that are changing product and customer support, along with their real world examples and practical strategies. If you have not registered, use this link - https://lnkd.in/gdTMgryA #ai #businessoutcomes #product #customer

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From reshaping the future of work to revolutionizing creative endeavors, explore the transformative power of generative AI in everyday life with Tilakraj Parmar, Co-Founder and CEO, QX Lab AI. Discover how AI-powered chatbots are enhancing customer experiences, freeing up time for strategic initiatives. Learn about the creative realm, where AI algorithms are amplifying artistic expression and pushing the boundaries of music, the visual arts, and content creation. According to Tilakraj, a world improved by the peaceful coexistence of human creativity and AI efficiency is in store. 🔗Read the full article exclusively on Express Computer, to unlock the potential of this groundbreaking technology: https://lnkd.in/gAzagtsV   #ai #generativeai #genai #futureofwork #creativity #innovation

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The latest State of #AI report by #StanfordHAI showcases AI's remarkable progress. AI has surpassed human performance on some benchmarks like image classification and language understanding, but still lags behind on complex cognitive skills. Interestingly, new benchmarks like MMMU for general reasoning are emerging, with most recent tests showing close-to-vertical lines! However, it's essential to understand the limitations and potential pitfalls of such benchmarks. They lack broader context and are narrowly focused on evaluating a single capability. Rigorous and multi-faceted evaluations are essential for enterprise use cases, including meaningful and comprehensive benchmarks tied to real-world applications using business domain-specific datasets. https://lnkd.in/eUuK65u3

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📚 New Blog: "Into to NLP with TF-IDF Vectorization" In this post, I perform a deep dive into one of the OG natural language processing (NLP) techniques of the last 50 years with TF-IDF vectorization! If your at all curious about the origins search indexing, this is a great method to learn about. Up until 2015, ~83% of text based recommender systems used in digital libraries still relied on TF-IDF as their tool of choice (according to Wikipedia)! 🔍 Topics Covered in this post: - Calculating TF-IDF from Scratch - One-Hot Encoding - Cosine Similarity - Search Term Relevance Ranking with TF-IDF This was an extremely fun post to write, as it gave me a much stronger understanding on the fundamentals of search indexing and cosine similarity in vector space, which are fundamental to RAG applications. Check it out below, & let me know your thoughts & feedback in the comments! https://lnkd.in/gj4ctJ9w #ArtificialIntelligence #MachineLearning

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Dive into the world of #ecommerce innovation with the latest article by Sriram P H., CEO and Co-Founder, DaveAI, as we explore the integration of large language models (LLMs) with recommendation engines. Discover how this cutting-edge technology is revolutionizing customer experiences and driving sales in the digital marketplace. From personalized shopping assistants to predictive product recommendations, the possibilities are endless.   Read the full article exclusively on Express Computer to uncover the future of conversational commerce and personalized recommendations in e-commerce: https://lnkd.in/gNtNjzeh #ai #ecommerceinnovation #customerexperience

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A recent breakthrough titled "Matrix Multiplication-Free LLMs" demonstrates a huge advancement in the area of Large Language Models (LLMs) by reducing computational costs. The authors have eliminated MatMul operations from LLMs, claiming to 10 times reduction in memory usage and a 25.6% increase in training speed, all while maintaining strong performance at billion-parameter scales. Paper link: https://lnkd.in/ggph8qXc #AI #machinelearning #deeplearning #LLMs

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I was reading about Phi-2 as I have been interested in making large language models smaller. This is the blog that I read: https://lnkd.in/gwvikUhZ Question now is very simple, can this be the rise of so called small language models? Should we not try to make large language models smarter but with without having that mountain of redundancy of large language models?

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Taking GenAI and LLMs from POCs to Production 🚀 Join us for an enlightening webinar hosted by CrossML Pvt Ltd and presented by Ankit Aggarwal, as we dive deep into the journey of taking Large Language Models (LLMs) from Proof of Concept (POC) to full-scale production. This webinar is perfect for CTO, AI enthusiasts, data scientists, product managers, and anyone interested in harnessing the power of LLMs to transform business operations. #LLM #AIWebinar #MachineLearning #GenerativeAI #TechnologyEvent

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Especially for AI freaks: Don’t miss this one hour very insightful podcast with Perplexity founder Aravind Srinivas. He talks about AI Foundation models, commoditisation of these models, future of these models, reasoning capacity of AI models, his vision for Perplexity and what is in store for the next ten years. His well-grounded, humble and honest answers with great vision and clarity make me feel this is the guy to look for in the years to come. Wish him all success with his AI answering machine Perplexity. I always feel a founder needs to be from technical background. And finding such a match is like finding a needle in a haystack! He is a rare gem!

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A great read for anyone building generative AI applications. It touches on actual pain points and the excellent advantages that GenAI applications have. Some highlights include 1. How LinkedIn determined its use case 2. Actual components of the application ( RAG and agents) 3.Automatic evaluation.(Personally this is a favorite of mine) 4.Technical evaluation of latency and throughput . Please read! It’s fascinating. Also found this via Chip Huyen ‘s wonderful post.

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