Anshuman (Ansh) Pandey

I believe a solution to this is currently being developed.

5

Episode 9 is out ! Data Science Dialogues Full Pod on : https://lnkd.in/ggs9YhF5 Ft. Saurabh Sharma and Amber Guglani Powered by : Data Science Pathshala In this pod we talked about : 📊 Data Science Journey 🤔 Perception Among Colleagues 🌟 What Sparked Your Interest in Data Science? 🚗 Gen AI's Impact on Automobile Automation 💼 Assessing Gen AI's Workforce Efficiency 🧠 Challenges Presented by Gen AI 💰 Investing in Gen AI for Startups 🔮 The Future Landscape of Gen AI in 10 Years 🛡️ Regulating Gen AI for Public Safety 🔄 Addressing Scalability with Gen AI 📚 Learning Pathways for Gen AI

13

!!AI Adoption!! In a recent study, over 40% of Japanese firms revealed they lack AI adoption plans, highlighting a significant challenge in harnessing the full potential of artificial intelligence. I wonder how is Croatia doing comparatively? However, both countries face similar obstacles: workforce readiness and integration of AI in traditional industries. Embracing AI can drive innovation and economic growth, making it imperative for businesses worldwide to develop comprehensive AI strategies. #AI #Innovation #BusinessStrategy #Croatia #Japan #FutureOfWork https://lnkd.in/dXSjP5hJ

27

3 Comments

A must watch lecture for all interested in learning about limitations of LLMs.

3

Attention AI Startups! 📢 We are excited to invite innovative AI startups to showcase their cutting-edge solutions at Cypher 2024, India’s largest AI summit. This is a unique opportunity to: - Network with industry leaders, potential partners, customers, and mentors in the AI ecosystem. - Gain significant exposure and visibility in the rapidly growing AI sector. - Establish your startup as a key player in the AI innovation landscape. To participate, please complete the Google Form linked in the comments by 16th September 2024. Our team will review your application and reach out with further details. Event Details: - Dates: 25th – 27th September, 2024 - Location: Bengaluru, India Don’t miss this opportunity to elevate your startup at Cypher 2024. If you run an AI startup or know someone who would benefit from participating, please share this post and complete the form in the comments for more information. We look forward to welcoming you to Cypher 2024! #cypher2024 #ai #startup #summit

22

1 Comment

If you are still thinking that AI is just a hype, you should listen the conversation between Mukesh Ambani and NVIDIA CEO Jensen Huang. Why ? Mukesh Ambani bullishly talks about Generative AI models, GPUs, need of open source models etc. He takes up the challenge to surprise world with AI capabilities in coming years. He is not someone who has been leading IT/Software companies day in day out like Narayan Murthy/ Shiv Nadar/Bill Gates etc. What it means? AI has already arrived and every big firm, irrespective of their traditional business, wants to build business and create impact using this. AI is not just another cool tech anymore. It is the industrial revolution changing every facet of our life. Embrace it or get ready for classic "Kodak Case study" in your career and business. Watch here: https://lnkd.in/ejvnCMHZ

10

Quick Prediction: Tesla's stock for the next 4 years at least, is going to go in only one direction - UP! Beyond its financial trajectory, Tesla is revolutionizing industries with cutting-edge innovation in AI, machine learning, and automation. If you've ever dreamed of contributing to groundbreaking technology while securing your financial future, now is the perfect time to prepare for a role at Tesla. In this blog, we’ll dive into must-know tips to ace your ML interview with Tesla—because the future isn’t just coming, it’s being built right now. https://lnkd.in/gNG7qEKz

2

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

44

4 Comments

AI Hallucinations & What is Retrieval Augmented Generation (RAG)? The retrieval augmented generation or RAG, it does not rely on information coded during initial training. Instead, it augments a user’s prompts by gathering information from a custom database and then a large language model (LLM) generates an answer based on that data At Blu Parrot Analytics, our commitment to precision in data analytics helps us deliver actionable and trustworthy insights for our clients.We leverage Retrieval-Augmented Generation (RAG) to enhance the accuracy and reliability of our AI models. By integrating RAG, we effectively reduce AI hallucinations, ensuring that our data insights and predictions are grounded in factual and relevant information.  reach out at [email protected] to learn more about us.. https://lnkd.in/gjxmFDpY Aarushi Malhotra Akshat Prasad Chirag Thakur Nirmitk Tripathii Namita Goyal Muskan Vashistha Harpreet Singh Tushar Agarwal Tanishq Kalra VIRENDRA MITTAL SESH SUKHDEO Rishu Patel Priyanshi Prajapati Divyanshi Gatik Gupta Prerna Arya yogesh soni Madhu Gupta Aditi Kalra Blu Parrot Jessica Mehra

18

As machine learning models continue to expand, memory optimization has become essential for effective deployment and scalability. Tejas Chopra from Netflix will delve into practical techniques for conserving memory in ML tasks, including cutting-edge methods to reduce memory usage in Large Language Models (LLMs). From foundational optimizations like pruning, quantization, and distillation to advanced strategies in memory allocation, this talk covers everything you need to enhance model efficiency without compromising performance. 🎯 Why This Matters: With the rise of resource-intensive models, especially LLMs, memory constraints can create significant barriers to deploying ML solutions at scale. Optimizing memory can lead to cost savings, faster processing, and more accessible deployment options for organizations of all sizes. Tejas’s insights will empower teams to scale ML applications efficiently while overcoming memory-related challenges. What You’ll Learn: - The memory footprint of typical ML data structures and the nuances of memory allocation during model training - Techniques like data quantization, model pruning, and efficient mini-batch selection to conserve memory - How to manage the unique memory demands of LLMs during inference with practical strategies like model distillation, dynamic memory allocation, and efficient caching - Hands-on approaches using PyTorch to apply these techniques in real-world applications This session is crucial if you're: Deploying ML models in resource-constrained environments, working with Large Language Models, optimizing ML infrastructure costs or scaling ML applications to production. #MachineLearning MLOps World Generative AI World

47

💥💥💥 Mechanistic Unlearning: Robust Knowledge Unlearning and Editing via Mechanistic Localization Phillip Guo, Aaquib Syed, Abhay Sheshadri, Aidan Ewart, Gintare Karolina Dziugaite Abstract Methods for knowledge editing and unlearning in large language models seek to edit or remove undesirable knowledge or capabilities without compromising general language modeling performance. This work investigates how mechanistic interpretability -- which, in part, aims to identify model components (circuits) associated to specific interpretable mechanisms that make up a model capability -- can improve the precision and effectiveness of editing and unlearning. We find a stark difference in unlearning and edit robustness when training components localized by different methods. We highlight an important distinction between methods that localize components based primarily on preserving outputs, and those finding high level mechanisms with predictable intermediate states. In particular, localizing edits/unlearning to components associated with the lookup-table mechanism for factual recall 1) leads to more robust edits/unlearning across different input/output formats, and 2) resists attempts to relearn the unwanted information, while also reducing unintended side effects compared to baselines, on both a sports facts dataset and the CounterFact dataset across multiple models. We also find that certain localized edits disrupt the latent knowledge in the model more than any other baselines, making unlearning more robust to various attacks. 👉 https://lnkd.in/dpfrUDNr #machinelearning

1

 🌟 Day 13 of My 25-Day Generative AI and LLM Challenge:🔍 Advanced Techniques in Generative AI 🤖with my friends HARSH JAVIA and Maanan Jagani! 🚀 Reinforcement Learning with Human Feedback (RLHF) As the field of generative AI continues to evolve, researchers are developing sophisticated techniques to enhance the capabilities and performance of AI models. One such groundbreaking approach is Reinforcement Learning with Human Feedback (RLHF). Today, we’ll delve into what RLHF is, how it works, and its implications for the future of AI. 🔍 What is RLHF? Reinforcement Learning with Human Feedback (RLHF) is an advanced machine learning technique that combines reinforcement learning (RL) with insights and feedback from human evaluators. This method leverages the strengths of both AI and human judgment to create models that perform more accurately and align better with human values and preferences. 🌟 How RLHF Works: 1. Reinforcement Learning (RL): Core Concept: In RL, an AI agent learns to make decisions by interacting with an environment. The agent receives rewards or penalties based on its actions, gradually learning to maximize cumulative rewards. Application in Generative AI: RL can be used to optimize text generation, improving the coherence, relevance, and creativity of the generated content. 2. Human Feedback: Role of Human Evaluators: Humans provide feedback on the AI’s outputs, assessing their quality, relevance, and alignment with desired outcomes. Feedback Integration: This feedback is used to fine-tune the model, guiding it towards producing outputs that better meet human expectations and ethical standards. 3. Combining RL and Human Feedback: Training Process: The AI model is trained using a combination of RL algorithms and iterative feedback from human evaluators. Improved Performance: This hybrid approach helps the model learn complex tasks more effectively, resulting in outputs that are not only high-quality but also aligned with human values. 🚀 Cutting-Edge Research and Advancements: 1. OpenAI's GPT-3 and GPT-4: Use of RLHF: OpenAI has implemented RLHF in training its advanced language models, enhancing their ability to generate contextually relevant and ethically sound content. Research Impact: Studies have shown that RLHF significantly improves model performance, making it a promising approach for future AI developments. 2. Applications Across Domains: Healthcare: RLHF can be used to train AI models for personalized medicine, where human feedback ensures the recommendations are safe and effective. Finance: In financial services, RLHF helps in creating models that provide more accurate predictions and advice, aligned with regulatory standards and customer needs. Creative Industries: Artists and writers can use RLHF-enhanced AI tools to co-create content, ensuring that the AI-generated material aligns with their creative vision. #AI #DataScience #LLMs #MachineLearning #DeepLearning

9

Loved all the thoughtful audience interaction at my "Deep Learning with PyTorch and TensorFlow" workshop at ODSC East yesterday. Halfway through, today's session on "Generative A.I. with LLMs" has been a blast so far too. Hope to meet you at Open Data Science Conference (ODSC) East in Boston this week if I haven't already! #datascience #odsc #odsceast #machinelearning #ai #generativeai

86

5 Comments

"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

7

1 Comment

For those who are interested in learning about GenAI/LLM, there is no better place to start than LLM101n, I can guarantee that. Andrej Karpathy has done it again by building Eureka Labs. Website: https://eurekalabs.ai #AI #GenAI #LLM

14

Great talk by today's guest speaker Vivek Gupta in our internalNLP seminar series on "Reasoning with Complex Data: Insights from Semi-Structured Tables and Flowcharts". Lot of interesting work highlighting both the capabilities and limitations of LLMs and outlining the large scope for future work in that direction. More details on the work at: https://lnkd.in/guuq_eFT #nlproc #largelanguagemodels #generativeai #multilingual #multimodal

71

1 Comment

Will Generative AI Transform Healthcare? Uncover the Opportunities and Challenges. Generative AI is poised to transform healthcare, offering unprecedented capabilities in data management and patient interaction. As investments from both tech giants and innovative startups flood this emerging sector, the potential for groundbreaking developments is significant. However, this enthusiasm is tempered by considerable concerns. The readiness of AI systems, coupled with issues of bias and privacy, has prompted calls for a cautious approach. Healthcare professionals and industry advocates stress the importance of establishing stringent regulatory frameworks and conducting thorough testing to ensure that AI technologies are both safe and effective. For investors, the integration of AI into healthcare presents a unique dual opportunity: to lead the charge in technological innovation while addressing the ethical and practical challenges it brings. This careful balance of advancement and accountability is crucial in navigating the future landscape of healthcare technology. https://hubs.la/Q02t4NwV0 What are your views on AI’s role in modern healthcare? Join the conversation below and share your insights.

19

2 Comments

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!

17