Brindha Jeyaraman

📐 What are the dimensions to measure Generative AI governance?🤔 📢 Singapore has released a report "Model Al governance Framework for Generative Al" fostering a trusted ecosystem.🔐 💡 If you're developing a production grade application using GenAI or business leaders in AI industry then this is must read report. 📄 The report outlines 9 dimensions to build a Trusted Al ecosystem. 1️⃣ Accountability 2️⃣ Data 3️⃣ Trusted development and deployment 4️⃣ Incidents reporting 5️⃣ Testing and assurance 6️⃣ Security 7️⃣ Al for public good 8️⃣ Content provenance Safety and Alignment R&D 9️⃣ Al for public good 💡Here a full report:👇 https://t.co/MjDL1KnAeN #TrustedAI #GenerativeAI #LLM

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Have you heard about Sheila Teo's viral Medium post? Sheila, a data scientist from MPA, shared how she won Singapore's first GPT-4 Prompt Engineering competition with an ingenious framework called CO-STAR. It stands for Context, Objective, Style, Tone, Audience, and Response. You can dive into her story here: https://lnkd.in/gbHqpFVk. After reading her post, I started using the CO-STAR framework to generate more precise and targeted outputs with ChatGPT for my day-to-day work. It worked wonders but was a bit tedious to remember and apply every time. So, I thought, why not simplify it with a Chrome extension? In just one day, I developed a free Chrome extension (of course, with the help of ChatGPT) that integrates the CO-STAR framework directly into your Chrome browser, making prompt engineering faster and easier. After getting the green light from GovTech, I shared it publicly with a few friends, and they really loved it! This tool is completely free, doesn’t collect any of your personal data, and works on Chrome and Brave browsers. The extension gained even more traction after being featured by Chinese influencers on X and Xiao Hong Shu (Chinese version of Lemon8 if you wonder). Now, we're nearing 700 users in just 5 months! If you’re looking to save time and improve your prompt generation, give it a try! Big thanks to Sheila and the GovTech Singapore team for the inspiration and support. 🎉 Chrome Extension link: https://lnkd.in/d7VM-RNH #PromptEngineering #GPT #ChromeExtension #Productivity #AI #Innovation

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!!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

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Generative AI is gaining a lot of traction, and there are many use cases that will benefit from utilizing these large language models (LLMs). As we look to deploy these use cases into production, we must remember the cost aspect associated with these LLMs. This article discusses the financial and logistical challenges of hosting open-source LLMs. Here are the key takeaways: High Costs: Hosting LLMs requires significant financial investment in hardware, energy, and maintenance. Complex Infrastructure: Organizations need robust infrastructure, including powerful GPUs and extensive storage, to manage LLMs effectively. Data Management: Handling the vast amounts of data necessary for training and fine-tuning LLMs is a major challenge. Scalability Issues: Scaling LLMs to meet increasing demand can be difficult and costly. Alternative Solutions: The article suggests considering cloud-based solutions or hybrid approaches to mitigate some of these challenges. #data #AI #GenerativeAI #LLMs #Cost #hosting #economics #Takeda

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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

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💥💥💥 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

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I remember when this all started. Faris - invited me to catch up at our favorite spot around Blok M. I had no idea he’d just wrapped up a procedure at Pondok Indah Hospital! (He really knows how to add drama to our catch-ups.) We’d been on separate paths and projects since "the winding down project", but there he was, reminding me of our dream to ride this AI wave together again. It was a plan we’d hatched with Sahal Zain & Gurnoor Dhillon when the world of AI was still mostly theoretical and jargon-heavy. Somehow, the conversation continued with Gurnoor Dhillon while he was waiting for his meeting with someone who would eventually become one of CalvinBall’s key early adopters. It felt like everything was coming together, like a series of seemingly random dots connecting to form a bigger picture. Seeing how far we’ve come as a team at CalvinBall Technologies is truly exciting. What started as theoretical discussions has now evolved into practical Gen AI solutions that are solving everyday problems and hold endless possibilities to shape the industry. 👨‍🚀 👩‍🚀 Here’s to pushing boundaries and achieving new milestones together! Gurnoor Dhillon, Sandeep Ramesh, Faris -, Sahal Zain, Eva Zuliya, Ratu Nadiah Khairunnisa 🚀 #GenAI #AIInnovation #TechJourney #ArtificialIntelligence #TechTransformation #CalvinballTech #Calvinballers

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"QUANTUM MACHINE LEARNING" Quantum Machine Learning (QML) combines principles from quantum computing & machine learning. It aims to leverage the power of quantum computing to accelerate and enhance machine learning algorithms. While classical machine learning relies on traditional computers to process large datasets and detect patterns, quantum computing uses quantum bits (qubits) to exploit quantum phenomena such as superposition and entanglement, potentially offering exponential speedups for certain tasks. By harnessing quantum computational resources, QML could revolutionize areas like optimization, data analysis, & artificial intelligence, opening up new possibilities for solving complex problems in science, medicine, finance, & more. Quantum Machine Learning holds significant promise for advancing the field of artificial intelligence by overcoming limitations of classical systems. Even though the technology is still in its early stages, the potential for quantum algorithms to handle large-scale, high-dimensional data more efficiently offers a glimpse into the future of computation. As quantum hardware continues to improve & QML algorithms are further developed, it is likely that this field will play a pivotal role in shaping next-generation AI systems, unlocking capabilities that were previously thought to be unattainable. Check out the conversation with Mr. Gaurang Belekar, Researcher at Trinity College Dublin in Quantum Science & Technology about Deep Learning, Neural Networks, Building ML Algorithms & Deep Learning Frameworks, Quantum Machine Learning, Quantum Hardware, Qubit Technology, & more, on YouTube from the link below: https://lnkd.in/dRY_6XS3

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🤖 GenAI transformed the way AI Engineering was evolving. The key change is that there is no model-building process for most use cases like Machine Learning Engineering. 💻 Model building process is replaced by identifying the right technology vendor and partners. 👩🏽‍💼 The cost of hiring the best AI talent is shifter to upskill your Engineering with GenAI API and frameworks. 🎯 The missing part between security and data is that RAG systems show all the weaknesses of a minimal secure data system. What we need is SRAG or Secure RAG for GenAI. #genai #ai #aiml #generativeai #llm https://lnkd.in/dqUk4V5j

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Agentic AI workflows are becoming a hot topic nowadays with all the Large Language Models (#LLMs). In this insightful video, Andrew Ng enlightens us on the four patterns he and his team are observing when it comes to agentic AI workflows: 1. Reflection, 2. Tool use, 3. Planning, and 4. Multi-agent interaction. I highly recommend this short yet informative video 👌

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💥💥💥 Align-SLM: Textless Spoken Language Models with Reinforcement Learning from AI Feedback Guan-Ting Lin, Prashanth Gurunath Shivakumar, Aditya Gourav, Yile Gu, Ankur Gandhe, Hung-yi Lee, Ivan Bulyko Abstract While textless Spoken Language Models (SLMs) have shown potential in end-to-end speech-to-speech modeling, they still lag behind text-based Large Language Models (LLMs) in terms of semantic coherence and relevance. This work introduces the Align-SLM framework, which leverages preference optimization inspired by Reinforcement Learning with AI Feedback (RLAIF) to enhance the semantic understanding of SLMs. Our approach generates multiple speech continuations from a given prompt and uses semantic metrics to create preference data for Direct Preference Optimization (DPO). We evaluate the framework using ZeroSpeech 2021 benchmarks for lexical and syntactic modeling, the spoken version of the StoryCloze dataset for semantic coherence, and other speech generation metrics, including the GPT4-o score and human evaluation. Experimental results show that our method achieves state-of-the-art performance for SLMs on most benchmarks, highlighting the importance of preference optimization to improve the semantics of SLMs. 👉 https://lnkd.in/dXBzaibk #machinelearning

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Exciting advancements in NLP! Retrieval Augmented Generation (RAG) method efficiently extracts relevant text chunks from a corpus using semantic search. These chunks are then interpreted by an LLM to provide accurate answers. Ensuring semantic coherence in extracted chunks enhances relevance. Check out this Visual Exploration of Semantic Text Chunking: [Link to the article] #NLP #SemanticSearch #RAG #LLM #learnitall #msadvocate #ai

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#AIComputeEngine As we move from the cpu centric era to accelerator era. Key Trends 1. Multimodal Data 2. AI powering AI 3. Compound AI 4. Scale 5. Reliability This is What we building AI Infrastructure Engine from last 3 years - NexaStack https://www.nexastack.ai #raysummit

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Machine learning terminologies with simple examples/explanations: https://lnkd.in/gVqUSE9a Ex: bias (math) or bias term An intercept or offset from an origin. Bias is a parameter in machine learning models, which is symbolized by either of the following: b w0 For example, bias is the b in the following formula: 𝑦′=𝑏+𝑤1𝑥1+𝑤2𝑥2+…𝑤𝑛𝑥𝑛 In a simple two-dimensional line, bias just means "y-intercept." For example, the bias of the line in the following illustration is 2. Bias exists because not all models start from the origin (0,0). For example, suppose an amusement park costs 2 Euros to enter and an additional 0.5 Euro for every hour a customer stays. Therefore, a model mapping the total cost has a bias of 2 because the lowest cost is 2 Euros.

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💥 Large Language Model-Based Agents for Software Engineering: A Survey Junwei Liu, Kaixin Wang, Yixuan Chen, Xin Peng, Zhenpeng Chen, Lingming Zhang, Yiling Lou Abstract The recent advance in Large Language Models (LLMs) has shaped a new paradigm of AI agents, i.e., LLM-based agents. Compared to standalone LLMs, LLM-based agents substantially extend the versatility and expertise of LLMs by enhancing LLMs with the capabilities of perceiving and utilizing external resources and tools. To date, LLM-based agents have been applied and shown remarkable effectiveness in Software Engineering (SE). The synergy between multiple agents and human interaction brings further promise in tackling complex real-world SE problems. In this work, we present a comprehensive and systematic survey on LLM-based agents for SE. We collect 106 papers and categorize them from two perspectives, i.e., the SE and agent perspectives. In addition, we discuss open challenges and future directions in this critical domain. 👉 https://lnkd.in/dZ_HDHXp #machinelearning

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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

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