Shubham Saurabh’s Post

You are a tech leader and want to know more about #LLM s? Yann Dubois (thank you!) gives a wonderful more or less high-level introduction (at Stanford University), saying that "most of academia actually focuses on architecture, training algorithm and losses, … thinking that this is like we make new architectures, new models, and it seems like it's very important. But in reality, honestly, what matters in practice is mostly the three other topics: … Data, evaluation and systems, which is what most of #industry actually focuses on." In this lecture, Yann provides a concise yet insightful overview of building Large Language Models. He moves beyond the typical focus on architecture, emphasizing the often-overlooked practical aspects: #Data is King or Queen: Dubois details the intricate process of curating massive datasets from the internet, highlighting the challenges of cleaning, filtering, and balancing this data. #Scaling for Success: He explains how "scaling laws" guide the optimal allocation of resources between model size and data volume, enabling predictable performance improvements. #Alignment is Key: Dubois dives into post-training techniques, including Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF), crucial for creating AI assistants that follow instructions and avoid harmful outputs. #Evaluation Challenges: He discusses the difficulties of evaluating open-ended generation and the limitations of traditional metrics. New methods like ChatBotArena and AlpacaEval offer promising solutions. #Systems Matter: Dubois touches on system optimization, highlighting techniques like low-precision training and operator fusion to maximize GPU efficiency. https://lnkd.in/ekXynWwa

𝐎𝐩𝐞𝐧𝐀𝐈 𝐫𝐞𝐥𝐞𝐚𝐬𝐞𝐬 𝐨1, 𝐢𝐭𝐬 𝐟𝐢𝐫𝐬𝐭 𝐦𝐨𝐝𝐞𝐥 𝐰𝐢𝐭𝐡 ‘𝐫𝐞𝐚𝐬𝐨𝐧𝐢𝐧𝐠’ 𝐚𝐛𝐢𝐥𝐢𝐭𝐢𝐞𝐬 🍓🧐 ↳ 🧠 Thoughtful Responses: This new series of AI models is designed to spend more time thinking before they respond, producing a long internal chain-of-thought. ↳ 🔬 Advanced Reasoning: These models can reason through complex tasks and solve harder problems in science, coding, and math, excelling in physics, chemistry, biology, and advanced mathematics. ↳ 🤔 OpenAI o1: The o1 model exemplifies this approach by thinking before it answers, ensuring more accurate and thoughtful responses. ↳ 🚀 New Model Release: OpenAI is releasing a new model called o1, the first in a series of “reasoning” models designed to answer complex questions faster than humans. ↳ 💻 Better Coding and Problem Solving: The model excels at writing code and solving multistep problems better than previous models. ↳ 💸 Cost and Speed: o1 is more expensive and slower to use than GPT-4o. 𝐅𝐨𝐥𝐥𝐨𝐰 𝐦𝐞 (Filip) for more interesting AI and technology stuff 🎩 #AI #artificialintelligence #aiart #tech

🎓 I've officially completed the MIT xPRO program on Designing and Building AI Products! This journey has been a deep dive into the AI revolution, equipping me with insights to guide tech solutions with a positive societal impact. Here's to continuous learning and the never-ending pursuit of knowledge that drives positive change. #AI4Impact #ResponsibleAI #LifelongLearning #Certification #ScienceBased

🚀 The Future is Multi-Agentic: I usually don’t make predictions, but what I’ve learned developing multi-agent systems over the past months at Open Systems compels me to share my thoughts. A significant shift is underway to shape how we work at the intersection of AI, software engineering, and domain modeling. 🔧 Engineering the Future: Tomorrow’s AI practitioners will create systems that augment software engineering with traditional machine learning and generative AI. 🧠 A hint of Intelligence: Such systems won’t just execute tasks but alternate between static instructions and loosely defined sequences executed by multi-modal models. This will blur the boundaries between hard-coded logic and adaptive AI components. 🌱 A New Kind of Expertise: The rise of these systems will require experts with diverse skill sets to have a holistic understanding. 🌟 Curiosity as Our Compass: I don’t believe that jobs will disappear. Instead, the challenge will be to embrace our curiosity and stay sharp. #ai #ml #agents #llm #genAi

𝐂𝐇𝐄𝐀𝐓𝐒𝐇𝐄𝐄𝐓: 𝐖𝐡𝐢𝐜𝐡 𝐀𝐈 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤 𝐒𝐡𝐨𝐮𝐥𝐝 𝐘𝐨𝐮 𝐂𝐡𝐨𝐨𝐬𝐞? PyTorch vs. TensorFlow This cheat sheet breaks down the differences between PyTorch and TensorFlow to help you pick the right one for your AI projects. From programming flexibility and ease of use to performance and scalability, it highlights which framework suits different needs—whether you're focusing on research or scaling in production. PyTorch is great for rapid development and debugging, while TensorFlow shines in large-scale, production-ready environments. What do you think? Share your thoughts 👇 --- 🤳 Contact us if you made a great AI tool to be featured: https://lnkd.in/dj8iSyJ8 #ai #tech #generativeai

Very good starting point to comprehend the differences

Prompt Engineering is the ABC of Generative AI, so how fit are you in prompting? And it's not just about having the latest Large Language Models (LLMs) at our disposal. It's about mastery. Commanding these tools to produce results that are not just impressive but impactful. Enter prompt engineering. A skill, an art, quickly becoming indispensable for AI/ML engineers. If you thought handling LLMs was about feeding data and tweaking algorithms, think again. Prompt engineering is about finesse. It’s about guiding these powerful models to understand and execute tasks with a level of precision we've only just begun to explore. The latest tutorial from SingleStore dives deep into this emerging field. It's not just a guide; it's a treasure trove for those ready to unlock the true potential of generative AI in software engineering. I couldn’t agree more with the insights shared. As we stand on the brink of what could be the most transformative era in tech, understanding, and mastering prompt engineering is not optional. It’s essential. This is your next must-read for all AI/ML engineers looking to stay ahead of the curve. Navigating the generative AI revolution requires more than just knowledge. It demands mastery. Are you ready to harness the full potential of your LLMs? --- Are you curious to find valuable resources and more interesting AI content? Dive into our blog on www.ellogy.ai #ellogy #contentgeneration #AIsoftwaredevelopment #AIbusinessanalyst Check this out: https://lnkd.in/d9nnrK8a

As we witness the exponential growth of AI, it's clear that the future we once imagined is arriving faster than anticipated. The progression from one level of AI capability to the next isn't just a linear, it's a catalyst for even more rapid development. The Path to Level-3 AI Agents The AI community is anticipating a major leap in capabilities after Sam Altman's announcement at the T-Mobile Capital Markets Day 2024 (link for the full video in the comments), sparking discussions across the tech world: "The shift to Level 2 took time, but it accelerates the development of Level 3. This will enable impactful agent-based experiences that will greatly impact advancements in technology." But what does this mean? Understanding the Levels of AI Agents: - Level 1: Rule-Based Automation, basic AI agents that operate on predefined rules without learning capabilities. - Level 2: Adaptive Learning, agents that learn and improve within specific tasks or domains using machine learning. - Level 3: Generalized Intelligence, agents capable of understanding and achieving user goals across diverse environments, adapting to new situations without explicit programming. Reflecting on OpenAI's Vision Looking back at OpenAI's technical goals from 2016 (https://lnkd.in/drJm3RGS), developed by Ilya Sutskever, Greg Brockman, Sam Altman, and Elon Musk, we can see how far-sighted their vision was: - Goal 1: Developing a metric for measuring AI progress - Goal 2: Building a household robot - Goal 3: Creating an agent with useful natural language understanding - Goal 4: Solving a wide variety of games using a single agent These goals, once seeming distant, are now within reach or already achieved. The rapid progress in natural language processing and reinforcement learning has brought us to the cusp of a new era in AI capabilities. As we stand on the brink of Level-3 AI, we must consider the profound implications for technology and society at large. These agents have the potential to: - Revolutionize productivity across industries - Transform human-computer interaction - Accelerate scientific research and discovery - Reshape education and skill development The journey from concept to reality in AI has been nothing short of remarkable. The synergy between Level-2 and Level-3 Agents is accelerating progress and opening new horizons. What are your thoughts on the rapid progression of AI capabilities? How do you envision Level-3 AI agents impacting your industry or daily life?

Great post by LinkedIn on ins and outs of building a GenAI product. Many has this perception that LLM can solve all engineering and AI problem. It probably can but with a lot of engineering efforts, as shown in the post. The first time I built a GenAI application I was so excited that I got an “almost there” version immediately, without much effort. Naively I thought I could easily get to the “this is what I want” version in a few days. This turned out to be a continuous effort for a couple of weeks. Just like the post mentioned the first 80% is easy to achieve and to bring it to surpass a 95% requires a much longer time frame. And this is a point that many enterprises (sometimes even developers) do not realise. Just like traditional ML applications, building with LLM also requires technicalities which might look different from what we usually do. Not forgetting the prompting part takes many trials to get to a stable state. If you are building with LLM or Gen AI or just want to know the good and the ugly, go through this post and let me know which points resonated with you! #llm #generativeai #largelanguagemodel #promptengineering

Great breakdown! 🚀 I’ve worked with both frameworks, and I totally agree. #PyTorch has been a lifesaver for quick experimentation and debugging in my research projects, while #TensorFlow’s robust production environment makes it ideal for scaling AI solutions. Curious to see how both evolve with the growing focus on multimodal models. What’s everyone’s go-to for production—#TensorFlow or #PyTorch?