Nirmal Gaud’s Post

🌟 The "Intersection Effect" is a fundamental phenomenon where two dissimilar entities collide, creating a powerful outcome greater than the sum of its parts. Put simply: 1+1 = 11 Intersection Effect is everywhere around us. 🌈✨ Examples abound: 🏙️ In living spaces, diverse cities like San Francisco, New York, and London thrive with economic innovation due to their rich cultural tapestry. (Alesina et al.) 🎶 In music, maestro Yo-Yo Ma's Silk Road Ensemble blends traditional music from China to Spain, creating breathtaking performances. In AI: 🤖 Deep Reinforcement Learning marries Deep Learning and RL, spawning game-changing algorithms like DQN (Volodymyr Mnih koray kavukcuoglu Ioannis Alexandros Antonoglou Martin Riedmiller) from Google DeepMind. ⚛️ Stable Diffusion Models bridge Physics and ML, unlocking insights into particle diffusion with projects like DALLE and Sora (OpenAI). 📚 Large Language Models, where self attention mechanism is a mix of ideas from sequence-to-sequence models and word embeddings, solving for long-range dependencies. (Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan Gomez, Lukasz Kaiser, Illia Polosukhin) from Essential AI, Google DeepMind, OpenAI, Cohere, Character.AI and others At Frontiers Capital, we champion the "Intersection Effect" by nurturing Scientist Entrepreneurs, combining scientific genius with entrepreneurial spirit to birth groundbreaking innovations. 🚀 #IntersectionEffect #Innovation #FrontiersCapital (Image: Intersection Effect when a river meets an ocean creating an amazing variety of marine and land life. Source: Getty; Pika Labs)

AI & the Future of Free Will: Insights from My Latest Article in La Vanguardia As neuroscience increasingly reveals, our human brains are imperfect prediction machines. Our prefrontal cortex generates hypotheses and simulations, allowing us to make decisions even in abstract realms like language, mathematics, and philosophy. But AI takes this a step further. Its ability to process and analyze vast amounts of data at incredible speeds far surpasses human limits. In the mid-19th century, Arthur Schopenhauer, in his work On the Freedom of the Will, asserted that free will is an illusion. Human decisions are inescapably tied to the chain of cause and effect that governs the universe. Schopenhauer says that our decisions are predetermined by factors beyond our consciousness. In this sense, while some great minds have successfully predicted significant events, such as technological advances or political shifts, there are countless unpredictable moments that escape us—like the financial crisis of 2008, 9/11, or the COVID-19 pandemic. AI, however, is changing the game. Machines like Deep Blue and AlphaGo have already demonstrated their superior predictive capabilities in strategy games. And the leaps in AI’s predictive power extend beyond games; soon, AI will help us combat pandemics, anticipate natural disasters, and even improve our ability to make more informed economic decisions. Recently, two scientists at Google DeepMind were awarded the Nobel Prize in Chemistry for their groundbreaking work in predicting protein structures using AI, showing us just a glimpse of what’s to come. The optimist in me believes that AI’s growing capabilities can dramatically enhance humanity’s ability to solve complex global issues, from climate change to public health crises. While the rise of AI may affect some aspects of our free will, it also opens doors to unparalleled accuracy and insight in decision-making. Yes, the future may bring a world where our choices are increasingly driven by machine-generated predictions. But this doesn't have to be a cause for concern—it can also be an opportunity. As AI advances, it will allow us to anticipate and navigate challenges more effectively, transforming industries, improving well-being, and perhaps even giving us more control over our lives than we realize. Read full article: https://lnkd.in/d6cy5a4d #AI #FutureOfTechnology #ArtificialIntelligence #PredictiveAnalytics #Innovation

🌟 Day 12 of my #100DaysOfLearning Challenge! 🌟 Here’s what I delved into: 1️⃣ LeetCode Practice: Tackled 2 problems, sharpening those problem-solving skills and exploring efficient solutions. 2️⃣ Research on Diffusion Models:    - Stable Diffusion:     - OOTDiffusion builds on Stable Diffusion, a popular latent diffusion model.    - Uses a variational autoencoder (VAE) with an encoder and decoder to work in latent space.    - Incorporates a UNet trained to denoise Gaussian noise, conditioned by CLIP text encodings.    - Employs cross-attention to link image representations and text prompts, enhancing the generative process. 3️⃣ Project Progress:   - Continuing the OOTDiffusion project.   - Deepening my understanding and implementation of the diffusion model. 🚀 Excited about the journey ahead and the endless possibilities of diffusion models! 💡 #AI #MachineLearning #LeetCode #DiffusionModels #StableDiffusion #VAE #UNet #TechInnovation #OOTDiffusion #Research #LearningJourney  

🔍 First Principles: The Key to Unlocking Neuroscience-Based Reasoning Systems 🧠 In a world of complex problems, one of the most powerful tools for innovation is first principles thinking. Whether building rockets or developing neuroscience-based AI systems, this method is about breaking down challenges into their most essential truths and rebuilding them innovatively. So, how do the first principles of thinking apply to neuroscience-based reasoning systems? 1️⃣ Understanding the Brain's Core Mechanisms: When designing systems that mimic human reasoning, the first principle is understanding the fundamental components of the brain—neurons, synapses, and how they communicate. Instead of relying on existing machine learning models, neuroscientists explain How neurons process and store information. What are the core processes behind decision-making, memory, and learning? 2️⃣ Reconstructing from the Basics: Once we have these fundamentals, we build up. Just as Elon Musk asked, "What is a rocket made of?" we ask, "What is the brain doing at its simplest level?" By focusing on these essential truths, we're able to design more efficient, biologically inspired AI systems that can reason, learn, and adapt like humans. 3️⃣ Innovation by Recombination: Neuroscience-based systems don't just iterate on traditional AI models. By leveraging what we've learned from neuroscience and recombining it with modern computation, we're creating systems that go beyond the current boundaries of technology—whether it's in healthcare, predictive analytics, or human-computer interaction. 🔗 Why it Matters: First principles thinking doesn’t just lead to incremental improvements. It opens up entirely new possibilities, driving breakthroughs in both AI and neuroscience. As we continue to map the brain’s functions, using first principles allows us to strip away complexity, question assumptions, and focus on what’s most important: creating systems that think for themselves. By anchoring innovation in first principles, we’re not just building better AI—we're crafting human-like reasoning systems that have the potential to transform industries and improve lives. What complex problem are you tackling using first principles? Share your thoughts below! 💡👇 #FirstPrinciples #Neuroscience #ArtificialIntelligence #Innovation #AI #Neurotech #ProblemSolving #MachineLearning #TechForGood

Day 13: Case Study: The Analog Ecosystem Powering Dynamic Neural Networks Imagine a supply chain that organizes itself, aligned to shared goals in real-time. That’s what we accomplished in the Australian fresh produce industry—a system once weighed down by inefficiencies, waste, and unpredictability. It was fragmented, reactive, and out of sync. The challenge was clear: How do we fix it? The solution wasn’t just to tweak the system, but to reimagine it completely. We stopped thinking of it as a traditional supply chain and envisioned it as a living ecosystem. An analog ecosystem powered by collaboration. We introduced ‘One Touch,’ a simple yet revolutionary system that connected growers, manufacturers, wholesalers, and retailers in real-time. They could see exactly what was happening in the supply chain, at every moment. Inventory, demand, availability—all dynamically adjusting, like neurons responding to live data. The impact? Over a billion dollars saved. Waste slashed. Inventory moved faster. What was once a broken system is now self-organizing, adapting, and thriving. This is the power of thinking differently. When you create an ecosystem that evolves with the world, it transforms how we live, work, and trade. Now, we’ve taken that analog ecosystem and digitally twinned it with a Dynamic Neural Network. And this is just the beginning of what’s possible. #SupplyChain #Sustainability #Innovation #ai #machine #NeuralNetworks #BalancedEcosystemScorecard

In March 2016, the world witnessed a historic moment in the realm of artificial intelligence as AlphaGo, developed by DeepMind Technologies, faced off against world champion Go player Lee Sedol in a five-game match held in Seoul, South Korea. 🤖 The Challenge: Go, an ancient Chinese game known for its complexity, had long been considered the ultimate test for artificial intelligence due to its vast number of possible board configurations and strategic depth. 🔍 The Solution: AlphaGo, powered by cutting-edge deep neural networks and trained through reinforcement learning, showcased the potential of machine learning to tackle complex, real-world problems. With over 30 million positions analyzed per second, AlphaGo was poised to take on the best human players. 🏆 The Triumph: In a series of thrilling matches, AlphaGo emerged victorious, defeating Lee Sedol, one of the greatest Go players of his generation, in four out of five games. The victory marked a watershed moment in the history of technology and machine learning, demonstrating the power of AI to master tasks previously thought to be beyond the reach of machines. 📈 The Impact: AlphaGo's success sparked widespread interest and debate, igniting conversations about the implications of AI on society, ethics, and the future of work. Its groundbreaking achievements continue to inspire new breakthroughs in machine learning and push the boundaries of AI research. 💡 Key Numbers: Over 30 million positions analyzed per second. Four out of five games won against world champion Lee Sedol. Millions of viewers tuned in worldwide to witness the historic matches. AlphaGo's victory showcased the potential of AI to tackle complex, real-world problems. 🌐 The Future: The story of AlphaGo serves as a testament to humanity's ongoing quest to unlock the mysteries of intelligence and our evolving relationship with technology. As we continue to harness the power of AI, we pave the way for a future where man and machine collaborate to achieve new heights of innovation and understanding. #AI #MachineLearning #Innovation #AlphaGo #DeepMind #Technology #FutureOfWork

Study up on the “hidden” types of AI with Rudina Seseri, the founder and managing partner at Glasswing Ventures, as she joins #theCUBE’s Managing Director, Paul Gillin, at #Supercloud7. “When it comes to gen AI, if I were to speak to it in the context of the enablers, you're really talking about transformer and attention technologies and methods. Aside from those, we have an architecture called recurrent neural nets where it is a type of deep learning architecture,” shares Seseri. “With it, you can move the data bidirectionally. It has memory and is very powerful. You have sub architectures and techniques like PINNs, which stands for physics informed neural nets, where you can bring the boundaries of the physical world to the algorithms to mirror the physical world with incredible ramifications, ranging from life sciences to oil and gas. That has nothing to do with gen AI, but it's one of many examples of what else is happening in the AI world,” she adds. 📺 Gain more firsthand insights: https://lnkd.in/ggAzahGU

In the realm of artificial intelligence (AI), the Logic Theory Machine stands as a pioneering milestone, representing a significant leap forward in the quest for machine intelligence. Developed in the late 1950s, this innovative concept laid the groundwork for advancements in logical reasoning and problem-solving that continue to shape the field of AI today.

When internal combustion engine were created in early 19st century it was a complete black box. You just put fuel in and you get moving force produced out of it. Sounds ridiculous, right? This is the situation with modern AI in the early 21st century. You send input, get output, and no one knows how it happens. Imagine looking inside an engine with 132 billion gears randomly moving. Would you understand it? And that is why Anthropic's latest research is groundbreaking in AI. They found a system in the billions of neural connections in their LLM. They discovered that entities and concepts aren't tied to specific single neurons but are instead spread across whole network. This means no single neuron represents Scarlett Johansson; instead, groups of neurons do. And a neuron can be a part of multiple groups. With this knowledge, you can find neuron groups and related concepts, observing how the model thinks and views the world. Even more fascinating, you can manipulate the model's thinking process to focus on specific topics or answers, even breaking rules to produce harmful texts or reveal secrets. Researchers say this requires vast compute power, but the possibilities are invaluable. I believe that entity mapping might become essential for universal industry-grade models. The tools and knowledge acquired during this process might also shed light on how human brains work. Amazing, right?

🤯It is beyond the “next word prediction”..🪸 🔍 Came across a fascinating paper by Massachusetts Institute of Technology today! It studies LLMs working structure deeply 🧠✨ The research dives into the geometry of concepts in Sparse Autoencoder (SAE) feature spaces, uncovering remarkable structures: • Atomic scale: Crystal-like patterns (e.g., man:woman::king:queen) appear when semantic noise is removed. 💎 • Brain scale: Functional “lobes” emerge, reminiscent of how regions in the brain specialize, like math or language. 🧠📚 • Galaxy scale: A fractal-like complexity revealed by eigenvalue distributions. 🌌✨ It’s incredible to see how these insights deepen our understanding of AI’s conceptual organization. Excited to think about the implications for interpretability and beyond! 📄 Here’s the link if you’d like to explore: 👀look comments #AI #DeepLearning #llm #SparseAutoencoders #Research