LG releases open source AI model Exaone Deep

Artificial Intelligence (AI) is transforming society through advanced algorithms and data structures, which enable machines to learn, adapt, and make decisions. Innovations such as deep learning, federated learning, and quantum computing are driving AI's evolution, enhancing efficiency and scalability. As AI systems grow more sophisticated, demands for innovative data structures rise to manage complex datasets. These advancements promise to revolutionize industries like healthcare and finance, but also raise ethical concerns about fairness and transparency. The balance between technological progress and ethical standards will be crucial as AI continues to shape the future. #ArtificialIntelligence #AIAlgorithms #DataStructures #DeepLearning #NeuralNetworks #FederatedLearning #QuantumComputing #EthicalAI #ExplainableAI #FutureOfAI

Artificial Intelligence (AI) is transforming society through advanced algorithms and data structures, which enable machines to learn, adapt, and make decisions. Innovations such as deep learning, federated learning, and quantum computing are driving AI's evolution, enhancing efficiency and scalability. As AI systems grow more sophisticated, demands for innovative data structures rise to manage complex datasets. These advancements promise to revolutionize industries like healthcare and finance, but also raise ethical concerns about fairness and transparency. The balance between technological progress and ethical standards will be crucial as AI continues to shape the future. #ArtificialIntelligence #AIAlgorithms #DataStructures #DeepLearning #NeuralNetworks #FederatedLearning #QuantumComputing #EthicalAI #ExplainableAI #FutureOfAI

A truly innovative paper: "Less is More: Recursive Reasoning with Tiny Networks." This research presents the Tiny Recursive Model (TRM), a groundbreaking approach to solving complex problems like Sudoku, Maze pathfinding, and ARC-AGI puzzles using unprecedentedly small neural networks. The findings demonstrate a highly promising new direction for the future of resource-efficient and robust AI. 🔍 Key Takeaways: 🧠 TRM massively outperforms LLMs and prior methods (like HRM) on hard puzzle benchmarks. For example, it achieves 87.4% test accuracy on Sudoku-Extreme, up from 55.0% by HRM. On the difficult ARC-AGI-2 benchmark, TRM obtains 7.8% accuracy, which is higher than results from large models like Gemini 2.5 Pro (4.9%). 💡 Extreme Parameter Efficiency: TRM achieves these state-of-the-art results using a single tiny network with only 7 million parameters. This is less than 0.01% of the parameter count of many Large Language Models (LLMs). 🛠️ Simplified Architecture and Training: TRM simplifies complexity by eliminating the need for the Implicit Function Theorem, fixed-point assumptions, and complex biological justifications relied upon by previous models like HRM. It achieves this by using a single, 2-layer network and a simplified Adaptive Computational Time (ACT) mechanism. 🚀 Optimal Design for Small Data: By leveraging deep recursion and deep supervision with tiny networks, TRM effectively bypasses significant overfitting penalties commonly seen when training large models on scarce data, maximizing generalization. I highly recommend exploring this crucial work which demonstrates that simplification and recursive refinement can unlock superior generalization. Read the full paper here: https://lnkd.in/ekvPeiqB Check out this article from VentureBeat on this paper: https://lnkd.in/erXHvWmE #AI #MachineLearning #ArtificialIntelligence #RecursiveReasoning #TinyModels #TRM #LLMs #ARCAGI #DeepLearning #Research #WithNotebookLM

Want to predict the next 10 years of AI? You have to understand the last 100.  This decade-by-decade breakdown (1920s-2020s) reveals the patterns in AI's rise, fall, and spectacular comeback. ✨1920s: The term “robot” appeared in Karel Čapek’s play, sparking the first public idea of self-sufficient intelligence, alongside the notion of the “computing machine.” ✨1930s:  Established the theoretical limits of computation with Alan Turing's Universal Turing Machine (UTM), which, in tension with Gödel's Incompleteness Theorems, defined the mathematical ceiling. ✨1940s: Norbert Wiener founded Cybernetics, while McCulloch and Pitts proposed the first logical model of artificial neurons, uniting control, communication, and feedback. ✨1950s: The decade that launched AI - from Turing’s 1950 paper and The Turing Test to the 1956 Dartmouth Conference, where John McCarthy and peers founded the field to pursue Artificial General Intelligence (AGI) via Symbolic AI. ✨1960s:  Featured the creation of ELIZA, which demonstrated the ELIZA Effect. Humans projecting comprehension onto machines based on fluent output while public apprehension was previewed in 2001: A Space Odyssey (1968). ✨1970s: The First AI Winter followed the Lighthill Report, which criticized Symbolic AI’s inability to handle real-world complexity. Ambitions shifted toward narrow, expert systems. ✨1980s: Defined by the Second AI Winter, caused by the brittleness and cost of maintaining commercial Expert Systems (e.g., XCON). Despite this, the decade revived neural networks through John Hopfield's work on Hopfield Networks which later earned him the Nobel Prize in 2024. ✨1990s: AI pivoted from rules to data. Machine learning took center stage, culminating in IBM’s Deep Blue defeating Garry Kasparov (1997), proving raw computational power could master human intellect in bounded domains. ✨2000s: Statistical AI (like SVMs) entered consumer tech. Pioneers Hinton, LeCun, and Bengio advanced neural networks, laying the groundwork for the deep learning era ahead through early 2000s. ✨2010s: Modern AI rose; the 2012 ImageNet moment and 2016 AlphaGo against Lee Seodal victory validated deep learning’s power. The 2017 Transformer architecture enabled massive parallelization and the birth of today’s large language models (OPENAI). ✨2020s: Generative AI solidified as a GPT, transforming communication and productivity. Breakthroughs like AlphaFold, billion-parameter LLMs (GPT-3), multi-modal models (DALL-E), and RLHF drove this shift. With feasibility proven, focus moved to governance and integration. "The climax of our current race toward AI may be either the best or the worst thing ever to happen to humanity, with a fascinating spectrum of possible outcomes."-  Max Tegmark For any queries, collaboration, or anything else, reach out to us at people.operations@stemonef.org Follow us here: YouTube: https://lnkd.in/gBVJQw9T LinkedIn: https://lnkd.in/gt-awacp © 2025 STEMONEF All rights reserved.

🚨 The AI game just changed forever. While everyone's obsessing over the next GPT update, MIT spinoff Liquid AI just dropped something that could make traditional LLMs look like yesterday's technology. Their new Liquid Foundation Models (LFMs) aren't just another incremental improvement—they're built on a completely different architecture called "liquid neural networks." Here's what's mind-blowing: ✅ LFM-1B dominated benchmarks like MMLU and ARC-C, setting NEW standards for 1B-parameter models ✅ They can handle up to 1 MILLION tokens efficiently with minimal memory usage ✅ While traditional LLMs need thousands of neurons, LFMs achieve the same performance with significantly fewer ✅ Perfect for edge deployments—think mobile apps, robots, and drones The kicker? Their smallest model (LFM-1B) is outperforming transformer-based models in the same category, while their largest (LFM-40B) can compete with much bigger models while maintaining superior efficiency. This isn't just about better performance—it's about making powerful AI accessible everywhere, from your smartphone to enterprise deployments. The AI revolution isn't slowing down. It's just getting started. What do you think—are we witnessing the beginning of the end for transformer-based models? #AI #MachineLearning #Innovation #Technology #LiquidAI #ArtificialIntelligence

🤖 Embracing the Power of Generative AI Artificial Intelligence is truly transforming the world — redefining industries, workflows, and the nature of work itself. While automation is replacing certain roles, it also opens new opportunities for those who learn to master and leverage AI effectively and efficiently. Generative AI, a cutting-edge branch of artificial intelligence, focuses on creating original content—from text and images to music and code—by learning patterns from existing data. Unlike traditional AI that primarily analyzes information, Generative AI produces novel and creative outputs through advanced algorithms, deep learning models, and neural networks. Its applications span across content creation, data science, and problem-solving in innovative ways. A special thanks to Scaler for offering valuable insights through the Masterclass on “How Generative AI is Changing the Role of a Data Scientist.” The session helped me grasp the fundamentals, prerequisites, and evolving landscape of data science in the age of AI.

SEPTEMBER 2025: The Month Agentic AI Got Faster and Smarter! The world of AI is moving faster than ever, and September 2025 delivered a wealth of breakthroughs specifically focused on AI Inferencing. We deep-dived into the top research papers, providing a detailed analysis. If you're building, deploying, or managing large-scale AI solutions for mission critical use cases, this analysis is mandatory reading. 👉 Read the full article here (Link Below): https://lnkd.in/gE3upvke #AgenticAI #AIInferencing #LLMOps #MLOps #AIResearch #LargeLanguageModels #DeepLearning #AryaXAI #AIEngineering

AI breakthrough - Samsung Semiconductor’s Tiny Recursive Model (TRM) just shattered assumptions about what’s possible with small neural networks. With only 7M parameters, TRM is matching or outperforming LLMs like GPT-4 and Gemini in advanced reasoning tasks, all while slashing costs and resource requirements. What does this mean for enterprise solutions, especially in customer service and support? • Cost savings - reduction in API fees or multi-cloud deployments. TRM is so efficient it can be self-hosted, dramatically lowering total cost of ownership. • Fast implementation - Its compact size allows rapid fine-tuning for organization-specific workflows, and integration with existing systems is much faster than with giant LLMs. • Customization and control - Enterprises gain transparency and privacy by hosting their own AI, tailoring models closely to compliance and data requirements. • Performance on real reasoning tasks - TRM’s recursive step-by-step approach enables logic-driven problems like support ticket resolution, where traditional LLMs can stumble. While generative LLMs remain the go-to for broad, creative tasks, recursive tiny models like TRM signal a new era for organizations that want efficiency, speed, and targeted intelligence at scale. #AI #EnterpriseAI #CustomerSupport #Innovation #MachineLearning Sources: - Forbes: https://lnkd.in/gxjT5Acy - Official TRM GitHub Repository: https://lnkd.in/giipa4cS - Research Paper: https://lnkd.in/gpC83MDP

Reflections from My Columbia CS Lecture: The Rise of Agentic AI This week, I had the pleasure of giving a guest lecture at Columbia University’s Computer Science Department on the rise of Agentic AI. We traced AI’s evolution across three decades: - 1990s: early neural networks that hinted at deep learning’s potential. - 2000s: embeddings and SVMs that taught us how representation and complexity could scale. - Today: foundation models trained on trillions of tokens — now powering agents that can reason, plan, and act. Agentic AI marks a shift from static generation to autonomous action. Frameworks like ThReaD and WMA-Agents show how systems can decompose tasks, simulate outcomes, and coordinate through APIs instead of fragile GUIs. With that autonomy comes new questions around alignment, governance, and security — from universal ethics down to organizational rules. At Foothill Ventures, we see open space across the Agentic AI infrastructure stack — inference, memory, data, reasoning, and execution layers — as well as vertical opportunities where products are 10× better, uniquely advantaged, and enabling new capabilities. My thanks to the Columbia University CS students and Prof Junfeng Yang for the thoughtful questions and spirited discussion during the seminar as well as the full afternoon of office hours — a reminder of how much curiosity and creativity still drive this field forward.

🛠️⚡️🌍 Efficient AI instead of “ever bigger” – why optimization is the new supercomputing As an engineer, my heart beats for efficiency and optimization – for systems that don’t just get bigger, but smarter. And that’s exactly what we’re seeing now: Samsung’s Tiny Recursion Model (TRM) proves that intelligence doesn’t require gigantism. Developed by Alexia Jolicoeur-Martineau, Senior AI Researcher at the Samsung Advanced Institute of Technology (SAIT), Montreal, Canada. Only ~7 million parameters, yet outperforming models 10 000× larger. This isn’t an upgrade – it’s a quantum leap. 🧩 In visual terms If 1 parameter = 1 cm, then the TRM spans just 70 km – roughly the distance from Dortmund to Düsseldorf. By comparison, the Llama-3 model (405 B parameters) would stretch 4 million km – more than 10× the distance between Earth and the Moon. And yet, the small TRM beats some of the giants – like OpenAI’s o3-mini or Google’s Gemini 2.5 – using less energy, fewer parameters, and far more mathematical elegance. 💡 Why this matters so much The Neural Scaling Laws are saturating! More compute no longer guarantees greater intelligence. What once held true – “more GPUs = better AI” – is now being replaced by three design principles: 1. Architecture – smart modular structures activate only what’s relevant. 2. Recursion – the model improves itself iteratively, like an engineer refining a design. 3. Sparsity – only essential neurons compute; the rest remain silent. These principles replace brute force with smart design – delivering orders-of-magnitude efficiency gains without billions in GPUs. And yes – that makes my ecological heart happy too. This marks the break with the Neural Scaling Laws – and the beginning of an era where progress is driven once again by engineering craftsmanship, not raw computation. ⚙️💡 🌱 Why it goes far beyond technology Smaller, specialized models mean: less energy, greater sustainability, lower costs – and the democratization of AI. Just as DeepSeek was a Sputnik moment for U.S. LLMs, this could be the epochal shift that quietly reorganizes the AI world – with the force of a technological nuclear explosion. 💬 What’s your take? Are we standing at the threshold of an efficiency era, where small becomes the new big? 💬 Share your perspective in the comments – and follow me to explore how efficiency will revolutionize both the ecological and economic foundations of AI. In my next post, I’ll show how this development could burst the AI bubble – and why that might just be the most important breakthrough of our time. 🔗 Sources Samsung TRM Preprint: https://lnkd.in/encWMPTK Llama 3 405B Paper: https://lnkd.in/eGMb7pPP DeepSeek-V3 Technical Report: https://lnkd.in/ekC_f9yc VentureBeat – TRM outperforms models 10 000× larger: https://lnkd.in/eMJJafby