Jordi LLUIS’ Post

🌟 John McCarthy: The Father of AI John McCarthy, often called the "Father of Artificial Intelligence," was a pioneering computer scientist who first coined the term Artificial Intelligence (AI) in 1955. His work laid the foundation for AI as a field, and he is celebrated for organizing the historic Dartmouth Conference in 1956, where he brought together leading minds to establish AI as a unique discipline. Major Contributions: 1. Inventing LISP:👉 McCarthy developed LISP, one of the first AI programming languages, which is still used today for its flexibility in handling symbolic expressions. 2. Time-Sharing Concept: 👉 He proposed the idea of time-sharing, allowing multiple users to access a single computer. This concept revolutionized computing and eventually led to the development of cloud computing. 3. Stanford AI Lab: 👉 McCarthy founded Stanford’s AI Laboratory, fostering research in areas like machine learning, autonomous systems, and robotics. Legacy: 👉 McCarthy’s vision for machines that could reason, solve problems, and use common sense inspired future generations of AI researchers. He was awarded numerous honors, including the Turing Award and the National Medal of Science, reflecting his enduring impact on technology and AI. John McCarthy’s work not only kickstarted the field of AI but continues to inspire advancements in computing and artificial intelligence across the globe 🚀 #AI #ArtificialIntelligence #Innovation #Technology #History

🚀 Transformers: Revolutionizing Machine Learning and AI 💡 In the dynamic world of artificial intelligence, Transformers have emerged as a groundbreaking architecture, transforming how we approach complex machine learning tasks. 🌐 🎯 Why Transformers Matter: 1️⃣ Unprecedented Scalability: Handle massive datasets with sophisticated attention mechanisms 2️⃣ Versatility: Excel across domains like NLP, computer vision, and multimodal learning 3️⃣ Parallel Processing: Enable efficient computation through self-attention mechanisms 🛠️ Key Transformer Optimization Techniques: - Efficient Attention Mechanisms: Sparse and linear attention variants - Model Pruning: Remove redundant parameters without sacrificing performance - Quantization: Reduce model size and computational complexity - Distributed Training: Scale across multiple GPUs and machines 💡 Pro Tip: Transformer architecture isn't one-size-fits-all. Carefully select model size, architecture, and optimization techniques based on your specific use case. The transformer revolution continues to push the boundaries of what's possible in AI. 🌈 💬 What transformer innovations are you most excited about? Share your thoughts! #TransformerAI #MachineLearning #DeepLearning #AIInnovation #ModelOptimization

🚨Paper Alert 🚨 ➡️Paper Title: DrEureka: Language Model Guided Sim-To-Real Transfer 🌟Few pointers from the paper 🤖Transferring policies learned in simulation to the real world is a promising strategy for acquiring robot skills at scale. 🤖However, sim-to-real approaches typically rely on manual design and tuning of the task reward function as well as the simulation physics parameters, rendering the process slow and human-labor intensive. 🤖In this paper authors have investigated the usage of  Large Language Models (LLMs) to automate and accelerate sim-to-real design. 🤖Their LLM-guided sim-to-real approach requires only the physics simulation for the target task and automatically constructs suitable reward functions and domain randomization distributions to support real-world transfer. 🤖Authors first demonstrated that their approach can discover sim-to-real configurations which are competitive with existing human-designed ones on quadruped locomotion and dexterous manipulation tasks. 🤖Then, authors showcased that their approach is capable of solving novel robot tasks, such as quadruped balancing and walking atop a yoga ball, without iterative manual design. 🏢Organization: University of Pennsylvania, NVIDIA, The University of Texas at Austin 🧙Paper Authors: Jason Ma, Will Liang, Hungju (Johnny) Wang, Sam Wang , Yuke Zhu, Jim Fan, Osbert Bastani, Dinesh Jayaraman 🤖Robot Used :  Unitree Robotics Go1 1️⃣Read the Full Paper here: https://lnkd.in/gU2Rzxmw 2️⃣Project Page: https://lnkd.in/gXX2KtgB 3️⃣Code: https://lnkd.in/gezTBeqn 🎥 Be sure to watch the attached Demo Video-Sound on 🔊🔊 Music by Roman Senyk from Pixabay Find this Valuable 💎 ? ♻️REPOST and teach your network something new Follow me 👣, Naveen Manwani, for the latest updates on Tech and AI-related news, insightful research papers, and exciting announcements.

Thoughts on DeepSeek and Wall Street reaction: DeepSeek's innovation in reducing training time for foundational models marks a significant advancement in AI development. By building upon OpenAI's concept of test-time scaling through reinforced learning, DeepSeek has potentially slashed training costs by 50x. This breakthrough demonstrates the viability of test-time scaling, paving the way for more cost-effective AI solution deployments, even at the edge. Wall Street's skeptical response on future compute needs echoes historical reactions to transformative technologies, reminiscent of the infamous misattribution to Bill Gates: "640k ought to be enough for anybody." We are now firmly in the age of accelerated computing, with Large Language Models (LLMs) representing just one facet of this revolution. As we venture into physical world automation like self-driving vehicles and robotics, computational demands are escalating exponentially due to the complexities of 3D environments and physics simulations. While test-time scaling may catalyze innovation in virtual automation, it does not diminish the overall need for accelerated computing. On the contrary, we are entering an era where compute and energy requirements will continue to surge as we grapple with increasingly complex, multi-dimensional challenges in AI and automation. The future demands for computational power are likely to far exceed current expectations, much like how the need for memory quickly outpaced the once-thought-sufficient 640KB. #ShaktiInvestmentGroup

John McCarthy (1927-2011), the pioneer who coined the term "Artificial Intelligence" in 1956, forever changed the course of technology. His groundbreaking work led to innovations we rely on today, including self-driving cars, virtual assistants, and advancements in machine learning. Key Contributions: Developed the Lisp programming language, a foundation for AI research. Pioneered the idea of machines simulating human intelligence, a vision that drives today’s most cutting-edge technologies. McCarthy’s legacy continues to inspire and drive the future of AI, shaping industries across the globe. His contributions remind us of the power of innovation to transform our world. #JohnMcCarthy #ArtificialIntelligence #AI #Innovation #MachineLearning #TechPioneer #DigitalTransformation #SpentDigitalLabs #AIImpact #TechLegacy

While we are busy taking advantage of the massive advancements that have taken place in the field of AI over the past decade or so, there were 4 visionaries and influential people who laid the foundations for the modern AI Alan Turing Known as the “father of modern computing”, Turing proposed the concept of a “universal machine” that could carry out calculations based on a set of instructions. His work laid the groundwork for modern computers and the development of AI. His “Turing Test” is still used today as a benchmark for a machine’s ability to exhibit intelligent behavior. John McCarthy McCarthy is credited with coining the term “Artificial Intelligence” in 1956. He also developed Lisp, a high-level programming language, which is widely used in AI research. He proposed the concept of time-sharing, allowing multiple users to use a computer simultaneously, which is a fundamental principle of modern computing. Arthur Samuel Samuel was a pioneer in the field of machine learning. He developed a program that could play checkers and improve its performance over time, which is considered one of the earliest examples of self-learning programs. Marvin Minsky Minsky was a co-founder of the MIT’s Media Lab and made several contributions to AI, cognitive psychology, mathematics, computational linguistics, robotics, and optics. He built the first neural network simulator and wrote several texts on AI and philosophy. These individuals who were way ahead of their time, made significant contributions to the field of AI, laying the groundwork for many of the advancements we see today. It is their work has shaped the evolution of AI over the past 50-60 years. Let us never forget. I am 100% sure, that I may have missed out many other individuals from the past who would have played an equally important role in the advancement of AI. If you know about those individuals, do drop their names in the comments, I would be keen to read up and post about them too. #aileaders #AIvisionaries #techinnovation #aicommunity #aistartups

Peter Norvig, a prominent figure in artificial intelligence, is known for his concise and insightful definition of AI: "Programming to make the computer do the right thing when we don't know what the right thing is."

Imagine teaching your AI model new information without needing astronomical computing power. That’s where LoRA (Low-Rank Adaptation) comes in—an efficient way to fine-tune models for specific tasks while preserving their core strengths. LoRA enables researchers and developers to create specialized models for creative writing, coding, or even mathematical proofs, making it easier to adapt AI for a wide range of needs. As an added bonus, see how Greg Robison uses LoRA to transform images of his adorable puppy into a pirate. And a Jedi Master. And a guest star on Seinfeld. #LoRA #AIModels #MachineLearning #AIDemocratization #ModelTraining https://lnkd.in/ghqnsTDE

AI Engineering - The Book has just been released by @chipro This is the most important book for developers right now. https://lnkd.in/eGK-s7QB It's no surprise that a large portion of it is dedicated to evaluation. As developers we have a ton to learn about building robust products with non-deterministic LLMs.

The uploaded file is an image depicting a humanoid robot contemplating in front of a chalkboard filled with mathematical equations, graphs, and scientific notations. The scene symbolizes artificial intelligence (AI) or machine learning (ML) in action, showcasing concepts like data analysis, problem-solving, and algorithmic thinking. The image reflects AI’s role in understanding and processing complex systems, and its potential applications in advanced mathematics, analytics, and decision-making processes.