Dr. Chengheri BAO’s Post

5 things you didn’t know about AI: The roots of AI trace back to the early 1950s when Alan Turing, a brilliant British mathematician, published "Computing Machinery and Intelligence," proposing the possibility of creating machines capable of human-like thinking and reasoning. Turing introduced the Turing Test to assess machine intelligence. The term "Artificial Intelligence" (AI) was coined in 1956 by computer scientist John McCarthy during a Dartmouth College conference, delineating a field dedicated to crafting intelligent machines akin to humans. In 1957, McCarthy, based at the Massachusetts Institute of Technology, developed LISP, a functional programming language tailored for AI algorithms. Prolog emerged in France in the seventies, serving a similar purpose. Expert Systems, characterized by rule-based collections, emerged in the 1960s, demonstrating remarkable effectiveness. IBM's Deep Blue showcased this efficiency by defeating chess world champion Garry Kasparov in 1997. In the late 1980s, MIT's Artificial Intelligence Lab pioneered insect-like robots such as Allen and Herbert. In 1990, Rodney Brooks and colleagues founded iRobot, creators of the famous Roomba robotic vacuum cleaners, commercially launched in 2002.

#Wednesdayseries #AISeries #FatherofAI #McCarthy 🤖 AI Wednesday: Celebrating the Father of Artificial Intelligence, John McCarthy! In recent years, "Artificial Intelligence" has become a buzzword across industries. But did you know that the field owes much of its foundation to John McCarthy? Often hailed as the "Father of AI," McCarthy's work continues to shape the landscape of AI today. 🧠 Who was John McCarthy? A pioneering computer scientist and cognitive scientist, McCarthy made groundbreaking contributions to AI. Here are a few fascinating highlights: 🔹 💡 Coined the term "Artificial Intelligence": In the 1950s, McCarthy defined AI as "the science and engineering of making machines that are smart." His definition still resonates today. 🔹 📅 Dartmouth Conference (1956): McCarthy organized this iconic gathering, often considered the "birthplace" of AI. Here, visionaries first conceptualized AI as a field of study, sparking research that would change the world. 🔹 🛠️ Invented Lisp Programming Language: Known for its power in AI applications, McCarthy’s Lisp remains foundational in robotics, scientific applications, and even internet services. 🔹 💻 Introduced Time-Sharing: He pioneered the concept of time-sharing, which allowed multiple users to interact with a central computer simultaneously—a breakthrough that reshaped computing. From defining AI to inspiring the research that drives today’s innovations, McCarthy’s contributions remain a bedrock of the AI field. Let’s celebrate his legacy! 🎉 #AI #ArtificialIntelligence #AIWednesday #JohnMcCarthy #Innovation #DartmouthConference #Lisp #ComputerScience #AIHistory #TechPioneers

🌟 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

Introducing pylon AI – the groundbreaking software designed for efficient image analysis using deep learning algorithms. Whether it’s object detection or segmentation, pylon AI makes complex image analysis tasks a breeze. One-of-a-kind among computer vision tools, our performance benchmarking feature empowers users to select the optimal processing hardware for their specific needs. Best of all, no programming skills are required.🚀 💥 Maximize Performance: Benchmark your AI model on various processors and AI chips. 💥 Optimize with Ease: Perfect your AI model for specific hardware, enhancing specific algorithms. 💥 User-Friendly Drag-and-Drop Interface: No coding needed, deploy with ease using pylon APIs. 💥 Modular Licensing: Pick the plug-ins you need for cost-effective, customizable solutions. 💥 Great Compatibility: Supports ONNX format from PyTorch, TensorFlow, and NVIDIA TAO. Embrace the new age of image analysis with pylon AI! 🌐💻 Learn more 👉 https://bit.ly/4eCNDns #AI #MachineLearning #ImageAnalysis #DeepLearning #ComputerVision

🚀NVIDIA released a paper on 𝙂𝙧𝙖𝙙𝙞𝙚𝙣𝙩 𝘽𝙤𝙤𝙨𝙩𝙞𝙣𝙜 𝙍𝙚𝙞𝙣𝙛𝙤𝙧𝙘𝙚𝙢𝙚𝙣𝙩 𝙇𝙚𝙖𝙧𝙣𝙞𝙣𝙜 (GBRL) 🚀 🌟 Here are the key takeaways:- ✅ Bridging GBT and RL: GBRL extends the strengths of Gradient Boosting Trees (GBT) into Reinforcement Learning, showing competitive performance across diverse tasks. 🌐 ✅ Innovative Tree-Based Actor-Critic Architecture: Introducing a shared structure for policy and value functions reduces memory and computational requirements, enhancing efficiency over millions of interactions. 🧠 ✅ High-Performance Implementation: A GPU-accelerated framework seamlessly integrates with popular RL libraries, offering a powerful tool for RL practitioners. ⚙️ ✅ Advantages in Structured Data: GBRL excels in environments with structured or categorical features, outperforming neural networks in such scenarios. 📊 ✅ Real-World Applications: The framework is designed for edge devices, making it suitable for tasks requiring lightweight implementations and interpretability, such as inventory management and traffic signal optimization. 🌍 ✅ Open Source and Collaborative: The implementation is available on GitHub, inviting collaboration and further innovation in the RL community. 🤝 ✅ Functional Gradient Descent: GBRL employs functional gradient descent to iteratively improve policy and value function approximations, ensuring robust learning even in high-dimensional environments. 🌳 ✅ Batch Learning for Stability: To address the non-stationary nature of RL, GBRL uses batch learning techniques, stabilizing training and allowing beneficial gradient directions to accumulate. 📈 🔗 Check out the full implementation: https://lnkd.in/gYBbGDjj Let’s drive the future of AI together! 💡 #ReinforcementLearning #MachineLearning #AI #GBT #NVIDIA #Innovation #OpenSource #TechCommunity

In recent AI benchmarking tests, Claude 3.5 Sonnet has demonstrated impressive performance across a variety of tasks, often leading in several categories. As per the benchmarks, Claude 3.5 Sonnet outperforms its predecessor and competitors, such as GPT-4o, in graduate-level reasoning, code generation, multilingual math, and grade school math. Meanwhile, a 59.4% in graduate-level reasoning teeters on the fine line between a failing mark and a passing mark. 🤭 In this AI race, Nvidia is poised to emerge as the undisputed winner, harnessing its superior computational power to rake in substantial revenue from AI companies. 💰 AMD, hello!!!😂

#IJCAIworkshop Workshop on No-Code Co-Pilots 👉 https://lnkd.in/dK4PkY2U 📅 Abstract Submission Deadline: 10 May The goal of this workshop is to bring together researchers from various disciplines, including programming languages, natural language processing, computer vision, knowledge representation, planning, human-computer interaction, and business process management, to chart a cross-disciplinary research agenda that will guide future work in the field of no-code copilots and the revolution that AI brings to it.

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.

Attended the AI Engineer World Fair 2024 Last Week! I had an incredible experience at the AI Engineer World Fair 2024. I was privileged to attend an array of insightful workshops and keynote sessions that have truly enriched my understanding and capabilities in AI engineering. 🔧 Workshops: 1. Architecting and Testing Controllable Agents: Explored methods to design and validate AI agents with controllable behavior. 2. Building with Anthropic's Claude - The Prompt Doctor is in: Learned advanced prompting techniques with Claude for effective AI interaction. 3. Knowledge Graphs & GraphRAG: Techniques for Building Effective GenAI Applications: Delved into the construction and application of knowledge graphs in AI. 4. LLM Quality Optimization Bootcamp: Focused on techniques to enhance the quality and performance of large language models. 🗣️ Keynote and Informative Sessions: 1. Opening Questions for AI Engineering: Addressed foundational and emerging questions in AI engineering. 2. Llamafile: Bringing AI to the Masses with Fast CPU Inference: Presented innovative ways to make AI accessible through efficient CPU inference. 3. Hasura Launch: Realtime Data Connectivity for AI: Introduced Hasura's solutions for real-time data integration in AI projects. 4. Building Reliable Agentic Systems: Discussed strategies for creating reliable and trustworthy AI systems. 5. LLM Judge: Explored the latest advancements in evaluating large language models. 6. Langchain New Innovations: LangGraph, LangGraph Studio, and Langraph Cloud: Unveiled new tools and platforms for enhanced AI development with Langchain. I am excited to apply these new insights and continue pushing the boundaries of what's possible with AI! #AI #AIEngineerWorldFair2024 #AIWorkshops #LLM #Langchain #LangGraph #Anthropic #KnowledgeGraphs #AIEngineering

🌟 Exciting News! 🌟 I'm thrilled to share that I've fine-tuned my Third Language Model (LLM) and hosted the model locally using Tesla T4 GPU for accelerated performance! 💻🚀 🔍 Prompt: "Why Cyient is Market Leader?" 💡 Result: Through fine-tuning the vrs-LAMA2_7b_chat_hf model, I received an insightful answer: Cyient is a market leader in the field of engineering and technology consulting services due to a number of factors: Expertise: Cyient boasts a team of seasoned engineers and consultants with a profound grasp of the latest technologies and industry trends. Innovation: Constant investment in research and development empowers Cyient to deliver cutting-edge solutions, staying ahead of the curve. Global Presence: With a footprint in over 15 countries, Cyient extends its services globally, catering to diverse client needs. Diversified Portfolio: Offering a spectrum of services from engineering to digital solutions, Cyient serves an array of clients across industries. Strong Partnerships: Cyient's robust partnerships enhance its offerings and strengthen its position as a market leader. 🚀 You can leverage my fine-tuned model for your tasks using this code snippet with GPU support: By Using the Code from the Comment I'm immensely proud of this achievement and grateful for the support of the AI community, OpenAI, and Hugging Face! 🙌 Check out the snapshot below for the prompt and the model's insightful response. 👇 #Cyient #AI #NLP #FineTuning #Engineering #Innovation #Technology #Leadership #LLM #generativeai #datascience #LAMA2 #huggingface