Kumar Singh

📦 Packing & Unpacking: Research Insights from MIT CTL 💡 Matthias Winkenbach, Director of Research at MIT CTL, recently sat down with MIT News to answer three questions about "Enhancing last-mile logistics with machine learning". 🤖 🧠 The article explores the many uses of AI "to make vehicle routing more efficient and adaptable for unexpected events" by unpacking 3 essential questions: 1️⃣ What is the vehicle routing problem, and how do traditional operations research (OR) methods address it? 2️⃣ How is [MIT CTL] employing machine learning to leverage and possibly outperform traditional OR methods? 3️⃣ What advantages does a method like [MIT CTL's] have over other state-of-the-art OR techniques? 👀 📖 Click the link to read the full interview with MIT News: https://lnkd.in/dQt2MxWj #SupplyChain #SupplyChainManagement #SupplyChainInnovation #ResearchCenter #Logistics #AI #ArtificialIntelligence Massachusetts Institute of Technology

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Collaborative Robotics hires a former Amazon AI leader, opens Seattle office after $100M round Collaborative Robotics, the Santa Clara, Calif., company founded in 2022 by former Amazon Robotics VP and Distinguished Engineer Brad Porter, has hired Michael Vogelsong, who co-founded Amazon’s Deep Learning Technologies team. Vogelsong will lead the company’s new Foundation Models AI research team from a newly opened Seattle office in an emerging AI corridor on the north side of Lake Union, close to the University of Washington and the Allen Institute for AI. In addition, the company is making a grant to the UW Allen School of Computer Science & Engineering to support research led by Sidd Srinivasa, a professor and former Amazon exec who is also an advisor to Cobot. Collaborative Robotics, also known as Cobot, is building a yet-to-be-revealed mobile collaborative robot that is already in use at a transload facility in the Seattle region as part of a global shipping and logistics operation. Porter, the Collaborative Robotics CEO, oversaw the development and deployment of hundreds of thousands of robotic units at Amazon. He describes Cobot’s robot as “human-capable, but not humanoid,” with fewer design constraints than those that mimic the human form. Competitors in the broader field of AI and robotics include Covariant, Figure AI, Agility Robotics, 1X, Sanctuary AI, and others. Collaborative Robotics’ expansion, which follows its $100 million Series B funding in April, illustrates Seattle’s rise as an AI hub, and the broader implications of fundamental AI advances for a new wave of robotics technology. https://lnkd.in/eH3-wUVD

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“By coupling simulation and machine learning, predictive models can be developed with limited historical data” Using accurate simulation to create synthetic data makes it possible to create as much data as required, covering as many and various scenarios as needed. Neural Network systems can thus be trained beyond what the system under study has actually encountered in the past. This is the major angle of attack of our article in International Journal of Production Research. Well done Tianyuan ZHANG, Matthieu Lauras, Greg (Gregory) Zacharewicz, Souad RABAH! KEDGE Business School, IMT Mines Albi, Ecole des Mines d'Alès https://lnkd.in/eAbBBCHc

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Blog Friday 🧠 Deep learning is revolutionizing AMRs, allowing them to perceive and adapt to their surroundings like never before. With computer vision at its core, robots can analyze environments, avoid obstacles, and optimize workflows. See how this innovation is transforming supply chains here, read more on our blog here: https://lnkd.in/gUWVzGxc   #ForwardXRobotics #AMRs #Automation #Deeplearning

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Mastering the Future: How Forecasting Simulation Propel Success In the fast-paced world of manufacturing, staying ahead of the curve is crucial. Accurate forecasting and advanced manufacturing simulations are indispensable tools for predicting market trends and optimizing production processes. This blog delves into the intricacies of forecasting, explores how manufacturing simulation supports it, and provides actionable insights for industry professionals. Read Complete Blog : - https://lnkd.in/d87FQZeH #leansimcorp #beawarechannel #simulation #industry #manufacturer #operations #projectmanagement #management #learn

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Interesting read! https://lnkd.in/eK2vyHCf #AI #artificialintelligence #deeplearning #machinelearning #ML #DL #deepreinforcementlearning #vendormanagement #supplychain #supplychainmanagement

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In the fast-evolving world of large language models (LLMs), designing efficient AI agents has become a critical focus. Anthropic has recently shared five impactful design patterns that are successfully powering real-world applications, particularly with Claude models. Discover the full breakdown of these innovative patterns and how they’re transforming LLM capabilities. 🚀 👉 Read more: https://lnkd.in/gszRNdbr . #LLM #ArtificialIntelligence #AIAgents #DesignPatterns

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💙 Great article from Satyajit Chaudhuri on How to MLForecast your Time Series Data! It's a comprehensive and helpful walkthrough of MLForecast 🤖 with implementation details, context on the forecasting process and a case study. Thanks Satyajit Chaudhuri for this excellent explanation and guide! 🙏🏽 https://lnkd.in/gD4QpWJx

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LEAN-GitHub: A Large-Scale Dataset for Advancing Automated Theorem Proving Researchers from The Chinese University of Hong Kong propose LEAN-GitHub, a large-scale Lean dataset that complements the well-utilized Mathlib dataset. This innovative approach provides an open-source Lean repositories on GitHub, significantly expanding the available data for training theorem-proving models. The researchers developed a scalable pipeline to enhance extraction efficiency and parallelism, enabling the exploitation of valuable data from previously uncompiled and unextracted Lean corpus. Also, they provide a solution to the state duplication problem common in tree-proof search methods. Read our take on this: https://lnkd.in/gGnt8XmK Paper: https://lnkd.in/gKAYmiZa Dataset: https://lnkd.in/gCZYWA6M

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AI Sees, Hears, and Reads: Multimodal Large Language Models (MM-LLMs) Large Language Models (LLMs) are great with text, but what if AI could understand the world like us - through sight, sound, and text? This is where MM-LLMs come in! Find out more: https://lnkd.in/gAQstRzH #MultimodalLargeLanguageModels #AI #MachineLearning #Tech #ArtificialIntelligence #NaturalLanguageProcessing #PrayingMantis #DeBug

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🧠 Curious about the business value of multimodal AI? Check out KX data scientist Ryan Siegler's latest blog.​ Unlike traditional unimodal systems, multimodal AI integrates diverse data sources to deliver nuanced and sophisticated outputs, much like how we as humans process multiple sensory inputs simultaneously.​ What does this mean when it comes to business?​ 📊 Leveraging your entire data estate for model training and RAG pipelines leads to analytics and predictions that harness all available data, regardless of modality.​ 🤖 This capability empowers you to perform enterprise searches or create chatbots that analyze text, images, tables, and graphs across all your documents and materials.​ ⚡ The outcome? More contextually relevant responses, data-driven insights, and enhanced decision-making.​ Want to learn more? Read the full blog: ​ https://lnkd.in/e7Xej5ex​ #KX #AI #DataScience #MultimodalAI #DataAnalytics #RAG ​

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🤸‍♂️ Learn from Charles Xie's insights on the future of #vector #databases and #AI #applications at genaisummit.ai 🤸‍♂️ Charles Xie is the Founder & CEO of Zilliz, and a trailblazer in the AI infrastructure space, revolutionizing the way enterprises approach vector databases for their AI applications. Zilliz stands out in a competitive market by offering a fully-managed vector database service built on the popular open-source project Milvus, which has garnered over 26,000 stars on GitHub and 14 million+ downloads. Zilliz Cloud's optimized AUTOINDEX, blazing-fast vector retrieval, and high scalability make it an attractive choice for enterprises. With its focus on ease of use, high performance, and robust security, Zilliz has become the go-to vector database for over 5,000 enterprises globally. Charles' leadership has been instrumental in driving the company's growth and adoption across various industries. 🔥 Don't miss this opportunity. #GENAISummitSF2024. Tickets on sale at genaisummit.ai 🔥 #artificialintelligence #airevolution #machinelearning #deeplearning #futureofai #techinvestment #technews #techstartup #techleaders #techtrend #technology #tech #SF #sanfrancisco

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Data has been a main focus of MIT CISR research across the years. At MIT CISR’s inception, Prof. Stuart Madnick was developing new approaches to database architecture and the logical and technical connectivity of databases. He and MIT colleagues went on to research how to improve data quality and data security. In the late 1970s, Jeffrey Meldman began to study data privacy. In the 1990s, as organizations became increasingly aware of the value of data as a corporate asset, CISR’s director, John F. Rockart, launched a project focused on effective approaches to managing and leveraging corporate data. Based on 30 case studies of data management initiatives undertaken by organizations to solve concrete business problems caused or exacerbated by poor data, the study found a range of successful approaches contingent on business context. The results, including actionable insights for managers, were published in a seminal paper, “Managing the Data Resource: A Contingency Perspective” by Dale L. Goodhue, Judith Quillard and John F. Rockart, in MIS Quarterly, Vol. 12, No. 3, September 1998. Barb Wixom began studying data in 1994 during her PhD program, informed by Stuart and Jack's earlier work beginning a life-long passion for data research. Barb brought that deep data expertise to MIT CISR in 2012 and established the current ongoing data research stream, connecting past and present research. Data continues to be a strong foundational pillar of MIT CISR. See more about Managing the Data Resource at https://lnkd.in/e9MzeCvV Review MIT CISR data research at https://lnkd.in/ejUhAirz See more about "Data Is Everybody's Business" at https://lnkd.in/egU9Ujf3 Read more about Jeffrey Meldman at https://lnkd.in/e8aumS3F Read more about Jack Rockart at https://lnkd.in/e6GGQ-TK Read more about Dale Goodhue at https://lnkd.in/enr2-B2n Founded in 1974, MIT CISR is celebrating its 50th anniversary in 2024. To review other memories from the center's history, visit the 50 Facts from 50 Years page on MIT CISR's website at https://lnkd.in/eu7RdEes #MITCISR50th #MITCISR

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⛅ Behind The Cloud #3-2024 ⛅ "From Past to Future: Statistical Models in Time Series Forecasting" We are pleased to share with you our 3rd edition of #BehindTheCloud - - the weekly insights into the world of #ArtificialIntelligence prepared by the 'Omphalos AI Research Team'. Today, we dive a little bit deeper into the world of Times Series Forecasting (TSF) and its important role in our AI-driven investment strategies—aiming to keep it accessible for #AI newcomers. 📈 Read our third article to learn: +++ 3 fundamental statistical methods for TSF +++ Their strengths and weaknesses +++ And finally, the challenges of statistical models 👉 https://lnkd.in/ezsTHfT9 Stay tuned and gain insights from our team on how TSF enhances our ability to deliver consistent, risk-adjusted returns. Don’t forget: Only in data we trust. And if you missed our first editions of “Behind the Cloud”, please click here: https://lnkd.in/egtH_4u7 🏖 Have a sunny weekend! 🏃 #AIFunds #OmphalosFund #TSF #TimeSeriesForecasting #Data

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