OEM Update Magazine’s Post

According to Honeywell's recent Industrial AI Insights global research study, nearly a third (29%) of Indian companies have already fully launched their artificial intelligence strategy, and 27% see data as their most important asset. “It's particularly encouraging that 57% of Indian respondents see AI as a catalyst for new revenue streams, significantly higher than the global average of 46%. It's clear that Indian industries are not just adopting AI, but leveraging it as a powerful growth engine,” shared Ashish Modi President, of Honeywell India with NDTV Profit. Read more: https://bit.ly/4digGMo Learn more about the global research study here: https://hwll.co/cwudr9sm #automation #AI #research #industry #technology #innovation #talent #skills #growth #digitalization #transformation #India Ashish Gaikwad Atul Pai Ranjit Kulkarni Niranjan Kalyandurg Anita Vijaykrishnan Rahul Khare PRAVEEN SHETTY Ritika Thakur Deepak Sivanandan Deepak Bisht Chaitanya Paspuleti Kamesh Keerti MANDEEP SINGH Chris Martin Mike Hockey Payal Gupta Dipankana Paul Jasmeet Bhatia Pradeep P Balijepalli Ankur Malhotra Kotresh Jogikalmath Hitesh Saini Nikhil Arora Ramanathan Ganapathy Subramanian Balaji Shankar G Pulkit Goyal Anamika Lakra Krishna Mani Meena VS Rahul Sule suju thomas Mahesh Joshi (MJ) Vij, Deepti Capt. Raman Raina Jinsy Elizabeth Raju Vandana M Sweta Mohanty

Accenture is helping companies utilise the power of Gen AI while keeping data confidentiality and security in check. A very insightful session! #accenture #hightech #genai

High Tech & Semicon Generative AI (Gen AI) workshop at Accenture Industrial Design office in Eindhoven. Exploring and hearing about relevant Gen AI use-cases from / for the High Tech and Semiconductor industry by Misja, Bart & Nadine. Think about: 📍Chip design: Optimize layouts for efficient semiconductor manufacturing. 📍OEE improvement: Proactive maintenance and downtime reduction strategies. 📍Quality Control: Anomaly detection ensures high-quality production. 📍Yield enhancement: AI-driven process optimization for increased manufacturing yield. 📍Fault detection: Early identification and diagnosis of manufacturing faults. 📍Code generation: AI automates code writing, streamlining software development. 📌 Besides relevant use-cases, also address Workforce readiness, IP infringement, legal implications, Bias, Inaccuracy, Misinformation, Proprietary and confidential company information, Protection of AI outputs and security #Accenture #Semicon #Semiconductor #SemiconductorIndustry #GenAI #Ai #artficialintelligence #GenerativeAI Nick Adjay Pei-Ying Guido Joost Eugénie Daniel Mateusz Li Wouter Yordana Frank Ashoka S. Menne Edward Marco Marlou Carmen

An industry topic which seems to be rearing it's head at most Manufacturing events is AI, Smart Manufacturing, and Industry 4.0 👷♀️. There are still many that view the change as a threat or daunting, with individuals citing that we'll see a shortage of jobs for humanity as a result. However, there are some really interesting viewpoints that argue differently and I thoroughly enjoyed a recent relevant discussion during the SUBCON and Smart Manufacturing & Engineering Week at the NEC - National Exhibition Centre 💭. Some discussion highlights for me were; ✔ With more than 2 million manufacturing jobs expected to remain unfilled by 2030, AI can add, rather than eliminate, new jobs across industrial sectors and empower employees to realize the potential of Industry 4.0. ✔ Skilled labour shortages can be caused by skills taking 3-5 years to train, AI can shorten this process to a few months. ✔ Technology can create heaps of new labor roles, specifically within manufacturing e.g Implementation, Research, Maintenance, Operators, Security, Computing, Data Scientists, Software Development and many more. Is AI a hot topic within your business? It seems the industry is going that way naturally, and quickly! #manufacturing #AI #smartmanufacturing

"AI won't take your job, someone using AI will". I have seen this line from two mutuals here recently and my question was "𝙬𝙝𝙖𝙩'𝙨 𝙩𝙝𝙚 𝙙𝙞𝙛𝙛𝙚𝙧𝙚𝙣𝙘𝙚?" Later, I added "𝙩𝙝𝙚𝙧𝙚 𝙞𝙨 𝙖 𝙩𝙬𝙞𝙨𝙩 𝙩𝙤 𝙞𝙩" Let's talk about it. 𝙎𝙥𝙤𝙞𝙡𝙚𝙧 𝙖𝙡𝙚𝙧𝙩: this post is not meant to pun or dunk on anyone. I don't know the writer's outlook on the topic. I'm only sharing mine. The posts were geared toward the energy (oil and gas) industry so I will be on the same keep to it. If AI won't take our job but someone using it, then for every number of engineers (say 10) who didn't integrate AI into their work, an equal number of engineers who do are supposed to replace them; otherwise, it's not adding up. I mean, If 10 engineers who are not AI-oriented are to man a station today, with the introduction of AI/ML devices on the same plant tomorrow; 10 engineers who know how to use AI should replace the previous 10. Only then can that line hold. Which I think is unlikely to happen. If that line is about the business, management, or the non-technical side of things, I will likely not agree less. But how can we talk about integrating AI into engineering without being at least a bit technical? Before AI/ML, the design of autonomous devices was a thing. Think of devices like an Automated Teller Machine (ATM) and how many supposed staff it took their place. Think of how many operators would have been needed to do the task of a control system (PLC) that regulates control valves without human intervention. Today, fewer operators are needed just to supervise the process. I worked as a Control Room Operator and I strongly believe that with IOT (which was mentioned too), AI will drastically reduce the number of operators needed if not replace them. In other words; If 2 AI-armed staff will replace 10 staff who are not, how can we say that "those who use AI replace them but not AI"? There is a twist there🙂. Maybe we should start reading between the lines and see the deeper meaning of things. "AI won't take your job, someone using AI will", is it totally true for something heavily technical like engineering? #ai #job #oilandgas #industry #ml Image credit: Noyan (LinkedIn)

Tech Mahindra has unveiled Project Indus, a groundbreaking initiative to develop an indigenous large language model (LLM) focusing initially on Hindi and its 37+ dialects. This project marks a significant step towards integrating advanced AI across various industries globally. Using the 'GenAI in a box' framework, which simplifies the deployment of AI models, Project Indus leverages Dell Technologies’ high-performance computing and Intel’s infrastructure solutions, including Intel® Xeon® Processors and Intel® Gaudi® AI Accelerators. This collaboration with Dell and Intel Corporation is set to redefine AI-driven solutions, creating industry-agnostic applications across sectors such as healthcare, education, finance, agriculture, and telecom. Nikhil Malhotra, Global Head of Makers Lab at Tech Mahindra, highlighted the project’s potential to drive innovation and operational excellence. With an estimated market growth to $1.3 trillion in the next decade, Project Indus could significantly impact how businesses leverage AI for customer support, content creation, and more, promoting accessible, scalable, and innovative AI deployments.  #AI #Innovation

Artificial Intelligence (AI) plays a pivotal role in the **semiconductor industry**, revolutionizing various aspects from design and manufacturing to supply chain management. Let's delve into how AI impacts this critical sector: 1. Design and Manufacturing : - **AI-driven design tools** enable faster and more efficient chip development. These tools optimize layouts, reduce power consumption, and enhance performance. - **Machine learning algorithms** analyze vast datasets to predict chip behavior, identify defects, and improve yield during fabrication. - **Process optimization**: AI helps streamline manufacturing processes, leading to higher productivity and cost savings. 2. Supply Chain Management: - **Predictive analytics** powered by AI enhance supply chain visibility. Companies can anticipate demand fluctuations, optimize inventory, and reduce lead times. - **Quality control**: AI algorithms detect defects early in the production process, minimizing waste and ensuring high-quality chips. - **Risk assessment**: AI models assess risks related to supply disruptions, geopolitical factors, and market dynamics. 3. Applications of AI in Semiconductors: - **Embedded AI**: Semiconductors are used in embedded AI applications such as **facial recognition**, **speech-to-text**, and **personal assistants**. - **High-Performance Computing (HPC)**: AI technologies drive complex simulations, data analytics, and decision support functions in HPC systems. - **Memory and Storage**: With the explosion of data, storage solutions witness significant growth, creating opportunities for value creation in the semiconductor industry. 4. **Global Landscape**: - While countries like **Taiwan**, **South Korea**, **Japan**, **USA**, and **China** lead in semiconductor production, no country is entirely self-sufficient. - The semiconductor industry remains capital-intensive, technology-driven, and reliant on skilled manpower. - Innovative processes, AI adoption, and strategic partnerships are essential for sustainable growth. In India, there's a growing focus on semiconductor research and development. As the tech revolution continues, India aims to strengthen its position in the global race for AI-driven semiconductor dominance. #ai #semiconductorindustry #semiconductor #india

#Technology advancements have carried #AI beyond the familiar areas of voice and image recognition to many new industrial applications. Heavy industry markets such and power, O&G, manufacturing, and others, are applying #artificalintellligence to improve factory productivity and inspection and maintenance. Check out this #Toshiba clip to hear from one of our engineers who is part of the team helping to support efforts. Learn more: https://lnkd.in/eXBbTBug #IIOT #energyinnovation #artificialintelligence

AI is a tool that all engineers should get familiar with. It's a great opportunity to automate routine task. Efficiency is the name of the game.

The field of engineering is undergoing a significant transformation, thanks to the advancements in Artificial Intelligence (AI). From automating routine tasks to solving complex engineering challenges, AI technologies are enhancing efficiency, accuracy, and innovation across various engineering disciplines. One of the most notable impacts of AI in engineering is its ability to process and analyze vast amounts of data faster than ever before. This capability enables engineers to make more informed decisions, optimize designs, and predict potential issues before they arise. Moreover, AI-driven tools and algorithms are facilitating the development of smarter, more sustainable engineering solutions that can adapt to changing conditions and requirements. Looking ahead, the integration of AI in engineering promises to unlock even greater possibilities. With the continuous evolution of AI technologies, we can anticipate more sophisticated applications that will further revolutionize the way engineers work and the solutions they create. As we stand on the brink of this exciting frontier, it's clear that AI advancements will continue to shape the future of engineering in profound ways. Embracing these technologies and exploring their potential will be key to driving innovation and achieving new levels of success in the engineering field.