Gopal Ratnam

𝗘𝘅𝗽𝗹𝗼𝗿𝗶𝗻𝗴 𝗔𝗜 𝗶𝗻 𝗠𝗮𝗻𝘂𝗳𝗮𝗰𝘁𝘂𝗿𝗶𝗻𝗴: 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗳𝗿𝗼𝗺 𝗠𝗜𝗧 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 𝗥𝗲𝘃𝗶𝗲𝘄 🚀 The latest report from MIT Technology Review, conducted in partnership with Microsoft, sheds light on the transformative role of AI in the manufacturing industry. Here are some pivotal takeaways: 1. Increased Investment 💸: Major manufacturers are ramping up their AI investments, with a focus on enhancing areas like engineering and factory operations. The drive for innovation is clear, but the path to full-scale implementation is fraught with challenges. 2. Data Dilemmas 📊: A significant barrier to AI adoption is data quality and integration. Most companies find their data infrastructure inadequate for AI deployment, highlighting the urgent need for better data governance and architecture. 3. Talent Shortages 🧠: The lack of specialized AI talent is a critical obstacle. As AI becomes more integral to operations, the demand for skilled professionals continues to outpace supply. The integration of generative AI might ease this gap by simplifying the technology's application. 4. Organizational Hurdles 🏢: Fragmentation within companies hinders AI effectiveness. Aligning AI initiatives with business strategies requires overcoming internal silos and fostering interdepartmental collaboration. 5. Business-Centric AI 🎯: Success in AI requires a business-first strategy. Prioritizing practical applications that address specific business needs ensures that AI investments deliver tangible benefits. The journey to integrating AI in manufacturing is complex but promising. For those in the industry, how are you navigating these challenges? #ArtificialIntelligence #Manufacturing #Innovation #DataGovernance #AITalent

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Infosys chair, Nilekani, predicts that many large AI models will become commoditized, with the true value stemming from the applications built on top of these models. Learn more about this insightful perspective here: [Link to the article] #AI #ArtificialIntelligence #TechTrends

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SpringUp Labs is pioneering an AI-driven platform for an Indian startup, by leveraging technologies like GPT-4, LLM and DALL·E to enhance creativity and productivity. Generative AI and Large Language Models (LLMs) are rapidly transforming numerous industries by automating tasks, enhancing creativity, improving efficiency, and creating new opportunities. These technologies are reshaping traditional workflows, allowing businesses to leverage AI in ways that were once unimaginable. Read more on our blog at : https://lnkd.in/d2w4VRuF

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6 key takeaways by Anurag Aggarwal, on how #ConversationalAI is revolutionizing customer experiences and driving operational efficiency: 1- Conversational AI thrives when it continuously learns from interactions. Regular feedback and audits ensure it meets evolving customer needs. 2- Ensure AI systems comply with privacy regulations like GDPR and CCPA, and prioritize data security to foster customer confidence. 3- To engage a global audience, businesses must offer AI support across multiple channels and languages, ensuring seamless, accessible experiences. 4- A hybrid approach—AI for simple queries, while human agents for complex issues, both combines efficiency with empathy. 5- Success metrics includes response times, customer satisfaction, and purchasing trends to track Conversational AI's impact. 6- AI systems can enhance both efficiency and customer trust when learning, privacy, and human collaboration go hand in hand. Via "One Expert, One Topic" series by TJ Thinakaran #AI #DataPrivacy #Security #Omnichannel #MultilingualAI #HumanAI #CustomerSupport #SuccessMetrics #BusinessStrategy #CustomerTrust

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💡 I recommend this interesting article on how 𝗻𝗲𝘂𝗿𝗼𝘀𝘆𝗺𝗯𝗼𝗹𝗶𝗰 𝗔𝗜 can overcome the limitations of LLMs. As an example, SAP Chief AI Officer Dr. Philipp Herzig explains that SAP introduced a formal parser to train an LLM on the ABAP programming language, 𝗶𝗺𝗽𝗿𝗼𝘃𝗶𝗻𝗴 𝗮𝗰𝗰𝘂𝗿𝗮𝗰𝘆 𝗳𝗿𝗼𝗺 𝟴𝟬% 𝘁𝗼 𝟵𝟵.𝟴%. 𝗡𝗼 𝗺𝗼𝗿𝗲 𝗵𝗮𝗹𝗹𝘂𝗰𝗶𝗻𝗮𝘁𝗶𝗼𝗻𝘀! ✅ #SAP #AI #generativeAI #neurosymbolicAI

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IBM is committed to replacing people with generative AI yet bans the use of LLMs from other vendors and lacks the skills to improve Watson which lags ChatGPT and other generative AI systems. This is in stark contrast to Microsoft who not only have the rights to all of OpenAI's "pre-AGI" technology but has released its own LLMs, Apple who have many sizes of LLMs now and free use of OpenAI services, Salesforce who have launched LLM agent systems, and Meta who have developed and released many LLMs, all while Google, the inventors of deep learning transformer models, have ramped up to compete with OpenAI. IBM have no clear strategy either way. https://lnkd.in/djquetv3

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🔝🦾AI firms want🇮🇳India to fast-track compute infra❕ 🔻In absence of any funding support from #venturecapitals, currently a lot of startups are not getting access to the right compute infra. ⚙️Artificial intelligence (#AI) infrastructure and software companies like #AMD, #Nvidia, Ola Krutrim, Reliance Jio, #Microsoft, People + AI and Yotta have urged the government to expedite creating compute infrastructure in the country as well as focus on sovereignty in AI systems. ⚡️This assumes significance as the government is looking to create a compute infrastructure through procurement of graphic processing units (GPUs) for startups that are looking for a variety of these chips to train their models. In absence of any funding support from venture capitals, currently a lot of startups are not getting access to the right compute infra. Read more…

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𝗙𝗶𝗿𝘀𝘁 𝗙𝗶𝗿𝗲𝘀𝗶𝗱𝗲 𝗖𝗵𝗮𝘁: 𝗗𝗲𝗺𝘆𝘀𝘁𝗶𝗳𝘆𝗶𝗻𝗴 𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝘀 (𝗟𝗟𝗠𝘀) 🔥 Excited to kick off the first fireside chat with 🔥 on the fascinating world of Large Language Models (LLMs) with Vignesh Kumar Ranjani Mani This series is designed for anyone curious about LLMs, from beginners to seasoned professionals. 💡👩‍💻 This is not academic but purely industry relevant "ears-on-the ground" know-how... 🚀In our first session, I delved on 📚 🔍 AI through the VC/PE Lens: I provided a behind-the-scenes look at AI from the perspective of Venture Capital and Private Equity firms. 💼💰 🏢 Organizational Use Cases: I discussed how organizations are leveraging LLMs to tackle real-world challenges. 📲 Product Management for AI SaaS: I dived deep into monetization models for AI-powered Software as a Service (SaaS) products. 💡💻 🔒 AI Hyperscalers : I gave a Ring-side view of how Hyperscalers are most sought given they offer "Security by Design" to customers... 💻 This is just the beginning! 💫🔑 I'll be covering a wide range of industry-relevant topics in upcoming sessions, with a focus on deep dives that build upon each other. 📚🌊Here's why you should join from the ground up 👇 🏛️ Solid Foundation: Gain a comprehensive understanding of LLM fundamentals. 🧠💪 📈 Progressive Learning: Our sessions will gradually increase in complexity, ensuring everyone can follow along. 📚📈 🌟 Advanced Insights: By the end, you'll be equipped with in-depth knowledge of LLMs and their applications. 🤓🔍 Stay tuned for the next fireside chat! 🔥📆 Vignesh Kumar Ranjani Mani #large_language_models #artificial_intelligence #venturecapital #saas #productmanagement #firesidechat #learninganddevelopment MindMajix

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Greetings from Seoul, where I’m representing IBM at the 2nd AI Safety Summit. Continuing the momentum from last year’s inaugural gathering in Bletchley Park, this year’s Summit is co-hosted by the UK Department for Science, Innovation and Technology and the Korean Ministry of Science and ICT, and aims to double down on progress to create and use AI safely and responsibly by governments and companies alike. Tomorrow, at the #AISeoulSummit I will share IBM’s point of view on why we believe an open approach to AI innovation is the best path forward to ensure safety and a healthy competitive landscape for companies of all sizes. An open innovation ecosystem – in which stakeholders recognize the value of community-built technology and the open exchange of information, ideas, and skills it cultivates – is the only way to grow AI responsibly.   IBM is proud to be a partner throughout this process and remains ready and committed to playing a pivotal role in bringing the power of responsible AI. Ultimately, effective regulation coupled with corporate accountability will allow businesses and society at large to reap the benefits of AI while addressing the potential for misuse and risk. See more on our latest commitment to safety here: https://lnkd.in/eVrYF3Si

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GenAI can accelerate the development of complex applications, interfaces, and supply chain solutions by 30%, significantly enhancing overall agility

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Amazing panel on Smart manufacturing and Supply chain with AI featuring Gayathri Radhakrishnan, Anshu Agarwal, and Rohini Chakravarthy with pragnatic insights on new ways AI is revolutionizing the manufacturing industry and improving supply chain efficiency. #pluto7, #googlecloud, #tiecon, and #sap for organizing such an informative event!

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IBM Concert #AIAutomation Powered by IBM watsonx to help #enterprises streamline application operations with detailed view of connected applications with trigger automations #generatieveai #AI #Watsonx https://lnkd.in/gU43-TEu

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Wipro Adopts Google Cloud AI Technologies including Gemini to Accelerate Innovation and Productivity Wipro Limited , a leading technology services and consulting company, announced the expansion of its strategic partnership with Google Cloud to scale its adoption of Google Cloud AI. Using Vertex AI and Gemini models, Wipro in its project execution to clients, is equipping its workforce with generative AI-powered tools to enhance developer productivity, accelerate cloud migrations, and deliver innovative GenAI solutions. As part of this partnership and Wipro’s ai360 initiative, Wipro FullStride Cloud Studio is integrating Gemini’s advanced reasoning and analysis capabilities into its core business platforms and innovation hubs. To rapidly scale its GenAI projects, Wipro is training its associates on Google Cloud’s AI technologies including Gemini to better help global enterprise customers scope, deploy, and manage AI projects that solve their unique business objectives. This will significantly enhance vital digital transformation projects such as application migrations and modernization, with GenAI-powered productivity improvements of up to 30%, according to Wipro’s estimates. Jo Debecker, Managing Partner and Global Head Wipro FullStride Cloud, said, “Through this expanded relationship with Google Cloud, we are able to significantly accelerate our clients’ digital transformation journeys and empower them to maximize their cloud-run businesses. With Gemini infused into our Wipro FullStride Cloud Studio platform, we will continue to help our clients leverage the power of cloud, adapt to an AI-driven future, and transform their businesses.” Kevin Ichhpurani, Corporate Vice President, Global Ecosystem and Channels at Google Cloud, said, “Through this partnership, Google’s powerful Gemini models and AI services are powering digital transformation within Wipro and with its broad set of enterprise clients. Wipro’s expanding expertise with Google Cloud AI will benefit its enterprise clients on many of their most critical projects, including cloud migrations, data analytics, application modernization, and more.” Matthias Wittpoth, Vice President of Vendor Management & IT Procurement at Metro Digital, said, "Metro Digital has embarked on a journey to improve the efficiency of our Incident & Service Request Management processes. The results we are seeing are: improved resolution time, reduced dependency on subject matter experts, and increased solution accuracy in less time. We expect to see further improvements in the service level agreements of our strategic partner Wipro, underscoring the effectiveness and efficiency of this innovative solution." MyGov India Wipro Google Cloud Mirabilia Business Magazine Jo Debecker Kevin Ichhpurani Matthias Wittpoth #wipro #googlecloudai #mirabiliabusinessmagazine

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Quantifying AI’s Impact: Key Performance Indicators for GCCs Key metrics and performance indicators are crucial to determining progress & success of your AI Center of Excellence (CoE) in the Global Capability Center (GCC). Here are some of the metrics to assess success: * Strategic Alignment - Evaluate the alignment of AI initiatives with business strategy * AI Project Success Rate - Measure the percentage of projects meeting objectives * Time to Deployment - Monitor the time taken from project initiation to deployment * AI Model Performance - Evaluate model accuracy, precision, recall, and other relevant metrics * Data Quality Scores - Assess data completeness, accuracy, and consistency * AI Skills Development- Track the growth of AI capabilities among CoE members and stakeholders: * Stakeholder Satisfaction - Gauge satisfaction levels through surveys * AI Use Case Adoption - Measure the number of new use cases implemented and adopted * Operational Cost Efficiency - Monitor costs per AI project, model deployment, and other relevant metrics * AI Risk Mitigation - Assess the effectiveness of risk management processes for disruptions, incidents, ethical guidelines, recovery etc. Let me know which metric has the most significant impact on your organization! If you have not started on your journey, connect with us and we can show you a path forward. #Coforge #CoforgeGCC #AIGCC Image created by Designer in Microsoft Copilot

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I really enjoyed this conversation as it allowed us to explore the fascinating world of Small Language Models and uncover why they're becoming the true workhorses of enterprise GenAI tasks! 💡 Given how frequently these questions come up in my discussions, I thought I'd share some key insights: 1️⃣ Why are SLMs important for enterprises? 🏢 Enterprises are leveraging LLMs and other GenAI solutions to improve their business processes, aiming to boost efficiency without inflating costs. While larger LLMs offer impressive capabilities, they come with significant cost implications - especially the proprietary models. Enter Small Language Models: though they might not match their giant cousins in raw performance initially, they can catch-up (and often outperform) through customization. Here's the game-changer: a task-customized SLM can deliver similar or superior performance compared to proprietary LLMs while being orders of magnitude more cost-effective! 💪 2️⃣ What is LLM customization? 🎯 To customize your LLM, you'll need two essential tools: - A solution to transform your enterprise data (knowledge) into training data and/or generate task-specific (skills) training data 📊 - An efficient method to train your SLM on this curated data 🔄 3️⃣ What makes it hard? 🤔 Data, of course! While numerous solutions exist to "bake the data into the model," we lack enterprise-grade approaches for converting enterprise data into training data or generating skills data. Sure, you could turn to data vendors, but that route is typically extremely expensive and often struggles to scale as your enterprise data grows. 📈 4️⃣ What tools are available for LLM customization today? ⚡ This is exactly where InstructLab shines! ✨ Unlike other customization offerings, IL provides both tools in one comprehensive open-source package, backed by the expertise of RedHat and IBM. IL features some of the most advanced synthetic data generation methods, enabling you to: - Convert your enterprise data into high-quality training data - Synthesize skills data using open-source LLMs - Leverage novel large-scale training methods to optimize your small model The results? 🎯 On numerous enterprise tasks, including RAG implementations, IL-tuned 7B/8B models consistently outperform proprietary models at a fraction of the cost! 🤝 Let me know if this was helpful, I'll be happy to create a comprehensive long(-er) form post sharing concrete results from real-world implementations. #GenAI #MachineLearning #EnterpriseAI #Innovation #TechTrends #InstructLab #SmallLanguageModels

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From the pages of an #IBMChampion's diary : Well , we all have been talking about democratizing AI models since their inception and I think IBM has nailed it through #InstructLab , giving everyone a chance to access it and it's not a myth that #InstructLab is made affordable, and open to all . Here are my 10 learnings with the #InstructLab BADGE . 1. #AI for Everyone: InstructLab by IBM and Red Hat Hat makes it super easy and economical for communities to tweak and improve large language models without starting from scratch.    2. Breaking Down Barriers: Think of InstructLab as the ultimate open-source playground where anyone can jump in and make #AI better for everyone.  3. Quick Customization, Low Costs: Why spend a fortune fine-tuning #AI? With #InstructLab, you can customize models faster and cheaper than ever.    4. Modernization Magic: IBM used InstructLab to turn its Granite model into a mainframe modernization whiz—and it’s setting records while doing it!  5. Your #AI, Your Way: Forget cookie-cutter models. With InstructLab, you’re in the driver’s seat, teaching your AI exactly what it needs to know.  6. Community Power: Got a cool idea for an #AI skill? #InstructLab lets you try it out, refine it, and share it with the world—right from your laptop.    7. Transparent and Trustworthy: Everything’s out in the open with #InstructLab , every contribution is visible and traceable, so no secrets here!  8. Open Source, Open Possibilities: #InstructLab is unlocking the full potential of AI by making it accessible, affordable, and open to all.  9. A Smarter Future, Together: IBM and Red Hat Hat are inviting you to be part of the AI revolution. With #InstructLab, we’re shaping the future of AI—one contribution at a time. 10. #InstructLab - a true and practical way to democratize your AI models . CC : Ahmed Azraq | Jolanda Roosen | Libby Ingrassia | Kathryn DuPont, PCC, ICD.D | Namitha K | Morgan M. | All other #IBM Champions

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Such a powerful insights from Sumit Singh :- embark on non-linear paths, embrace risks, cultivate learning behaviors, and practice humbleness…😇 #learning #optimism #leadership #humbleness

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