Kaushal Misra

🤖 AI isn't just ChatGPT, folks. It's a whole universe of game-changing tech for business leaders. Here's the real scoop: 1/. Machine Learning: Predicts trends and detects fraud 📊 2/. Deep Learning: Powers NLP and computer vision 🧠 3/. Expert Systems: Makes decisions like human pros 🏥💼 4/. Robotics: Automates physical tasks 🦾 5/. NLP: Enables human-like communication 💬 6/. Computer Vision: Interprets visual data 👁️ 7/. Reinforcement Learning: Learns through trial and error 🎮 8/. Generative AI: Creates new content 🎨✍️ The takeaway: AI is your secret weapon for smarter business decisions. It's not about replacing humans, but supercharging our capabilities. Question for you: Which AI tech excites you most for your industry? Drop your thoughts below! 👇 #ArtificialIntelligence #BusinessStrategy #LeadershipInTech Share Rewrite

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Exciting to hear Chirantan "CJ" Desai of ServiceNow and Jensen Huang of NVIDIA discuss how #AI has scaled millions of times, over the last 10 years (#MooresLaw) at #Know2024 yesterday. And the era of "Manufacturing of #Digital #Intelligence at scale" to come! 😯 with "every aspect of computing moving to intention-driven computing", where the 'system' will understand the problem, without you having to be prescriptive! 🏅 The opportunity to move from "instruction-driven AI" to "intention-driven AI" represents a massive opportunity for everyone in #business, #commerce, public service, #publicsector, #notforprofit and all sectors. 𝗕𝘂𝘁 𝗥𝗲𝗺𝗲𝗺𝗯𝗲𝗿; 𝗜𝘁'𝘀 𝗔𝗹𝗹 𝗮𝗯𝗼𝘂𝘁 𝗦𝗲𝗿𝘃𝗶𝗰𝗲! #AllAboutService #COOs #ServiceExcellence #CXP #BusinessTransformation

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The transformative potential of generative AI in healthcare is truly remarkable! The 80% reduction in processing time achieved by Mainsoft is particularly impressive and highlights how AI can optimize operations without compromising quality of care. This balanced approach ensures that AI enhances healthcare while maintaining the highest standards of patient safety and confidentiality!

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Data protection via use of Custom AI.. Highlights -- 1. Entrprises are customizing AI applications with thier own business data using Retreeval Augmanted Generation (RAG). 2.By connecting AI models to handpicked data source RAG enables faster ,effective and case specific generative AI deployment 3.Using RAG the copilot pulls live database on local servers keepinh all computer operations secure and in-house. 4. For many organisation biggest challenge to achieve AI success lies in collecting and praparing right data to train effective model. #GenerativeAI #data #privacy #LLM #copilot #businessorganisation #HBR #NIVIDEA #GALTER #Profmkb #galterprofmkb

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Ever wondered how AI agents could revolutionize your workday? Think about the tasks you do daily. Now, imagine AI agents taking over or enhancing those tasks. How much more productive could you be? Now, apply that thinking to entire areas of work like customer service, sales & marketing, or manufacturing operations and supply chain logistics. The impacts and opportunities are huge! In customer service, AI agents can handle repetitive tasks, allowing your team to focus on complex issues. In manufacturing, they can streamline supply chain logistics, reducing delays and increasing efficiency. Building, connecting, and orchestrating AI agents isn't easy - this is where our AI Refinery™ and Distiller Framework come in to help. These tools simplify the process, ensuring seamless integration and maximum impact. How do you see AI agents transforming your industry? Share your thoughts below!

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Explore how behavioral science enhances generative AI accuracy! #Accenture's Chief Responsible AI Officer, Arnab Chakraborty, discusses an MIT field experiment that introduced "speedbumps" to help users detect AI errors and improve reliability. Dive into insights on responsible AI deployment and its impact. https://accntu.re/47xjV0A

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Unlock insights into Large Language Model evolution with our comprehensive white paper! Chaminda Wijayasundara, VP of Technology and genAI Leader, delves deep into the landscape of LLMs, comparing commercial giants, open-source wonders, and compact transformers. Download the white paper: https://lnkd.in/ejeA2Bc6 #EngineeringFirst #generativeAI #LLM #OpenSource #virtusa

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#Horasis welcomes Ness Digital Engineering Digital Engineering CEO Ranjit Tinaikar to Advisory Board to offer his counsel and experience on matters related to Intelligent Engineering and innovation. Today, software solutions run front and center to the progress of both the public and private sectors. Whether it’s a provider of water management modernizing its data platform or an ed-tech company enhancing learning outcomes, the number of use cases for these solutions is truly endless. At the same time, delivering reliable technology in 2024 also has its challenges. To begin to address this and more, Horasis is proud to announce the appointment of Dr. Tinaikar to its Technology Advisory Board. As the CEO of Ness Digital Engineering, Dr. Tinaikar is responsible for spearheading important solutions that improve how software services are delivered across the globe. Part of this work is encapsulated within an emerging term called ‘Intelligent Engineering,’ an approach that looks to leverage the power of data, AI and intelligence to improve engineering productivity. Dr. Tinaikar was earlier a Partner at McKinsey, where he was one of the earliest leaders in forming its digital practice, founding the Lean Software Development and IT strategy practices. Read the article in full below. https://lnkd.in/ebMGcxh3

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Just completed an enlightening course on various aspects of Artificial Intelligence through the MIT Sloan Executive Education and MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) collaboration. The class covered key concepts of Generative AI, Machine Learning, and NLP while delving into legal risks and the critical question of AI ethics. A truly enriching experience! If you have any questions, don't hesitate to reach out! Check out the course here: [MIT Artificial Intelligence Online Short Course](https://lnkd.in/gvd5FjTy)

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Embracing Synthetic Data: The Future of AI Training Synthetic data is revolutionizing AI by addressing one of its biggest challenges—access to high-quality, diverse datasets. By generating realistic, artificial data, we can enhance model training, improve accuracy, and maintain privacy. This innovation is pivotal in overcoming data scarcity and bias, paving the way for more robust and ethical AI solutions. https://lnkd.in/dY-AUDdc However, one needs to know that continuous reliance on synthetic data without fresh inputs can doom future AI models to inefficiency and reduced diversity known as “model collapse”, this can happen where future AI systems get progressively dumber due to the increase of AI-generated data on the internet. Will be keen to hear views on how are organisations balancing the conundrum between synthetic and human data. #AI #SyntheticData #MachineLearning #DataScience #Innovation #data #

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Great news to increase value of generative AI. CodiumAI leverages this technology to automate code generation, testing and validation with increased effectiveness thanks to integrated RAGS capabilities. It should be particularly impactful in large organizations and/or regulated industries where code creation and QA processes may be quite complex and sensitive. "At the heart of CodiumAI’s enterprise solution is its advanced Retrieval Augmented Generation (RAG) capabilities. This powerful technology indexes an organization’s full codebase, allowing CodiumAI’s tools to gain a deep understanding of the unique context of each company’s code. By leveraging this comprehensive knowledge, the platform can provide more accurate and relevant suggestions, ensuring that AI-generated code aligns seamlessly with existing codebases and practices. Complementing this deep understanding of code context, CodiumAI’s enterprise platform also features a sophisticated database of best practices."

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Explore how behavioral science enhances generative AI accuracy! #Accenture's Chief Responsible AI Officer, Arnab Chakraborty, discusses an MIT field experiment that introduced "speedbumps" to help users detect AI errors and improve reliability. Dive into insights on responsible AI deployment and its impact. https://accntu.re/47xjV0A

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🚀 From Sports to AI Innovator: My Leadership Journey🌟 As a school games captain, I learned the power of leading by example, building trust, and fostering teamwork—values that continue to shape my leadership today. Chess ♟️ taught me strategic thinking; tennis 🎾 instilled resilience; football ⚽ emphasised teamwork and adaptability—skills that fuel my professional journey. 💡 Vision & Innovation: Proud to lead the Generative AI CoE, driving business value to customers. 🏆 Industry Recognition: Celebrating impactful solutions and high-performing teams that earn acknowledgment and success. 🌱 Empowerment: Building a culture of continuous learning and harnessing the strengths of diverse teams. 🌟 Leadership Philosophy: Creating an environment where everyone feels valued, motivated, and empowered to thrive. Thanks to for sharing my story with 3AI with this amazing network. What inspires your leadership path? Let’s connect and share your journeys! 💬 #Leadership

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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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The competition to develop the most advanced Large Language Models (LLMs) has seen major advancements, with the four AI giants, OpenAI, Meta, Anthropic, and Google DeepMind, at the forefront. These LLMs are reshaping industries and significantly impacting the AI-powered applications we use daily, such as virtual assistants, customer support chatbots, and translation services. As competition heats up, these models are constantly evolving, becoming more efficient and capable in various domains, including multitask reasoning, coding, mathematical problem-solving, and performance in real-time applications.

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Amidst the buzz around generative AI, industries are reevaluating how these technologies can address their challenges. While the conversation has long centered on AI applications like chatbots in customer service, there is a broader horizon to explore. The visual shared below contrasts traditional roles with the transformative impact of generative AI, highlighting a shift towards AI-driven task execution with humans in supervisory roles. As the Founder of NVIDIA, Jensen Huang, pointed out, humans are still the experts guiding these systems. Generative AI may not be a silver bullet, but it certainly widens our scope for innovation, offering novel solutions to persistent issues.

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Key to building trustworthy AI systems is to design decision algorithms that are auditable, accountable & thus, less biased. #AI #GenAI ##DeepLearning #MachineLearning

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