Key Ward’s Post

🚀 Join the Exclusive 'Introduction to Industrial AI' Workshop - Transforming AI Theory into Industrial Practice on March 4th! 🌟 Hosted by Kence Anderson, CEO of Composabl & ex-Microsoft Bonsai. 🔍 Bridging the AI-Industry Divide: This masterclass, designed for AI innovators, is your key to bridging the crucial gap between AI technology and industrial applications. With 87% of machine learning models never reaching production, our workshop is a critical step in changing this narrative. 👥 Who Should Attend? Perfect for AI Founders, Industrial Business Executives, Plant Managers, Process Engineers, Control Engineers, Line Supervisors, Industrial Data Scientists, and Innovation Team Members. 📚 Key Benefits: ✔ Develop a deeper understanding of AI capabilities and jargon ✔ Acquire a practical framework for integrating AI into various organizational levels ✔ Gain the knowledge to select the most appropriate technologies, service providers, and vendors for industrial applications ✔ Learn strategies to enhance manufacturing, production, and logistics with AI 🚀 For AI Founders: This isn't just a course, it's your strategic advantage in the competitive world of industrial AI. Equip yourself with the knowledge and tools to outshine in securing manufacturing clients. 🔗 Be an AI Pioneer in Industry – Register Now! 👉 https://lnkd.in/gjHRgrsx #ATDC #GeorgiaAIM #A3 #AmazonRobotics

Before a company embarks on a GenAI journey, there are 4 key things they should have in place before starting out: 1. Clear goals and use cases defined - The problems to solve and desired outcomes need to be clearly specified. 2. Governance and oversight processes - Procedures for developing, testing, deploying and monitoring generative models safely and responsibly are important. 3. Technical expertise and resources - Specialized AI/ML skills and sufficient computing power for training models must be available. 4. High-quality data - Generative models require large datasets to learn meaningfully. The company needs access to data that is sufficiently robust, varied, and cleaned/labeled to train models for their specific use cases. Poor or limited quality data can hamper model performance and limit what generative techniques are feasible. Ensuring data quality should be a priority early on. The data needs to match the defined goals and use cases. Data governance processes also help make data available, usable and managed appropriately over the long run for retraining models as needed. Establishing these four pillars - clear direction, responsible processes, technical abilities, and high-quality data - provides a strong foundation for companies to thoughtfully develop and apply generative AI in a way that sets the initiative up for success. #genai #data #governance #aisafety

🚀 Taking AI to the Next Level in Manufacturing 🚀 Few technological advances have generated as much excitement as AI, especially generative AI. According to a recent study by MIT Technology Review Insights, manufacturers are at the forefront of AI development, seeing it as integral to creating hyper-automated, intelligent factories. 🔍 Key Findings: 1. Talent, Skills, and Data: Major constraints on scaling AI. Quality and governance of data, along with access to cloud-based compute power, are significant challenges. 2. Investment Expectations: 58% of executives plan to increase AI spending by over 10% in engineering and design, with 43% planning the same for factory operations. 3. Specific Gains: Desired AI improvements include speed, efficiency, reduced failures, security, innovation, safety, and a reduced carbon footprint. 4. Data Foundations: Scaling AI requires strong data quality, integration, and governance. Modernizing data architecture and improving interoperability between systems is crucial. Manufacturers must address these barriers to unlock AI’s full potential. The path forward includes enhancing talent and skills, improving data quality, and ensuring robust data governance. 🔗 [Link is shared in the comments section] #Manufacturing #AI #Innovation #Technology #DataScience #MITTechnologyReview #Microsoft

🚀 Exciting Developments in Engineering: How AI and ML Can Help Engineer Solutions 🚀 In today's rapidly evolving world, engineering challenges are becoming increasingly complex, requiring innovative solutions to address them effectively. That's where Artificial Intelligence (AI) and Machine Learning (ML) come into play! These cutting-edge technologies are revolutionizing the way engineers approach problem-solving and paving the way for groundbreaking advancements across various domains. 🔍 Enhanced Problem-Solving: AI and ML algorithms can analyze vast amounts of data to identify patterns and insights that may not be apparent to human engineers. By leveraging these insights, engineers can develop more efficient and effective solutions to complex engineering problems. ⚙️ Optimized Design Processes: From optimizing product designs to streamlining manufacturing processes, AI and ML techniques offer invaluable support to engineers. These technologies enable engineers to explore a wide range of design possibilities, identify optimal solutions, and accelerate the development cycle. 🛠️ Predictive Maintenance: By analyzing sensor data and equipment performance metrics, AI and ML algorithms can predict potential failures before they occur. This proactive approach to maintenance not only minimizes downtime but also extends the lifespan of machinery, leading to significant cost savings for businesses. 🌐 Global Collaboration and Innovation: AI and ML technologies facilitate collaboration among engineers worldwide by enabling real-time data sharing and collaboration on engineering projects. This global collaboration fosters innovation, allowing engineers to tap into diverse perspectives and expertise to solve complex challenges. 💡 Continuous Learning and Improvement: One of the most exciting aspects of AI and ML is their ability to learn and adapt over time. As engineers gather more data and insights, AI algorithms can refine their models and algorithms, leading to continuous improvements in engineering solutions. In conclusion, the integration of AI and ML technologies into the engineering workflow is unlocking new possibilities and reshaping the future of engineering. By harnessing the power of AI and ML, engineers can tackle challenges with greater efficiency, innovation, and effectiveness, driving progress and shaping a better tomorrow for all. Let's continue to embrace these transformative technologies and push the boundaries of what's possible in engineering! 💡✨ #AI #ML #Engineering #Innovation #FutureTech #Energy #powersystem #datascience

Which tasks are cost-effective to automate with computer vision? A research team of Maja Svanberg, wensu li, Martin Fleming, Brian C. Goehring, Neil C. Thompson tried to answer this question in detail. As a result, they have released a study that indicates the human labor is still mostly cheaper compared to AI for now: “We focus on #ComputerVision, where cost modeling is more developed. We find that at today’s costs, US businesses would choose not to automate most vision tasks that have ‘AI Exposure,’ and that only 23% of worker wages being paid for vision tasks would be attractive to automate. This slower roll-out of AI can be accelerated if costs falls rapidly or if it is deployed via AI-as-a-service platforms that have greater scale than individual firms, both of which we quantify. Overall, our findings suggest that AI job displacement will be substantial, but also gradual–and therefore there is room for policy and retraining to mitigate unemployment impacts,” the study stated. The study was funded by the MIT-IBM Watson AI Lab and used online surveys to collect data on around 1,000 visually assisted tasks across 800 occupations. In general, the research can be very useful for a huge number of businesses whose leaders want to assess the economic feasibility of implementing #automation based on computer vision technology: there are here many suitable formulas for how to calculate this. For a comprehensive overview of the research and its insights, you can access it by following the link in a comment below.

❌ 8 Misconceptions about AI-Powered Engineering! 🧠 In this video, our CEO Matthias Bauer addresses common misconceptions about artificial intelligence (AI), particularly in engineering applications, shedding light on the realities of AI's capabilities and limitations. 📊 1) Data Requirements: Contrary to the belief that AI needs vast data, specific engineering problems can often be addressed with as few as 20-50 simulations. ⏳ 2) Training Time: While some might think AI models take weeks to train, engineering-focused models usually require only hours to a few days, aiming for overnight training. ✨ 3) AI is Not Magic: A common misconception is viewing AI as magic, whereas it's a technology with specific applications and limitations. 🏗️ 4) Engineering Applications: Engineering models focus on solving precise problems rather than broad applications, which influences both data needs and training time. 🛠️ 5) Practical Implementation: The practical application of AI in engineering is more about efficiency and targeted solutions than about overcoming massive data or time challenges. 🔄 6) Expectation vs. Reality: Bridging the gap between what people expect from AI and what it can deliver is crucial in leveraging its potential in engineering. 🤔 7) Perception Issues: Correcting misconceptions about AI can lead to a better understanding and utilization of AI in various industries, especially engineering. 📈 8) Efficiency in AI Use: The focus on efficiency and specificity in engineering AI applications demonstrates how AI can be effectively integrated into real-world problem-solving. 💬 Comment your doubts and questions about AI-powered engineering!👇 🤝 Read our customer success stories: https://lnkd.in/dwF82znJ 🔗 Learn more about NAVASTO's AI-solutions for engineering: https://lnkd.in/d2n3TepT #AIEngineering #DeepLearning #Engineering #Design #Simulation #AI #MachineLearning

Building on our previous post about the challenges and opportunities of integrating AI/ML into engineering workflows, let's dive deeper into the strategies and best practices for successful implementation. Strategies for Successful AI/ML Integration in Engineering #AI #ML #EngineeringWorkflows #ImplementationStrategies 1. Start with a clear vision and objectives 🎯   - Define specific use cases and desired outcomes   - Align AI/ML initiatives with business goals and priorities   - Communicate the vision to stakeholders and secure buy-in 2. Assess and prepare your data infrastructure 📊   - Evaluate the quality, quantity, and relevance of your data   - Establish data governance policies and procedures   - Invest in data storage, processing, and security technologies 3. Foster collaboration between domain experts and data scientists 🤝   - Create cross-functional teams with diverse skill sets   - Encourage knowledge sharing and continuous learning   - Promote a culture of experimentation and iteration 4. Develop a robust AI/ML pipeline 🔬   - Select appropriate tools and frameworks for your use cases   - Implement data preprocessing, feature engineering, and model training   - Establish processes for model validation, testing, and deployment 5. Integrate AI/ML outputs into engineering workflows 🔄   - Identify touchpoints for AI/ML integration (e.g., design, simulation, optimization)   - Develop user-friendly interfaces and visualizations   - Automate data flow and update processes 6. Monitor, evaluate, and iterate 📈   - Establish performance metrics and KPIs   - Regularly assess the impact and ROI of AI/ML initiatives   - Continuously refine and optimize models based on feedback and results By following these strategies and best practices, engineering organizations can overcome the challenges and harness the full potential of AI/ML in their workflows. Remember, successful AI/ML integration is an ongoing journey, not a one-time event. It requires commitment, collaboration, and a willingness to learn and adapt. What strategies have you found effective for integrating AI/ML into engineering workflows? Share your experiences and insights in the comments below! 💬 #BestPractices #SuccessfulImplementation #ContinuousImprovement #EngineeringInnovation #machinelearning #artificalintelligence

As the tech landscape evolves, understanding how to interact with AI tools becomes crucial. This course has provided me with a solid foundation in prompt engineering, enhancing my ability to streamline processes and drive efficiencies in projects.

Unveiling the Power of MoE Models in AI! Are you ready to discover a groundbreaking AI model architecture that outperforms traditional models? Introducing Mixture of Experts (MoE) models! What makes MoE models superior to other AI models? 1. 🔍 Specialized Expert Networks: MoE models consist of multiple specialized expert networks, each focusing on a specific task or subset of data. This division of labor allows MoE models to handle complex problems more efficiently than traditional models. 2. 📡 Dynamic Routing: MoE models use dynamic routing mechanisms to direct input data to the most appropriate expert networks. This intelligent data handling ensures that the model effectively utilizes its computational resources, resulting in better performance. 3. 📈 Scalability: MoE models are highly scalable, allowing for the addition of more expert networks as the complexity of the problem or the size of the data increases. This scalability makes them well-suited for handling large and complex datasets. 4. 🚀 Improved Performance: MoE models often achieve higher accuracy, better generalization, and reduced computational costs compared to other models due to their specialization and dynamic routing capabilities. 5. 💡 Training Efficiency: Training MoE models can be more efficient than training traditional models because each expert network focuses on a specific subset of data or task. This allows for more effective optimization and parallelization during training, saving time and resources. Now that you know the advantages of MoE models, let's discuss their potential together! Share your thoughts on MoE models in the comments below: 1. How can businesses leverage MoE models to solve complex problems? 2. What industries will benefit the most from adopting MoE models? 3. Have you considered exploring MoE models for your AI projects? Join the conversation and help us explore the exciting possibilities of MoE models! #MoEModels #ArtificialIntelligence #AIDiscussion #Innovation

Why are simple AI models better? They're less of a cost center - complex models have huge cost: 💠 Compute: They can rack up costs from data engineering to predictions 💠 Dev time: They consume time and delay time to market 💠 Maintenance: Complex models demand constant, painful maintenance, and updates So, what are the benefits of simple models? 💠Your projects stay on time and within budget. 💠Stakeholders and users grasp the value - you need advocates 💠Alignment improves with models that are easier to understand and manage. 💠You achieve faster iterations, enabling rapid improvements. 💠You lower the risk of model failure in production, ensuring reliability. There is a real risk of projects stalling or failing altogether. Data and AI gets seen a cost center - rather than a competitive edge. In these economic times, data scientists must champion business value and costs. The era of focusing only on models is over. Data and cost matter. Competitive edge now hinges on strategic alignment, cost-efficiency, and impactful outcomes. Build simple - then build up to complex models. #datalife360 #datastrategy #ai #ml #datascience