The Neil Ashton Podcast

• S2 EP11 - Foundational AI Models for Fluids

  In this episode of the Neil Ashton podcast, the discussion revolves around foundational models in fluid dynamics, particularly in the context of computational fluid dynamics (CFD). Neil shares insights from a recent panel discussion and explores the potential of AI in predicting fluid behavior. He discusses the evolution of AI in CFD, the challenges of data availability, and the differing adoption rates between industries. The episode concludes with predictions about the future of foundational models and their impact on the engineering landscape.Chapters00:00 Introduction to the Podcast and Topic01:09 Foundational Models in Fluid Dynamics10:09 The Evolution of AI in CFD19:52 Future Predictions and Industry Dynamics

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• S2 EP10 - Dr. Kurt Bergin-Taylor, Head of Innovation - Tudor Pro Cycling

  In this episode of the Neil Ashton podcast,  Neil discusses the intersection of cycling and engineering with Kurt Bergin-Taylor, head of innovation at Tudor Pro Cycling. They explore how technology and science are transforming cycling into a more competitive and innovative sport, akin to Formula One. The conversation covers various aspects of cycling, including the importance of aerodynamics, nutrition, and the holistic approach to rider performance. Kurt shares insights from his academic background and experiences in professional cycling, emphasizing the need for tailored training and the integration of technology in enhancing performance. They discuss the future of cycling innovation, emphasizing the importance of individualization in gear, collaborative relationships with partners, and the evolving mindset of young cyclists. Kurt highlights the significance of data and AI in optimizing performance and strategies in cycling, while also addressing the need for viewer engagement in the sport. Finally Kurt shares valuable advice for aspiring engineers looking to enter the cycling industry, stressing the importance of mentorship and practical experience.Chapters00:00 Introduction to the Podcast and Themes04:55 Kurt Bergin-Taylor: Background and Role at Tudor Pro Cycling10:08 The Structure and Dynamics of a Pro Cycling Team12:59 Innovation in Cycling: Aerodynamics, Thermal Management, and Safety19:14 Nutrition, Training, and Performance in Cycling29:18 Future Innovations in Cycling Equipment and Systems30:42 Understanding Individualization in Cycling Gear34:30 Collaborative Innovation in Cycling Equipment38:20 The Evolving Mindset of Young Cyclists42:28 Enhancing Viewer Engagement in Cycling46:24 The Future of Data and AI in Cycling50:05 Advice for Aspiring Engineers in CyclingTakeaways- Cycling is increasingly influenced by technology and engineering.- Tudor Pro Cycling is focused on long-term performance and innovation.- Aerodynamics plays a crucial role in cycling performance.- Thermal management is essential for riders in extreme conditions.- Nutrition has dramatically improved in cycling over the last decade.- Training methodologies must be tailored to individual riders.- The relationship between power output and speed is complex.- Safety innovations are critical as speeds increase in cycling.- Understanding the whole system of rider and equipment is vital.- Professional cyclists have different recovery capabilities compared to amateurs. Individualization in cycling gear is crucial for performance.- Collaborative innovation with partners enhances product development.- Young cyclists are more educated but sometimes overlook tactical aspects.- Data-driven insights are essential for optimizing race strategies.- Viewer engagement can be improved through real-time data sharing.- AI and machine learning are emerging tools in cycling optimization.- Mentorship is vital for aspiring professionals in the cycling industry.- Practical experience and initiative can open doors in professional sports.- Cycling offers a holistic approach to engineering and performance.- The cycling industry is growing, providing more opportunities for engineers.

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• S2, EP9 - New Job Update! (and a small apology..)

  A short episode to give a brief update on what I've been doing and to say sorry for not putting out episodes recently. I've joined NVIIDA as a Distinguished CAE Architect and have been rather busy! New episodes will be coming soon! Listen to the episode to learn more. 

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• S2, EP8 - Neil Ashton - Career advice for Engineers

  In this episode of the Neil Ashton podcast, Neil discusses career advice for aspiring engineers, focusing on the differences between various types of companies, job roles, and the growing importance of software skills in the engineering field. The conversation highlights the pros and cons of working in large enterprises, startups, and consulting firms, as well as the diverse career paths available beyond traditional engineering roles. In this conversation, Neil discusses the evolving landscape of engineering careers, particularly focusing on the increasing relevance of software development and the tech sector. He highlights the diverse career paths available within tech, including software development, product management, and solution architecture, as well as the growing importance of AI in engineering. Neil emphasizes the opportunities for engineers to transition into tech roles and the need for a strong understanding of the tech ecosystem to navigate career decisions effectively.Chapters00:00 Introduction to Engineering Careers03:01 Exploring Company Types in Engineering06:05 Understanding Job Roles in Engineering09:00 The Shift Towards Software in Engineering11:52 Diverse Career Paths Beyond Traditional Engineering14:47 The Role of Consulting in Engineering18:03 Navigating the Job Market in Engineering20:57 The Importance of Software Skills in Engineering24:03 Conclusion and Future Trends in Engineering Careers30:08 The Rise of Software Development in Engineering31:59 The Tech Sector's Growing Relevance to Engineers36:41 Career Paths in Tech: Software Development and Management44:27 Understanding Product Management in Tech48:15 The Role of Solution Architects in Tech52:04 Consulting and Support Roles in Tech55:54 AI's Impact on Engineering and Software Development#careers #engineering #tech #sde #amazon #aws #google #jobs

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• S2, EP7 - Prof. Michael Mahoney - Perspectives on AI4Science

  In this episode of the Neil Ashton podcast, Professor Michael Mahoney discusses the intersection of machine learning, mathematics, and computer science. The conversation covers topics such as randomized linear algebra, foundational models for science, and the debate between physics-informed and data-driven approaches. Prof. Mahoney shares insights on the relevance of his research, the potential of using randomness in algorithms, and the evolving landscape of machine learning in scientific disciplines. He also discusses the evolution and practical applications of randomized linear algebra in machine learning, emphasizing the importance of randomness and data availability. He explores the tension between traditional scientific methods and modern machine learning approaches, highlighting the need for collaboration across disciplines. Prof Mahoney also addresses the challenges of data licensing and the commercial viability of machine learning solutions, offering insights for aspiring researchers in the field.Prof. Mahoney website: https://www.stat.berkeley.edu/~mmahoney/Google scholar: https://scholar.google.com/citations?user=QXyvv94AAAAJ&hl=enYoutube version: https://youtu.be/lk4lvKQsqWUChapters00:00 Introduction to the Podcast and Guest05:51 Understanding Randomized Linear Algebra19:09 Foundational Models for Science32:29 Physics-Informed vs Data-Driven Approaches38:36 The Practical Application of Randomized Linear Algebra39:32 Creative Destruction in Linear Algebra and Machine Learning40:32 The Role of Randomness in Scientific Machine Learning41:56 Identifying Commonalities Across Scientific Domains42:52 The Horizontal vs. Vertical Application of Machine Learning44:19 The Challenge of Common Architectures in Science46:31 Data Availability and Licensing Issues50:04 The Future of Foundation Models in Science54:21 The Commercial Viability of Machine Learning Solutions58:05 Emerging Opportunities in Scientific Machine Learning01:00:24 Navigating Academia and Industry in Machine Learning01:11:15 Advice for Aspiring Scientific Machine Learning ResearchersKeywordsmachine learning, randomized linear algebra, foundational models, physics-informed neural networks, data-driven science, computational efficiency, academic advice, numerical methods, AI in science, engineering, Randomized Linear Algebra, Machine Learning, Scientific Computing, Data Availability, Foundation Models, Academia, Industry, Research, Algorithms, Innovation

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