What Tech Can Learn from Structural Engineering
What does building bridges have to do with building algorithms? Why this licensed civil and structural engineer suddenly intersted in IT and tech products. How can someone who designs skyscrapers and bridges contribute to the world of artificial intelligence and machine learning? These questions might seem far-fetched at first, but they open up a fascinating dialogue about the nature of innovation and leadership.
As both a Florida-State Licensed Professional Engineer and an AWS certified ML Architect, I've straddled both worlds and seen firsthand how principles from structural engineering can revolutionize tech practices.
Responsibility: When a tech enthusiast makes a mistake in the code, the code breaks. However, if a structural engineer makes an engineering error, people die. The building you currently sit in or the bridge you pass thru on I4 this morning, consider thanking a structural engineer! A structural engineer who went rigerous schooling, more than 4 years of experience, and licensure exam of mathematical principles, tech-coding practices, physics, and project management.
Safety and Security: When was the last time you heard of a modern building collapsing due to poor engineering? It's rare, right? That's because structural engineers are masters of risk assessment and mitigation. We take every detail seriously. In an era where AI ethics and data security are paramount, this risk mitigation skillset and mindset is invaluable.
Merit and Licensure: Engineering license is the hardest license to practice the profession in any industry. To practise engineering in the US one has to first become a training engineer first after passing a state recognized training exam (FE Exam). Second is passing Professional Engineer exam + 4 years experience + endorsement from 5 other licensed engineers - gets an engineering license! This is harder than the residency requirement of medical doctors, bar requirement of the lawyers, etc.
Systems Thinking: We are trained to view complex projects as interconnected systems, and view each system contributing to the whole. The same thought process in tech can be compared only to senior level tech VPs or ML-system design architects in combination with software developer who executes the code at finer level.
Just as a bridge is a complex system of interconnected components, so is a large-scale software architecture. How many tech projects have failed because tech professionals didn't account for all the moving parts? Licensed structural engineers are trained to see the big picture while managing intricate details - a crucial skill that applies in the tech leadership.
Regulatory compliance: Tech companies often struggle with new regulations. But for civil and structural engineers, working within strict building codes is second nature. This experience of compliance can be a game-changer when dealing with emerging tech laws and data privacy regulations.
Project management: Large-scale infrastructure projects require meticulous planning, vision, and execution, skills and mindset that transfer well to software development lifecycles which are relatively of shorter duration.
Quality control: Engineers are trained to ensure high standards of safety and reliability when producing plans and engineering calculations, which is vital in developing robust tech solutions.
Interdisciplinary collaboration: Structural engineers often work with various specialists, fostering skills in cross-functional team leadership. Engineers often work with diverse teams, including architects, contractors, clients, and regulatory agencies. This experience jives will to help tech companies foster collaboration and manage complex relationships.
Public Engagement: An engineer's ability to communicate technical information to non-technical audiences can be valuable for engaging with stakeholders and building trust.
5. Data-Driven Decision Making:
Simulation and Modeling: Structural engineers often relay on computer simulations and modeling (finite element modeling) to predict the behavior of structures under wind, earthquakes, and gravity. This engineering simulation is an art as well as sciencce just like developing ML and AI models.
Real-World Testing: Testing the waters and validating designs is very similar to deploying the models for the real world with an exception that there is no scope of error. Therefore, enough contigency and redundancy is built in a complex system of infrastructure that even if a smaller system fails, it would not stop the larger system from running.
6. Long-term thinking: Infrastructure projects are designed not for next year but to serve for several decades. Long-term sustainability and scalability in tech have a lot to learn from this approach of building once and evolving forever.
7. Resource optimization: Structural engineers like me are trained to manage resources efficiently. Planning for each and every component on the critical path in the product development cycle is often overlooked piece in tech when forming strategies for optimizing organizational and computational resources.
8. Practical problem-solving: Engineers are experienced to find pragmatic solutions, which can help ground AI and tech development in real-world applications. These parallels aren't just theoretical. Companies like Autodesk have successfully bridged the gap between engineering and software, revolutionizing both fields. Their success underscores the potential of this interdisciplinary approach.
9. Ethical considerations: The emphasis on public safety in civil engineering can contribute to discussions on responsible AI development.
Let's look at some real-world examples:
The collapse of the Tacoma Narrows Bridge in 1940 led to revolutionary changes in how we approach structural dynamics. Similarly, early failures in AI deployment (like biased algorithms) are reshaping how we approach AI development and ethics.
The construction of the Channel Tunnel required unprecedented international cooperation and problem-solving. Tech companies facing global expansion and cross-cultural challenges could learn from this approach.
The implementation of seismic design in buildings has saved countless lives. This proactive approach to safety mirrors the need for robust security measures in software and AI systems.
As both a Florida-State Licensed Professional Engineer and an AWS certified ML Architect, I've straddled both worlds and seen firsthand how principles from structural engineering can revolutionize tech practices.
Responsibility: When a tech enthusiast makes a mistake in the code, the code breaks. However, if a structural engineer makes an engineering error, people die. The building you currently sit in or the bridge you pass thru on I4 this morning, consider thanking a structural engineer! A structural engineer who went rigerous schooling, more than 4 years of experience, and licensure exam of mathematical principles, tech-coding practices, physics, and project management.
Safety and Security: When was the last time you heard of a modern building collapsing due to poor engineering? It's rare, right? That's because structural engineers are masters of risk assessment and mitigation. We take every detail seriously. In an era where AI ethics and data security are paramount, this risk mitigation skillset and mindset is invaluable.
Merit and Licensure: Engineering license is the hardest license to practice the profession in any industry. To practise engineering in the US one has to first become a training engineer first after passing a state recognized training exam (FE Exam). Second is passing Professional Engineer exam + 4 years experience + endorsement from 5 other licensed engineers - gets an engineering license! This is harder than the residency requirement of medical doctors, bar requirement of the lawyers, etc.
Systems Thinking: We are trained to view complex projects as interconnected systems, and view each system contributing to the whole. The same thought process in tech can be compared only to senior level tech VPs or ML-system design architects in combination with software developer who executes the code at finer level.
Just as a bridge is a complex system of interconnected components, so is a large-scale software architecture. How many tech projects have failed because tech professionals didn't account for all the moving parts? Licensed structural engineers are trained to see the big picture while managing intricate details - a crucial skill that applies in the tech leadership.
Regulatory compliance: Tech companies often struggle with new regulations. But for civil and structural engineers, working within strict building codes is second nature. This experience of compliance can be a game-changer when dealing with emerging tech laws and data privacy regulations.
Project management: Large-scale infrastructure projects require meticulous planning, vision, and execution, skills and mindset that transfer well to software development lifecycles which are relatively of shorter duration.
Quality control: Engineers are trained to ensure high standards of safety and reliability when producing plans and engineering calculations, which is vital in developing robust tech solutions.
Interdisciplinary collaboration: Structural engineers often work with various specialists, fostering skills in cross-functional team leadership. Engineers often work with diverse teams, including architects, contractors, clients, and regulatory agencies. This experience jives will to help tech companies foster collaboration and manage complex relationships.
Public Engagement: An engineer's ability to communicate technical information to non-technical audiences can be valuable for engaging with stakeholders and building trust.
5. Data-Driven Decision Making:
Simulation and Modeling: Structural engineers often relay on computer simulations and modeling (finite element modeling) to predict the behavior of structures under wind, earthquakes, and gravity. This engineering simulation is an art as well as sciencce just like developing ML and AI models.
Real-World Testing: Testing the waters and validating designs is very similar to deploying the models for the real world with an exception that there is no scope of error. Therefore, enough contigency and redundancy is built in a complex system of infrastructure that even if a smaller system fails, it would not stop the larger system from running.
6. Long-term thinking: Infrastructure projects are designed not for next year but to serve for several decades. Long-term sustainability and scalability in tech have a lot to learn from this approach of building once and evolving forever.
7. Resource optimization: Structural engineers like me are trained to manage resources efficiently. Planning for each and every component on the critical path in the product development cycle is often overlooked piece in tech when forming strategies for optimizing organizational and computational resources.
8. Practical problem-solving: Engineers are experienced to find pragmatic solutions, which can help ground AI and tech development in real-world applications. These parallels aren't just theoretical. Companies like Autodesk have successfully bridged the gap between engineering and software, revolutionizing both fields. Their success underscores the potential of this interdisciplinary approach.
9. Ethical considerations: The emphasis on public safety in civil engineering can contribute to discussions on responsible AI development.
Let's look at some real-world examples:
The collapse of the Tacoma Narrows Bridge in 1940 led to revolutionary changes in how we approach structural dynamics. Similarly, early failures in AI deployment (like biased algorithms) are reshaping how we approach AI development and ethics.
The construction of the Channel Tunnel required unprecedented international cooperation and problem-solving. Tech companies facing global expansion and cross-cultural challenges could learn from this approach.
The implementation of seismic design in buildings has saved countless lives. This proactive approach to safety mirrors the need for robust security measures in software and AI systems.