The IEEE (Institute of Electrical and Electronics Engineers) is a leading organization that brings together experts to discuss and develop new technologies. I enjoy attending IEEE webinars—not just for learning, but because I’m genuinely curious about the innovations shaping our world. This past month, I attended two particular webinars that piqued my interest: “The Automotive Industry in Turmoil – Chiplets to the Rescue?” and “Agentic AI Safety.”
I’ve been fascinated by transportation—how we get from one place to another. Over the years, we’ve seen incredible advancements, from traditional cars to self-driving ones like Waymo. But I believe the real transformation is still ahead. During my undergraduate studies in electronics engineering, I developed a strong interest in the Internet of Things (IoT), which plays a key role in smart transportation. These IEEE sessions gave me a deeper understanding of the latest challenges and innovations in the industry.
The Automotive Industry in Turmoil – Chiplets to the Rescue?
The automotive industry is facing a huge challenge with semiconductor shortages. Traditionally, cars have relied on large, single-piece chips, but supply chain disruptions have made this approach unsustainable. This webinar discussed how chiplets—a modular way of designing semiconductors—can help. Instead of relying on one big chip, chiplets use smaller, specialized modules that work together, improving efficiency and lowering costs.
Many big tech companies, including Intel, AMD, and TSMC, are investing in chiplet technology to solve these shortages. For the automotive sector, this means better, faster, and more reliable computing power for electric vehicles (EVs), self-driving technology, and advanced driver-assistance systems (ADAS). The webinar featured professionals from these leading companies who shared insights on how chiplets will shape the future of automotive technology.
SLM in Field Fleet Analytics
SLM in Field Fleet Analytics generally refers to Service Level Management (SLM) within the context of field fleet analytics, which is the practice of managing and ensuring the performance of services provided by a fleet of assets or resources, often in real-time.
- Service Level Management (SLM) involves defining, agreeing upon, and monitoring specific performance metrics or targets (service levels) that an organization commits to deliver. These service levels could include parameters like response times, availability, and efficiency. SLM ensures that the performance of services consistently meets agreed expectations.
- Field Fleet Analytics involves the use of data analysis to track and optimize the performance of assets or resources that are deployed in the field, such as equipment, machinery, or any mobile resources used for various tasks. The analytics look at data like operational efficiency, utilization rates, maintenance cycles, and cost management, to name a few.
Combining SLM with Field Fleet Analytics means using data analytics to monitor and ensure that the field assets or resources meet the defined service levels—whether that’s maximizing uptime, minimizing costs, or optimizing performance. By doing so, businesses can improve operational efficiency, reduce downtime, and ensure that resources are deployed effectively.
Agentic AI Safety
Agentic AI Safety refers to the measures and strategies implemented to ensure that agentic AI systems—those that can make independent decisions and act autonomously—operate in a safe, predictable, and ethical manner. As agentic AI becomes more advanced and is used in critical systems like autonomous vehicles, healthcare, and finance, safety becomes a primary concern. The goal of Agentic AI Safety is to mitigate risks and ensure that these AI systems do not cause harm to humans, the environment, or themselves, even as they make decisions without direct human intervention.
Key aspects of Agentic AI Safety include:
- Ethical Decision-Making: Ensuring that AI systems make decisions based on ethical guidelines and respect for human rights, avoiding actions that could lead to harm or injustice.
- Accountability: Developing systems to monitor and track the decisions made by AI, making sure that there is clarity on who or what is responsible for its actions, especially in case of failures or unintended consequences.
- Transparency: Ensuring that the decision-making process of AI systems is understandable and explainable to humans, so that their actions can be trusted and verified.
- Safety Protocols: Implementing fail-safes, testing, and redundant systems to make sure that AI systems behave predictably and safely, even in unexpected or hazardous situations. This might include backup systems or emergency stop mechanisms.
- Regulations and Standards: Developing and adhering to industry regulations, safety standards, and guidelines to ensure the safe deployment and operation of autonomous AI systems.
In practical terms, Agentic AI Safety becomes critical in areas such as autonomous vehicles (where AI must make split-second decisions), healthcare (where AI might prescribe treatments or make diagnoses), and military applications (where AI systems might make decisions with life-or-death consequences).
The focus on Agentic AI Safety is growing as AI systems become more autonomous, and it’s a field that aims to ensure these systems benefit society while minimizing risks.
How Chiplets and Agentic AI Work Together
As I listened to both webinars, I couldn’t help but wonder: what if chiplets and agentic AI worked together? At first, they might seem like two separate technologies, each tackling different challenges, but as I thought more about it, I realized that these two innovations could complement each other in powerful ways.
In my mind, I began to picture a future where chiplets provide the foundational hardware for AI systems, allowing them to scale and process data more efficiently. With the growing complexity of self-driving cars and other autonomous systems, the computational power required to handle the massive amounts of data they generate is staggering. AI models need to analyze sensor data, interpret traffic patterns, and make real-time decisions—things that require immense processing speed and power. Chiplets, with their modular design, could provide the perfect solution, offering a way to build processors that can be customized for specific AI needs, allowing them to perform faster and more efficiently.
What if, instead of relying on a single monolithic chip, we had chiplets designed to process specific tasks required by agentic AI? One chiplet could focus on processing sensor data, while another could handle decision-making algorithms, all working together to improve the performance of autonomous systems. The beauty of chiplets is that they allow for flexibility and adaptability—something that is essential in an environment as dynamic and complex as self-driving vehicles.
I could also see how integrating these technologies could reduce the overall cost and improve the scalability of autonomous systems. If chiplets make hardware more modular and efficient, and agentic AI makes intelligent decisions more autonomously, the synergy between them could drastically speed up the development and deployment of safer, more reliable autonomous vehicles.
It was exciting to think about how these two technologies could evolve in tandem. They could not only improve the capabilities of self-driving cars but also make them safer, more efficient, and more responsive to changing conditions. As I pondered the potential, I couldn’t help but feel a sense of optimism about the future of transportation—where AI-driven decisions powered by chiplet-based processors could make our roads smarter and safer for everyone.
Conclusion
No matter how advanced technology becomes, safety should always be the priority. People will continue to innovate and create incredible machines, but ensuring that these machines protect human lives is what truly matters. That’s why I’m passionate about learning and contributing to advancements that make transportation safer.
Since I have been involved with safety in my current workplace, which is in transit, I have been immensely grateful to have a place where I contribute to decision-making. There is still a lot to be done, but my work will not stop until it is safe for every person to travel anywhere. Whether it’s through chiplets improving AI performance or agentic AI making smarter decisions, the ultimate goal should be to create a world where technology serves and safeguards us.
Attending IEEE webinars helps me stay informed and work toward this vision—one where innovation and safety go hand in hand. Seeing industry leaders share their expertise reinforces my belief that no matter how far technology advances, protecting people should always be at the heart of every innovation. As an engineer, my aim is to contribute to technologies that prioritize safety—because no matter what humans decide to do with machines like cars, our responsibility is to ensure they protect us.