I'm Retiring From My Full Time Engineering Faculty Position at Holyoke Community College

“Often when you think you’re at the end of something, you’re at the beginning of something
else.” – Fred Rogers

After an enriching and fulfilling career spanning five decades, I am announcing my retirement from my full-time engineering faculty position at Holyoke Community College (HCC) effective August 30, 2024. This decision comes with mixed emotions, as I reflect on the journey that has shaped not only my professional life but also my personal growth.

Teaching has been more than just a job; it has been a vocation that allowed me to inspire and nurture countless students, guiding them through the complexities of engineering principles and practices. My time in academia has been marked by moments of profound satisfaction, seeing students evolve into competent and confident engineers and technologists, ready to make their mark in the world. Each graduation ceremony, each successful project, and each breakthrough in understanding (and of course each belly-flop failure) has reinforced my belief in the power of education.

The field of engineering itself has undergone tremendous transformation during my tenure. From the early days of teaching with chalk and blackboards to leveraging cutting-edge technology and online platforms, I have witnessed and embraced these changes, ensuring that my students received the most current and relevant knowledge. The shift towards digital learning, in particular, has been a remarkable journey, one that demanded continuous adaptation and learning on my part.

Retirement from HCC marks the beginning of a new chapter, where I am eager to channel my energy and expertise into some exciting consultancy projects, writing, mentoring, and of course some part-time teaching. The flexibility will allow me to spend more time with my family and yeah (!) - get in a little more fishing. 

Will this be a “sabbatical”  or a permanent change? Having experienced "retirement" in the past and not having it “stick”, I've learned to appreciate the unpredictability of life. This journey has taught me to embrace uncertainty and always stay open to whatever opportunities and possibilities the future may bring.

My academic career has been a tapestry woven with dedication, passion, and countless rewarding experiences. I carry with me a wealth of memories and the profound satisfaction of having contributed to the field of engineering and to the lives of so many young minds. 

An Exponential Leap: The Emergence of AGI - Machines That Can Think

Tech companies are in a rush. They're trying to lock in as much electricity as they can for the next few years. They're also buying up all the computer components they can find. What's all this for? They're building machines that can think and referring to the tech as Artificial General Intelligence, or AGI.

On June 3 Ex-OpenAI researcher (yeah he was fired) Leopold Aschenbrenner published a 162 page interesting document titled SITUATIONAL AWARENESS The Decade Ahead. In his paper Aschenbrenner describes AGI as not just another incremental tech advance – he views it as a paradigm shift that's rapidly approaching an inflection point.

I’ve read the whole thing - here's my short list of highlights by topic.

Compute Infrastructure Scaling: We've moved beyond petaflop systems. The dialogue has shifted from $10 billion compute clusters to $100 billion, and now to trillion-dollar infrastructures. This exponential growth in computational power is not just impressive—it's necessary for the next phase of AI development.

AGI Timeline Acceleration: Current projections suggest AGI capabilities surpassing human-level cognition in specific domains by 2025-2026. By the decade's end, we're looking at potential superintelligence—systems that outperform humans across all cognitive tasks.

Resource Allocation and Energy Demands: There's an unprecedented scramble for resources. Companies are securing long-term power contracts and procuring voltage transformers at an alarming rate. We're anticipating a surge in American electricity production by tens of percentage points to meet the demand of hundreds of millions of GPUs.

Geopolitical Implications: The race for AGI supremacy has clear national security implications. We're potentially looking at a technological cold war, primarily between the US and China, with AGI as the new nuclear equivalent.

Algorithmic Advancements: While the mainstream still grapples with language models "predicting the next token," the reality is far more complex. We're seeing advancements in multi-modal models, reinforcement learning, and neural architecture search that are pushing us closer to AGI.

Situational Awareness Gap: There's a critical disparity between public perception and the reality known to those at the forefront of AGI development. This information asymmetry could lead to significant societal and economic disruptions if not addressed.

Some Technical Challenges Ahead:

- Scaling laws for compute, data, and model size

- Achieving robust multi-task learning and zero-shot generalization

- Solving the alignment problem to ensure AGI systems remain beneficial

- Developing safe exploration methods for AGI systems

- Creating scalable oversight mechanisms for increasingly capable AI

An over reaction by Aschenbrenner?  Some think so. Regardless - this stuff is not going away and as an educator and technologist, I feel a responsibility to not only teach the tech but also have students consider the ethical and societal implications of this kind of work. The future isn't just coming—it's accelerating towards us at an unprecedented rate. Are we prepared for the AI  technical, ethical, and societal challenges that lie ahead?

How Is Battery Percentage Measured On My Device?

I get this question often - how is battery percentage measured and calculated by devices like our
phones, leaf blowers, electric cars, etc?  

It turns out most modern Battery Management Systems (BMS) use a combination of four variables to estimate the remaining charge in a battery.

 

1.    Voltage Measurement: Used to provide quick and direct State Of Charge (SOC) estimates  but can be inaccurate due to load variations. There are two measurements typically  considered:

• Open-Circuit Voltage (OCV): This measures the voltage when the battery is not under load. Each battery type has a characteristic voltage curve that relates voltage to SOC.
• Under-Load Voltage (ULV): This measures the voltage while the battery is under load. Compensation is required to account for the voltage drop due to internal resistance. 

2.    Coulomb/Charge Counting:  Precise over short periods but can drift over time due to measurement inaccuracies.

• Coulomb/Charge Counting involves tracking the current flowing in and out of the battery. By integrating the current over time, you can estimate the total charge added or removed. This method needs a known initial state of charge to be accurate.      

3.    Impedance Tracking: Provides additional data to refine SOC estimates but requires complex algorithms and computations.

• Impedance Tracking uses the battery's internal impedance (reciprocal resistance) which changes with the state of charge. By measuring the impedance, the state of charge can be estimated.

4.    Temperature Compensation: Battery performance and voltage readings can be significantly affected by temperature. 

• Temperature sensors are used to adjust the SOC calculations.

Battery Capacity

Battery capacity C for a new battery is a given and specified by the battery manufacturer in ampere-hours (Ah) or milliampere-hours (mAh) units, depending on the size of the battery. As an example, the iPhone 15 Pro is equipped with a single battery rated at 3274mAh.  If you are wondering about your Tesla or Prius - batteries are wired in parallel – for example a 2020 Long Range Tesla Model 3 battery pack has 46 cells in parallel with each cell rated at 5 Ah. So that Tesla battery pack has about 46x5 Ah = 230 Ah capacity. Over time, battery capacity will decrease.

 

Some Variables

Let’s look at a couple of simple calculations used by our devices to calculate battery charge/percentage. Before we look at the formulas, let’s identify some variables used in the calculations.

 

Initial Calibration:

• Battery starts fully charged (100% SOC).
• OCV is measured to establish a reference point.

Discharge Phase:

• Voltage is continuously monitored.
• Coulomb counting tracks the charge removed.
• Impedance is checked periodically to refine the SOC estimate.
• Temperature compensation adjusts the readings. 

State of Charge Calculation:

• Combining the voltage, coulomb count, and impedance data, the BMS computes the SOC.
• This value is then translated into a percentage to display the remaining battery life. 

Finally - A Couple Formulas

There are two formulas commonly used - the first uses current (Coulomb Counting) and the second voltage (Voltage-Based SOC Estimation)

 

Formula for Coulomb Counting Estimation:

Formula for Voltage-Based SOC Estimation:

Battery percentage calculations rely on sensor measurements that account for the various factors affecting battery performance. Using real time sensor data, BMS algorithms continue to advance, providing estimates of remaining battery charge.

Chaos Engineering: Embracing Disorder In Resilient Systems

In my last post I wrote about resilience engineering – interesting stuff but can it be tested? Of course!

Growing up in the 1960s one of my favorite shows was Get Smart, a classic comedy TV series parodying the spy genre, following bumbling secret agent Maxwell Smart and his adventures against the villainous organization KAOS, The International Organization of Evil.

Like the TV show, in the world of engineering, unpredictability and chaos are often viewed as adversaries to be conquered. However, a paradigm shift has occurred in recent years, with some engineers embracing chaos as a means to build more resilient systems. This approach, aptly named "Chaos Engineering," is a discipline that advocates deliberately injecting failure into systems to test their robustness and identify weaknesses before they cause real-world disasters.

 

At its core, Chaos Engineering is about embracing the inherent unpredictability of complex systems and using it to our advantage. Instead of waiting for failures to occur in production, engineers proactively introduce controlled chaos to observe how systems respond under adverse conditions. By doing so, they gain valuable insights into system behavior and dependencies, enabling them to build more resilient architectures.

[image credit: https://www.bmc.com/blogs/chaos-engineering/]

The principles of Chaos Engineering are grounded in scientific experimentation. Engineers start by defining a hypothesis about how their system should behave under stress. They then design and execute experiments that simulate real-world failures, such as server crashes, network latency spikes, or database outages. These experiments are carefully controlled to minimize the impact on users while still providing meaningful insights into system behavior.

 

One of the key benefits of Chaos Engineering is its ability to uncover hidden weaknesses in distributed systems. In today's world of microservices and cloud computing, systems are becoming increasingly complex, with numerous interdependencies and failure points. Traditional testing methods often fail to uncover these issues until they manifest in production. Chaos Engineering helps mitigate this risk by actively probing for weaknesses in a controlled environment.

 

Netflix is perhaps the most famous proponent of Chaos Engineering, with its "Chaos Monkey" tool being widely used to inject failures into production systems. By regularly causing disruptions in their infrastructure, Netflix ensures that engineers are constantly aware of potential weaknesses and can design systems to withstand them. This approach has helped Netflix achieve unprecedented levels of uptime and scalability, even in the face of unexpected events like server outages or network failures.

 

However, Chaos Engineering is not without its challenges. Introducing chaos into a system requires careful planning and coordination to ensure that experiments do not cause widespread outages or data loss. Moreover, interpreting the results of chaos experiments can be complex, as system behavior is often non-linear and influenced by numerous factors.

 

Despite the challenges, the benefits of Chaos Engineering are undeniable. By proactively testing for weaknesses and building resilience into their systems, engineers can improve uptime, reduce downtime, and ultimately deliver a better experience for users.

Resilience Engineering: Understanding System Failures, Causes, And Enhancing Resilience For Better Recovery

Resilience engineering is a multifaceted concept that has gained increasing importance in various fields, particularly in engineering, systems design, and risk management. It refers to the ability of a system, organization, or individual to adapt and recover from unexpected challenges, disturbances, or failures while maintaining essential functions and performance. In essence, resilience engineering aims to understand how systems fail, why they fail, and how they can be designed or improved to withstand and recover from failures effectively. 

[image source: https://rote.se/upload/images/resilience-engineering.png]

One of the core principles of resilience engineering is recognizing that failures are inevitable in complex systems. Instead of focusing solely on preventing failures, resilience engineering emphasizes building systems that can gracefully degrade or adapt when failures occur. This approach acknowledges the interconnectedness and unpredictability inherent in complex systems, whether they are technological, organizational, or socio-technical.

 

At the heart of resilience engineering is the concept of "anticipating the unexpected." This involves actively seeking out potential sources of failure, understanding their potential impacts, and developing strategies to mitigate or recover from them. Rather than relying solely on past data or traditional risk assessment methods, resilience engineering encourages a proactive and dynamic approach to risk management that takes into account uncertainties and emergent properties of complex systems.

 

Central to resilience engineering is the idea of "resilience barriers" or "safety margins." These are mechanisms, redundancies, or practices built into a system to prevent or mitigate the escalation of failures. Unlike traditional safety measures, resilience barriers are designed to be flexible and adaptable, allowing for rapid response and recovery in the face of unforeseen events. Examples of resilience barriers include backup systems, cross-training of personnel, flexible protocols, and decentralized decision-making structures.

Resilience engineering also emphasizes the importance of learning from failures. Instead of viewing failures as purely negative events to be avoided, resilience engineering sees them as valuable opportunities for learning and improvement. This involves conducting thorough post-event analyses, sharing insights across organizational boundaries, and implementing changes to prevent similar failures in the future. By fostering a culture of learning and adaptation, resilience engineering helps organizations become more agile and responsive to change.

 

In recent years, resilience engineering has found applications in diverse domains, including aviation, healthcare, cybersecurity, and disaster management. For example, in aviation, resilience engineering has led to the development of Crew Resource Management (CRM) programs, which focus on improving communication, decision-making, and teamwork among flight crews to enhance safety and resilience in the face of unexpected events.

 

Interested in learning more? Here’s a 9 minute 51 secondf introductory video by Dr. David Woods, a professor in the Department of Integrated Systems Engineering at the Ohio State University. https://youtu.be/r8awKlk7JPM?feature=shared

 

Additonal videos from Dr Woods are posted on the YouTube C/S/E/L BackChannel  here.

 

As our world becomes increasingly interconnected and complex, the principles of resilience engineering will continue to be essential for ensuring safety, reliability, and effectiveness in all walks of life.

Community College Engineering Student Transfer

Yesterday I checked in via LinkedIn with a Holyoke Community College Engineering program
graduate who transferred to a nationally ranked top ten engineering university. The student is studying Electrical Engineering there and I asked how things were going. Here’s a screen shot of the response I got with identification information removed – including student name and the transfer university. Pretty cool!

The student compliments my two classes (Circuits 1 and Circuits 2) but there is so much more. Both classes are Calculus and Differential Equations based so the students need to really know their math stuff before I get them. The math, physics and chemistry instruction is  exceptional at Holyoke Community College – as it is at so many other community colleges in the country.  It is not just the STEM classes that prepare students for my classes though. To get their degree our students need to take additional courses including English Composition, History, Social Sciences and in some cases Business courses. These courses are critical, complementing the technical knowledge, skills and abilities gained in engineering courses, producing well-rounded professionals capable of addressing complex challenges with creativity, empathy, and ethical awareness.

 

I see it every day with students coming to my classes prepared to learn, solve problems, communicate and understand some pretty complex stuff. Amazing faculty doing amazing things in their classrooms makes it pretty easy for me to teach those classes.

We (community colleges) often face unjust criticism due to misconceptions. Despite offering quality education, we’re sometimes seen as inferior to four-year institutions. We provide valuable opportunities and options with smaller classes, dedicated faculty, and affordable tuition. And let's not forget transfer to four year institutions.

 

Thanks to the unnamed student – you certainly made the day!

Distributed Inference And Tesla With Some SETI Nostalgia

In this post, I’m setting aside any political stuff and focusing solely on tech.

 

In recent months, the electric vehicle (EV) market has seen a decline, marked by falling sales and an increase in unsold inventory. Tesla, in particular, has received a significant share of negative attention. During Tesla's first-quarter earnings call last week, Elon Musk diverged from the norm by highlighting Tesla's broader identity beyond its role in the automotive industry. He emphasized the company's engagement in artificial intelligence and robotics, suggesting that pigeonholing Tesla solely within the EV sector overlooks its broader potential.

 

Musk's suggestion to actively utilize Tesla's computational power hints at a larger strategic vision. He envisions a future where idle Tesla vehicles contribute to a distributed network for AI model processing, termed distributed inference. This concept could leverage the collective computational strength of millions of Tesla cars worldwide, extending the company's impact beyond transportation.

 

Very interesting – I drive maybe 1-2 hours per day, the rest of the time my car is not being used. What if all that computing horsepower could be used while I’m not using it? Musk’s concept brings up memories of the sunsetted SETI@home computer application. SETI was a distributed computing project that allowed volunteers to contribute their idle computer processing power to analyze radio signals from space in the search for extraterrestrial intelligence (SETI). SETI@home used data collected by the Arecibo Observatory in Puerto Rico and the Green Bank Telescope in West Virginia to search for patterns or anomalies that could indicate the presence of intelligent alien civilizations.

 

Participants in SETI@home downloaded a screensaver or software client onto their computers, which would then process small segments of radio telescope data during periods of inactivity. The processed data would be sent back to the project's servers for analysis. By harnessing the collective power of millions of volunteer computers around the world, SETI@home was able to perform computations on an unprecedented scale. The project was launched in 1999 by the University of California, Berkeley, and it quickly became one of the largest distributed computing projects in history. Although the original SETI@home project ended in 2020, its legacy lives on as an example of the power of distributed computing and the widespread public interest in the search for extraterrestrial life.

 

Musk's vision underscores Tesla's potential to revolutionize not only the automotive sector but also broader domains such as artificial intelligence and robotics. It signifies a strategic shift towards leveraging Tesla's resources and expertise in a SETI-like way to drive innovation and create value in new and unexpected ways.