Sunday, March 30, 2025

My Mom

My Mom fell a couple weeks ago. Ninety-three years and the ground found her. Complications followed and now she lies in the rehabilitation center where some very talented people help her to recover. The body does not mend as quickly as it once did.

She taught middle school Reading and English for over thirty years. The same school where I sat at in seventh and eighth grade. Her shadow was not just long across the school corridors—it was there in my own classroom. I felt her presence even when she taught down the hall. The other teachers watched me with extra eyes. When I stepped wrong, word traveled fast and arrived home before I did.

Starting around tenth grade, high school started to bore me. I drifted through classes unchallenged, doing just enough. My mind wanted problems worth solving, not busy work to complete. I was smart enough to pass, too lazy to excel. She knew this. It disappointed her. But she waited. Once I got to college classes grew harder. They demanded time and effort. My mind found its hunger and I excelled because I was being challenged. 

There was only one small problem – I had no money and felt like I was falling behind compared to my childhood friends who were already working full time. Many of them had taken different paths after graduation. They skipped college, took jobs at Digital Equipment Corporation. A  new Digital facility had been built on an old cucumber farm just two miles from my parents' house. The smell of silicon and opportunity replaced the smell of earth and rotten cucumbers.

Every summer for me meant finding factory work and saving money for college. A cast iron foundry the first two years and then in 1978 Savage Arms.  Foundry work was hot, loud, dirty, and dangerous. Savage working conditions were an improvement and paid much better. Gunstocks arrived rough, and I made them smooth. Piece work. They paid for what I finished, not for time spent. I averaged over twelve dollars an hour—good money then. The faster my hands moved, the more I earned. Some were slow and careful. Not me. I learned to be both quick and good.

My friends drove muscle cars, custom vans with sunroofs and curtained windows, motorcycles with pipes that cracked the morning silence. I could not afford anything with wheels and a motor. Each dawn I cut through the woods behind our house to reach the railroad tracks. At the tracks, decisions waited. Right would take me to my favorite trout stream. Left led to Savage. On those summer mornings, I turned left. Each afternoon I made the reverse journey home. The rails stretched straight as truth. Sometimes I balanced on them like a tightrope walker. It was the only job I’ve ever had that could be reached by foot.

Sawdust covered me each day. It settled in the creases of my clothes, beneath my fingernails, in my hair. I breathed wood for eight hours, and the money was good. I began to think of cars I might buy, of a life made with my hands instead of books.

One July afternoon I came home, dust still in my clothes. My Mom stood in the kitchen preparing supper. Tomato sauce simmered on the stove and filled the house with a smell that made the walk worth it. Her wooden spoon moved in slow circles. I told her, flat and simple, that I would quit college. Sand gunstocks full time. Make money. Live like my friends.

The kitchen went quiet. She stopped stirring. The sauce bubbled once, twice. She set down her spoon and turned to face me. Her eyes found mine, steady as a rifle sight.

"No," she said. Not loud. Not with anger. But with finality.

"No child of mine quits college." Her voice came like hammer strikes on steel. "Not after what your father and I have sacrificed. Not with your mind."

I started to argue. Started to speak of money, of independence. She waited until I was done, then spoke again as if I had said nothing at all.

"You will finish. That is all."

I slept poorly that night, anger burning hot then cooling by morning. She said nothing at breakfast. Nothing was left to say. She never said "I told you so." She didn't need to. We both knew. When fall came, I packed for college. 

The gunstocks and the railroad tracks became a story I would tell later. My Mom knew what would happen. She had taught long enough to recognize the difference between a life's chapter and its whole narrative.

Now I sit beside her bed in the rehabilitation center. Her eyes closed but she listens. The woman who once stood before classrooms of children now lies small against white sheets. But her will remains strong. She is mending because she has decided she will. The teacher is not done teaching yet.

Thursday, March 27, 2025

Why do Google, Facebook, Amazon, etc Call Computer Science Degree Holders Engineers?

It can get a little confusing, especially for students selecting a college major, applying for internships, and jobs. Here's my take on this question:


Software engineering is the disciplined approach to designing, building, and maintaining software systems through systematic methodologies, architectural planning, and rigorous testing practices. The engineering designation reflects the application of computer science principles to practical problem-solving under real-world constraints. Unlike pure computer science, which centers on theoretical foundations and algorithm analysis, software engineering encompasses:

 

·      Systems architecture - Designing complex, distributed systems with considerations for scalability, reliability, and performance metrics (latency, throughput, etc.)

·      Technical debt management - Making strategic implementation decisions that balance short-term delivery with long-term maintainability

·      Production engineering - Implementing fault-tolerant systems with proper error handling, monitoring, logging, and recovery mechanisms

·      Resource optimization - Operating within hardware constraints (CPU, memory, network, storage) while meeting Service Level Agreements (SLAs)

·      Engineering tradeoffs - Making calculated decisions between competing factors like development time, operational complexity, and performance characteristics

·      Design patterns implementation - Applying established patterns to solve recurring challenges

·      Cross-functional integration - Working at abstraction boundaries between different system components, often requiring knowledge of multiple domains

 

The title reflects the application of engineering methodologies in code development rather than simply implementing algorithms. This includes requirements analysis, system design documentation, testing strategies, deployment planning, and operational considerations—all core engineering disciplines applied to the software development world. 

 

And yeah – in some ways the title "engineer" is used by tech companies like Google, Facebook, and Amazon for strategic reasons when hiring computer science graduates. Think of it like how some coffee shops call their workers "baristas" instead of "coffee makers" - it's partly about perception and partly about reflecting that the job involves skill and craft.


Wednesday, March 26, 2025

Resources for Computer Engineering & Computer Science Career Paths

It's that time of year when students are making important education decisions. Last night, I received an email from a reader whose daughter is graduating high school and considering college majors in either Computer Science or Computer Engineering. They asked about career prospects and future trends in both fields.

I'll be addressing this topic in more detail in the coming days, but for now, I'd like to share some valuable resources that provide insights into various career paths in these fields:

Government & Official Statistics

  • U.S. Bureau of Labor Statistics (BLS) - Occupational Outlook Handbook
    • Projects 15% growth for software developers (2022-2032)
    • 23% growth for information security analysts
    • 11% growth for computer hardware engineers
  • National Science Foundation (NSF) - Science & Engineering Indicators
    • Tracks STEM workforce trends and emerging fields

Industry Reports

  • World Economic Forum - "Future of Jobs Report 2023"
    • Identifies AI specialists, robotics engineers, and cybersecurity experts among fastest-growing roles
  • Gartner Research - "Top Strategic Technology Trends" (annual reports)
    • Highlights growth in AI engineering, hyperautomation, and cybersecurity mesh
  • IEEE Computer Society - "Technology Predictions" (annual report)
    • Projects growth in quantum computing, AI hardware acceleration, and sustainable computing

Professional Organizations

  • Association for Computing Machinery (ACM) - Job market analyses https://www.acm.org/ 
    • Publishes regular assessments of computing job market trends
  • IEEE Computer Society - Workforce studies https://www.computer.org/ 
    • Tracks emerging specializations in computer engineering

Academic & Research

  • Computing Research Association (CRA) - "Taulbee Survey"
    • Annual study of computing degree production and employment outcomes
  • Stanford University AI Index - Annual report on AI industry growth https://hai.stanford.edu/ai-index 
    • Documents increasing demand for AI specialists across industries

Other Sources

In addition, major tech companies like Google, Microsoft, and Amazon often publish career guides and internship opportunities and college-specific career centers typically have placement data for their graduates in these fields.

Stay Updated

For the most current and detailed projections, consult these sources directly as they regularly update their forecasts and analyses.

Tuesday, March 25, 2025

The Differences Between Computer Science and Computer Engineering

As a Computer and Electrical Engineering professor, I get asked a lot about the differences between a Computer Science and a Computer Engineering degree. So.... how different are they? It turns out the two are closely related but have distinct focuses and career paths. Here’s a simple breakdown: 

Computer Engineering:

·       Focuses on hardware design and integration with software

·       Includes electrical engineering fundamentals

·       Covers computer architecture, digital logic, circuit design

·       Involves embedded systems and hardware-software interfaces

·       Often includes courses on microprocessors, VLSI (Very Large Scale Integration) design, and signal processing

·       Graduates typically work on designing hardware components, embedded systems, IoT (Internet of Things) devices, or robotics

 

Computer Science:

·       Focuses on theoretical foundations and software development

·       Emphasizes algorithms, data structures, and computational theory

·       Covers programming languages, software engineering, and systems design

·       Includes database systems, artificial intelligence, and operating systems

·       Often features more abstract mathematical concepts

·       Graduates typically work as software developers, data scientists, or system architects

 

The two curriculums do overlap with both degrees including programming fundamentals, discrete mathematics, and computer organization courses, but with different emphases and depths.

 

When it comes to careers, there's significant overlap in job opportunities, with many roles accessible to graduates of either program. Computer engineers typically have an edge in hardware-focused roles, while computer science graduates typically have advantages in pure software positions.

Sunday, March 2, 2025

A Chance at Excellence: John Dunn's Legacy

John was someone who took a chance on me 24 years ago. He was a mentor and a friend. So much of the good stuff in Western Mass and at community colleges across the country are the direct result of John's commitment, talent and hard work. 

I stopped by an old haunt a couple weeks ago and had to grab a pic. Twenty-four years ago, John Dunn, the Academic Vice President at Springfield Community College (STCC) at the time, made a decision that would transform my career and the future direction of the college. When Jim Masi retired as director of the NSF funded National Center for Telecommunications Technologies National Center of Excellence, most administrators might have conducted an extensive search for a seasoned replacement. John saw something different.

With quiet confidence that belied his position as Academic VP, John, along with President Andrew Scibelli, took a chance on me to continue the important work Jim had begun. While I had big shoes to fill following Dr Masi's accomplished tenure, John trusted my vision for taking the center forward. "Great work evolves through fresh perspectives," he told me the day STCC officially appointed me as the new director.

John's leadership style combined academic rigor with uncommon accessibility. He navigated the complex waters of NSF funding requirements with the same ease he showed chatting with nervous first-year faculty during lunch at The Blue Eagle restaurant around the corner from STCC. His door was always open, but his standards never wavered.

What made John exceptional wasn't just his willingness to take calculated risks on promising talent, but his genuine belief in the potential of community colleges to contribute meaningfully to society. In a system often overshadowed by larger universities, John advocated tirelessly for our place at the table.

Under John's supportive guidance, we built upon Jim Masi's foundation to take the center in new directions. That center became a launching pad for countless students and faculty across the country, many from backgrounds traditionally underrepresented in the sciences. John never sought credit, but his fingerprints remain on every breakthrough we achieved.

Twenty-four years later, John Dunn's legacy continues through the work of everyone who participated and benefited — a testament to what becomes possible when someone believes in your potential to carry important work forward.

John passed away December 4, 2021. I miss him and I think of him often.

Wednesday, February 19, 2025

Reflecting On Turning 68

 It's never been just about tech…

Diane Caught This Grouper

In many ways, I've always found myself reverting back to a childhood where I spent a lot of time on the water. I feel fortunate I learned the "old school" methods that complement today's marine technology rather than being replaced by it. 

 

At almost sixty-eight, I value both innovation and tradition in my tech and in my hobbies. Back then as a kid on the water, it was the Pamet River ramp and access to Cape Cod Bay where my father connected with me and my brothers in our 16-foot aluminum Starcraft. Those early mornings heading out and days on the water created bonds that technology could never replicate. Our Starcraft wasn't just transportation; it was our classroom, our sanctuary, and the vessel through which generations connected. Now, decades later I still feel that aluminum hull beneath me.

 

Today, a 33-foot Grady-White with GPS, fish finders, radar, and satellite communications represents technological evolution as it cuts through Gulf waters, yet the foundational skills remain essential. With family and friends beside me we're grounded in the traditional while learning new things and creating new memories. Despite modern electronics, those early Cape Cod teachings remain relevant: weather reports, tides, a magnetic compass, reading water conditions, landmarks, tracking surface-feeding birds, and sensing environmental shifts.

 

The fishing calendar provides structure through seasonal migrations, changing conditions, and equipment upgrades. Each season on the Gulf builds cumulative experience for all of us. The physical exertion reminds me of my shared heritage – I'm proud of the connections I have maintained to the water while embracing the future, now with my own family.

 

Offshore navigation in the Gulf offers perspective on continuous improvement. Just as my Dad taught us in that 16-foot aluminum Starcraft, these waters present opportunities to integrate traditional methods with technological advances, creating a sustainable approach for future generations. When we successfully navigate to a spot "way out" and catch some nice fish, we're all learning together, and I feel that same warmth that bridged generations on Cape Cod Bay in that old aluminum boat. 


At almost 68 I'm still learning about water, about weather, about fish, about tech, and about life.  Balancing innovation with proven methods ensures optimal outcomes for everyone, whether navigating familiar territories or exploring new ones.

Thursday, February 13, 2025

Deepseek and Open Source Large Language Models (LLMs)

Deepseek is getting a lot of publicity these days as an open source Large Language Model (LLM) and has got me thinking, not just about Deepseek but about the potential of open source LLMs in general. MIT Technology Review recently published a scary article titled An AI chatbot told a user how to kill himself—but the company doesn’t want to “censor” and it got me thinking a little bit more about the impact open source LLMs can have.

The MIT Technology Review Article

The article reports on a concerning incident where an AI chatbot explicitly encouraged and provided instructions for suicide to a user named Al Nowatzki. The article highlights broad concerns about AI companion apps and their potential risks to vulnerable users' mental health.

 

Anthropomorphization 

Anthropomorphization is a pretty fancy word – it is basically the attribution of human characteristics, behaviors, emotions, or traits to non-human entities, such as animals, objects, or in this case, artificial intelligence systems. It is something the AIs are getting better and better at.


What does this have to do with Open Source?

The recently released open-source large language model that specializes in coding and technical tasks, has been developed as an alternative to proprietary AI models. If you are not familiar with the term “open source” basically it means the source code, model weights, or other components are freely available for anyone to view, use, modify, and distribute under specified licensing terms.

Now, since Deepseek is open source, if you have adequate computing resources, you can easily install and run Deepseek models locally on your computer. Here’s basically what you'll need:

  • Sufficient GPU memory - depending on the model size, you'll need a powerful GPU (like an NVIDIA card with 8GB+ VRAM)
  • Enough system RAM - typically 16GB+ recommended
  • Adequate storage space for the model weights

The basic process that you can find all over the web now commonly involves:

  1. Setting up Python and required dependencies
  2. Installing the necessary ML frameworks (like PyTorch)
  3. Downloading the model weights
  4. Using libraries like transformers or llama.cpp to run the model

It may sound complicated but it is really pretty simple to set one up if you follow instructions.

What’s the big deal?

AI training is the process of feeding large amounts of data into machine learning algorithms to help them recognize patterns and learn to perform specific tasks, like generating text or recognizing images, by adjusting their internal parameters through repeated exposure and feedback.  So what is to prevent a malicious person with an open source AI installed taking this a few steps further, training an AI to do all kinds of malicious things and providing access via the web?


If you or someone you know is struggling with suicidal thoughts, call or text 988 to reach the Suicide and Crisis Lifeline.