The Dash Family: One Simple Way AI Detectors Analyze Punctuation

I've been hearing a lot lately about em dashes and AI....

The dash family, consisting of the em dash (—), en dash (–), and hyphen (-) has become significant in AI detection systems that determine if content was written by humans or machines.

·      The em dash (—), longer than its relatives takes its name from typography, where it occupies the width of the letter "M." Writers use it to replace commas, parentheses, or colons, adding emphasis or creating breaks in sentences.

·      The en dash (–) is shorter than the em dash but longer than a hyphen. Named for its width approximating the letter "N," it indicates ranges (2010–2020) or connections between words (Chicago–New York flight). Many writers often misuse this punctuation mark.

·      The hyphen (-), the shortest of the three, joins compound terms (cost-effective) or breaks words at line ends. Despite appearing simple, proper hyphen usage follows complex rules that writers frequently struggle with.

AI detection tools examine usage patterns of all three marks because language models often handle punctuation differently than human writers. Detection algorithms analyze the distribution and contextual placement of dashes. Human writers typically use each dash type with specific intent, while AI systems historically struggled with these patterns.

As language models have evolved, they've improved their punctuation capabilities. Modern AI can mimic human dash usage more convincingly, forcing detection tools to rely on more complex indicators beyond punctuation analysis. For writers concerned about their work being flagged, understanding these detection mechanisms helps. Using dashes according to proper style guidelines, rather than arbitrary patterns, remains the best approach.

The dash family shows how subtle language elements help distinguish between human and AI-written content, a revealing example of technology's impact on language.

The Birth of Modern Computer Communications: The Hayes Smartmodem

Another looking back post - it was 1982, and I was still trying to figure out what I wanted to do in life. At one time I thought I wanted to go to medical school but after working in a hospital microbiology lab, I realized that was not the path for me. Maybe I could do something in the communications field….

In the early 1980s, connecting computers over phone lines was a complex and frustrating process requiring specialized knowledge and equipment. That all changed in 1981 when Dennis Hayes and Dale Heatherington introduced the Hayes Smartmodem, a revolutionary device that would fundamentally transform how computers communicate. The Hayes Smartmodem 300 was the first modem to combine communication hardware with an intelligent microprocessor control system. Unlike previous modems that required manual configuration, the Smartmodem could be controlled through a standard command set—the now-famous "AT commands" (where AT stood for "Attention"). This innovation allowed software to directly control the modem, automating the complex process of establishing connections.

The AT Command Revolution

The AT command set, sometimes called the Hayes command set, revolutionized communications because it created a standardized way for computers to control modems through simple text commands.

 

Basic Structure and Function

AT commands follow a simple structure: they begin with "AT" followed by specific command
letters and parameters. For example:

·      ATD (Dial) - Instructs the modem to dial a number

·      ATH (Hang up) - Terminates the current connection

·      ATA (Answer) - Instructs the modem to answer an incoming call

·      ATZ (Reset) - Resets the modem to its default configuration

The genius of this system was its simplicity. Before Hayes, controlling modems required specialized hardware interfaces or complicated software. The AT command set turned modem control into simple text strings that any program could generate.

Historical Impact

When Dennis Hayes introduced this command set with the Smartmodem in 1981, he effectively created the first "smart" modem that could be programmed and controlled by software. This innovation:

• Allowed software to handle complex connection procedures automatically
• Enabled features like auto-dialing and auto-answering
• Created a standard that was widely adopted across the industry
• Made modems accessible to non-technical users

Legacy and Modern Applications

Remarkably, variants of the AT command set are still used today in many communication devices. Modern cellular modems, some smartphones, and IoT devices continue to use AT commands for configuration and control. For example, sending an SMS from some embedded systems still involves AT commands like AT+CMGS.

The AT command set represents one of those rare technological innovations that was so fundamentally sound that its basic principles have outlived the hardware for which it was originally designed. From controlling 300 bps modems in the early 1980s to configuring LTE and 5G modules today, the basic concept of "Attention + Command" has proven remarkably durable.

This standardization was perhaps the Hayes Smartmodem's most enduring contribution to computing history - creating a common language that allowed computers and communication devices to work together seamlessly, helping to build the connected world we know today.

Google Gemini Progression April 18-25, 2025

**Google Gemini Advanced is free for college students through finals 2026**

This brief two-minute video demonstrates the rapid evolution of Google's Gemini AI. Within just one week, the AI advanced significantly - starting on April 18, 2025 when it could solve electromagnetic problems but couldn't generate corresponding images, to later being capable of creating detailed, dimensionally accurate visualizations of those same problems on April 25, 2025.This brief two-minute video demonstrates the rapid evolution of Google's Gemini AI. Within just one week, the AI advanced significantly - starting on April 18, 2025 when it could solve electromagnetic problems but couldn't generate corresponding images, to later being capable of creating detailed, dimensionally accurate visualizations of those same problems on April 25, 2025.

Here's my takeaways:

·      Speed of development: A one-week timeframe for implementing significant new capabilities in a complex AI system demonstrates extraordinary engineering progress.

·      Cross-modal integration: The transition from purely computational problem-solving to visual representation shows successful integration between mathematical reasoning and image generation systems.

·      Technical complexity: Electromagnetic problems often involve complex vector fields, differential equations, and spatial relationships that are challenging to visualize accurately.

·      Practical applications: This capability could revolutionize fields like engineering, physics education, and scientific visualization by making complex theoretical concepts more accessible through visual representation.

·      Dimensional accuracy: The ability to create detailed images with precise dimensions suggests the AI understands both the mathematical relationships and their physical implications.

This development represents an important step toward AI systems that can not only solve technical problems but also communicate their solutions through multiple modalities, potentially making complex STEM concepts more accessible and actionable.

The Well-Designed Career: Engineering Success Beyond the Technical

After four years of hard work it’s graduation time and for many, time for that first professional paycheck! 

Across my career, I've witnessed tremendous technological advancements and workplace transformations. While technical skills remain fundamental, work-life balance is equally critical for long-term success and fulfillment. The following insights represent lessons I wish someone had shared with me when I began my journey in 1979 - wisdom that might help today's graduates navigate their careers most effectively.

1. Establish boundaries early. The engineering field often glorifies long hours and constant availability, but this can lead to burnout. Setting clear work-life boundaries from the beginning of your career helps establish sustainable patterns.
2. Navigate technological flexibility wisely. Remote work options have expanded significantly, offering greater flexibility but also blurring the line between work and personal time. Being intentional about disconnecting is crucial.
3. Embrace non-linear career paths. Engineering careers often involve periods of intense work followed by more balanced phases. This natural ebb and flow means you shouldn't judge your entire career by any single moment.
4. Set limits on continuous learning. The rapid pace of technological change requires ongoing education, but this shouldn't consume all your personal time. Negotiate for professional development during work hours when possible.
5. Prioritize physical and mental health. Regular exercise, social connections outside work, and meaningful hobbies provide necessary balance to the analytical nature of engineering work.

After nearly six decades in engineering, these lessons highlight the importance of intentional boundaries, adaptability to changing work environments, acceptance of career fluctuations, balanced professional development, and holistic wellbeing practices. New graduates who implement these insights early can build rewarding careers that support both professional achievement and personal fulfillment throughout their working lives.

Congrats to the Class of 2025!

Evolving Engineering Education: AI's Impact on the Classroom

About six weeks into the current spring semester, I stepped in to teach an electromagnetics course when a professor from the Electrical and Computer Engineering Department at the University of Hartford needed to take emergency leave. Returning to teach this subject after six years has been eye-opening. The contrast between teaching methodologies in 2019 versus now reveals a significant transformation in engineering education—one largely driven by the integration of artificial intelligence tools into the classroom experience. 

My Teaching Journey

After serving three amazing and fulfilling semesters as a Visiting Professor at the University of Hartford, in September 2019 I moved to the Engineering Department at Holyoke Community College. There, I spent five more amazing and fulfilling years (2019-2024) teaching Circuits 1 and 2 courses to electrical engineering students who typically transfer to university programs like the excellent one at Hartford to complete their Bachelor of Science in Electrical Engineering (BSEE). These foundational classes at Holyoke, usually taken by second-year students, provide the essential groundwork compared to the more advanced electromagnetics course I've now returned to at Hartford.

What's fascinating is how the AI classroom revolution unfolded around me at Holyoke without my complete recognition. While teaching circuits courses day-to-day, AI tools were gradually integrating into my teaching—so incrementally that the transformation wasn't immediately obvious. It was only upon returning to teach electromagnetics at Hartford after six years that the dramatic contrast became apparent.

 

Problem-Solving Transformation

The traditional approach to electromagnetics problems—careful application of Maxwell's equations, vector calculus, and boundary conditions through meticulous manual calculations using advanced calculus—now exists alongside powerful AI alternatives that can generate solutions almost instantaneously.

In a recent electromagnetics classroom lecture, I worked through a standard homework problem using the conventional pencil-and-paper method, spending about 10 minutes to complete the derivation and solution. When I ran the same problem through Gemini AI, the contrast was striking. Within seconds, Gemini produced the correct solution, presented step-by-step with conceptual connections that enhanced understanding. However, I found that running the same problem multiple times through Gemini sometimes did not produce the correct answer, though the level of detail in the solution made it easy to identify the error. Gemini is just one of many and as these AI systems continue to improve, these errors will become less and less frequent.

 

Redefining Educational Focus

This technological shift is reframing the fundamental questions we ask in engineering education:

• Instead of "How do we solve this problem?" we're increasingly asking "How do we interpret and verify these solutions?"
• Rather than spending most of our time on calculation mechanics, we can focus on "What deeper insights can we gain from these results?"
• The emphasis moves from computation to critical evaluation: "How do we assess the validity and limitations of AI-generated solutions?"

Finding Balance in Engineering Education

Despite these changes, foundational knowledge remains essential. Students still need to understand Maxwell's equations, boundary conditions, and vector analysis. The difference is that AI now serves as a powerful tool for exploration, verification, and extending understanding beyond textbook problems.

For today's engineering students, proficiency with AI tools is becoming as important as understanding the core principles of their discipline. They need to learn when to rely on their foundational knowledge, when to leverage AI assistance, and most importantly, how to critically evaluate AI-generated solutions.

 

Looking Forward

This unexpected return to teaching electromagnetics at Hartford after a six year gap has provided a unique vantage point to witness the evolution of engineering education. The combination of traditional engineering fundamentals with cutting-edge AI tools promises to produce graduates better equipped to tackle the complex technological challenges of tomorrow.

As educators, our role continues to evolve. We're no longer just teachers of technical content, but guides helping students navigate the increasingly AI-augmented landscape of engineering practice. This includes fostering the critical thinking skills needed to effectively collaborate with AI systems while maintaining the fundamental understanding that makes such collaboration meaningful.

AI Jobs in 2025: What Engineers Should Know

According to Stanford's latest AI Index Report, the demand for AI skills continues to grow in 2025. After a temporary slowdown, AI job postings have rebounded significantly, with positions requiring AI skills now representing 1.8% of all U.S. job postings, up from 1.4% in 2023.

Job Market Trends

The report, which analyzes data from LinkedIn and Lightcast (tracking over 51,000 websites), shows AI jobs are here to stay. Singapore leads globally with 3.27% of job postings requiring AI skills, followed by Luxembourg (1.99%) and Hong Kong (1.89%). The United States comes in at 1.75%.

Interestingly, adoption of AI coding tools like GitHub Copilot appears to be creating more jobs rather than eliminating them. According to LinkedIn economist Peter McCrory, companies using these AI assistants are actually increasing their software engineering hiring, though new hires typically require fewer advanced programming skills.

Shifting Skill Requirements

While Python remains the top specialized skill in AI job postings for 2023-2024, the broader skills landscape is evolving:

• Generative AI skills saw nearly 4x growth year-over-year
• Data analysis, SQL, and data science remain highly sought after
• Most AI-related skills increased in demand compared to 2023
• Only autonomous driving and robotics skills declined

McCrory notes that LinkedIn members "are increasingly emphasizing a broader range of skills and increasingly uniquely human skills, like ethical reasoning or leadership."

Workforce Impact and Concerns

Despite fears about AI eliminating jobs, the evidence is mixed. A McKinsey survey found 28% of software engineering executives expect generative AI to decrease their workforce in the next three years, while 32% anticipate growth. The overall percentage of executives expecting workforce reductions appears to be declining.

Diversity Challenges

A concerning trend is the persistent gender gap in AI talent. LinkedIn data shows women in most countries are less likely to list AI skills on their profiles, with males representing nearly 70% of AI professionals on the platform in 2024. This ratio has remained "remarkably stable over time," according to the report.

Academia vs. Industry

The report highlights how expensive AI training has shifted innovation from academia to industry. AI Index steering committee co-director Yolanda Gil noted: "Sometimes in academia, we make do with what we have, so you're seeing a shift of our research toward topics that we can afford to do with the limited computing [power] that we have."

Looking Forward

As AI tools become more integrated into workflows, the distinction between "AI jobs" and regular positions continues to blur. Success in this evolving landscape will likely require a combination of technical proficiency and uniquely human capabilities. The report emphasizes the importance of cross-sector collaboration between industry, government, and education to provide researchers with necessary resources and help educators prepare students for emerging roles in AI.

For engineers looking to stay competitive, developing a mix of technical AI skills (particularly Python and generative AI) while cultivating leadership and ethical reasoning capabilities appears to be the winning formula for 2025 and beyond.

Grokking: The "Aha!" Moment in Artificial Intelligence Podcast

A couple of robot friends discussed my last blog post on Grokking and my robot friends and I made it into a little over 10 min podcast.