Views & Visions

It wasn’t that long ago when artificial intelligence, or “AI” was something most people only heard about in passing, often in headlines about Google DeepMind’s AlphaGo program defeating one of the world’s best Go players, or IBM’s Watson winning a round of Jeopardy!. Although these early innovations captured the public’s imagination, AI was still thought of as a distant dream. For many people, AI was still an abstract idea, confined to the laboratories of major technology companies and academic institutions, or something they might see in a plotline of a Star Trek episode, rather than something that might shape their own work and impact their daily lives.

In just a few short years, that distant dream has become an everyday reality. AI has woven itself into our everyday lives, from powering the systems that help us navigate traffic, to recommending our next TV show to watch, or predicting severe weather events before they occur. The same technology that once ran on supercomputers and only talked about in academic conferences, now sits in our pockets, guiding decisions both big and small.

Whether it’s detecting fraudulent credit card transactions, translating languages in real time, or filtering spam before we ever see it, AI has become the unseen engine behind much of the convenience and efficiency of daily life. Now, with the rise of generative AI platforms such as ChatGPT, Gemini, Claude, Grok, and CoPilot, AI has almost become ubiquitous in our modern-day society, as our everyday companion, which can write, suggest, research, design, and imagine alongside us.

AI in Action: Modern Applications

AI’s impact can be felt across a variety of industries, fields, or disciplines, and is being leveraged in new and exciting ways.  For example:

• Medicine: AI computer vision algorithms can assist doctors in identifying patterns in patient charts, or medical imaging that might otherwise go unnoticed, improving diagnostic speed and accuracy. One recent application of AI was able to predict future occurrences of breast cancer from past, pre-cancerous mammogram images.
• Accounting and Finance: AI systems are being leveraged by banks to analyze everyday financial transactions to identify anomalies, detect fraudulent activity, and predict emerging risks. 
• Art, Music, and Content Creation: Content creators are leveraging AI to generate artwork, write music, and even movie scripts. I’m sure we’ve all seen the power of AI tools like DALL-E to create astounding, and at times funny artwork, just based off a simple prompt. Even architects are using AI to give inspirational ideas for future skyscrapers.
• Environmental Monitoring: The latest AI models are being used with earth observation remote sensing and weather satellite data (see photo)  to monitor deforestation, track urban growth, assess storm damage, and predict global climate patterns. A recent study in the prestigious journal Nature found that the AI-powered Aurora system generated weather forecasts and predicted hurricane trajectories faster and more accurately than any operational center.

Within the workplace, AI has become a powerful tool for improving productivity through automating cumbersome tasks. Professionals across almost every field continue to discover new ways on how to integrate AI into their workflows. For example,

• Lawyers can leverage AI to summarize complex case documents and highlight relevant precedents.
• Educators can run expert content through AI systems to tailor lessons to various students’ learning styles.
• Marketers and communicators use natural language processing, and generative AI models to craft content and test messaging.

While it is clear that AI is reshaping how we work, there is also growing uncertainty surrounding its accuracy, and ability to perform complex tasks. As an AI researcher myself, I always recommend, as part of best practices, to carefully checking the output of any AI model, using human validation. AI models, particularly the large language models or LLMs that underly many generative AI platforms, are highly prone to hallucinations where the model produces information that sounds convincing but is factually incorrect or entirely fabricated. These errors can occur because the model is designed to predict the most likely next word in a sequence, not to reason or verify facts. This is why human oversight remains essential. 

There is also some worry about whether AI will replace workers. Now, it’s true that some companies may choose to downscale their workforce in favor of AI tools – we’ve seen that Amazon recently announced they were reducing their workforce by 14,000 employees. While this can be a little unsettling, it’s important to remember that history has shown that technological revolutions don’t just eliminate jobs, they also create entirely new ones. Just as the industrial revolution led to the rise of engineers, technicians, and factory managers, and the birth of the Internet in the digital age, created countless new opportunities, the AI revolution is ushering in a new generation of roles focused on managing, interpreting, and guiding intelligent systems. In my view, the most successful professionals in the years ahead won’t be those displaced by machines, but those who learn to work effectively with them.

What’s Next?

While AI remains a nascent technology, cutting edge applications of AI are already moving far beyond text and image generation. We are entering an era where AI is being embedded into cyber-physical systems, industrial environments, and operational technologies. These are the systems that underly our critical infrastructure, which make our modern-day civilization possible. For example, assisting with balancing power supply and demand within our power grids, optimizing transportation routes and anticipating congestion in our ports, trains, and road systems, to optimizing water, fertilizer, and energy use in precision agriculture. We’re even seeing the integration of AI and Internet of Things (IoT) devices, everyday objects equipped with sensors, from self-driving cars to smart thermostats. AI is transforming how our world senses, responds, and adapts to changing situations in real time. Even in emergency management situations, AI is helping first responders map disaster zones, predict wildfire spread, and coordinate relief efforts more efficiently than ever before. The possibilities are seemingly endless.

If the last decade was about teaching machines to think, the next will be about learning how humans and machines can think together.

The future of work in the age of AI will not be defined by competition between humans and machines, but by collaboration. AI may be among the most transformative tools that humanity has ever created, but its true promise lies not in what it can do without us, but what it can help us become.

Dr. Christopher Ramezan is an Assistant Professor of Cybersecurity and Management Information Systems at West Virginia University, where he directs the Cyber-Resilience Resource Center, a statewide initiative supporting businesses, municipalities, and critical infrastructure. He also coordinates WVU’s Business Cybersecurity Management program, teaching courses in information security, AI, cyber analytics, and industrial control systems. An award-winning educator and researcher, Dr. Ramezan has been recognized as West Virginia Educator of the Year, a WVU Foundation Awardee for Outstanding Teaching – WVU's highest teaching award, and one of West Virginia’s 40 Under 40. He was also named the Academic Lead for the U.S. National Blue Team in NATO’s Locked Shields Exercise, the world’s largest live-fire cyber defense exercise by the Joint Force Headquarters United States Cyber Command. His research applies AI to strengthen network security and critical infrastructure resilience, supported by over $1.6M in funded projects. Prior to academia, he worked for nearly a decade in the cybersecurity and IT field and holds more than 20 cybersecurity certifications. He possesses a Ph.D. in Remote Sensing, specializing in developing machine learning and AI methods for automating the analysis of space-based multispectral and hyperspectral satellite data, light detection and ranging systems, and synthetic aperture radar.