AI Mythbusting with a Leading UNB AI Researcher: Your AI Questions, Answered
Dr. Scott Bateman, of UNB's Research Institute for Data Science and Artificial Intelligence, tackles popular questions about generative AI.
Artificial intelligence (AI) is evolving fast, but that doesn't mean humans should be left behind. In this Q & A, UNB's Dr. Scott Bateman, of the Research Institute in Data Science and Artificial Intelligence at the University of New Brunswick, busts myths about AI and computer science and shares how students and researchers at UNB are shaping the future of human-AI collaboration.
Myth #1: AI will replace human creativity
UNB: With each passing day, AI gets better at writing code, creating art, and solving problems. Is human creativity becoming obsolete?
Bateman: When we think about AI these days, most often we are thinking about Large-Language Models (LLMs). LLMs are fantastic because they use a huge amount of data that already exists. They can find connections in that data and summarize it in a way that is easy to understand. But they are not creative in the same way people are.
They rely on data that they have already seen to make a guess at what sounds like something we want to hear; they are good at mimicking what people have already created. This can seem like creativity, but it's a shallow type of creativity because it doesn't invent.
People will always have the innate ability for deeper creativity, inventing ideas and thoughts that no one has had before. Human emotion and our understanding of larger contexts and constraints allow for this kind of innovative thinking – and also help us understand whether a creative idea is one that others will be receptive to.
Solving society's biggest problems will require deep creativity.
Myth #2: AI will become more emotionally intelligent than people
UNB: You talked about AI being a good mimic. What about its ability to mirror human empathy and compassion?
Bateman: For some types of tasks, AI can do a reasonable job. For example, we have seen a rise in chatbots handling communications with customers, either via chat or sometimes voice. This works most of the time because people largely have the same questions about products and services that can be well-defined and tested beforehand.
But sometimes, chatbots fail. A Canadian airline recently had a chatbot give a passenger incorrect information about bereavement fares. The airline had to honour the chatbot's mistake in a situation where human sensitivity and compassion would have been appreciated by the customer.
This simple example reinforces the idea that those who take advantage of AI must be held accountable. It also suggests that while AI can streamline some tasks, we have to use it carefully. To maintain our humanity, human oversight is required.
Myth #3: AI will take computer science jobs
UNB: There's a lot of fear that AI is making human workers obsolete. What are your thoughts on this?
Bateman: I believe that humans will never be obsolete in the workforce. We need human oversight to identify when things are wrong or not working. We need humans to make sure that AI systems are used ethically, safely and securely. We will always need humans for deep, creative insights.
One possibility that we have to guard against is the amount of work people are responsible for. We have seen a steady increase in workloads with the introduction of transformative technologies (calculators, personal computers, the internet and smartphones). This is referred to as the "productivity paradox," where new capabilities lead to much higher expectations.
To build on the airline example, say a customer service representative who was once responsible for handling dozens of customers on the phone over the course of a day now must provide oversight on hundreds of cases that were filtered through a chatbot. This creates an increase in expectation, and is another reason why human oversight and management is and will remain crucial. Because while humans might not be able to get through a task list as quickly as AI, the quality of work can suffer, and critical mistakes can be made.
Reports similar to this one have been well documented and ideally should be avoided. Many of these reports suggest that people across a wide range of fields already feel overburdened.
Preparing students for an AI-driven job market
UNB: How is UNB preparing students to thrive in an AI-driven job market?
Bateman: There are a few ways UNB is doing a good job of preparing students.
- World-class fundamentals: We provide fundamental computer science education so graduates can tackle challenges when AI provides a bad solution or real creativity is required.
- Adapted coursework: We incorporate new practices and adjust assessments to prevent over-reliance on AI while still supporting appropriate AI uses that reinforce industry best practices.
- New offerings: We've created courses covering the latest in AI, machine learning, cybersecurity, software engineering, systems architecture, human-computer interaction, social issues, and ethics.
These topics go far beyond coding and help prepare computer scientists to address bigger and broader challenges in real-world contexts.
Myth #4: AI breakthroughs only happen in Silicon Valley
UNB: Big tech companies dominate conversations around AI. How are UNB researchers making an impact?
Bateman: UNB researchers are using AI to address problems across every discipline. Here's a quick, incomplete list:
What ties these projects together is that researchers rely on deep critical thinking — not just AI tools — to drive innovation with positive societal impact.
Myth #5: Computer science is just for math geniuses
UNB: A lot of folks think you need to be a math whiz to succeed in computer science. Is that true?
Bateman: Math and computer science share common roots, but programming and math are different. Some areas of CS rely on math more than others, but most coding jobs require only a small amount of math. Strong math skills can be an asset, but they are not strictly required.
Computer programmers need to communicate ideas effectively, be good listeners, and be willing to learn what people need so they can translate that into great solutions. Communication and people skills are often the most valuable assets for a coder.
There are so many career paths in coding and computer science. It's intertwined in almost every area, from business to health to the arts. We will continue to need more coders, each with their own strengths and weaknesses — and that's a good thing!
The future of AI and human collaboration
UNB: What kinds of opportunities will UNB computer science students be part of in the next few years?
Bateman: UNB CS students will have amazing opportunities. They'll understand how transformative technologies work and help code our future. Coders create the tools people use everywhere, from phones and watches to kitchens and cars.
Society needs thoughtful people with excellent skills, ethics and an understanding of the importance of their work. The world needs more UNB computer science graduates to help build the tools of the future and guide us in a rapidly transforming world.
Looking ahead
UNB: What excites you most about the future of AI and human problem-solving?
Bateman: I imagine a future where people remain at the centre of decision-making, using AI assistants for little tasks that slow us down, like sorting emails or searching for documents. While small, these tasks add up and eat up time. I hope AI frees up our time so we can focus on deeper, more creative pursuits and tackle bigger problems. In other words, doing the things people are really good at.
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