Artificial intelligence (AI) is evolving fast, but that doesn't mean humans should be left behind. According to UNB's Dr. Scott Bateman, they're needed now more than ever.
Dr. Scott Bateman, a leading researcher at the University of New Brunswick's (UNB) Research Institute for Data Science and Artificial Intelligence (RIDSAI), SPECTRAL Spatial Computing Research Centre and Human-Computer Interaction Lab argues that human creativity, problem-solving and decision-making are more crucial than ever in a world driven by AI.
In this two-part Q&A series, Bateman busts myths about AI and computer science and shares how students and researchers at UNB are shaping the future of human-AI collaboration.
PART 1
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.
I like this simple example because it 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 the 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.
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.
First, we are doing what we have always done: providing world-class education in computer science that ensures students build a fundamental knowledge base. This is so important because when AI provides a bad solution or when real creativity is required, our graduates are prepared to tackle challenges and think critically and innovatively.
Second, we are always adapting our courses by incorporating new practices and the latest content. This includes adjusting how we assess students to make sure they are not becoming overly reliant on AI, while still providing opportunities for appropriate AI uses that support learning and reinforce industry best practices.
Third, we are creating new course offerings that cover the latest developments in AI, machine learning, cybersecurity, software engineering, systems architecture, human-computer interaction, social issues and ethics.
These topics go far beyond coding. They help prepare computer scientists to address bigger and broader topics and provide a platform for deep understanding and creativity not just related to building computer systems, but how to make sure they work in real-world situations.
PART 2
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: I can share lots of quick examples!
UNB researchers are using AI to address many problems, and this extends far beyond computer science and into every discipline. Here is a quick and incomplete list:
- Dr. Erik Scheme and Dr. Jon Sensinger with the Institute of Biomedical Engineering, are world leaders in applying machine learning to rehabilitation technologies and to help amputees better control prosthetic limbs.
- Sensinger and Dr. Juan Antonio Carretero from the university's faculty of engineering also developed a groundbreaking AI algorithm that enables AI to seek out information while performing its tasks, setting it apart from traditional AI models.
- Dr. Argyri Panezi in the faculty of law is building a critical understanding of the implications of generative AI on copyright and privacy through the Legal Innovation Lab.
- Dr. Matt McGuire from the faculty of education is the McKenna Fellow in Digital Education. He works directly with the New Brunswick Department of Education and Early Childhood Development to help educators build an understanding of how to use AI in their teaching and how they can best support young people in successfully adopting AI technologies.
- Dr. Paul Cook in the faculty of computer science uses AI and natural language processing to create technologies to help preserve endangered languages and to help teach Indigenous languages like Wolastoqey and Mi'gmaq.
- Dr. Stijn De Baerdemacker in the department of chemistry uses AI and machine learning in his research to accelerate the discovery of new chemical compounds.
- The many researchers and faculty members with the Canadian Institute for Cybersecurity use AI and machine learning to help identify cybersecurity threats and identify new viruses and malware.
- Computer science faculty members are using AI to help software developers build reliable and secure computer systems more easily.
- Dr. Daniel Tubb in the anthropology department uses the latest AI systems to help digitize important local records and histories in rural Colombia.
- Dr. M. Willis Monroe in the classics and ancient history department is using AI and natural language processing to help decipher ancient scripts from Iran.
There are many more researchers at UNB who are using AI in new ways that have a positive societal impact.
These projects are making important advancements in a wide range of areas, from health to public policy to cultural preservation. What is critical to note in all these cases is that not one of the researchers is exclusively using a tool like ChatGPT to do this work. Most often they use a wide range of tools and even create their own tools to do the work. And in some cases, they don't use AI directly at all. Instead, they rely on the same skills that universities have always taught: deep critical thinking.
Regardless of what area people work in, the latest AI innovation provides useful tools. And understanding how a tool works allows us to use it in better and more creative ways. Knowing how to think critically is essential to understanding when to use the tool, the quality of what the tool helps us create and when the tool should or should not be used.
UNB: UNB is collaborating with industry partners on game-changing AI innovations. Can you tell us more about their impact?
Bateman: This is a tough one to encapsulate because the ways that UNB researchers and students engage with industry are so vast. Let's talk about why these types of collaborations work so well.
UNB is second to none in terms of the ease and accessibility partners and collaborators have when it comes to gaining access to our deep pool of talented students and researchers.
UNB's culture is unlike any other university I have worked with or visited, which includes universities across Canada, the U.S., Europe and Asia.
It has a real advantage because as a medium-sized school, we can simplify partnerships, so they focus directly on collaboration with researchers and students.
Companies collaborating with UNB see the value our researchers provide and the strength of our students' skills (whether bachelor, master's or PhD students). Partners gain immediate access to our impressive talent pipeline.
This leads to deeply synergistic opportunities where researchers are exposed to real world problems that they can take back to the classroom, while also lending their expertise. This benefits both our partners and our students, who in the process learn about innovation and creative problem solving.
Students almost always work directly with industrial partners on research and development projects. Because students are so close to the solid fundamentals they've learned in the classroom, they provide a fresh perspective and often, the most creative solutions.
It's common for these relationships to turn into employment offers for students.
Many universities collaborate with industry, but at UNB, it's baked into the culture. This, and our size, coupled with the calibre of students, researchers and experts, is why our partners seek collaboration again and again.
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 do share common roots, and when I hear this, it reminds me that some of the things that make someone good at math are the same things that make them good at computer science: the ability to think in abstract, to identify patterns and the creative use of established problem-solving strategies.
But programming and math are two different things. Some areas of computer science do rely on math more than others, but for most coding jobs, the amount of math needed is quite small.
So, I disagree that being a math whiz is required. Many coders, myself included, were never the best math students my first-year calculus course was my worst grade in university. So, while mathematicians and coders share some common traits, strong math skills can be an asset for a coder, but it is not strictly required.
Computer programmers need to be able to communicate ideas effectively, be good listeners, and be willing and able to learn about what people need and want so that they can translate that into a great solution. More often than not, communication and people skills are the most valuable assets for a coder.
There are so many career paths in coding and computer science. It is intertwined deeply 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!
Where is AI and human collaboration heading?
UNB: What kinds of opportunities will UNB computer science students be part of in the next few years?
Bateman: UNB computer science students will have amazing opportunities. Not only will they be able to fundamentally understand how the technology that is transforming the world around us works, but they will have an opportunity to help code our future.
This is because coders create the tools that people use on their phones and watches, in their kitchens and cars; everywhere there is a computer.
Society needs thoughtful people with excellent skills, ethics and an understanding of the importance of their work to provide safe, secure and reliable information and assistance.
The world needs more UNB computer science graduates to help create the tools of the future and to guide us in a rapidly transforming world.
UNB: What excites you most about the future of AI and human problem-solving?
Bateman: I think what excites me most is the rate of progress that new AI tools will enable.
I imagine a future where people remain at the centre of decision-making and in full control of the work that they do, but use AI assistants, where available, for little tasks that currently slow us down, like sorting through emails or searching for a document.
While these small tasks seem simple and insignificant, they add up in the course of a day and eat up time. I hope that AI frees up our time so we can focus on deeper and more creative pursuits and tackle bigger problems. In other words, to do things that people are really good at.
