At the Vector Institute’s annual AI conference, Remarkable, industry leaders, researchers, and policymakers convened to explore the latest advancements in artificial intelligence, discuss challenges in regulation and adoption, and highlight emerging technologies that could shape the future of AI-driven enterprises. The conference covered a range of topics, but key discussions revolved around the delicate balance between AI regulation and innovation, the growing importance of reinforcement learning (RL), and the need for Canada to scale its AI infrastructure to remain competitive on the global stage.
One of the most pressing concerns raised at the event was the challenge of establishing AI regulations that protect against risks without stifling innovation. AI is advancing at an unprecedented pace, making it difficult for regulators to anticipate and address future challenges effectively. Laura Gilbert, head of AI for government at the Ellison Institute of Technology Oxford, emphasized that regulation is a complex and evolving issue. She cautioned that any attempt to draft overly rigid or forward-looking regulations could inadvertently hinder AI’s progress. “Any regulation that looks to protect us against something in the future in any sort of specificity means that we will set up regulation that is not flexible enough, that’s not future-proof and could actually put us at risk,” Gilbert explained during a fireside chat. Instead, she advocated for a more adaptable approach, where regulators continuously reassess and refine AI policies as the technology evolves.
Beyond regulatory concerns, corporate AI adoption was another hot topic at the conference. Many businesses are eager to embrace AI but struggle with finding the right balance between speed and caution. Foteini Agrafioti, senior vice president of data and AI at RBC, urged companies to avoid rushing into AI investments without proper research and validation. She highlighted that even large organizations like RBC have faced challenges when adopting AI, learning “the hard way” that thorough testing and hypothesis validation are crucial before scaling AI-driven initiatives. Her advice to businesses was clear: AI should be deployed strategically, with a strong foundation in data integrity, ethical considerations, and a clear understanding of its potential risks and limitations.
However, not all speakers agreed that caution was the primary concern. Dante Morra, founder and chair of the CAN Health Network, took a different stance, arguing that Canada’s healthcare sector is moving far too slowly when it comes to AI adoption. While many organizations are hesitant due to concerns over reputational risks, Morra believes that the real danger lies in failing to integrate AI quickly enough. He pointed out that every day of delay results in lost opportunities, not just for improving healthcare efficiency but also for maintaining Canada’s global standing in AI innovation. “Every single day, our access goes down. Every single day, our chance to win in the new healthcare economy is less,” Morra stated. He urged decision-makers to rethink their approach to AI risks, advocating for a shift in mindset where the consequences of inaction are weighed just as heavily as the risks of potential failures.
In addition to regulatory and adoption challenges, the conference also spotlighted the rise of reinforcement learning (RL) as a powerful AI technique for businesses. While generative AI has dominated the conversation in recent years, Ian Scott, Deloitte’s chief science officer, believes that RL is set to play a major role in enterprise AI applications. RL operates by training AI agents through trial and error, using mathematical reward systems to refine decision-making processes over time. This method enables AI systems to optimize complex workflows, making them highly valuable for industries ranging from finance and logistics to manufacturing and healthcare. According to Scott, businesses should start preparing now to integrate RL-driven solutions into their operations, as this approach will likely become a key driver of automation and efficiency improvements in the near future.
Beyond individual businesses, discussions at the Remarkable conference also addressed Canada’s position in the global AI race. With AI adoption accelerating worldwide, there are growing concerns that Canada lacks the necessary infrastructure and computational resources to compete with AI powerhouses like the U.S. and China. Industry leaders stressed that investment in AI compute capacity, cloud infrastructure, and research funding is essential to ensure that Canadian AI companies can scale and remain competitive.
The conversations at Remarkable 2024 highlighted the delicate balancing act that policymakers, business leaders, and researchers must navigate as AI continues to transform industries. Whether it’s crafting flexible and effective regulations, ensuring AI adoption in critical sectors like healthcare, or leveraging emerging technologies like reinforcement learning, the conference made it clear that the AI landscape is evolving rapidly. Staying ahead in this fast-moving field requires a thoughtful approach that blends caution with boldness, regulation with adaptability, and investment with strategic deployment.