Creative processes are typically divided into three types: combinatorial, exploratory, and transformational. Here, we provide a graphical theory of transformational scientific creativity, synthesizing Boden's insight that trans-formational creativity arises from changes in the "enabling constraints" of a conceptual space (Boden 1992) and Kuhn's structure of scientific revolutions as resulting from paradigm shifts (Kuhn 1962). We prove that modifications made to axioms of our graphical model have the most transformative potential and then illustrate how several historical instances of transforma-tional creativity can be captured by our framework.

Inventor and futurist Ray Kurzweil, researcher and Brookings Institution fellow Chinasa T. Okolo, director of the U.S. Artificial Safety Institute (AISI) Elizabeth Kelly, and Cognizant CEO Ravi Kumar S, discussed the transformative power of AI during a panel at a TIME100 Impact Dinner in San Francisco on Monday. During the discussion, which was moderated by TIME's editor-in-chief Sam Jacobs, Kurzweil predicted that we will achieve Artificial General Intelligence (AGI), a type of AI that might be smarter than humans, by 2029. "Nobody really took it seriously until now," Kurzweil said about AI. "People are convinced it's going to either endow us with things we'd never had before, or it's going to kill us." Cognizant sponsored Monday's event, which celebrated the 100 most influential people leading change in AI. Jacobs probed the four panelists--three of whom were named to the 2024 list--about the opportunities and challenges presented by AI's rapid advancement.

TIME hosted its inaugural TIME100 Impact Dinner: Leaders Shaping the Future of AI, in San Francisco on Monday evening. The event kicked off a weeklong celebration of the TIME100 AI, a list that recognizes the 100 most influential individuals in artificial intelligence across industries and geographies and showcases the technology's rapid evolution and far-reaching impact. TIME CEO Jessica Sibley set the tone for the evening, highlighting the diversity and dynamism of the 2024 TIME100 AI list. With 91 newcomers from last year's inaugural list and honorees ranging from 15 to 77 years old, the group reflects the field's explosive growth and its ability to attract talent from all walks of life. The heart of the evening centered around three powerful toasts delivered by distinguished AI leaders, each offering a unique perspective on the transformative potential of AI and the responsibilities that come with it.

Large Language Models (LLMs) have emerged as a cornerstone of artificial intelligence research and development, revolutionizing how machines understand and process natural language. These models, based on advanced deep learning architectures, have become increasingly sophisticated, capable of generating human-like text, answering questions, summarizing content, and performing a plethora of other tasks. The remarkable growth in the capabilities of LLMs can be attributed to advancements in computational power, the availability of large-scale datasets, and the continuous refinement of algorithmic techniques. A key element in the success of LLMs is their use of transformer-based architectures, which employ self-attention mechanisms to capture contextual information across long text sequences. Transformers have demonstrated a remarkable ability to scale, enabling the development of larger models with billions of parameters.

Over the last year, our attention has been focused on a series of issues that are of global importance. Last year, Extinction Rebellion pushed climate change to the top of the agenda. This year, COVID-19 has made effective public health monitoring a priority. In recent weeks, anger at systemic racial injustice has fuelled public protests. While these are very different issues, they share something in common: they will not be addressed if we do not use data-driven technologies to understand, monitor and improve complex systems - whether that is the healthcare system, the justice system or the energy grid.

Artificial intelligence (AI) systems are becoming smarter every day, beating world champions in games like Go, identifying tumors in medical scans better than human radiologists, and increasing the efficiency of electricity-hungry data centers. Some economists are comparing the transformative potential of AI with other "general purpose technologies" such as the steam engine, electricity, or the transistor. But current AI systems are far from perfect. They tend to reflect the biases of the data used to train them and to break down when they face unexpected situations. They can be gamed, as we have seen with the controversies surrounding misinformation on social media, violent content posted on YouTube, or the famous case of Tay, the Microsoft chatbot, which was manipulated into making racist and sexist statements within hours.

Artificial Intelligence (AI) systems are becoming smarter every day, beating world champions in games like Go, identifying tumours in medical scans better than human radiologists, and increasing the efficiency of electricity-hungry data centres. Some economists are comparing the transformative potential of AI with other "general purpose technologies" such as the steam engine, electricity or the transistor. But current AI systems are far from perfect. They tend to reflect the biases of the data used to train them and to break down when they face unexpected situations. They can be gamed, as we have seen with the controversies surrounding misinformation on social media, violent content posted on YouTube, or the famous case of Tay, the Microsoft chatbot, which was manipulated into making racist and sexist statements within hours.

As the transformative potential of AI technologies becomes clear, companies and countries around the world are frenetically competing to become leaders. They're doing so because evidence increasingly suggests that AI will be one of the leading economic drivers of our time, with recent estimates of worldwide AI spending reaching US$78 billion by 2022--creating worldwide business value of US$3.9 trillion.1 Thanks to the leadership of a small group of academics and institutions, Canada has been an early research and talent leader in the AI space to date. But the state of Canada's leadership in AI is precarious at best. Our research shows that Canada's current efforts are insufficient if we truly want to lead in an AI-driven world and shape what it might look like. Not enough Canadian businesses are investing in AI, given its transformative potential.

Greg Brockman, cofounder of nonprofit AI research organization OpenAI, had an interest in artificial intelligence from a young age, but he didn't come to it right away. Brockman studied computer science at Stanford before transferring to MIT, where he dropped out to launch online payments platform Stripe. As a founding engineer, Brockman helped scale the business from four people to 250. But he had his heart set on another field: artificial general intelligence, or systems that can perform any intellectual task that a human can. Brockman left Stripe to pursue a career in AI, building a knowledge base from the ground up.

Greg Brockman, cofounder of nonprofit AI research organization OpenAI, had an interest in artificial intelligence from a young age, but he didn't come to it right away. Brockman studied computer science at Stanford before transferring to MIT, where he dropped out to launch online payments platform Stripe. As a founding engineer, Brockman helped scale the business from four people to 250. But he had his heart set on another field: artificial general intelligence, or systems that can perform any intellectual task that a human can. Brockman left Stripe to pursue a career in AI, building a knowledge base from the ground up.