Four Advances Redefining AI Innovation — Insights from Professor Chris Callison-Burch
Artificial Intelligence is no longer just advancing. It’s accelerating in ways that will redefine how humans and machines work together. For Professor Chris Callison-Burch, a leading researcher in natural language processing and director of Penn Engineering’s Master of Science in Engineering in Artificial Intelligence (MSE-AI) online degree program, this moment is both thrilling and pivotal.
“I think now is the best time there’s ever been to experiment with AI,” says Callison-Burch. “With tools like ChatGPT freely available, anyone can explore one of the most powerful models ever built.”
Yet what distinguishes this moment is not just the technology; it’s how leaders, researchers, and future engineers choose to apply it. At Penn Engineering, that choice is guided by a tradition of combining technical rigor with human impact.
Dr. Chris Callison-Burch
Four Advances in AI (Post-ChatGPT) That Inspire Callison-Burch
1. Bigger and Better Models
“A super interesting aspect of AI is the growth of bigger and better models,” says Callison-Burch. “Performance gains come from three factors: the number of neural units, the size of training data, and the compute resources invested. Over time, models have steadily expanded in all three areas—and as a result, their capabilities have advanced dramatically.”
Scaling laws show that as models increase in size and scope, they demonstrate stronger reasoning, deeper factual recall, and the ability to handle vastly larger contexts. For example, the graph below illustrates the performance of GPT-3 on a variety of professional exams (blue bars) vs. the performance of GPT-4 (green bars) on the same exams—showing how newer, bigger models have improved performance.
For students in the MSE-AI Online program, exploring these scaling laws isn’t an abstract exercise. They study directly with the researchers pushing these boundaries, learning how to harness – not just observe – the next leap in capability.
2. Multimodal AI
“Language models were originally trained solely on text. Multimodal models now move beyond text, generating outputs such as images, music, and expressive voices,” Callison-Burch explains.
This shift means AI can now process text, images, and even voice, answering questions about visuals, generating new content, and holding natural-sounding conversations. Callison-Burch and one of his PhD students, in collaboration with the Allen Institute for Artificial Intelligence, recently developed CoSyn (Code Guided Synthesis)—a tool designed to train vision-language models on high-quality data. Enhancing AI’s ability to interpret charts, graphs and scientific images is an important step toward enabling AI to interpret scientific literature and accelerate scientific discovery.
Example of a Ternary Plot generated by CoSyn
The ability to teach machines to interpret complex visuals could accelerate breakthroughs across domains, from drug discovery to climate modeling, areas where Penn faculty are already extending AI’s reach.
3. Tool Use and Reasoning
“As models scale, ‘reasoning models’ have emerged,” notes Callison-Burch. “Instead of generating a final answer immediately, these systems take time to ‘think aloud’ and revise their steps. Large language models can also make function calls—booking tickets, checking itineraries, making purchases. This capacity, often referred to as agentic AI, marks a shift from AI as a tool for writing to AI as an agent capable of taking meaningful action.”
This step-by-step reasoning and ability to use external tools makes models more reliable and versatile. As an example, an AI agent can review your schedule and book your flight.
But beyond convenience, the same reasoning capabilities could help optimize global supply chains, accelerate drug discovery, or inform policy decisions—domains where Penn faculty are actively testing AI’s potential.
4. Emerging Creative Domains
“One of my favorite demonstrations of AI at the moment is being able to generate songs,” Callison-Burch highlights. “When given a prompt, an AI system can write lyrics, compose music, and synthesize a catchy song. It’s not about replacing musicians, but about creating personalized, expressive works that are really impressive.”
Similar advances are appearing in filmmaking, with tools like Google’s Veo2 model producing increasingly cinematic outputs. These creative applications are an example of how AI is used for innovation at the intersection of technology and creativity.
For Penn Engineering faculty, these creative domains aren’t side projects. They represent the next frontier of human–machine collaboration, where technical fluency meets imagination to redefine how culture and technology evolve together.
MSE-AI Students at the Forefront
For Callison-Burch, the true excitement lies in seeing how students will carry these advances forward. “The education our MSE-AI online students receive will help them become the leaders of this field. They will shape the world with their knowledge of how AI works.”
Students aren’t just learning the fundamentals—they’re preparing to apply AI in ways that drive entrepreneurship, spark creativity and innovation, and deliver solutions once thought impossible—equipping them not just to participate in the field but to redefine it.
Advice for Aspiring AI Engineers
Dr. Chris Callison-Burch
For newcomers, Callison-Burch recommends diving in directly: “Try using AI in your own work—spend some time figuring out how it integrates into your workflow. Learn which tasks it does well and which tasks it doesn’t do well—discover its potential firsthand.”
This philosophy reflects the ethos of Penn Engineering Online: experiment boldly, question critically, and apply your expertise in ways that move the field forward.
For those ready to go deeper, Penn’s MSE-AI Online degree program, led by Callison-Burch, offers a rigorous curriculum in deep learning, AI, natural language processing, and systems programming— equipping graduates to thrive in a rapidly evolving field.
From Aspiring Learner to Innovator
The breakthroughs of today—scaling models, multimodal systems, reasoning capabilities, and creative applications—are just the beginning. The next chapter will be written by those who experiment boldly, think critically, and apply their knowledge in ways that push the boundaries of possibility.
The breakthroughs of today are only the foundation. The future of AI will be defined by those prepared not just to use the tools, but to lead their evolution—and at Penn Engineering’s MSE-AI Online program, that future is already taking shape.
Ready to Take the Next Step?
To learn more about diving into artificial intelligence with Chris Callison-Burch and Penn Engineering Online, explore the MSE-AI Online master’s degree program:
