AI increasingly augments research practices, redefines scientific workflows, and evolves teaching and training—requiring new skills, ethical frameworks, and collaborative infrastructures across disciplines.

Join us on Thursday, March 19 for Matthias Scheutz’s lecture, Virtues and Vices of Robot Foundation Models!

Robot foundation models are increasingly hyped in industry and academia alike, as demonstrated by the sky-high levels of funding recently formed robot startups are receiving on the promise to successfully develop and deploy those models. All of this activity using generative AI, in particular, transformer-based models, is ultimately predicated on the hope that these models will eventually be able to solve the obstacles other approaches in AI and robotics have hitherto failed to overcome. But is this a realistic expectation or just (expensive) wishful thinking? In this lecture, Matthias Scheutz will discuss opportunities of foundation models for robotic architectures and illustrate them with examples from our own work, and also look at their downsides and failure cases. He will quantify the vast amount of resources required to train and operate those models in comparison to alternatives, raising the question of whether their performance is actually worth the cost.

About

Matthias Scheutz is the Karol Family Applied Technology Professor of computer science and mechanical engineering in the Department of Computer Science at Tufts University in the School of Engineering, the Director of the Human-AI Interaction Center at the Tufts Institute of Artificial Intelligence, and the Director of the Human-Robot Interaction (HRI) Laboratory and the HRI Masters and PhD programs. He is a AAAI and AAAS Fellow with close to 500 peer-reviewed publications in artificial intelligence, artificial life, agent-based computing, natural language understanding, cognitive modeling, robotics, human-robot interaction and foundations of AI and cognitive science. His current research focuses on complex ethical AI-enabled robots with natural language interaction, problem-solving, and fast instruction-based learning capabilities in open worlds.

Hybrid Event

Whether you’re a student or simply interested in how Artificial Intelligence is changing science and engineering practices–this lecture series is open to everyone:

• We look forward to meeting you in person in TU Wien’s beautiful Festsaal at 1040 Vienna, Karlsplatz 13, 1st floor, room AA0148.
• If you can’t make it person, switch on your laptop and join us via Zoom (Meeting ID: 661 3918 8284, Passcode: serwyN6e).

Public Lecture Series AI in Science and Engineering

Our public lecture series AI in Science and Engineering explores how AI is transforming these disciplines. Bringing together computer science, mathematics, engineering, and science in general, the series highlights how AI is reshaping discovery, design, and deployment in science and engineering.

Engaging with these changes is essential: AI already permeates daily life and has fundamentally altered how science and engineering are practiced, from data collection and analysis to experiment design, simulation, and validation. Understanding AI’s capabilities and limits helps researchers build systems that are safe, robust, and equitable. It also informs how we teach and train the next generation—integrating responsible innovation and interdisciplinary collaboration so that the scientific and engineering communities can advance AI that serves people and strengthens the foundations of science and engineering.

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