Speaker Biographies

 

Austin Beaulier
Photogrammetry Artist and 3D Specialist, USA
The World is Your Asset Pack: Transforming Images and Videos into 3D Worlds

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This talk puts forth the idea that artificial intelligence is most powerful for artists when it is used to transform real-world inputs, rather than generate content from scratch. It presents repeatable workflows that keep human artists in the loop, leveraging a combination of paid and open-source tools (including Blender, Adobe Substance, Unreal Engine, and various large language models) to convert photographs, videos and physical objects into finished 3D assets, scenes, and interactive experiences. Through case studies of viral social videos, the session demonstrates a complete progression from single-image material creation to AI pipelines that produces assets suitable for film, gaming, and mixed reality media.

 

Furthermore, the presentation explores how AI-assistant layout and scripting can accelerate scene blocking when recreating iconic sets and spaces. The core of these workflows is to increase iteration speed while ensuring the human artist remains central to the creative process, preserving their authorship and artistic voice.

 

The talk provides tangible value for two key audiences. For artists, it offers a practical demonstration of workflow optimization, supported by audience engagement metrics and a cost-time analysis against traditional manual baselines. For educators and humanists, it addresses the challenge of applying scholarly standards to digital objects by introducing a classroom template for documenting sources, licensing assets, and establishing provenance. 

 

Attendees will leave with practical workflows, effective prompts, and ethical checklists for capture, transformation, composition and publishing, enabling faster, more innovative creation without compromising artistic vision or integrity.

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Chetan Gupta
Hitachi America, USA
Industrial AI 2.0: From Hype to Real-World Transformation

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Bio: Chetan Gupta is a distinguished leader in Applied and Industrial AI, recognized for his contributions within Hitachi and across the global AI community. As head of Hitachi’s AI research, he advances both core technologies and real-world applications across industry, energy, finance, and transportation. He is the author of  250+ papers and patents and holds a Ph.D. in Mathematics along with M.S. in Mathematical Computer Science and Chemical Engineering. Honored with numerous awards, Chetan continues to shape the future of AI through groundbreaking projects, thought leadership, and educational initiatives.

Abstract: Artificial Intelligence has evolved rapidly in the last few years — but the real story is not just about bigger models or flashy demos. The real story is how AI is moving beyond content generation to deliver measurable value in enterprises and in the physical world. In this keynote, I will share how we are combining traditional machine learning, generative AI, agentic AI, and physical AI to create real-world outcomes. I will also discuss the challenges to adoption — from technical hurdles like robustness and data quality, to organizational and governance barriers — and the opportunities ahead as we work to make AI an integral part of how the real world operates.

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Mario Schlener
Evolver, USA

How to Scale Enterprise Adoption of AI and GPT Technologies “Machine Intelligence” TODAY and Not Waiting for AGI to Happen TOMORROW

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The biggest blocker yesterday, today and also tomorrow will be the acceptance, trust and openness to unlearn behaviours of Humans to use and experiment with Machine Intelligence driven processes and systems – similar to using a driver-less car vs. a taxi or uber and feel confident that we are safe using the driver-less car trained on billions of safety scenarios i.e. at least safe in the same way or even better off compared to rely on a taxi or uber managed by a human that we don’t know apart from his rating on the app. Thus how do we build Machine Intelligence systems that have the capability to generate and gain the trust of Humans using it? To develop proper Machine Intelligence systems the fundamental constraints of Machine Intelligence (i.e. Intelligence is based on Logic Inspired Approach or Biologically Inspired Approach) has to be understood, appropriately addressed and mitigated. The two different paradigms for Intelligence: 1.) Logic Inspired Approach and 2.) Biologically Inspired Approach need to be well understood to manage the common known formal and informal fallacies of Machine Intelligence systems i.e. Multi-Agentic Systems, LLMs, etc.

 

Will share examples of core fundamental epistemological limitations of LLMs and how to manage them to make AI and GPT technologies ready of enterprise use TODAY.