Patrick van der Smagt, Director of AI Research at Volkswagen Group, discussed the use of generative machine learning models for predicting and controlling complex stochastic systems in robotics. The talk highlighted examples in robotics and beyond and addressed the challenges of achieving quality and trust in AI systems. He also mentioned his involvement in a European industry initiative on trust in AI and his membership in the AI Council of the State of Bavaria. Why it matters: Understanding control in robotics, along with trust in AI, are key issues for further development of autonomous systems, especially in industrial applications within the GCC region.
The paper introduces the Unscented Autoencoder (UAE), a novel deep generative model based on the Variational Autoencoder (VAE) framework. The UAE uses the Unscented Transform (UT) for a more informative posterior representation compared to the reparameterization trick in VAEs. It replaces Kullback-Leibler (KL) divergence with the Wasserstein distribution metric and demonstrates competitive performance in Fréchet Inception Distance (FID) scores.
MBZUAI faculty Kun Zhang is researching methods to improve the reliability of generative AI, particularly in healthcare applications. Current generative AI models often act as "black boxes," making it difficult to understand why a specific result was produced. Zhang's research focuses on incorporating causal relationships into AI systems to ensure more accurate and meaningful information. Why it matters: Improving the trustworthiness of generative AI is crucial for sensitive sectors like healthcare and ensuring responsible AI deployment across the region.
Axel Sauer from the University of Tübingen presented research on scaling Generative Adversarial Networks (GANs) using pretrained representations. The work explores shaping GANs into causal structures, training them up to 40 times faster, and achieving state-of-the-art image synthesis. The presentation mentions "Counterfactual Generative Networks", "Projected GANs", "StyleGAN-XL”, and “StyleGAN-T". Why it matters: Scaling GANs and improving their training efficiency is crucial for advancing image and video synthesis, with implications for various applications in computer vision, graphics, and robotics.
A DeepMind researcher presented work on incorporating symmetries into machine learning models, with applications to lattice-QCD and molecular dynamics. The work includes permutation and translation-invariant normalizing flows for free-energy estimation in molecular dynamics. They also presented U(N) and SU(N) Gauge-equivariant normalizing flows for pure Gauge simulations and its extensions to incorporate fermions in lattice-QCD. Why it matters: Applying symmetry principles to generative models could improve AI's ability to model complex physical systems relevant to materials science and other fields in the region.
VinAI Research presented research projects focused on advancing image generation and manipulation using GANs and Diffusion Models. The research aims to improve GANs regarding utility, coverage, and output consistency. For Diffusion Models, the work focuses on improving the models’ speed to approach real-time performance and prevent negative social impact of diffusion-based personalized text-to-image generation. Why it matters: This talk indicates ongoing research and development in generative AI in Southeast Asia, an area of growing interest globally.
Researchers at the Rosalind Franklin Institute are using generative AI, including GANs, to augment limited biological datasets, specifically mirtron data from mirtronDB. The synthetic data created mimics real-world samples, facilitating more comprehensive training of machine learning models, leading to improved mirtron identification tools. They also plan to apply Large Language Models (LLMs) to predict unknown patterns in sequence and structure biology problems. Why it matters: This research explores AI techniques to tackle data scarcity in biological research, potentially accelerating discoveries in noncoding RNA and transposable elements.
Jorge Amador, a PhD student at KAUST's Visual Computing Center, presented a talk on physically-based simulation for generative AI models. The talk covered the use of synthetic data generation and physical priors to address the need for high-quality datasets. Applications discussed include photo editing, navigation, digital humans, and cosmological simulations. Why it matters: This research explores a promising technique to overcome data scarcity issues in AI, particularly relevant in resource-constrained environments or for sensitive applications.