Exploring Cutting-Edge AI Research at ICLR 2023
Research towards AI models that can generalise, scale, and accelerate science
Exciting news is on the horizon as the 11th International Conference on Learning Representations (ICLR) is set to kick off on May 1st in Kigali, Rwanda. This groundbreaking event marks the first major artificial intelligence (AI) conference to be held in Africa since the start of the pandemic.
Attendees from all corners of the globe will converge to showcase their pioneering work in deep learning, covering a wide spectrum of AI, statistics, data science, and practical applications in machine vision, gaming, and robotics. DeepMind is honored to be a Diamond sponsor and a champion for diversity, equity, and inclusion at the conference.
Our talented teams at DeepMind are gearing up to present 23 papers at ICLR 2023, highlighting some of the most innovative and impactful research in the field. Here’s a sneak peek at some of the key highlights:
Unpacking the Quest for AGI
While AI has made significant strides in text and image processing, there is still much ground to cover in terms of generalization and scalability. Achieving artificial general intelligence (AGI) remains a pivotal milestone, one that could revolutionize our daily lives.
One of our novel approaches involves training models to tackle dual problems simultaneously, enhancing their ability to reason and generalize across tasks. Additionally, our exploration into neural networks’ generalization capabilities sheds light on the importance of augmenting models with external memory for improved performance.
Another intriguing challenge we address is enhancing models’ proficiency in long-term tasks with sporadic rewards, introducing new methodologies and training datasets to foster human-like exploration strategies over extended timeframes.
Innovative Strategies in AI
As we push the boundaries of AI capabilities, it becomes essential to refine existing methodologies for real-world efficiency. Our research delves into leveraging language models for multi-step reasoning tasks by leveraging their logical structures, enabling interpretable and verifiable responses.
Moreover, we explore ways to bolster model robustness against adversarial attacks without compromising performance on regular inputs, showcasing the potential for adaptive models that offer flexibility in managing this tradeoff dynamically.
On the reinforcement learning front, we introduce algorithm distillation as a means to enhance models’ generalization capabilities across diverse tasks, thus mitigating the challenges of task-specific RL algorithms. Additionally, we present a groundbreaking approach that significantly reduces the data and energy requirements for training RL agents to human-level performance.
The Intersection of AI and Science
AI continues to revolutionize scientific endeavors by enabling researchers to analyze complex datasets and propel scientific advancements. Our work demonstrates how AI accelerates progress in scientific discovery while also benefiting from insights gained through scientific research.
From predicting molecular properties with unprecedented accuracy to enhancing quantum chemistry calculations with new transformer models, our research showcases AI’s potential in revolutionizing drug discovery and scientific modeling. Furthermore, our simulation of collisions between intricate shapes opens up possibilities across robotics, graphics, and design domains.
For a comprehensive list of DeepMind’s papers and the complete schedule of events at ICLR 2023, be sure to check out the official ICLR 2023 event page.