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Publications
Motor cortex latent dynamics encode arm movement direction and urgency independently
Current approaches to building general-purpose AI systems tend to produce systems with both beneficial and harmful capabilities. Further pro… (see more)gress in AI development could lead to capabilities that pose extreme risks, such as offensive cyber capabilities or strong manipulation skills. We explain why model evaluation is critical for addressing extreme risks. Developers must be able to identify dangerous capabilities (through"dangerous capability evaluations") and the propensity of models to apply their capabilities for harm (through"alignment evaluations"). These evaluations will become critical for keeping policymakers and other stakeholders informed, and for making responsible decisions about model training, deployment, and security.
Current approaches to building general-purpose AI systems tend to produce systems with both beneficial and harmful capabilities. Further pro… (see more)gress in AI development could lead to capabilities that pose extreme risks, such as offensive cyber capabilities or strong manipulation skills. We explain why model evaluation is critical for addressing extreme risks. Developers must be able to identify dangerous capabilities (through"dangerous capability evaluations") and the propensity of models to apply their capabilities for harm (through"alignment evaluations"). These evaluations will become critical for keeping policymakers and other stakeholders informed, and for making responsible decisions about model training, deployment, and security.
Current approaches to building general-purpose AI systems tend to produce systems with both beneficial and harmful capabilities. Further pro… (see more)gress in AI development could lead to capabilities that pose extreme risks, such as offensive cyber capabilities or strong manipulation skills. We explain why model evaluation is critical for addressing extreme risks. Developers must be able to identify dangerous capabilities (through"dangerous capability evaluations") and the propensity of models to apply their capabilities for harm (through"alignment evaluations"). These evaluations will become critical for keeping policymakers and other stakeholders informed, and for making responsible decisions about model training, deployment, and security.
Current approaches to building general-purpose AI systems tend to produce systems with both beneficial and harmful capabilities. Further pro… (see more)gress in AI development could lead to capabilities that pose extreme risks, such as offensive cyber capabilities or strong manipulation skills. We explain why model evaluation is critical for addressing extreme risks. Developers must be able to identify dangerous capabilities (through"dangerous capability evaluations") and the propensity of models to apply their capabilities for harm (through"alignment evaluations"). These evaluations will become critical for keeping policymakers and other stakeholders informed, and for making responsible decisions about model training, deployment, and security.
Realistically distributing object placements in synthetic training data improves the performance of vision-based object detection models
Setareh Dabiri
Vasileios Lioutas
Berend Zwartsenberg
Yunpeng Liu
Matthew Niedoba
Xiaoxuan Liang
Dylan Green
Justice Sefas
Jonathan Wilder Lavington
Frank Wood
Adam Ścibior
When training object detection models on synthetic data, it is important to make the distribution of synthetic data as close as possible to … (see more)the distribution of real data. We investigate specifically the impact of object placement distribution, keeping all other aspects of synthetic data fixed. Our experiment, training a 3D vehicle detection model in CARLA and testing on KITTI, demonstrates a substantial improvement resulting from improving the object placement distribution.
Large language model (LLM)-based decision-making agents have shown the ability to generalize across multiple tasks. However, their performan… (see more)ce relies on massive data and compute. We argue that this inefficiency stems from the forgetting phenomenon, in which a model memorizes its behaviors in parameters throughout training. As a result, training on a new task may deteriorate the model's performance on previous tasks. In contrast to LLMs' implicit memory mechanism, the human brain utilizes distributed memory storage, which helps manage and organize multiple skills efficiently, mitigating the forgetting phenomenon. Thus inspired, we propose an internal working memory module to store, blend, and retrieve information for different downstream tasks. Evaluation results show that the proposed method improves training efficiency and generalization in both Atari games and meta-world object manipulation tasks. Moreover, we demonstrate that memory fine-tuning further enhances the adaptability of the proposed architecture.
