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Big tech company hype sells generative artificial intelligence (AI) as intelligent, creative, desirable, inevitable and about to radically reshape the future in many ways. Published by Oxford University Press, our new research on how generative AI depicts Australian themes directly challenges this perception. We found when generative AIs produce images of Australia and Australians, these outputs are riddled with bias. They reproduce sexist and racist caricatures more at home in the country's imagined monocultural past. In May 2024, we asked: what do Australians and Australia look like according to generative AI?

Reward curves for TOP-RAD and RAD on pixel-based tasks from the DM Control Suite are shown in Figure 7. Figure 7: Results across 10 seeds for DM Control tasks. Each individual run was performed on a single GPU and lasted between 3 and 18 hours, depending on the task and GPU model. The procedures for updating the critics and the actor for TOP-TD3 are described in detail in Algorithm 2 and Algorithm 3. Algorithm 2: UpdateCritics In order to enable adaptation, we make use of an approach inspired by recent results in the model selection for contextual bandits literature. Bandit problems, the "arm" choices in the model selection setting are not stationary arms, but learning algorithms. The objective is to choose in an online manner, the best algorithm for the task at hand.The In figure 5, Ant-v2 we show this to be the case.

One of the principal scientific challenges in deep learning is explaining generalization, i.e., why the particular way the community now trains networks to achieve small training error also leads to small error on held-out data from the same population. It is widely appreciated that some worst-case theories--such as those based on the VC dimension of the class of predictors induced by modern neural network architectures--are unable to explain empirical performance. A large volume of work aims to close this gap, primarily by developing bounds on generalization error, optimization error, and excess risk. When evaluated empirically, however, most of these bounds are numerically vacuous. Focusing on generalization bounds, this work addresses the question of how to evaluate such bounds empirically. Jiang et al. [ 9 ] recently described a large-scale empirical study aimed at uncovering potential causal relationships between bounds/measures and generalization. Building on their study, we highlight where their proposed methods can obscure failures and successes of generalization measures in explaining generalization. We argue that generalization measures should instead be evaluated within the framework of distributional robustness.

TinyNAS is co-designed with TinyEngine, a memory-efficient inference library to expand the search space and fit a larger model.