OpenAI is officially getting into the hardware business. In a video posted to X on Wednesday, OpenAI CEO Sam Altman and former Apple designer Jony Ive, who worked on flagship products like the iPhone, revealed a partnership to create the next generation of AI-enabled devices. Also: I tried Google's XR glasses and they already beat my Meta Ray-Bans in 3 ways The AI software company announced it is merging with io, an under-the-radar startup focused on AI devices that Ive founded a year ago alongside several partners. In the video, Altman and Ive say they have been "quietly" collaborating for two years. As part of the deal, Ive and those at his design firm, LoveFrom, will remain independent but will take on creative roles at OpenAI.

Zero-shot reinforcement learning (RL) promises to provide agents that can perform any task in an environment after an offline, reward-free pre-training phase. Methods leveraging successor measures and successor features have shown strong performance in this setting, but require access to large heterogenous datasets for pre-training which cannot be expected for most real problems. Here, we explore how the performance of zero-shot RL methods degrades when trained on small homogeneous datasets, and propose fixes inspired by conservatism, a well-established feature of performant single-task offline RL algorithms. We evaluate our proposals across various datasets, domains and tasks, and show that conservative zero-shot RL algorithms outperform their non-conservative counterparts on low quality datasets, and perform no worse on high quality datasets. Somewhat surprisingly, our proposals also outperform baselines that get to see the task during training.

We begin by formally defining multihead self-attention and Transformer. Our definition is equivalent to Vaswani et al. (2017) [68], except we omit layer normalization for simplicity as in [81, 23, 34]. Consequently, each equivalence class γ in Definition 3 is a distinct set of all order-l multi-indices having a specific equality pattern. Now, for each equivalence class, we define the corresponding basis tensor as follows: Definition 4. I. Given a set of features X R Proof of Lemma 1 (Section 3.3) To prove Lemma 1, we need to show that each basis tensor B Here, our key idea is to break down the inclusion test (i, j) µ into equivalent but simpler Boolean tests that can be implemented in self-attention (Eq. To achieve this, we show some supplementary Lemmas.

Recent Convolutional Neural Networks (CNNs) have achieved significant success by stacking multiple convolutional blocks, named procedures in this paper, to extract semantic features. However, they use the same procedure sequence for all inputs, regardless of the intermediate features.

This section provides more details on the augmentation process of Figure 1. For Image Filtering (IF), s equals to 1.5, so the image is blurred by convolving with K = 1.5 G3+ Testing sets are not involved in our augmentation search process. ImageNet [2] is a challenging large scale dataset, containing about 1.28 million training The testing set is not used. Mean values and standard deviations are reported. The hyperparameters for re-training used in this paper are listed in Tab.

Decision trees have been widely used as classifiers in many machine learning applications thanks to their lightweight and interpretable decision process. This paper introduces Tree in Tree decision graph (TnT), a framework that extends the conventional decision tree to a more generic and powerful directed acyclic graph.

OpenAI has fully acquired Io, a joint venture it cocreated last year with Jony Ive, the famed British designer behind the sleek industrial aesthetic that defined the iPhone and more than two decades of Apple products. In a nearly 10-minute video posted to X on Wednesday, Ive and OpenAI CEO Sam Altman said the Apple pioneer's "creative collective" will "merge with OpenAI to work more intimately with the research, engineering, and product teams in San Francisco." OpenAI says it's paying 5 billion in equity to acquire Io. The promotional video included musings on technology from both Ive and Altman, set against the golden-hour backdrop of the streets of San Francisco, but the two never share exactly what it is they're building. "We look forward to sharing our work next year," a text statement at the end of the video reads.

Ensemble-based debiasing methods have been shown effective in mitigating the reliance of classifiers on specific dataset bias, by exploiting the output of a biasonly model to adjust the learning target. In this paper, we focus on the bias-only model in these ensemble-based methods, which plays an important role but has not gained much attention in the existing literature. Theoretically, we prove that the debiasing performance can be damaged by inaccurate uncertainty estimations of the bias-only model. Empirically, we show that existing bias-only models fall short in producing accurate uncertainty estimations. Motivated by these findings, we propose to conduct calibration on the bias-only model, thus achieving a three-stage ensemble-based debiasing framework, including bias modeling, model calibrating, and debiasing. Experimental results on NLI and fact verification tasks show that our proposed three-stage debiasing framework consistently outperforms the traditional two-stage one in out-of-distribution accuracy.

This work brings together three active research areas: variational inference, variational Kalman filtering, and gradient flows on the Wasserstein space.

A.2 Mercury Data Distribution and Customized Data Structures Except for all built-in Python data structures, Mercury imports another two structures to enhance the diversity and complexity as shown in Figure 4. Table 6: Mercury-eval encompasses 256 tasks, the difficulty of which has been balanced for model evaluation. Mercury-train Figure 4: Mercury supports two customized comprises the remaining 1,633 tasks for data structures: TreeNode and ListNode. Each executed code within the sandbox is subject to certain constraints to ensure fair utilization of resources and to prevent any single code from monopolizing the system resource. Specifically, there are two primary constraints: a time limit and a memory limit. The time limit restricts how long the code can execute before being forcibly terminated, thereby ensuring that no infinite loops or excessively long computations negatively impact the availability of the sandbox.