OpenAI unveiled several work-related tools at its Dev Day event today, including a hub designed for collaboration. Coworkers will be able to set up ChatGPT Spaces, where they, ChatGPT and Dots (new personal AI agents OpenAI is offering up) can tap into shared information. The company says users can keep files that they and their colleagues upload -- as well as what they generate using ChatGPT -- organized in a team space to make it easy to find what they need. This feature will be available for those on OpenAI's Pro, Business and Enterprise plans, first on the ChatGPT desktop app and the web before arriving on mobile. Aligned with that, a plugin for meetings can take notes and generate summaries (including action items), and add those to a space.

OpenAI has launched a plugin for the Apple Messages app, and as Bloomberg notes, it could raise privacy and security concerns. At the moment, it's only available to ChatGPT Work and Codex users on Mac, allowing them to search messages and even draft and send replies through the chatbot on their desktop. In the example OpenAI posted, the user asked ChatGPT to look for conversations they'd missed the day before in their Messages app. They then used the chatbot to write and send their reply. The feature is, at least, opt-in.

I wore the world's first HDR10 smart glasses TCL's new E Ink tablet beats the Remarkable and Kindle Anker's new charger is one of the most unique I've ever seen I wore the world's first HDR10 smart glasses TCL's new E Ink tablet beats the Remarkable and Kindle Anker's new charger is one of the most unique I've ever seen You can use 70+ Adobe tools without leaving ChatGPT now - here's how Adobe's new plugin works both in Work and Codex, and is available today in ChatGPT. Adobe's new plugin for ChatGPT puts the company's tools directly in Work and Codex. Design software must adapt around competitive AI products. The SaaSpocalypse still hasn't arrived. Adobe is expanding its partnership with OpenAI's ChatGPT through a new integration: an Adobe for ChatGPT plugin .

Deep learning has emerged as a pivotal tool for accelerating research in the life sciences, with the low-level processing of biomedical images (e.g., registration, fusion, restoration, super-resolution) being one of its most critical applications. Platforms such as ImageJ (Fiji) and napari have enabled the development of customized plugins for various models. However, these plugins are typically based on models that are limited to specific tasks and datasets, making them less practical for biologists.

We're seeing coding be one of the leading applications of artificial intelligence tools, and OpenAI is continuing to expand on its offerings in that space. The company has launched a Chrome extension for its Codex platform. The new browser-based capabilities of the plugin include testing web apps, collecting context from across open tabs and using Chrome DevTools in parallel while the user performs other tasks. This extension could also help Codex be more appealing to casual users and additional professions beyond developers since so many computing tasks happen in browsers. Codex can now take on more of your browser dev work.

Dataset condensation (DC) distills a large real-world dataset into a small synthetic dataset, with the goal of training a network from scratch on the latter that performs similarly to the former. State-of-the-art (SOTA) DC methods have achieved satisfactory results through techniques such as accuracy, gradient, training trajectory, or distribution matching. However, these works all perform matching in the high-dimension pixel space, ignoring that natural images are usually locally connected and have lower intrinsic dimensions, resulting in low condensation efficiency. In this work, we propose a simple-yet-efficient dataset condensation plugin that matches the raw and synthetic datasets in a low-dimensional manifold.

The key idea of the proof is to exploit the problem representation in terms of confusion matrices. Here we set up and discuss the example in 3.2 in more detail. In the following, whenAj is used to denote an event inside a probability, it refers to the event {Aj =1}. First step is to extract the error incurred by plugging inˆη rather than η. C.2 WeightedERM In the weighed ERM approach (referred to as cost-sensitive classification for the binary case [1]) we parametrizeh: X [K]by a function classF of functions: X RK.

We consider the problem of recovering an unknown latent code vector under a known generative model.