Correlation clustering is a central problem in unsupervised learning, with applications spanning community detection, duplicate detection, automated labeling and many more. In the correlation clustering problem one receives as input a set of nodes and for each node a list of co-clustering preferences, and the goal is to output a clustering that minimizes the disagreement with the specified nodes' preferences. In this paper, we introduce a simple and computationally efficient algorithm for the correlation clustering problem with provable privacy guarantees. Our additive error is stronger than those obtained in prior work and is optimal up to polylogarithmic factors for fixed privacy parameters.

A central goal in the cognitive sciences is the development of numerical models for mental representations of object concepts. This paper introduces Variational Interpretable Concept Embeddings (VICE), an approximate Bayesian method for embedding object concepts in a vector space using data collected from humans in a triplet odd-one-out task. VICE uses variational inference to obtain sparse, non-negative representations of object concepts with uncertainty estimates for the embedding values. These estimates are used to automatically select the dimensions that best explain the data. We derive a PAC learning bound for VICE that can be used to estimate generalization performance or determine a sufficient sample size for experimental design.

Ensembling neural networks is an effective way to increase accuracy, and can often match the performance of individual larger models. This observation poses a natural question: given the choice between a deep ensemble and a single neural network with similar accuracy, is one preferable over the other? Recent work suggests that deep ensembles may offer distinct benefits beyond predictive power: namely, uncertainty quantification and robustness to dataset shift. In this work, we demonstrate limitations to these purported benefits, and show that a single (but larger) neural network can replicate these qualities. First, we show that ensemble diversity, by any metric, does not meaningfully contribute to an ensemble's ability to detect out-of-distribution (OOD) data, but is instead highly correlated with the relative improvement of a single larger model.

In addition to gobbling up most of the internet, ChatGPT now wants access to your OneDrive and SharePoint files, too. One of the earliest uses of AI was to summarize documents and folders of documents, and there's only so many times you can ask it whether Spider-Man would beat Wonder Woman in a fair fight. It would be more productive for AI to collate and make sense of your own personal information, assuming you want to grant access to it. According to OpenAI, ChatGPT can now connect to your OneDrive or SharePoint document libraries, assuming you're a paid ChatGPT Plus, Pro, or Team user who lives outside the EEA, Switzerland, and the UK (via Windows Central). You'll obviously have to connect ChatGPT and give it permission to start poring over your cloud documents.

It might be time to say your goodbyes to the iconic "I'm Feeling Lucky" button below the Google Search bar. In its place will be AI Mode, a feature that's been quietly rolling out to users this week, according to The Verge. It's part of Google's ongoing push to merge its core search engine with Gemini, its flagship AI model. First announced in March, AI Mode started as an experimental opt-in via Google Labs. Earlier this May, it became available to all Labs users.

That photo you'd like to share on TikTok seems a bit blah. If only there were some way you could make it more exciting, dynamic, and visual. You can, thanks to a new AI-powered image-to-video feature known as AI Alive. Unveiled on Tuesday, AI Alive creates a brief video clip out of any still photo. Available within TikTok's Story Camera, the AI tool taps into AI to automatically add the right prompt and transform your photo.

Breakthroughs, discoveries, and DIY tips sent every weekday. Archaeopteryx represents a pivotal point in the grand evolutionary journey linking dinosaurs to their avian descendants. But paleontologists still have questions about the Jurassic era animal's anatomy and behavior roughly 165 years after its discovery. One of the most pressing lingering mysteries is how Archaeopteryx managed to fly above its fellow feathered dinosaur relatives. After more than two decades spent in a private collection, one of the most detailed and complete fossil sets arrived at the Chicago's Field Museum in 2022.

Microsoft quietly announced earlier this week that it plans to shut down a longstanding tool supplying search engine startups and other software developers with a raw feed of Bing search results. The Bing Search APIs, or application programming interfaces, were once vital to many niche Google alternatives, but fell out of favor more recently as Microsoft hiked fees for the service and restricted its use. The shutoff, which is scheduled to begin on August 11, still came as a surprise to several developers who spoke with WIRED. Customers learned of it on Monday via an email from Microsoft and a post on its website. They were directed to consider using "Grounding with Bing Search as part of Azure AI Agents," a Microsoft service that allows chatbots like ChatGPT to augment AI-generated responses with "real-time public web data."

Since the release and viral success of ChatGPT in late 2022, generative AI has been integrated into an ever-expanding number of tech platforms and gadgets. As is often the case with powerful new technologies, generative AI's growth has outpaced our ability to build frameworks for safe and responsible use. Teachers, for example, must now contend with the fact that many (if not all) of their students are using generative AI tools like ChatGPT and Gemini to complete assignments. The long-term implications of this sudden surge for the education system remain to be seen. Similarly, business leaders now face the challenge of managing a generative AI-powered workforce and ensuring that the technology facilitates, rather than hinders, employee performance.

We consider learning Nash equilibria in two-player zero-sum Markov Games with nonlinear function approximation, where the action-value function is approximated by a function in a Reproducing Kernel Hilbert Space (RKHS). The key challenge is how to do exploration in the high-dimensional function space. We propose a novel online learning algorithm to find a Nash equilibrium by minimizing the duality gap. At the core of our algorithms are upper and lower confidence bounds that are derived based on the principle of optimism in the face of uncertainty. We prove that our algorithm is able to attain an O(\sqrt{T}) regret with polynomial computational complexity, under very mild assumptions on the reward function and the underlying dynamic of the Markov Games.