Kicking off the current generative AI frenzy, ChatGPT is already a relatively capable AI, able to answer questions, generate content, and chat with you about almost any topic. Rather, the company has big plans for its popular AI, envisioning an evolution that would turn it into a "super assistant." OpenAI's goals for ChatGPT came to light courtesy of a confidential and highly redacted document introduced as part of the Justice Department's antitrust case against Google. In the internal file named "ChatGPT: H1 2025 Strategy," OpenAI described the near future of ChatGPT as an intuitive super assistant that understands you and acts as your interface to the internet. Also: How much energy does AI really use?

Lanyard-clad attendees with branded tote bags and pink-shirted volunteers flowed through London's Brick Lane on Monday, marking the launch of the inaugural SXSW London festival. Taking place over multiple stages and venues in Shoreditch and Hoxton, SXSW London has officially kicked off its first full day of panels, keynotes, demonstrations, movie premieres, and music gigs. And luckily, Londoners are no strangers to a queue, with SXSW's penchant for long lines outside Austin venues replicated in the UK capital. Playing to the strengths of fellow conferences, the biggest topics of SXSW London are the impact of AI on essentially anything you could think of, the creator economy and online communities, and self-driving tech -- I spied a Wayve autonomous vehicle carefully navigating the pedestrian-filled Brick Lane (with a human driver behind the wheel, just in case). London mayor Sadiq Khan officially launched the festival with a speech Monday morning, championing London as "a global centre for AI investment and innovation," emphasising a focus on ethical and accessible AI development, and playing to the audience with a ChatGPT anecdote.

How many people visit AIs like ChatGPT, Copilot, and Google Gemini each month? Whether you're just curious about such stats or need them for your own business or research, you can now turn to Comscore for the answers. On Thursday, the analytics firm announced that it has officially added AI usage information to its regular analysis. Comscore clients will be able to find out the number of monthly visits for 117 different AI tools across nine categories, covering both PC and mobile use. Also: How much energy does AI really use?

Artificial intelligence startup Anthropic has hit 3 billion in annualized revenue, marking a 200% increase in just five months, according to a Friday report from Reuters. Anthropic's annualized revenue -- or its total projected earnings over the course of the year, assuming its current rate of income continues -- was close to 1 billion in December, according to the Reuters report, which cited anonymous sources close to the matter. It crossed the 2 billion threshold in late March and reached 3 billion last month. Also: Anthropic's free Claude 4 Sonnet aced my coding tests - but its paid Opus model somehow didn't Founded in 2021 by siblings Dario and Daniela Amodei, both former OpenAI employees, Anthropic has built its business model around its Claude family of generative AI chatbots. The company has also positioned itself as a leader in the responsible deployment of powerful AI tools.

Decoding non-invasive brain recordings is pivotal for advancing our understanding of human cognition but faces challenges due to individual differences and complex neural signal representations. Traditional methods often require customized models and extensive trials, lacking interpretability in visual reconstruction tasks.

We address the problem of aggregating an ensemble of predictors with known loss bounds in a semi-supervised binary classification setting, to minimize prediction loss incurred on the unlabeled data. We find the minimax optimal predictions for a very general class of loss functions including all convex and many non-convex losses, extending a recent analysis of the problem for misclassification error. The result is a family of semi-supervised ensemble aggregation algorithms which are as efficient as linear learning by convex optimization, but are minimax optimal without any relaxations. Their decision rules take a form familiar in decision theory -- applying sigmoid functions to a notion of ensemble margin -- without the assumptions typically made in margin-based learning.

The design of revenue-maximizing combinatorial auctions, i.e. multi item auctions over bundles of goods, is one of the most fundamental problems in computational economics, unsolved even for two bidders and two items for sale. In the traditional economic models, it is assumed that the bidders' valuations are drawn from an underlying distribution and that the auction designer has perfect knowledge of this distribution. Despite this strong and oftentimes unrealistic assumption, it is remarkable that the revenue-maximizing combinatorial auction remains unknown. In recent years, automated mechanism design has emerged as one of the most practical and promising approaches to designing high-revenue combinatorial auctions. The most scalable automated mechanism design algorithms take as input samples from the bidders' valuation distribution and then search for a high-revenue auction in a rich auction class.

Representing 3D scenes from multiview images remains a core challenge in computer vision and graphics, requiring both reliable rendering and reconstruction, which often conflicts due to the mismatched prioritization of image quality over precise underlying scene geometry. Although both neural implicit surfaces and explicit Gaussian primitives have advanced with neural rendering techniques, current methods impose strict constraints on density fields or primitive shapes, which enhances the affinity for geometric reconstruction at the sacrifice of rendering quality. To address this dilemma, we introduce GSDF, a dual-branch architecture combining 3D Gaussian Splatting (3DGS) and neural Signed Distance Fields (SDF). Our approach leverages mutual guidance and joint supervision during the training process to mutually enhance reconstruction and rendering. Specifically, our method guides the Gaussian primitives to locate near potential surfaces and accelerates the SDF convergence. This implicit mutual guidance ensures robustness and accuracy in both synthetic and real-world scenarios. Experimental results demonstrate that our method boosts the SDF optimization process to reconstruct more detailed geometry, while reducing floaters and blurry edge artifacts in rendering by aligning Gaussian primitives with the underlying geometry.

The blind application of machine learning runs the risk of amplifying biases present in data. Such a danger is facing us with word embedding, a popular framework to represent text data as vectors which has been used in many machine learning and natural language processing tasks. We show that even word embeddings trained on Google News articles exhibit female/male gender stereotypes to a disturbing extent. This raises concerns because their widespread use, as we describe, often tends to amplify these biases. Geometrically, gender bias is first shown to be captured by a direction in the word embedding.

We propose a geometric algorithm for topic learning and inference that is built on the convex geometry of topics arising from the Latent Dirichlet Allocation (LDA) model and its nonparametric extensions. To this end we study the optimization of a geometric loss function, which is a surrogate to the LDA's likelihood. Our method involves a fast optimization based weighted clustering procedure augmented with geometric corrections, which overcomes the computational and statistical inefficiencies encountered by other techniques based on Gibbs sampling and variational inference, while achieving the accuracy comparable to that of a Gibbs sampler. The topic estimates produced by our method are shown to be statistically consistent under some conditions. The algorithm is evaluated with extensive experiments on simulated and real data.