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?

"I recently made a commitment that my wealth will be given away over the next 20 years. The majority of that funding will be spent on helping you address challenges here in Africa," he said in an address at the African Union (AU) headquarters. Mozambique's former First Lady Graça Machel welcomed his announcement, saying it came in a "moment of crisis". "We are counting on Mr Gates' steadfast commitment to continue walking this path of transformation alongside us," she said. The US government has cut aid to Africa, including programmes to treat patients with HIV/Aids, as part of US President Donald Trump's "America First" policy, raising concerns about the future of healthcare on the continent.

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.

We live in uncertain times. From geopolitical tension, macroeconomic instability, and digital transformation, business leaders must develop strategies that help their organizations survive and thrive in a complex world. Five business leaders give us their top tips. Gabriela Vogel, vice president analyst in Gartner's Executive Leadership of Digital Business practice, said her company's research stresses the importance of developing tech-enabled flexibility. "You must be able to adapt your organization to all of the crazy changes happening around the world. And part of that approach is achieved with technology," she said.

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.