There's been a lot of talk about how AI tools like ChatGPT are changing education. Students are using AI to do research, write papers, and get better grades. So today on the show, we debate whether using AI in school is actually cheating. Plus, we dive into how students and teachers are using these tools, and we ask what place AI should have in the future of learning. Write to us at uncannyvalley@wired.com.

Although recent point cloud analysis achieves impressive progress, the paradigm of representation learning from a single modality gradually meets its bottleneck. In this work, we take a step towards more discriminative 3D point cloud representation by fully taking advantages of images which inherently contain richer appearance information, e.g., texture, color, and shade. Specifically, this paper introduces a simple but effective point cloud cross-modality training (PointCMT) strategy, which utilizes view-images, i.e., rendered or projected 2D images of the 3D object, to boost point cloud analysis. In practice, to effectively acquire auxiliary knowledge from view images, we develop a teacher-student framework and formulate the crossmodal learning as a knowledge distillation problem. PointCMT eliminates the distribution discrepancy between different modalities through novel feature and classifier enhancement criteria and avoids potential negative transfer effectively. Note that PointCMT effectively improves the point-only representation without architecture modification. Sufficient experiments verify significant gains on various datasets using appealing backbones, i.e., equipped with PointCMT, PointNet++ and PointMLP achieve state-of-the-art performance on two benchmarks, i.e., 94.4% and 86.7% accuracy on ModelNet40 and ScanObjectNN, respectively. Code will be made available at https://github.com/ZhanHeshen/PointCMT.

The use of momentum in stochastic gradient methods has become a widespread practice in machine learning. Different variants of momentum, including heavyball momentum, Nesterov's accelerated gradient (NAG), and quasi-hyperbolic momentum (QHM), have demonstrated success on various tasks. Despite these empirical successes, there is a lack of clear understanding of how the momentum parameters affect convergence and various performance measures of different algorithms. In this paper, we use the general formulation of QHM to give a unified analysis of several popular algorithms, covering their asymptotic convergence conditions, stability regions, and properties of their stationary distributions. In addition, by combining the results on convergence rates and stationary distributions, we obtain sometimes counter-intuitive practical guidelines for setting the learning rate and momentum parameters.

SAVE 38%: As of May 23, you can get the TCL 98-inch QM7K QD-Mini LED 4K Smart TV (98QM7K, 2025 Model) for 2,499.99, It's also the lowest price we've seen for this model. Memorial Day is just a few days away, and Amazon's offering massive discounts on TVs of all sizes, including this 98-inch TCL 4K smart TV. As of May 23, you can get the TCL 98-inch QM7K QD-Mini 4K Smart TV (98QM7K, 2025 Model) for 2,499.99, It's also the lowest price we've seen for this model.

Various graph contrastive learning models have been proposed to improve the performance of learning tasks on graph datasets in recent years. While effective and prevalent, these models are usually carefully customized. In particular, although all recent researches create two contrastive views, they differ greatly in view augmentations, architectures, and objectives. It remains an open question how to build your graph contrastive learning model from scratch for particular graph learning tasks and datasets. In this work, we aim to fill this gap by studying how graph information is transformed and transferred during the contrastive learning process and proposing an information-aware graph contrastive learning framework called InfoGCL. The key point of this framework is to follow the Information Bottleneck principle to reduce the mutual information between contrastive parts while keeping task-relevant information intact at both the levels of the individual module and the entire framework so that the information loss during graph representation learning can be minimized. We show for the first time that all recent graph contrastive learning methods can be unified by our framework. We empirically validate our theoretical analysis on both node and graph classification benchmark datasets, and demonstrate that our algorithm significantly outperforms the state-of-the-arts.

Yesterday in an announcement blog post, AI company Anthropic unveiled Claude 4, its new generation of AI models consisting of Claude 4 Opus and Claude 4 Sonnet with a range of new abilities. Both Claude 4 models are hybrid models, which means they're capable of giving you short-and-quick answers or thinking longer on their responses with deeper reasoning. Claude 4 Opus is excellent at solving complex problems and at programming. The model can maintain its performance in long tasks over several hours with thousands of different steps. Meanwhile, Anthropic says Claude 4 Sonnet is a huge upgrade over Claude 3.7 Sonnet's abilities.

Breakthroughs, discoveries, and DIY tips sent every weekday. After decades of being tortured, shoved, kicked, burned, and bludgeoned, robots are finally getting their chance to fight back. This weekend, Chinese robotics maker Unitree says it will livestream the world's first boxing match between two of its humanoid robots. The event, titled Unitree Iron Fist King: Awakening, will feature a face-off between two of Unitree's 4.3-foot-tall G1 robots. The robots will reportedly be remotely controlled by human engineers, though they are also expected to demonstrate some autonomous, pre-programmed actions as well.

Figure 9: Example showing how a single line of HTML code is rendered by a browser's renderer. In this example, we can see that the tags

delimit different blocks which are therefore spaced by line breaks while other tags, such as , are rendered on the same line of text that precedes and follows them.

Iteratively Reweighted Least Squares (IRLS) is an easy to implement family of algorithms for solving these problems that has been studied for over 50 years. However, these algorithms often diverge for p>3, and since the work of Osborne (1985), it has been an open problem whether there is an IRLS algorithm that is guaranteed to converge rapidly for p>3. We propose p-IRLS, the first IRLS algorithm that provably converges geometrically for any p 2 [2, 1). Our algorithm is simple to implement and is guaranteed to find a high accuracy solution in a sub-linear number of iterations. Our experiments demonstrate that it performs even better than our theoretical bounds, beats the standard Matlab/CVX implementation for solving these problems by 10-50x, and is the fastest among available implementations in the high-accuracy regime.

As of yesterday, Microsoft has begun rolling out a new update to Windows 11 Insiders on the Dev and Canary Channels. This update brings new AI features to Notepad, Paint, and the Snipping Tool. Notepad now has the ability to write text from scratch using generative AI, which is meant to aid you by quickly producing drafts based on your prompts and instructions. To use AI text generation, simply right-click anywhere in the document and select Write. Type in your instructions, then either click Keep Text or Discard on the results.