Neural Information Processing Systems http://nips.cc/

Tractable models of human perception have proved to be challenging to build. Hand-designed models such as MS-SSIM remain popular predictors of human image quality judgements due to their simplicity and speed. Recent modern deep learning approaches can perform better, but they rely on supervised data which can be costly to gather: large sets of class labels such as ImageNet, image quality ratings, or both. We combine recent advances in information-theoretic objective functions with a computational architecture informed by the physiology of the human visual system and unsupervised training on pairs of video frames, yielding our Perceptual Information Metric (PIM)1. We show that PIM is competitive with supervised metrics on the recent and challenging BAPPS image quality assessment dataset and outperforms them in predicting the ranking of image compression methods in CLIC 2020. We also perform qualitative experiments using the ImageNet-C dataset, and establish that PIM is robust with respect to architectural details.

Graph neural networks (GNNs) have been successful in learning representations from graphs. Many popular GNNs follow the pattern of aggregate-transform: they aggregate the neighbors' attributes and then transform the results of aggregation with a learnable function. Analyses of these GNNs explain which pairs of non-identical graphs have different representations. However, we still lack an understanding of how similar these representations will be. We adopt kernel distance and propose transform-sum-cat as an alternative to aggregate-transform to reflect the continuous similarity between the node neighborhoods in the neighborhood aggregation. The idea leads to a simple and efficient graph similarity, which we name Weisfeiler-Leman similarity (WLS). In contrast to existing graph kernels, WLS is easy to implement with common deep learning frameworks. In graph classification experiments, transform-sum-cat significantly outperforms other neighborhood aggregation methods from popular GNN models. We also develop a simple and fast GNN model based on transform-sum-cat, which obtains, in comparison with widely used GNN models, (1) a higher accuracy in node classification, (2) a lower absolute error in graph regression, and (3) greater stability in adversarial training of graph generation.

In this episode of “Uncanny Valley,” we get into how the Elon Musk-Sam Altman trial goes way beyond their rivalry and could have major implications both for OpenAI and also the AI industry at large.

Insurance apps often collect driving, location and health data in exchange for premium discounts. Adjusting app permissions can help limit what information is shared.

Explore how MSI's line-up - from Mini PCs to powerful workstations - adapts to every level of performance need. Most modern workplace AI doesn't require a full tower PC or a dedicated GPU. The MSI Cubi NUC AI+ 3MG proves that with a design that fits in the palm of your hand, weighs just over a pound, and starts at $569 for barebones configurations. This compact little system is shorter than a credit card and takes up just 0.5 liters in volume - the size of a small paperback book. The beating heart of this tiny powerhouse is an Intel Core Ultra 9 386H Panther Lake CPU with up to 16 cores and a high clock-speed, even with such a compact design.

Elon Musk Seemingly Admits xAI Has Used OpenAI's Models to Train Its Own While answering questions under oath, Musk argued it's standard practice for AI labs to use their competitors' models. While testifying on Thursday in federal court, Elon Musk seemed to indicate that his AI lab may have used OpenAI's models to train xAI's own. He touched upon the topic while sitting on the witness stand answering cross-examination questions from an OpenAI attorney amid his ongoing legal battle against the ChatGPT-maker . Do you know what distillation is? It means to use one AI model to train another AI model.

OpenAI is rolling out Advanced Account Security for people concerned that their ChatGPT or Codex accounts could be potential targets of phishing attacks. For anyone who fears their ChatGPT and Codex accounts might be targeted by attackers, OpenAI announced on Thursday that it is adding an optional new level of account protection that adds an extra layer of security. Dubbed Advanced Account Security, the feature enforces strict access controls that would make account takeover attacks very difficult. Such measures are not a new idea in the realm of account security. Google, for example, has offered its Advanced Protection account security tier for nearly a decade . But as mainstream AI services rapidly proliferate around the world, there is a pressing need for an array of basic protections to be put in place.

OpenAI desires less regulation, but it still doesn't know how its chatbot works. Get your news from a source that's not owned and controlled by oligarchs. OpenAI admitted it had to develop a specific instruction in the code of its latest model of ChatGPT to stop it from repeatedly referencing "goblins, gremlins, and other creatures." In an explanation posted Wednesday, the company said the "strange habit" came from its chatbot personality feature --specifically for users who chose the "Nerdy" personality. You are an unapologetically nerdy, playful and wise AI mentor to a human.