Jensen Huang Says Nvidia's New Vera Rubin Chips Are in'Full Production' The chip giant says Vera Rubin will sharply cut the cost of training and running AI models, strengthening the appeal of its integrated computing platform. Nvidia CEO Jensen Huang says that the company's next-generation AI superchip platform, Vera Rubin, is on schedule to begin arriving to customers later this year. "Today, I can tell you that Vera Rubin is in full production," Huang said during a press event on Monday at the annual CES technology trade show in Las Vegas. Rubin will cut the cost of running AI models to about one-tenth of Nvidia's current leading chip system, Blackwell, the company told analysts and journalists during a call on Sunday. Nvidia also said Rubin can train certain large models using roughly one-fourth as many chips as Blackwell requires.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This is an interesting paper - the application of graphical methods to analyze missing data patterns may prove to be very useful. The paper contains the word causal graph in the title and in the introduction. However, none of the results seem to depend on the graph being causal. The results are entirely about conditional independence and factorizations.

Each year, MIT Technology Review honors 35 outstanding people under the age of 35 who are driving scientific progress and solving tough problems in their fields. Today we want to introduce you to the computing innovators on the list who are coming up with new AI chips and specialized datasets--along with smart ideas about how to assess advanced systems for safety. Earlier this year, the $800 million Vera Rubin Observatory commenced its decade-long quest to create an extremely detailed time-lapse movie of the universe. Rubin is capable of capturing many more stars than any other astronomical observatory ever built; it also sees many more satellites. Up to 40% of images captured by the observatory within its first 10 years of operation will be marred by their sunlight-reflecting streaks. Meredith Rawls, a research scientist at the telescope's flagship observation project, Vera Rubin's Legacy Survey of Space and Time, is one of the experts tasked with protecting Rubin's science mission from the satellite blight.

Unobserved confounding arises when an unmeasured feature influences both the treatment and the outcome, leading to biased causal effect estimates. This issue undermines observational studies in fields like economics, medicine, ecology or epidemiology. Recommender systems leveraging fully observed data seem not to be vulnerable to this problem. However many standard practices in recommender systems result in observed features being ignored, resulting in effectively the same problem. This paper will show that numerous common practices such as feature engineering, A/B testing and modularization can in fact introduce confounding into recommendation systems and hamper their performance. Several illustrations of the phenomena are provided, supported by simulation studies with practical suggestions about how practitioners may reduce or avoid the affects of confounding in real systems.

Counterfactuals play a pivotal role in the two distinct data science fields of causal inference (CI) and explainable artificial intelligence (XAI). While the core idea behind counterfactuals remains the same in both fields--the examination of what would have happened under different circumstances--there are key differences in how they are used and interpreted. We introduce a formal definition that encompasses the multi-faceted concept of the counterfactual in CI and XAI. We then discuss how counterfactuals are used, evaluated, generated, and operationalized in CI vs. XAI, highlighting conceptual and practical differences. By comparing and contrasting the two, we hope to identify opportunities for cross-fertilization across CI and XAI.

We can put a good figure on how much we know about the universe: 5%. That's how much of what's floating about in the cosmos is ordinary matter--planets and stars and galaxies and the dust and gas between them. The other 95% is dark matter and dark energy, two mysterious entities aptly named for our inability to shed light on their true nature. Previous work has begun pulling apart these dueling forces, but dark matter and dark energy remain shrouded in a blanket of questions--critically, what exactly are they? Enter the Vera C. Rubin Observatory, one of our 10 breakthrough technologies for 2025.

There has been widespread use of causal inference methods for the rigorous analysis of observational studies and to identify policy evaluations. In this article, we consider coarsened exact matching, developed in Iacus et al. (2011). While they developed some statistical properties, in this article, we study the approach using asymptotics based on a superpopulation inferential framework. This methodology is generalized to what we termed as coarsened confounding, for which we propose two new algorithms. We develop asymptotic results for the average causal effect estimator as well as providing conditions for consistency. In addition, we provide an asymptotic justification for the variance formulae in Iacus et al. (2011). A bias correction technique is proposed, and we apply the proposed methodology to data from two well-known observational studies.

In hundreds of videos since taken down by YouTube, right-wing influencers working for Tenet Media--a company the US Department of Justice alleges was financed and guided by a state-backed Russian news network--showed interest in a highly specific set of topics, according to a WIRED analysis. Using closed captioning of the videos we downloaded before the videos were removed, we've compiled lists of terms frequently mentioned in them, along with a searchable database: The content of these videos was described by prosecutors as "consistent" with Russia's aim of sowing political discord in the US. Among the areas covered: free speech, illegal immigrants, diversity in video games, supposed racism toward white people, and Elon Musk. While an indictment unsealed earlier this week does not name Tenet, WIRED and other outlets were able to identify it because prosecutors gave its motto as that of a business identified as "U.S. Company-1." Prosecutors allege that two employees of the state-backed Russian network RT, Kostiantyn Kalashnikov and Elena Afanasyeva, who are charged with conspiracy to commit money laundering and to violate the Foreign Agents Registration Act, paid Tenet and its parent company 9.7 million to produce and distribute videos supporting Russian aims.