A viral video of a colony of bunnies seemingly enjoying jumping on a trampoline, posted in July, had more than 200m views - but was identified as AI-generated. A viral video of a colony of bunnies seemingly enjoying jumping on a trampoline, posted in July, had more than 200m views - but was identified as AI-generated. Macquarie Dictionary announces'AI slop' as its word of the year, beating out Ozempic face AI slop is here, it's ubiquitous, it's being used by the US president, Donald Trump, and now, it's the word of the year. The Macquarie Dictionary dubbed the term the epitome of 2025 linguistics, with a committee of word experts saying the outcome embodies the word of the year's general theme of reflecting "a major aspect of society or societal change throughout the year". "We understand now in 2025 what we mean by slop - AI generated slop, which lacks meaningful content or use," the committee said in a statement announcing its decision.

Nvidia is worth $5 trillion. Here's what it means for the market Jensen Huang, chief executive officer of Nvidia, poses for a photo on the sidelines of the APEC CEO Summit in Gyeongju, South Korea, on Friday. Nvidia made history last week when it became the first company ever to have a market value of $5 trillion. The chipmaker at the heart of the artificial intelligence revolution is not only by far the biggest company on the planet, it also may be the most influential stock in Wall Street history. Nvidia has been the primary driver of the market's gains since the start of 2023, delivering massive returns to shareholders and minting billions for Chief Executive Officer Jensen Huang. This is obviously a massive outlier from a historical perspective, really something to behold for the ages," said Matt Miskin, co-chief investment strategist at Manulife John Hancock Investments.

Late Breaking Results: The Art of Beating the Odds with Predictor-Guided Random Design Space Exploration Felix Arnold Huawei, Switzerland Maxence Bouvier Huawei, Switzerland Ryan Amaudruz Huawei, Switzerland Renzo Andri Huawei, Switzerland Lukas Cavigelli Huawei, Switzerland Abstract --This work introduces an innovative method for improving combinational digital circuits through random exploration in MIG-based synthesis. High-quality circuits are crucial for performance, power, and cost, making this a critical area of active research. Our approach incorporates next-state prediction and iterative selection, significantly accelerating the synthesis process. This novel method achieves up to 14 synthesis speedup and up to 20.94% better MIG minimization on the EPFL Combinational Benchmark Suite compared to state-of-the-art techniques. We further explore various predictor models and show that increased prediction accuracy does not guarantee an equivalent increase in synthesis quality of results or speedup, observing that randomness remains a desirable factor .

Summary and Contributions: ***** UPDATE ***** I realize I might have been harsh in my evaluation. I believe the paper would have been more suited for a more theory oriented statistics conference / journal, but this is a recurrent problem in NeurIPS and I shouldn't have taken it out on the authors. While their theoretical result is really interesting, I also didn't appreciate that the authors barely mentioned previous work on statistical learning bounds with optimal transport. There have been recent efforts on the topic by several teams, and they should at least acknowledge them. However, if other reviewers took the time to thoroughly review the proof of the main result, I'm willing to increase my score.

Most of the reviewers were excited about this work, and I'm pleased to recommend it for publication. In the revision, please address all promised changes in the rebuttals and/or requested in the reviews. The outlier R1 has some valid points about the exposition as well as discomfort with the length of the appendix (it's true this is difficult to review in the NeurIPS environment), but these are not reasons to reject the work. That said, the authors of this paper are encouraged to take R1's expository suggestions seriously in their revision to make the work as approachable as possible.

We consider the problem of estimating the Wasserstein distance between the empirical measure and a set of probability measures whose expectations over a class of functions (hypothesis class) are constrained. If this class is sufficiently rich to characterize a particular distribution (e.g., all Lipschitz functions), then our formulation recovers the Wasserstein distance to such a distribution. We establish a strong duality result that generalizes the celebrated Kantorovich-Rubinstein duality. We also show that our formulation can be used to beat the curse of dimensionality, which is well known to affect the rates of statistical convergence of the empirical Wasserstein distance. In particular, examples of infinite-dimensional hypothesis classes are presented, informed by a complex correlation structure, for which it is shown that the empirical Wasserstein distance to such classes converges to zero at the standard parametric rate.

Bots are better and faster than humans at online CAPTCHA tests designed to keep them out of websites. The finding calls into question whether we should continue using these kind of security measures, given how much frustration they can cause for people.

Learning how to adjust to an opponent's position is critical to the success of having intelligent agents collaborating towards the achievement of specific tasks in unfriendly environments. This pa(cid:173) per describes our work on a Memory-based technique for to choose an action based on a continuous-valued state attribute indicating the position of an opponent. We investigate the question of how an agent performs in nondeterministic variations of the training situ(cid:173) ations. Our experiments indicate that when the random variations fall within some bound of the initial training, the agent performs better with some initial training rather than from a tabula-rasa.

MIT chemical engineers have shown that specialized particles can oscillate together, demonstrating a phenomenon known as emergent behavior. Taking advantage of a phenomenon known as emergent behavior in the microscale, MIT engineers have designed simple microparticles that can collectively generate complex behavior, much the same way that a colony of ants can dig tunnels or collect food. Working together, the microparticles can generate a beating clock that oscillates at a very low frequency. These oscillations can then be harnessed to power tiny robotic devices, the researchers showed. "In addition to being interesting from a physics point of view, this behavior can also be translated into an on-board oscillatory electrical signal, which can be very powerful in microrobotic autonomy. There are a lot of electrical components that require such an oscillatory input," says Jingfan Yang, a recent MIT PhD recipient and one of the lead authors of the new study.

Beating the Clock: Leveraging Machine Learning to Scope Ransomware Response. Tuesday, June 08 | 1:15p.m. – 1:35p.m. ET. Session Type: …