There's a deep, forbidding moat that surrounds Nvidia--and it has nothing to do with hardware. Forgive me for starting with a cliché, a piece of finance jargon that has recently slipped into the tech lexicon, but I'm afraid I must talk about "moats." Popularized decades ago by Warren Buffett to refer to a company's competitive advantage, the word found its way into Silicon Valley pitch decks when a memo purportedly leaked from Google, titled "We Have No Moat, and Neither Does OpenAI," fretted that open-source AI would pillage Big Tech's castle. A few years on, the castle walls remain safe. Apart from a brief bout of panic when DeepSeek first appeared, open-source AI models have not vastly outperformed proprietary models.

Things to Do in L.A. Fears of an AI breakthrough force the U.S. and China to talk Quiet discussions have taken place ahead of President Trump's state visit to China this week to explore reviving talks on an emergency channel, officials told The Times. This is read by an automated voice. Please report any issues or inconsistencies here . Discussions have taken place ahead of President Trump's state visit to China to explore reviving talks on an emergency channel for AI matters between Washington and Beijing, officials say. Any talks between the United States and China over AI regulations will be fraught with suspicion and risk.

Every time my cat Mira walks across a keyboard, I learn a few new Mac and PC keyboard shortcuts I never knew about. All cats love keyboards (but this is not a photo of my cat). My cat Mira is perfect, and has never done anything wrong. She also loves walking on laptop keys--both my MacBook and my wife Kathy's Windows PC . You might think that walking on laptops is an example of Mira doing something wrong. And, in any case, we've both learned a lot about how our computers work because of this.

When you purchase through links in our articles, we may earn a small commission. Get a lifetime of Microsoft Office 2021 Pro + Windows 11 Pro for $34.97. Others just clean up your entire setup in one move--this is closer to the second. No subscription fees every month, no renewals--just a clean, one-time upgrade to both your workflow and your system. On the productivity side, Office 2021 Pro gives you the full stack: Word, Excel, PowerPoint, Outlook, Access, and Publisher.

SoftBank will partner with South Korea's Cosmos Lab and DeltaX to enable mass production of large-scale battery cells from the fiscal year starting next April. SoftBank Group's mobile unit said it plans to begin large-scale battery cell manufacturing at its plant in Sakai, Osaka Prefecture, to address growing power demand for AI services. SoftBank Corp. will partner with South Korea's Cosmos Lab and DeltaX to enable mass production from the fiscal year starting next April, the company said in a statement Monday. The aim is to output energy storage systems at a scale of one gigawatt-hour per year, SoftBank said, which would make it one of the largest facilities in Japan, according to data from BloombergNEF. SoftBank could scale up to a capacity of several GWh, Bloomberg reported last month.

We study dynamic measure transport for generative modeling, focusing on transport maps that connect a source measure $P_0$ to a target measure $P_1$ by integrating a velocity field of the form $v_t(x) = \mathbb{E}[\dot X_t \mid X_t = x]$, where $X_\bullet = (X_t)_t$ is a stochastic process satisfying $(X_0,X_1)\sim{P_0}\otimes{P_1}$ and $\dot X_t$ is its time derivative. We investigate when $X_\bullet$ induces a \emph{straight-line flow}: a flow whose pointwise acceleration vanishes and is therefore exactly integrable by any first-order method. First, we develop multiple characterizations of straight-line flows in terms of PDEs involving the conditional statistics of the process. Then, we prove that straight-line flows under endpoint independence exhibit a sharp dichotomy. On the one hand, we construct explicit, computable straight-line processes for arbitrary Gaussian endpoints. On the other hand, we show that straight-line processes do not exist for targets with sufficiently well-separated modes. We demonstrate this obstruction through a sequence of increasingly general impossibility theorems that uncover a fundamental relationship between the sample-path behavior of a process with independent endpoints and the space-time geometry of this process' flow map. Taken together, these results provide a structural theory of when straight-line generative flows can, and cannot, exist.

Previous research has investigated the potential of refugee matching for boosting refugee outcomes, first considered by Bansak et al. (2018). This paper demonstrates the stability of counterfactual impact evaluation results in the context of refugee matching in the United States using a range of off-policy evaluation methods. In order to estimate counterfactual impact and test the robustness of our results, we employ several evaluation methods, including inverse probability weighting (IPW) and multiple variants of augmented inverse probability weighting (AIPW). We also consider various modifications, including alternative modeling architectures and different assignment procedures. The impact estimates remain consistent in magnitude in all scenarios as well as statistically significant in most cases. Furthermore, the estimates are also consistent with the results originally presented in Bansak et al. (2018).

Time-varying networks arise in a variety of ubiquitous applications, such as functional brain connectivity [Thompson et al., 2017, Zhang et al., 2020], gene and genomic regulatory processes [Zhang and Cao, 2017, Bartlett et al., 2021], and social or economic environments [Snijders et al., 2010, Kolar et al., 2010]. In these contexts, measurements collected at different time points record how observed connections fluctuate, forming a sequence of network snapshots that reflect the temporal evolution of the underlying system. For example, fMRI studies yield time-indexed measurements of activity across brain regions, from which researchers construct connectivity networks that change over the scanning period [Bassett et al., 2011, Rubinov and Sporns, 2010]. Similarly, in political systems such as the U.S. Senate, legislative cosponsorship records give rise to network snapshots that naturally vary across sessions [Fowler, 2006, Kirkland and Gross, 2014]. General reviews of time-varying network analysis, including methodological developments and representative applications, are provided in Holme and Saram aki [2012] and Kim et al. [2018].

LLM-as-a-Judge evaluation has become a standard tool for assessing base model performance. However, characterizing performance via the naive estimator, i.e., raw judge outputs, is systematically biased. Recent work has proposed estimators to correct this bias, but their reliability depends critically on judge quality and, for model comparisons, on calibration stability. Sharing calibration across compared models is practically attractive but can introduce severe bias, including cases where the comparison estimate points in the wrong direction with high apparent confidence. We study these failure modes through analytical results, simulations over judge quality ($J$) and cross-model calibration instability ($ΔJ$), and a real-data MMLU-Pro case study with sign reversal. We propose $J$ and $ΔJ$ as diagnostics for when corrected estimates, especially shared-calibration comparisons, are likely unreliable, and provide reporting guidance for LaaJ evaluation.

AI agents are increasingly deployed in multi-task settings, where the task to perform is specified at test time, and the agent must generalize to unseen tasks. A major concern in such settings is safety: often, an agent must not only execute unseen tasks, but do so while avoiding risks and handling ones that materialize. Empirical evidence suggests that even when the ability to execute generalizes to unseen tasks, the ability to do so safely frequently does not. This paper provides theory and experiments indicating that failures of agentic safety to generalize across tasks are not merely due to limitations of training methods, but reflect an inherent property of safety itself: the relationship between a task and its safe execution is more complex than the relationship between a task and its execution alone. Theoretically, we analyze linear-quadratic control with $H_{\infty}$-robustness, and prove that the mapping from task specification to an optimal controller has higher Lipschitz constant with safety requirements than without, yielding a Lipschitz bound of independent interest. Empirically, we demonstrate our conclusions in simulated quadcopter navigation with a neural network agent and in CRM with an LLM agent. Our findings suggest that current efforts to enhance agentic safety may be insufficient, and point to a need for fundamentally different approaches.