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AI agents are not your "coworkers" Marketing AI agents as digital employees may make human workers worse at spotting errors and more likely to offload accountability. Imagine coming in to work to learn that a new underling will report to you. The worker is not a person but an AI tool--one that your company nonetheless calls Alex, an "employee" with a title and defined responsibilities. How well do you think you would work with Alex? If you're anything like the managers recently studied by Emma Wiles, a Boston University business professor, treating Alex as a "coworker" and not a software tool would lead you to do a worse job. Wiles found that people caught 18% fewer errors when the work was said to have come from an agentic "AI employee" rather than a chatbot. It turns out that what's in a name matters.

Science A.I.'s First Big Math Breakthrough Is Not What It Seems But it can help us do genuinely creative work--for a reason you might not expect. Sign up for the Slatest to get the most insightful analysis, criticism, and advice out there, delivered to your inbox daily. Last month, OpenAI announced that its latest version of ChatGPT had solved a major math problem, one that had stumped experts for 80 years. This was considered among the most important unsolved problems in combinatorics, a prominent branch of math and computer science dealing with finite objects and arrangements. As opposed to previous A.I.-powered breakthroughs that involved back-and-forth conversations between a chatbot and a human expert, this was cracked with a single prompt.

For such neural networks, we prove a non-constant lower bound of that are compatible with certain polyhedral complexes, more precisely with the the best known lower bound in general is still 2. We focus on neural networks linear functions on R .

Although Multimodal Large Language Models (MLLMs) have demonstrated remarkable achievements in recent years, they remain vulnerable to adversarial examples that result in harmful responses. Existing attacks typically focus on optimizing adversarial perturbations for a certain multimodal image-prompt pair or fixed training dataset, which often leads to overfitting. Consequently, these perturbations fail to remain malicious once transferred to attack unseen image-prompt pairs, suffering from significant resource costs to cover the diverse multimodal inputs in complicated real-world scenarios. To alleviate this issue, this paper proposes a novel adversarial attack on MLLMs based on distribution approximation theory, which models the potential image-prompt input distribution and adds the same distribution-fitting adversarial perturbation on multimodal input pairs to achieve effective cross-image/prompt transfer attacks. Specifically, we exploit the Laplace approximation to model the Gaussian distribution of the image and prompt inputs for the MLLM, deriving an estimate of the mean and covariance parameters. By sampling from this approximated distribution with Monte Carlo mechanism, we efficiently optimize and fit a single input-agnostic perturbation over diverse image-prompt pairs, yielding strong universality and transferability. Extensive experiments are conducted to verify the strong adversarial capabilities of our proposed attack against prevalent MLLMs spanning a spectrum of images/prompts.

Devices that monitor seniors for safety are appealing to worried loved ones and underresourced home care agencies. It was January of 2026 in North Seattle, and my 86-year-old father was struggling to move around his house. "I'm stumbling around here," my 86-year-old father told a guest in his home this past January. "Oooh, ooh, careful," the guest replied. "Yeah, I almost fell down."

In this work, we investigate new activation functions for achieving arbitrary-accuracy Sobolev approximation by fixed-size neural networks. We first show that any function in $W^{2,\infty}((a,b)^d)$ can be approximated with arbitrary accuracy, measured in the $W^{1,\infty}$-norm, by a fixed-size neural network using the Elementary Universal Activation Function ($\mathrm{EUAF}$). To extend this result to $W^{s,\infty}((a,b)^d)$ for $s\in\mathbb{N}$, we introduce a smooth activation $\mathrm{DUAF}_{\infty}$ from the family of Differentiable Universal Activation Functions ($\mathrm{DUAF}_n$). We prove that any function in $W^{s,\infty}((a,b)^d)$ can be approximated with arbitrary accuracy in the $W^{s-1,\infty}$-norm by a fixed-size $\mathrm{DUAF}_{\infty}$-activated network. We further construct sigmoidal variants $\widetilde{\mathrm{DUAF}}_n$ and show that, for every $1\leq s\leq n$, fixed-size $\widetilde{\mathrm{DUAF}}_n$-activated networks still approximate any $f\in W^{s,\infty}((a,b)^d)$ with arbitrary accuracy in the $W^{s-1,\infty}$-norm. In all these results, the width and depth bounds are computed explicitly, and the proposed activations are elementary.

LLMs typically respond quickly, but their SVG outputs tend to be short and structurally simplistic.

No one can say for sure what's going on in the president's head. His 25 greatest obsessions can get us a little closer. This is the year the first baby boomers--those born in 1946--turn 80, and that cohort includes Donald Trump. We have all recently lived through what it means to have an 80-year-old commander in chief, but at a political moment that's simultaneously more horrific, erratic, and just plain befuddling than anything this country has seen in ages, we wanted to understand the brain of 80-year-old president. Plenty of people are trying to discern whether his recent rants and raves are due to a more serious cognitive decline--we understand the instinct; we've done it too --but we went a different (if related) route. The more we dug into Trump's many fixations, the more we realized that this man still thinks he lives in the 1980s. We also discovered--without too much surprise--that he often seems to fundamentally misunderstand the works he treasures most deeply. These items might not replace a brain map, but they do create a certain holistic view of what animates and splinters Trump's mind. Sometimes, they just help explain his worldview. Other times, they seem to have had real influence on policy and the America that Trump is trying to create. Welcome to Trump Brain, the 25 things that define who the president is--and what he wants. Please enable javascript to fully experience this interactive. When millions of people took to the streets in October to protest Trump's authoritarianism, the president responded by dunking on his critics online. Specifically, he posted an A.I.-generated video of a fighter jet, piloted by himself in a literal crown, dropping human excrement onto the crowds. It was perhaps Trump's most juvenile use of A.I. slop yet--the kind of low-quality, feverish content made possible by artificial intelligence. Trump undoubtedly is the perfect president for the A.I. slop era. In some ways, this is because he's the ideal audience for it: Like many older internet users delighted by the technology, Trump seems to enjoy mindless, cartoonish, childish content. One of the videos he shared depicted him playing soccer with Cristiano Ronaldo in the Oval Office.

So Claude and I tried to make a database for tracking the petty grievances of the masses. The dog that ushered me into the technological future was "low and thick." That's all my mother registered before it T-boned her in a city park earlier this year: dense, heavy, and traveling fast enough to fracture her right tibia. Let's discuss what this set in motion in my life: Having successfully learned nothing about coding for two and a half decades, I would soon be attempting my very first software development project. If you've ever had a low and thick dog break your mom's shin bone, you know the stream of lesser indignities that follows.