How the artists in this year's survey do or, more often, don't acknowledge those who paved the way for them. Machado makes pieces that one might call documents of reverence, excavated burial grounds. If nothing else, the 2026 Whitney Biennial, curated by Marcela Guerrero and Drew Sawyer (at the Whitney Museum through August 23rd), introduces viewers to what I call ChatGPT art--facsimiles of facsimiles by makers who have little if any relationship to what they're putting out there, aside from its being a product in service of a career. Indeed, it's difficult to think of the people who grew up with and apparently condone the use of A.I. sources in the creation of "art" as artists themselves, especially if you define art as a creative expression of thoughts or feelings that have changed, and contributed to the vision of, the artists who made it. It's true that, nearly from the beginning, postmodern art challenged the notion of originality, or, more specifically, the weight of originality--often with great joy and wit and not a little fear.

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

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

Many tasks in AI require the collaboration of multiple agents. Typically, the communication protocol between agents is manually specified and not altered during training. In this paper we explore a simple neural model, called CommNet, that uses continuous communication for fully cooperative tasks. The model consists of multiple agents and the communication between them is learned alongside their policy. We apply this model to a diverse set of tasks, demonstrating the ability of the agents to learn to communicate amongst themselves, yielding improved performance over non-communicative agents and baselines. In some cases, it is possible to interpret the language devised by the agents, revealing simple but effective strategies for solving the task at hand.

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

Our information estimator uses only these noisy measurements and a noise model to quantify how well measurements distinguish objects. Many imaging systems produce measurements that humans never see or cannot interpret directly. Your smartphone processes raw sensor data through algorithms before producing the final photo. MRI scanners collect frequency-space measurements that require reconstruction before doctors can view them. Self-driving cars process camera and LiDAR data directly with neural networks.

Matching users to the right items at the right time is a fundamental task in recommendation systems. As users interact with different items over time, users' and items' feature may evolve and co-evolve over time. Traditional models based on static latent features or discretizing time into epochs can become ineffective for capturing the fine-grained temporal dynamics in the user-item interactions. We propose a coevolutionary latent feature process model that accurately captures the coevolving nature of users' and items' feature. To learn parameters, we design an efficient convex optimization algorithm with a novel low rank space sharing constraints. Extensive experiments on diverse real-world datasets demonstrate significant improvements in user behavior prediction compared to state-of-the-arts.

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

Most learning algorithms are not invariant to the scale of the signal that is being approximated. We propose to adaptively normalize the targets used in the learning updates. This is important in value-based reinforcement learning, where the magnitude of appropriate value approximations can change over time when we update the policy of behavior. Our main motivation is prior work on learning to play Atari games, where the rewards were clipped to a predetermined range. This clipping facilitates learning across many different games with a single learning algorithm, but a clipped reward function can result in qualitatively different behavior. Using adaptive normalization we can remove this domain-specific heuristic without diminishing overall performance.

The AI Race Is Pressuring Utilities to Squeeze More From Europe's Power Grids As data center developers queue up to connect to power grids across Europe, network operators are experimenting with novel ways of clearing room for them. European countries are racing to bring new data centers online as AI labs across the globe continue to demand more compute. The primary limiting factor is energy--and specifically, the ability to move it. Though Europe is on track to generate enough energy, utilities experts say, grid operators broadly lack the infrastructure needed to transport it to where it needs to go. That's throttling grid capacity and, by extension, the number of new power-hungry data centers that can connect without risking blackouts.