This story appears in the March 2026 print edition. While some stories from this issue are not yet available to read online, you can explore more from the magazine . Get our editors' guide to what matters in the world, delivered to your inbox every weekday. America Isn't Ready for What AI Will Do to Jobs Does anyone have a plan for what happens next? In 1869, a group of Massachusetts reformers persuaded the state to try a simple idea: counting. The Second Industrial Revolution was belching its way through New England, teaching mill and factory owners a lesson most M.B.A. students now learn in their first semester: that efficiency gains tend to come from somewhere, and that somewhere is usually somebody else. They were operating at speeds that the human body--an elegant piece of engineering designed over millions of years for entirely different purposes--simply wasn't built to match. The owners knew this, just as they knew that there's a limit to how much misery people are willing to tolerate before they start setting fire to things. Still, the machines pressed on. Check out more from this issue and find your next story to read. So Massachusetts created the nation's first Bureau of Statistics of Labor, hoping that data might accomplish what conscience could not. By measuring work hours, conditions, wages, and what economists now call "negative externalities" but were then called "children's arms torn off," policy makers figured they might be able to produce reasonably fair outcomes for everyone. A few years later, with federal troops shooting at striking railroad workers and wealthy citizens funding private armories--leading indicators that things in your society aren't going great--Congress decided that this idea might be worth trying at scale and created the Bureau of Labor Statistics. Measurement doesn't abolish injustice; it rarely even settles arguments. But the act of counting--of trying to see clearly, of committing the government to a shared set of facts--signals an intention to be fair, or at least to be caught trying. It's one way a republic earns the right to be believed in. The BLS remains a small miracle of civilization.

We present a novel method for diffusion-guided frameworks for view-consistent super-resolution (SR) in neural rendering. Our approach leverages existing 2D SR models in conjunction with advanced techniques such as Variational Score Distilling (VSD) and a LoRA fine-tuning helper, with spatial training to significantly boost the quality and consistency of upscaled 2D images compared to the previous methods in the literature, such as Renoised Score Distillation (RSD) proposed in DiSR-NeRF (1), or SDS proposed in DreamFusion. The VSD score facilitates precise fine-tuning of SR models, resulting in high-quality, view-consistent images. To address the common challenge of inconsistencies among independent SR 2D images, we integrate Iterative 3D Synchronization (I3DS) from the DiSR-NeRF framework. Our quantitative benchmarks and qualitative results on the LLFF dataset demonstrate the superior performance of our system compared to existing methods such as DiSR-NeRF.

Differentiable Search Index (DSI) utilizes Pre-trained Language Models (PLMs) for efficient document retrieval without relying on external indexes. However, DSIs need full re-training to handle updates in dynamic corpora, causing significant computational inefficiencies. We introduce PromptDSI, a rehearsal-free, prompt-based approach for instance-wise incremental learning in document retrieval. PromptDSI attaches prompts to the frozen PLM's encoder of DSI, leveraging its powerful representation to efficiently index new corpora while maintaining a balance between stability and plasticity. We eliminate the initial forward pass of prompt-based continual learning methods that doubles training and inference time. Moreover, we propose a topic-aware prompt pool that employs neural topic embeddings as fixed keys. This strategy ensures diverse and effective prompt usage, addressing the challenge of parameter underutilization caused by the collapse of the query-key matching mechanism. Our empirical evaluations demonstrate that PromptDSI matches IncDSI in managing forgetting while significantly enhancing recall by over 4% on new corpora.

Scaling up language models has led to unprecedented performance gains, but little is understood about how the training dynamics change as models get larger. How do language models of different sizes learn during pre-training? Why do larger language models demonstrate more desirable behaviors? In this paper, we analyze the intermediate training checkpoints of differently sized OPT models (Zhang et al.,2022)--from 125M to 175B parameters--on next-token prediction, sequence-level generation, and downstream tasks. We find that 1) at a given perplexity and independent of model sizes, a similar subset of training tokens see the most significant reduction in loss, with the rest stagnating or showing double-descent behavior; 2) early in training, all models learn to reduce the perplexity of grammatical sequences that contain hallucinations, with small models halting at this suboptimal distribution and larger ones eventually learning to assign these sequences lower probabilities; 3) perplexity is a strong predictor of in-context learning performance on 74 multiple-choice tasks from BIG-Bench, and this holds independent of the model size. Together, these results show that perplexity is more predictive of model behaviors than model size or training computation.

To further explore the intersection of 5G and manufacturing, register for the 5G Manufacturing Forum. Global manufactuers are starting to adopt 5G to improve manufacturing processes. Low latency and high reliability are needed to support critical applications in the manufacturing field. Several top manufacturers are already taking advantage of 5G implementation to improve operations in different industrial environments. Here we briefly describe some implementations by large manufacturers globally.

Southfield, MI, June 2016 – Stefanini, a 1B global IT provider, announced today that the company is launching Sophie, its artificial intelligence platform with the ability to turn data into valuable solutions. Aware of the latest trends, Stefanini has invested and developed this platform over the last 3 years as a Research & Development and pilot project for clients in Brazil, and now, the company is launching the platform in the United States. "We are very proud to introduce Sophie for our clients in North America, reinforcing Stefanini's commitment to connect people and technology innovations with a goal to create business value," said Antonio Moreira, Stefanini CEO, North America and Asia Pacific. "Our artificial intelligence platform can improve the end-user experience and deliver smarter and more efficient services," affirmed Mr. Moreira. Technology research firm Gartner forecasts that by 2017, autonomics-based managed services and cognitive platforms will fuel a significant reduction in the cost of IT services by automating repetitive tasks currently tackled by humans.