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Businesses finally seeing AI ROI, but 62% can't handle the storage demands A Seagate study finds that 99% of IT leaders expect AI to drive increased data storage needs, but only 38% are prepared to meet them, revealing a significant readiness gap. Kayla Solino is a ZDNET Editor based in New York City and New Jersey. A new study finds 62% of organizations are ill-prepared to tackle surging storage needs. Organizations must lean in to AI's growing data demands and infrastructure readiness. "Sustainable scaling" may be the key to optimizing AI growth, according to Seagate's study.

Things to Do in L.A. Tap to enable a layout that focuses on the article. Fewer than half of Americans expect midterm elections to be'free and fair,' poll finds A worker carries ballots at L.A. County's processing center in June. This is read by an automated voice. Please report any issues or inconsistencies here . See more from the L.A. Times in Google Search.

Your friend with'nothing to hide' is more likely to spy on you, study shows New research explores behavioral trends behind that'nothing to hide' phrase. Plus: 6 ways to improve your phone privacy. Charlie Osborne is a cybersecurity journalist and photographer who writes for ZDNET and CNET from London. If you've said you have'nothing to hide,' do you still want device privacy? New research finds those who say it are actually more likely to spy on others.

I wore the world's first HDR10 smart glasses TCL's new E Ink tablet beats the Remarkable and Kindle Anker's new charger is one of the most unique I've ever seen I wore the world's first HDR10 smart glasses TCL's new E Ink tablet beats the Remarkable and Kindle Anker's new charger is one of the most unique I've ever seen Three out of four of the 138 developers I surveyed use Claude Code. Here's what they say matters in daily AI coding workflows. Claude Code dominates, but Codex has crucial advantages. Whichever AI you choose, human review remains essential. I explained how I use both tools and provided some suggestions for exploring them.

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AI-assisted interviews promise to reduce respondent burden in surveys by allowing respondents to describe experiences naturally while an AI system noisily maps those accounts into structured survey variables. That mapping is a measurement process that is fallible, versioned, adaptive, and potentially behaves differently across subgroups. This paper proposes Adaptive Matrix Validation (AMV), a design in which each respondent completes an AI-assisted interview, which is then mapped into tabular data by the AI. Respondents are also asked a small, randomized set of structured questions, which are used for statistical adjustment. The estimator first calibrates the mapped values using validation answers from other respondents, then corrects the remaining error with the validation answers observed for the target respondent. The paper develops estimators for item means, subgroup estimates, and regression coefficients when outcomes, predictors, or both are mapped from interviews. It also gives planning formulas the number of validation questions required and the sample size. A design-calibration simulation, an American Time Use Survey emulation, and a CHAMPS verbal-autopsy narrative study show when sparse validation can improve precision and when it cannot

Americans really don't want AI data centers close to their homes Americans really don't want AI data centers close to their homes AI companies are spending astronomical sums of money on building data centers as quickly as possible in order to increase their compute power. But the majority of Americans don't want that infrastructure close to their homes, according to a Gallup survey . The polling company asked 1,000 adults across the US about their views on AI data centers, and 71 percent were against having one in their local area. Almost half of the respondents (48 percent) were strongly opposed. On the flip side, just seven percent were strongly in favor of having a data center close to their home.

Digital personas powered by Large Language Models (LLMs) are increasingly proposed as substitutes for human survey respondents, yet it remains unclear when they can reliably approximate human survey findings. We answer this question using the LISS panel, constructing personas from respondents' background variables and pre-2023 survey histories, then testing them against the same respondents' held-out post-cutoff answers. Across four persona architectures, three LLMs, and two prediction tasks, we assess performance at the question, respondent, distributional, equity, and clustering levels. Digital personas improve alignment with human response distributions, especially in domains tied to stable attributes and values, but remain limited for individual prediction and fail to recover multivariate respondent structure. Retrieval-augmented architectures provide the clearest gains, but performance depends more on human response structure than on model choice: personas perform best for low-variability questions and common respondent patterns, and worst for subjective, heterogeneous, or rare responses. Our results provide practical guidance on when digital personas could be appropriate for survey research and when human validation remains necessary.

Public attitudes toward artificial intelligence are heterogeneous, ordinally measured, and poorly captured by any single dependency graph. Existing ordinal structure learners assume a shared directed acyclic graph (DAG) across all respondents; recent heterogeneous ordinal graphical-model approaches focus on subgroup discovery rather than confirmatory cluster-specific DAG estimation; and latent profile analyses discard dependency structure entirely. We introduce a heterogeneous ordinal structure-learning framework combining monotone Gaussian score embedding, Bayesian nonparametric (BNP) complexity discovery via a truncated stick-breaking prior, and confirmatory fixed-K estimation with cluster-specific sparse DAG learning. The key methodological insight is a discovery-to-confirmation workflow: the nonparametric stage calibrates plausible archetype complexity, while inner-validated confirmatory refitting yields stable, interpretable structural estimates. On the 2024 Pew American Trends Panel AI attitudes survey, Wave 152 (W152) survey, (N = 4,788, 8 ordinal items), the confirmatory K*=5 model reduces holdout transformed-score mean squared error (MSE) by 25.8% over a single-graph baseline and by 4.6% over mixture-only clustering. A controlled tiered semi-synthetic benchmark calibrated to W152 structure validates recovery across difficulty regimes and transparently reveals failure modes under stress conditions.