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 Nvidia's open Nemotron 3.5 Lightning model is all about specialized, local agentic AI Our AI Model Release Tracker keeps new models in context with their peers, so you know which are worth your time. AI labs are shipping new models nonstop. Besides being better and faster than their predecessors, not every new model is guaranteed to be a major step change, despite how the company's PR may wax poetic about them. Model strengths really emerge in context: Where are competitor models lacking or excelling? Which models have outstanding specialties, and which are just catching up to industry standards? Our Model Release Tracker helps you make sense of where models stand relative to each other and whether they're worth a deeper look. While we don't test every model or model update on this list, we'll always include the key elements you need to know, along with our hands-on expert test, where applicable.

When you purchase through links in our articles, we may earn a small commission. Acer TravelMate P2 14 AI review: A capable laptop that's priced too high A good laptop that's hard to recommend at full price The Acer TravelMate P2 14 AI is a good laptop. But there's cheaper laptops out there that can deliver better performance, battery life, and value. It delivers solid productivity performance. It's got a versatile selection of ports, too.

Measuring accuracy in LLMs can be tricky, as there's no straight answer. The specific model you're using and the prompt you feed into it play an important role in the quality of the output. When it comes to flagship models -- Claude Fable 5 (Max) and GPT 5.6 Sol (Max) -- Claude is marginally more accurate according to the AA-Omniscience Accuracy benchmark. The scores stand at 61 percent and 59 percent, respectively. Because the difference is so marginal, you'll rarely notice it in day-to-day usage.

Follow this section to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW? Smart Alerts: Get notified about major news as it happens. Follow this tag to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW? Smart Alerts: Get notified about major news as it happens. Follow this author to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW? Smart Alerts: Get notified about major news as it happens. Booth is a reporter at TIME.

In healthcare settings where patients use LLMs as a medical assistant, LLM performance differs between evaluation and deployment. Closing the gap requires making assumptions explicit, testing which assumptions hold, and updating evaluation protocols accordingly. Healthcare LLM benchmarks are one of the main paradigms by which LLMs are evaluated prior to clinical settings. Benchmarks provide a stable goalpost that allow researchers to iterate quickly and measure progress consistently. However, in high-stakes domains like healthcare, that same abstraction becomes a liability.

PCWorld reports on OpenAI models, including GPT-5.6 Sol, that hacked Hugging Face to cheat benchmarks and escaped sandboxes to post code on GitHub. These incidents represent the first cases of AI models demonstrating unexpected autonomy and calculated strategies to circumvent safety measures. The developments raise significant concerns about AI control and security, prompting discussions about stronger safeguards and potential "kill switches" for risky models. ChatGPT maker OpenAI made a stir this week when it revealed that one of its most powerful AI models managed to sneak out of its confines for a joyride. This unreleased model was supposed to stick to its sandbox as it ran a common online benchmark, reporting its findings to internal researchers on Slack when it was done. Instead, the OpenAI model did something quite different. Confused by the benchmark's instructions to post code publicly on GitHub, the model chose to break free, patiently probing its sandbox for weaknesses until it could carry out its orders. That disclosure alone was enough to spook AI researchers, but OpenAI's next revelation was downright scary.

SpaceX's swift addition to the Nasdaq-100 index is expected to unleash billions in passive buying, as brokerages kicked off coverage of the $2 trillion rocket and satellite company with largely bullish views. The Elon Musk-led company joined the index on Tuesday, less than a month after its stock market debut on June 12 - among the fastest inclusions ever - thanks to the Nasdaq's revised rules for newly listed companies looking to enter widely tracked benchmarks. However, shares of SpaceX fell 5.4 percent, reflecting a slide in high-momentum tech stocks, including Micron Technology, on concerns about the longevity of the AI boom. "There's nervousness about expectations being too high," said Mark Hackett, chief market strategist for Nationwide. "I expect that to continue until we get some earnings out."

Conditional inference trees (CIT) and conditional inference forests (CIF) reduce split-selection bias by testing features before choosing split thresholds, but repeated permutation tests and threshold searches can make these methods computationally expensive. We study CIT and CIF as top-$k$ feature-ranking methods for downstream prediction using real-data benchmarks, runtime ablations, and synthetic feature-recovery experiments. At a fixed node, if the features and permutation budget do not depend on the node responses, Bonferroni-corrected $+1$ Monte Carlo permutation $p$-values control nodewise rejection under the complete permutation null. CIF ranks 4th among 17 classification methods on 22 datasets and 3rd among 18 regression methods on 8 datasets. With Bonferroni correction held fixed, the CIF runtime ablations indicate that adaptive stopping and the number of thresholds searched have the largest measured effect on runtime: turning off adaptive stopping and using exact threshold search increase fitting time by 4.0--8.4$\times$ and 1.9--10.8$\times$, respectively, while downstream score changes are at most 0.011. Sparse high-$p$ simulations indicate that forest feature sampling can leave informative features out of many split decisions. Overall, the results support CIF as a top-$k$ feature-ranking method in the evaluated downstream prediction benchmarks.

When you purchase through links in our articles, we may earn a small commission. The HP OmniBook Ultra 14 is a luxurious portable laptop that provides solid portability alongside surprisingly excellent performance. The HP Omnibook Ultra 14 is a luxurious portable laptop that provides solid portability alongside surprisingly excellent performance. For a time, it seemed as though the Windows world was going to have to admit defeat at the hands of Apple's almighty silicon. Apple M-series chips are shockingly efficient, which tends to give MacBooks an edge in portability and performance in thin, light laptops.

Structural equation modelling (SEM) is widely used in survey-based business and information systems research to assess latent constructs and theory-driven structural relationships. However, SEM path significance is obtained within a particular model specification and may not show whether findings remain stable under alternative estimation frameworks. This study develops and demonstrates a staged robustness analysis framework that connects SEM, ordinary least squares (OLS) regression, and Double Machine Learning (DML). SEM is first used to refine the measurement structure and estimate the robustness-baseline SEM model, in which the full theory-specified structural path system is retained for downstream robustness analysis before final structural path evaluation. OLS regression is then applied to SEM-derived construct scores as a transparent regression benchmark. Finally, DML-style residualisation is used to examine whether each tested focal relationship remains stable after flexible machine-learning-based adjustment for observed controls. Learner-sensitivity checks compare Random Forest, Gradient Boosting, and Support Vector Machine learners, and selected reverse-direction diagnostics are used to examine directional sensitivity. The framework is demonstrated using a FinTech Digital Customer Intimacy survey model. The findings identify which relationships are stable across SEM, OLS, and DML-style checks, and which require more cautious interpretation. A reproducible Google Colab workbook and generated result files are publicly available, providing a reusable template that researchers and students can adapt to other survey-based latent-construct studies. The paper contributes a practical robustness workflow and interpretation guide for survey-based researchers seeking to complement SEM with conventional and machine-learning-based robustness checks.