Google has updated Gemini for Home so that it no longer acts like a strict parent when you ask it for cocktail recipes. In the past, you may have encountered a message that says I cannot provide recipes for alcoholic beverages when you ask the AI assistant for a margarita recipe on Google smart home devices, such as the Nest Hub . Now, Google has updated its safeguards to prevent adult users from encountering filters meant for younger ones. Adults will now experience improved availability for general queries, including recipes for age-gated beverages, the company said in the Google Home support page . If Gemini still isn't responding when you ask it for instructions on how to make a cocktail, you may have to check you Parental Control settings and your Gemini for Home response filter settings in the Google Home app.

You may be using the wrong plates. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. The ideal dinner party size is somewhere between five and eight guests. Breakthroughs, discoveries, and DIY tips sent six days a week. You don't have to be Martha Stewart to host a successful dinner party .

Life My Dad Used to Have All the Answers. It feels like he adopted a robot child, and that child will stop at nothing to wedge a divide between us. Like many twentysomethings, I ask my dad a lot of questions. How do I fix the leak under my sink? What does "federal withholding" mean?

From recipe changes to aging taste buds, here's why those peanut butter cups don't hit like they used to. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. There's a reason you might remember Hershey's chocolate differently. Breakthroughs, discoveries, and DIY tips sent six days a week. Brad Reese, grandson of Reese's Peanut Butter Cups inventor H.B. Reese, caused a stir this year with his claims that The Hershey Company had changed his grandfather's recipes beyond recognition .

Preprocessing leakage arises when scaling, imputation, or other data-dependent transformations are estimated before resampling, inflating apparent performance while remaining hard to detect. We present fastml, an R package that provides a single-call interface for leakage-aware machine learning through guarded resampling, where preprocessing is re-estimated inside each resample and applied to the corresponding assessment data. The package supports grouped and time-ordered resampling, blocks high-risk configurations, audits recipes for external dependencies, and includes sandboxed execution and integrated model explanation. We evaluate fastml with a Monte Carlo simulation contrasting global and fold-local normalization, a usability comparison with tidymodels under matched specifications, and survival benchmarks across datasets of different sizes. The simulation demonstrates that global preprocessing substantially inflates apparent performance relative to guarded resampling. fastml matched held-out performance obtained with tidymodels while reducing workflow orchestration, and it supported consistent benchmarking of multiple survival model classes through a unified interface.

To facilitate these annotations, we used both our user application and Label Studio.