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There's a Free Video Editor Inside Google Drive That Anyone Can Use You may not have noticed everything Google's free software suite offers--especially a web app called Google Vids. Mention the online apps available as part of Google Workspace, and most people will immediately think of Google Drive, Gmail, Google Docs, Google Sheets, and Google Slides--those speedy, lightweight office apps that let you get work done from any browser tab. There's more to the suite of Google Workspace tools than you might have realized though, including an online video editing package called Google Vids . Originally launched in 2024 for business and enterprise users, the app was made available to individuals in 2026 and continues to get regular updates from Google. Since it's both free to use and designed to run in a web browser, it's not capable of pro-level, high-resolution, multitrack video editing.

Adobe offers a free video editor for your phone--here's what it can do. 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. Adobe Premiere can help you get your videos looking better, for free. We may earn revenue from the products available on this page and participate in affiliate programs. With so many smartphone apps in the modern age locked (or partially locked) behind paywalls, it's important to grab the free ones when they become available--and on rare occasions, it's the biggest software companies on the planet offering apps free of charge.

Make complex data easy to digest with Adobe Acrobat's AI tools The demands and pace of modern business can mean that there's less time than you'd like to work through dense materials and produce insights. It's not unusual to be given dense, long PDF documents from which you need to create a report or presentation, with the expectation being that you fully understand the content. It can all feel overwhelming. How do you deliver concise and informed insights if there aren't enough hours in the day to fully understand all the nuances? Then there's the problem of how you present this information to colleagues or clients.

Try Adobe's AI presentation generator today for free! Putting together a profession presentation can be a daunting prospect. Not only do you have to gather all of the relevant information and images, but then there's the task of getting them to look good on the screen while succinctly conveying the message you want to get across. Even if you have existing documents with all the facts laid out, there's still the challenge of transferring them onto slides, often manually. Adobe Acrobat is on hand to help though, with its AI presentation generator that's ready to save time and lighten your workload.

AI agents that can model the human process of research will accelerate discoveries in science. Every few decades, someone announces that science has reached its end. In 1903, the revered physicist Albert Michelson wrote that the "facts of physical science have all been discovered." In the 1980s, Stephen Hawking predicted that theoretical physics might be finished by the end of the century. With the explosive arrival of artificial intelligence, the feeling is in the air again--this time accompanied by a Nobel Prize. In 2024, Demis Hassabis and John Jumper of Google DeepMind were awarded part of the Nobel in chemistry for their neural network AlphaFold, which predicts the three-dimensional structures of proteins by learning from thousands of experimentally measured shapes.

When you purchase through links in our articles, we may earn a small commission. Outlook lets you save frequently used messages as templates. Here's how to set them up. Need to send the same email over and over? Microsoft Outlook lets you save messages as reusable templates, but the setup depends on which version you've got.

When you purchase through links in our articles, we may earn a small commission. Did ChatGPT give you a perfect answer? Take an AI's effective answer and turn it into a reusable template. I've written before about how to ask ChatGPT to improve a so-so prompt . But what about those times with ChatGPT, Claude, or Gemini absolutely crushes it, giving you exactly what you needed from your prompt?

Inverse protein folding addresses the challenge of designing amino acid sequences that fold into a predetermined tertiary structure, bridging geometric and evolutionary constraints to advance protein engineering. Inspired by the pivotal role of multiple sequence alignments (MSAs) in structure prediction models like AlphaFold, we hypothesize that structural alignments can provide an informative prior for inverse folding. In this study, we introduce DualMPNN, a dual-stream message passing neural network that leverages structurally homologous templates to guide amino acid sequence design of predefined query structures. DualMPNN processes the query and template proteins via two interactive branches, coupled through alignment-aware cross-stream attention mechanisms that enable exchange of geometric and co-evolutionary signals. Comprehensive evaluations across on CATH 4.2, TS50 and T500 benchmarks demonstrate DualMPNN achieves state-ofthe-art recovery rates of 65.51%, 70.99%, and 70.37%, significantly outperforming base model ProteinMPNN by 15.64%, 16.56%, 12.29%, respectively. Further template quality analysis and structural foldability assessment underscore the value of structural alignment priors for protein design.

The use of large language models (LLMs) to simulate human behavior has gained significant attention, particularly through personas that approximate individual characteristics. Persona-based simulations hold promise for transforming disciplines that rely on population-level feedback, including social science, economic analysis, marketing research, and business operations. Traditional methods to collect realistic persona data face significant challenges: they are prohibitively expensive and logistically challenging due to privacy constraints, and often fail to capture multi-dimensional attributes, particularly subjective qualities. Consequently, synthetic persona generation with LLMs offers a scalable, cost-effective alternative. However, current approaches rely on ad hoc and heuristic generation techniques that do not guarantee methodological rigor or simulation precision, resulting in systematic biases in downstream tasks. Through extensive large-scale experiments including presidential election forecasts and general opinion surveys of the U.S. population, we reveal that these biases can lead to significant deviations from real-world outcomes. Based on the experimental results, this position paper argues that a rigorous and systematic science of persona generation is needed to ensure the reliability of LLM-driven simulations of human behavior. We call for not only methodological innovations and empirical foundations but also interdisciplinary organizational and institutional support for the development of this field. To support further research and development in this area, we have opensourced approximately one million generated personas, available for public access and analysis at Tianyi-Lab/Personas.