The platform appears to closely resemble TikTok and is powered by Sora 2, OpenAI's latest video generation model. OpenAI is preparing to launch a stand-alone app for its video generation AI model Sora 2, WIRED has learned. The app, which features a vertical video feed with swipe-to-scroll navigation, appears to closely resemble TikTok--except all of the content is AI-generated . There's a For You-style page powered by a recommendation algorithm. On the right side of the feed, a menu bar gives users the option to like, comment, or remix a video.

The Salvador Convention Center, where RoboCup 2025 will take place. RoboCup is an international scientific initiative with the goal of advancing the state of the art of intelligent robots, AI and automation. The annual RoboCup event, where teams gather from across the globe to take part in competitions across a number of leagues, will this year take place in Brazil, from 15-21 July. We spoke to Marco Simões, one of the General Chairs of RoboCup 2025 and President of RoboCup Brazil, to find out what plans they have for the event, some new initiatives, and how RoboCup has grown in Brazil over the past ten years. RoboCup will be held in Salvador, Brazil.

Hassabis received half of the award alongside a colleague, John Jumper, for the design of AlphaFold: an AI tool that can predict the 3D structure of proteins using only their amino acid sequences--something Hassabis describes as a "50-year grand challenge" in the field of biology. Released freely by Google DeepMind for the world to use five years ago, AlphaFold has revolutionized the work of scientists toiling on research as varied as malaria vaccines, human longevity, and cures for cancer, allowing them to model protein structures in hours rather than years. The Nobel Prizes in 2024 were the first in history to recognize the contributions of AI to the field of science. If Hassabis gets his way, they won't be the last. AlphaFold's impact may have been broad enough to win its creators a Nobel Prize, but in the world of AI, it is seen as almost hopelessly narrow.

The adoption of networked care models is set to revolutionize health-care delivery. As we move forward, expect to see health-care organizations increasingly investing in technologies that enable seamless data sharing and interoperability. Imagine a scenario where a patient's entire medical history, including imaging data from various specialists, is instantly accessible to any authorized health-care provider. This level of connectivity not only improves diagnosis and treatment but also enhances the overall patient experience. True data integration is becoming the norm in health care.

Earlier today, President Joe Biden signed the most sweeping set of regulatory principles on artificial intelligence in America to date: a lengthy executive order that directs all types of government agencies to make sure America is leading the way in developing the technology while also addressing the many dangers that it poses. The order explicitly pushes agencies to establish rules and guidelines, write reports, and create funding and research initiatives for AI--"the most consequential technology of our time," in the president's own words. The scope of the order is impressive, especially given that the generative-AI boom began just about a year ago. But the document's many parts--and there are many--are at times in tension, revealing a broader confusion over what, exactly, America's primary attitude toward AI should be: Is it a threat to national security, or a just society? Is it a geopolitical weapon?

There are various methods to prepare the feature dataset, which is a crucial input for a machine learning prediction model. One approach is to code technical indicators using Python and feed them as input to the model. Another simpler approach is to utilize Amibroker's AFL Exploration, which provides built-in indicators and also supports custom indicators that are easy to code, explore, and prepare as feature datasets. The exploration dataset can then be extracted in CSV format. In machine learning, features represent the input data points or independent variables used to describe various aspects of the object under study.

Artificial Intelligence (AI) is changing the way we live and work. From healthcare to transportation, education to entertainment, AI is rapidly transforming every aspect of our lives. As the technology continues to evolve, it's important to understand what it is, how it works, and how to prepare for the future. AI is a type of technology that can perform tasks that typically require human intelligence. It uses algorithms, machine learning, and other advanced techniques to analyze data, recognize patterns, and make decisions.

ChatGPT and similar systems will increasingly be part of our lives, including health care. We need guidelines to ensure their ethical deployment. Generative AI systems like ChatGPT, a chatbot based on a generative pre-trained transformer, have captured the public's attention, resulting in a flurry of positive and negative speculation about their potential. They have even found their way into popular comic strips. In one Dilbert strip, for instance, the boss asks Wally if his status report was written by a commercial grade AI.

Towards the end of last year, management consultant McKinsey published an article where the first paragraph was created by ChatGPT, the generative artificial intelligence (AI) language model. The article's authors admitted that the AI's attempt was "not perfect but overwhelmingly impressive". They noted that products like ChatGPT and GitHub Copilot take technology into realms once thought to be reserved for humans. "With generative AI, computers can now arguably exhibit creativity. They can produce original content in response to queries, drawing from data they've ingested and interactions with users," they said.

President & CEO of BBB National Programs, a non-profit organization dedicated to fostering a more accountable, trustworthy marketplace. In recent years, government scrutiny over the use of artificial intelligence (AI) tools in the recruiting and hiring process has risen. Since I wrote about this topic last year, there has been significant activity within several federal government agencies regarding the use of AI and machine learning in the employment context. A better understanding of these actions can help business leaders reduce their risk of legal liability and better understand how to use AI and machine learning responsibly in their organizations. The Equal Employment Opportunity Commission (EEOC) has been particularly active through its EEOC initiative on AI and algorithmic fairness and its joint HIRE initiative with the U.S. Department of Labor.