On its 20th anniversary, YouTube is venturing into an era of AI-generated video, and may never be the same. Google figured out early on that video would be a great addition to its search business, so in 2005 it launched Google Video. Focused on making deals with the entertainment industry for second-rate content, and overly cautious on what users could upload, it flopped . In 2006, Google snapped up that year-old company, figuring it would sort out the IP stuff later. Though the $1.65 billion purchase price for YouTube was about a billion dollars more than its valuation, it was one of the greatest bargains ever.

OpenAI announced today that it is releasing a new family of artificial intelligence models optimized to excel at coding, as it ramps up efforts to fend off increasingly stiff competition from companies like Google and Anthropic. The models are available to developers through OpenAI's application programming interface (API). OpenAI is releasing three sizes of models: GPT 4.1, GPT 4.1 Mini, and GPT 4.1 Nano. Kevin Weil, chief product officer at OpenAI, said on a livestream that the new models are better than OpenAI's most widely used model, GPT-4o, and better than its largest and most powerful model, GPT-4.5, in some ways. GPT-4.1 scored 55 percent on SWE-Bench, a widely used benchmark for gauging the prowess of coding models.

On Monday, 404 Media's Samantha Cole reported that the 2.4 trillion company asked workers to download videos from YouTube, Netflix and other datasets to develop commercial AI projects. The graphics card maker is among the tech companies appearing to have adopted a "move fast and break things" ethos as they race to establish dominance in this feverish, too-often-shameful AI gold rush. The training was reportedly to develop models for products like its Omniverse 3D world generator, self-driving car systems and "digital human" efforts. NVIDIA defended its practice in an email to Engadget. The company equated the practice to a person's right to "learn facts, ideas, data, or information from another source and use it to make their own expression."

Santa Cruz Cabralia, Bahia, Brazil – With a loud whir, the drone takes flight. Minutes later, the humming sound gives way to a distinctive rattling as the machine, hovering about 20 metres above the ground, begins unloading its precious cargo and a cocktail of seeds rains down onto the land below. Given time, these seeds will grow into trees and, eventually, it is hoped, a thriving forest will stand where there was once just sparse vegetation. That is what the startup which operates this drone, a large contraption that looks a bit like a Pokemon ball with antennae, hopes. The 54 hectares (133 acres) here which have been badly degraded by agriculture and cattle farming in the Brazilian state of Bahia are just the start.

AI models using individual's work without permission (or compensation) is nothing new, with entities like The New York Times and Getty Images initiating lawsuits against AI creators alongside artists and writers. In March, OpenAI CTO Mira Murati contributed to the ongoing uncertainty, telling The Wall Street Journal she wasn't sure if Sora, the company's new text-to-video AI tool, takes data from YouTube, Instagram or Facebook posts. Now, YouTube's CEO Neal Mohan has responded with a clear warning to OpenAI that using its videos to teach Sora would be a "clear violation" of the platform's terms of use. In an interview with Bloomberg Originals host Emily Chang, Mohan stated, "From a creator's perspective, when a creator uploads their hard work to our platform, they have certain expectations. One of those expectations is that the terms of service is going to be abided by. It does not allow for things like transcripts or video bits to be downloaded, and that is a clear violation of our terms of service. Those are the rules of the road in terms of content on our platform."

Sir Lucian Grainge, the chairman and C.E.O. of Universal Music Group, the largest music company in the world, is curious, empathetic, and, if not exactly humble, a master of the humblebrag. His superpower is his humanity. A sixty-three-year-old Englishman, who was knighted in 2016 for his contributions to the music industry and has topped Billboard's Power 100 list of music-industry players several times in the past decade, Grainge is compact and a bit chubby, with alert eyes behind owlish glasses. He isn't trying to be noticed. He presides over a public company worth more than fifty billion dollars, but he could be a small-business owner who sells music in a London shop, as did his father, Cecil.

Artificial intelligence just doesn't pop up when you install tools and software. It takes planning and, most of all, it takes data. But getting the right data to make AI and machine learning algorithms -- and understanding it -- is where many organizations are slipping up, a recent study finds. Organizations face difficulties with data silos, explainability, and transparency, a study of 150 data executives commissioned by Capital One and Forrester Consulting finds. They say internal, cross-organizational, and external data silos slowed machine learning deployments and outcomes.

Dialog is a useful way for a robotic agent performing a task to communicate with a human collaborator, as it is a rich source of information for both the agent and the human. Such task-oriented dialog provides a medium for commanding, informing, teaching, and correcting a robot. Robotic agents engaging in dialog must be able to interpret a wide variety of sentences and supplement the dialog with information from its context, history, learned knowledge, and from non-linguistic interactions. We have identified a set of nine system-level requirements for such agents that help them support more effective, efficient, and general task-oriented dialog. This set is inspired by our research in Interactive Task Learning with a robotic agent named Rosie. This paper defines each requirement and gives examples of work we have done that illustrates them.

In this work, we look at the challenge of learning in an action game,Infinite Mario. Learning to play an action game can be divided intotwo distinct but related problems, learning an object-relatedbehavior and selecting a primitive action. We propose a framework that allows for the use of reinforcement learning for both ofthese problems. We present promising results in some instances of thegame and identify some problems that might affect learning.

Google is returning to using humans for YouTube moderation after repeated errors with its AI system. Moderating a large network like YouTube is no easy task. Aside from the sheer volume of content uploaded every day, moderators are subjected to the worst of humanity and often end up requiring therapy. AI has been hailed as helping to deal with some of the aforementioned issues. Google was left with little choice but to give more power to its AI moderators as the COVID-19 pandemic took hold… but it hasn't been smooth sailing.