Editor-Curated Robotics Industry Briefing - RobotToday

How Musicians Can Get Paid for Training AI

In response to the challenges posed by generative AI on the music industry, startups like Sureel and SoundVerse are developing innovative solutions to ensure musicians are compensated fairly for their work. Following Warner Music Group's acquisition of Sureel, the company has partnered with the Swedish copyright agency STIM to create a system that tracks how music is used in AI training. This software allows creators to specify the terms of use for their music, ensuring they receive royalties based on its influence in AI-generated outputs. The ongoing debate centers on how to accurately attribute the contributions of various training data to the outputs produced by AI systems. SoundVerse advocates for a model that rewards artists continuously throughout the AI lifecycle, rather than through one-time payments. This approach aims to maintain the economic incentives that drive creativity while addressing concerns about AI's potential to undermine cultural vibrancy and artist livelihoods. As copyright lawsuits give way to negotiated agreements between major music labels and AI companies, there is a growing opportunity to establish fair compensation practices. Experts emphasize the need for transparent and equitable attribution systems that reflect the complex relationship between training data and AI outputs. Ultimately, the success of these initiatives may depend on collaboration across disciplines, including musicology, law, and economics, to create policies that support a sustainable creative sector in the age of AI.

IEEESpectrumAI By Oliver Bown 3 hours ago Copyright Training-data Generative-ai Music

Deepfake Detection Dataset Aims to Keep Up With Generative AI

A collaborative effort involving researchers from Microsoft, Northwestern University, and the non-profit organization Witness has led to the development of a new dataset aimed at enhancing the detection of AI-generated media. Announced in a study published on April 10 in IEEE Intelligent Systems, the Microsoft-Northwestern-Witness (MNW) deepfake detection benchmark is designed to address the growing challenge of distinguishing real from fake content in an era where generative AI technology is rapidly advancing. The dataset includes a diverse array of AI-generated images, audio, and videos, reflecting the current landscape of generative AI. Thomas Roca, a principal research scientist at Microsoft, emphasized the increasing sophistication of AI-generated media, which can easily be produced by anyone using accessible applications. This proliferation raises significant concerns, including identity fraud and the creation of harmful content. The MNW benchmark aims to improve the effectiveness of detection systems by providing a wider variety of AI-generated materials, including those that have undergone post-processing manipulations. Researchers acknowledge that while this dataset could potentially be misused to develop new evasion techniques, it is crucial for enhancing the ability to assess the authenticity of media as generative AI continues to evolve. The team plans to update the dataset biannually to incorporate the latest developments in generative AI and detection challenges, with the goal of fostering transparency and raising standards in the fight against deepfake content.

IEEESpectrumAI By Michelle Hampson May 03, 2026 Deepfakes Generative-ai Artificial-intelligence Microsoft Journal-watch

Sarang Gupta Builds AI Systems With Real-World Impact

Sarang Gupta, a data scientist at OpenAI in San Francisco, has leveraged his childhood curiosity and engineering skills to make significant contributions to the field of artificial intelligence. From a young age, Gupta demonstrated a knack for problem-solving, fixing household items and later developing software solutions, including an online ordering system for a local restaurant. After earning dual degrees in industrial engineering and business management from the Hong Kong University of Science and Technology, he began his career at Goldman Sachs, where he automated trade reconciliation processes, enhancing operational efficiency. In 2020, Gupta earned a master's degree in data science with a focus on AI from Columbia University, where he collaborated on projects that aimed to improve journalism through technology. He then joined Asana as a product data scientist, leading the launch of AI-powered features to enhance user experience. His work gained momentum alongside the rise of generative AI, prompting him to transition to OpenAI in September 2025. At OpenAI, Gupta collaborates with the marketing team to develop data-driven models that optimize customer outreach and measure the effectiveness of various marketing channels. He emphasizes the transformative potential of AI across industries and plans to continue his work in this rapidly evolving field. Gupta, an IEEE member since 2024, values the organization for its resources and networking opportunities, which he believes inspire and enhance his professional journey.

IEEESpectrumAI By Julianne Pepitone Apr 14, 2026 Ieee-member-news Openai Generative-ai Chatgpt Careers Type-ti

How Your Virtual Twin Could One Day Save Your Life

In May 2019, a cardiac surgeon at Boston Children’s Hospital successfully performed a complex heart surgery on a child with a severe congenital defect, utilizing advanced virtual twin technology. This innovative approach involved creating a detailed 3D model of the child's heart and vascular system from MRI and CT scans, allowing the surgical team to simulate various strategies and predict outcomes before the operation. The procedure was critical due to the unique nature of the child's heart condition, which had no established surgical manual. The Living Heart Project, initiated in 2014, has since guided nearly 2,000 surgeries by employing virtual twin modeling, which combines engineering principles with medical expertise to enhance surgical precision and patient outcomes. This project, now involving over 150 organizations globally, aims to revolutionize medical treatment by providing a dynamic, predictive tool that can simulate the human body's responses. The technology not only aids in surgical planning but also has the potential to streamline clinical trials. By creating virtual patient cohorts, researchers can test treatments more efficiently, reducing the time and costs associated with traditional trials. The FDA has recognized the significance of this approach, collaborating with the project to establish guidelines for in silico clinical trials, marking a significant shift in how medical innovations are developed and validated. As virtual twins expand beyond cardiac applications to other organs, they promise to transform healthcare by enabling personalized medicine and fostering a deeper understanding of patient physiology, ultimately improving treatment outcomes and patient engagement in their health management.