GenAI

GenAI

China’s Response to OpenAI’s Sora Model: StepFun Unveils a 30-Billion-Parameter AI System

On 17 February 2025, Chinese company StepFun publicly released its open-source text-to-video generation model, Step-Video-T2V, featuring 30 billion parameters. Positioned as a direct competitor to OpenAI’s Sora, the model interprets bilingual (English and Chinese) text prompts and can generate videos of up to 204 frames in 544×992 resolution.

by poltextLAB AI journalist

The NIST AI Risk Management Framework: A Key Tool in Regulating GenAI

The Artificial Intelligence Risk Management Framework (AI RMF) issued by the National Institute of Standards and Technology (NIST) on 26th January 2023 is gaining increasing significance in regulating GenAI. The framework is built on four primary functions—governance, mapping, measurement, and management—which assist organisations in developing and evaluating trustworthy

by poltextLAB AI journalist

Can an AI-created work be copyrighted? The US Copyright Office's stance

The United States Copyright Office issued a landmark statement on copyright protection for works created with artificial intelligence in January 2025. After processing 10,000 professional observations, the office concluded that AI-assisted creations may enjoy legal protection provided they contain a sufficient degree of human creativity, whilst purely AI-generated works

by poltextLAB AI journalist

New European Research Centre: Developing Artificial Intelligence Based on the CERN Model

According to the plan unveiled in January by the Centre for Future Generations research institute, a €35 billion initial investment would create an international research centre for European artificial intelligence development. In line with former ECB President Mario Draghi's comprehensive technology report, the proposal suggests establishing an institution

by poltextLAB AI journalist

The Place of GenAI in the AI Hierarchy: From Neural Networks to Large Language Models

Generative AI relies on a specialised branch of machine learning (ML), namely deep learning (DL) algorithms, which employ neural networks to detect and exploit patterns embedded within data. By processing vast volumes of information, these algorithms are capable of synthesising existing knowledge and applying it creatively. As a result, generative

Generative Artificial Intelligence: Just Hype or Reality?

Gartner’s 2024 Technology Hype Cycle—which outlines the dynamics of expectations surrounding technological innovations across five distinct phases—indicates that generative AI has surpassed the peak of inflated expectations. Nevertheless, the associated hype remains persistent (see figure below), and the technology continues to hold the potential to become truly