Industry News

Tracks company news, strategic changes, funding activities, and personnel adjustments across the blockchain and crypto industries, delivering a full-spectrum industry overview for our users.

Zhou Shen's New Song 'Chronicle of the Moon' Released: Clearly Marked as Strictly Prohibited for AI Training and Voice Imitation

On April 1, Chinese singer Zhou Shen released the theme song "Yue Zhi Ji" for the fantasy drama "Yue Lin Qi Ji," with a notable copyright declaration explicitly prohibiting the use of the work for artificial intelligence (AI) training. This marks the first case in China where such a restriction was imposed at the time of a song’s release, setting a precedent for protecting vocal rights and creative sovereignty in the digital age. The statement specifies that unauthorized use—including covers, re-recording, remixing, or distribution—is strictly forbidden. It explicitly bans the use of the work in AI training, imitation, learning, or generation activities, addressing growing concerns over AI voice replication and algorithm-driven music plagiarism. Zhou Shen has previously emphasized that while AI can achieve technical precision, it cannot replicate the emotional depth and artistic essence of human performance. As AI music commercializes by 2026, this proactive measure establishes a legal and technical framework to safeguard original vocal works and clarifies authorization standards for AI training data. Industry experts believe such declarations will simplify copyright enforcement by establishing intent at the source. Leading artists like Zhou are helping define ethical and legal boundaries for human-AI collaboration, reaffirming that irreplaceable human emotion remains central to artistic creation.

marsbit04/02 01:49

Zhou Shen's New Song 'Chronicle of the Moon' Released: Clearly Marked as Strictly Prohibited for AI Training and Voice Imitation

marsbit04/02 01:49

Dialogue with BlackRock's Head of Digital Assets: How Do Tokenized Stocks Work?

The article "Dialogue with BlackRock's Digital Asset Head: How Do Tokenized Stocks Work?" features a discussion with industry experts including Robert Mitchnick (BlackRock), Rob Hadick (Dragonfly), and Noah Levine (a16z). The conversation explores the evolution and mechanics of tokenized assets, particularly stocks. Key takeaways highlight that tokenization is primarily an "access" story, enabling broader investor participation in traditionally hard-to-reach asset classes, rather than just an efficiency improvement. Stablecoins are evolving from payment tools into foundational financial infrastructure, acting as an entry point for investment and asset management. Most current "tokenized stock" offerings are transitional, often representing derivative-like structures rather than true on-chain ownership, with limitations like transfer restrictions due to whitelisting and compliance requirements. The discussion covers three main structures for tokenized equities: SPV-based models, rights-based tokens (e.g., Securitize’s approach), and native on-chain issuance (e.g., Superstate). The latter is seen as the most promising for enabling true composability and functionality like collateralization. Regulatory clarity and infrastructure development are critical for advancing toward permissionless, liquid markets. Initiatives like the NYSE’s partnership with Securitize for 24/7 trading are noted, though the core demand is for improved asset utilization efficiency, not just extended hours. The piece also differentiates stablecoins (serving cross-border and crypto-native users) from tokenized deposits (focused on banking efficiency), predicting both will coexist. Privacy emerges as a growing need in on-chain capital markets, with technologies like ZK-proofs gaining relevance. Long-term, tokenization could flatten financial market structures by reducing intermediaries, lowering costs, and expanding access, ultimately integrating crypto infrastructure into mainstream finance.

marsbit04/01 14:54

Dialogue with BlackRock's Head of Digital Assets: How Do Tokenized Stocks Work?

marsbit04/01 14:54

Embodied Intelligence Breakthrough: Amap Fully Open-Sources Universal Robot Base Model ABot-M0

Embodied Intelligence Breakthrough: AutoNavi Open-Sources Universal Robot Base Model ABot-M0 AutoNavi has announced the full open-source release of ABot-M0, the world's first unified architecture-based embodied manipulation base model. This model is designed to enable "one general brain to adapt to multiple forms of robots," aiming to break down barriers between heterogeneous hardware and accelerate the adoption of embodied intelligence in industrial and household settings. ABot-M0 demonstrated exceptional performance in industry tests, achieving a task success rate of 80.5% on the Libero-Plus benchmark—a nearly 30% improvement over the previous benchmark, Pi0. It also set new state-of-the-art records on benchmarks like Libero and RoboCasa. The open-source release addresses long-standing challenges in the field, such as data isolation and deployment difficulties, by providing resources across three key dimensions: - **Data:** The UniACT dataset, the largest of its kind, with over 6 million real operation trajectories and full data pipeline tools. - **Algorithm:** The model architecture and training framework, featuring innovative components like Action Manifold Learning (AML) and a dual-stream perception architecture. - **Model:** End-to-end pre-trained models and a complete toolchain for out-of-the-box deployment, significantly lowering the barrier to adaptation. According to AutoNavi's ABot-M0 technical lead, this open-source initiative aims to build a bridge between academic research and industrial application, enabling robots of various forms to possess a smart, reliable, and universal "brain."

marsbit04/01 08:19

Embodied Intelligence Breakthrough: Amap Fully Open-Sources Universal Robot Base Model ABot-M0

marsbit04/01 08:19

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