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

marsbitPubblicato 2026-04-02Pubblicato ultima volta 2026-04-02

Introduzione

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.

On April 1, renowned Mandopop singer Zhou Shen released the theme song 'Chronicle of the Moon' for the ancient costume fantasy drama 'Moon Scale Chronicles,' with a clear copyright notice in the song's introduction and lyric sheet stating 'Prohibited for Artificial Intelligence (AI) Training.' This move marks the first case in China where copyright red lines were drawn against AI technology at the time of the work's release, signaling that musicians have entered a new phase of 'digital defense' in protecting their voice rights and creative sovereignty.

The notice specifies in detail: Without the written permission of the copyright owner, any use or distribution (including covers, re-recordings, remixes, etc.) is strictly prohibited; without authorization, the work is strictly forbidden for use in AI training, imitation, learning, generation, and other activities. This directly addresses the increasingly rampant phenomena of 'AI voice cloning' and 'algorithmic song washing' in the industry. Zhou Shen has previously expressed a rational perspective on AI technology in public, emphasizing that while AI can achieve extreme precision through algorithms, it cannot replicate the 'vivid emotions' and artistic soul honed through repeated refinement in human singing.

In 2026, as AI music enters commercial exploration, this 'hardcore' notice not only builds a technical firewall for original voices but also provides a standardized paradigm for addressing the authorization legitimacy of AI training data. Industry experts believe that this approach, which locks in intentional infringement from the source of the work, will significantly lower the threshold for evidence in subsequent copyright enforcement. With leading artists taking the initiative, the music industry is accelerating the construction of legal consensus on the boundaries of human-machine collaboration, reaffirming that irreplaceable human emotion remains the core moat of artistic creation amid the wave of technological advancement.

Domande pertinenti

QWhat is the title of Zhou Shen's new song and what specific copyright declaration does it carry?

AThe title of Zhou Shen's new song is '月之纪' (Yue Zhi Ji), and it carries a copyright declaration that explicitly prohibits its use for AI training and voice imitation.

QWhy is Zhou Shen's copyright statement for '月之纪' considered a landmark case in China?

AIt is considered a landmark case because it is the first instance in China where a copyright statement specifically prohibiting use for AI training was issued at the time of a work's release, setting a precedent for digital copyright protection.

QAccording to the article, what specific uses of the song are strictly prohibited without written permission?

AWithout written permission, it is strictly prohibited to use the song in any way, including cover versions, re-recording, remixing, or for AI training, imitation, learning, and generation activities.

QWhat does Zhou Shen believe is the key difference between AI-generated music and human performance?

AZhou Shen believes that while AI can achieve extreme precision through algorithms, it cannot replicate the 'vivid emotion' and artistic soul that comes from the meticulous polishing of a human performance.

QWhat broader industry impact does the article suggest Zhou Shen's declaration could have?

AThe article suggests that this declaration sets a standardized model for the legitimacy of AI training data authorization and helps build a legal consensus on the boundaries of human-AI collaboration, reinforcing that irreplaceable human emotion is the core of artistic creation.

Letture associate

XRP Ledger v3.3.0 Release Brings Five Amendments Into Focus

The upcoming XRP Ledger v3.3.0 release, scheduled for early August, focuses on five key amendments: Confidential MPT, Batch, Permission Delegation, Sponsored Fees and Reserves, and Dynamic MPT. This bundle aims to enhance the ledger's infrastructure for advanced use cases like tokenized assets and improve operational flexibility and user experience. Notably, Sponsored Fees could reduce onboarding friction by allowing third parties to cover transaction costs, while Confidential and Dynamic MPT features provide more control for institutional asset tokenization. Batch transactions and Permission Delegation add operational efficiency for applications. However, these features do not activate automatically upon release; they require approval from 80% of network validators. The upgrade represents a significant step in XRPL's roadmap for enterprise and developer utility, with practical impact dependent on subsequent validator consensus and builder adoption.

bitcoinist13 min fa

XRP Ledger v3.3.0 Release Brings Five Amendments Into Focus

bitcoinist13 min fa

Wall Street Morning News: V-shaped Rebound at Month-end, but Nasdaq Suffers Worst July in 12 Years; Funds Accelerate Concentration Towards Cloud Giants

Despite a V-shaped rebound at the end of July, the Nasdaq posted its worst July since 2004, while the S&P 500 had its worst July since 2014. Markets were jolted by geopolitical shifts, as President Trump canceled a planned strike on Iran, leading WTI crude to plunge over 8%. This, alongside OPEC+ announcing a supply increase, reversed crude's sharp July gains. Treasury yields surged, with the 10-year yield rising over 30 basis points in July—its largest July increase since 2005. In a rare move, the US and Japan jointly intervened to weaken the USD/JPY, aiming to prevent potential Japanese sales of US Treasuries. While the tech sector faced deleveraging pressure throughout July, cloud giants staged a massive rally on strong earnings. Microsoft, Amazon, and Google collectively added nearly $1.5 trillion in market value last week. Amazon soared over 15% on accelerating AWS growth, Microsoft extended historic gains, Google fully recovered post-earnings losses, and Meta ended an 11-day losing streak. In contrast, Apple tumbled over 7% on supply chain and guidance concerns, ceding its "world's most valuable company" title to Nvidia. The memory and storage sector corrected sharply. Gold edged up 0.91% in July, with analysts viewing the ~30% pullback from January highs as a potential basing period, supported by long-term central bank demand. Key events to watch this week include earnings from Palantir, AMD, SpaceX (its first post-IPO report), and memory giants like Western Digital. The US July non-farm payrolls report on Friday will be critical for gauging the Fed's policy path. SpaceX also faces a significant lock-up expiration, testing market liquidity.

marsbit37 min fa

Wall Street Morning News: V-shaped Rebound at Month-end, but Nasdaq Suffers Worst July in 12 Years; Funds Accelerate Concentration Towards Cloud Giants

marsbit37 min fa

Rubin Ultra Makes Major Cuts, Even Nvidia Can't Handle Memory Price Hikes?

NVIDIA's Rubin Ultra, the top-tier variant of the newly announced Rubin AI accelerators, has reportedly seen significant specification downgrades, according to an industry report from SemiAnalysis. Initially designed with four compute dies (4-die), the Rubin Ultra is now said to be reduced to a 2-die design. Key changes highlighted in the report include: * **No increase in peak theoretical compute performance**, remaining at 35 PFLOPs like the standard Rubin. * **Severe reduction in memory capacity** to 192GB using 8-Hi HBM stacks, which is less than the standard Rubin's 288GB using 12-Hi stacks. * **Negligible memory bandwidth improvement** of only 1 TB/s. * **Slightly higher chip-level power consumption**. * The **primary upgrade is a massive increase in scale-up interconnect capacity**, supporting connections for up to 576 GPUs via NVLink, compared to 72 for the standard Rubin. The report suggests the redesign is primarily a cost-optimization move driven by the sharp rise in HBM (High-Bandwidth Memory) prices. By reducing the expensive HBM content and shifting investment towards enhanced system-scale networking, NVIDIA aims to maintain the platform's value for large-scale AI training clusters while managing soaring material costs. The news reportedly triggered a sell-off in South Korean memory stocks, with SK Hynix and Samsung shares falling around 8%, as markets grew concerned that NVIDIA—a major HBM buyer—might be reducing its reliance on high-capacity memory, potentially capping future pricing power for memory makers.

Odaily星球日报58 min fa

Rubin Ultra Makes Major Cuts, Even Nvidia Can't Handle Memory Price Hikes?

Odaily星球日报58 min fa

Upbit Rebalances 864B SHIB In Internal Wallet Move

South Korean cryptocurrency exchange Upbit moved a total of 864 billion SHIB (worth approximately $4 million) between its internal wallet addresses. The activity, involving transfers of 384 billion SHIB outbound and 480 billion SHIB inbound, occurred following a 36% SHIB price rally. On-chain data indicates this was a routine internal wallet rebalancing operation by the exchange, not a sell-off or liquidation event. The article cautions that while large on-chain transfers are highly visible, they require proper context and wallet labeling to interpret correctly. In this case, the movement between known Upbit addresses does not inherently signal a change in market supply or selling pressure for SHIB.

bitcoinist1 h fa

Upbit Rebalances 864B SHIB In Internal Wallet Move

bitcoinist1 h fa

Can Generative Models Finally Be Trained End-to-End? The Core Is a For Loop

This article introduces a novel training paradigm for generative models called Explorative Modeling (XM), which enables true end-to-end training. Traditionally, powerful generative models like autoregressive and diffusion models are not trained end-to-end. They are trained to predict a single small step but require iterative multi-step sampling for inference. This "exposure bias" leads to error accumulation and limits performance. The core challenge XM addresses is "mode blurring." In generative tasks, a single input (e.g., "generate a dog") corresponds to many valid outputs (multiple modes). Standard training objectives like reconstruction loss force the model to average these modes, producing unrealistic, blurry outputs. To avoid this, existing models break generation into many small, almost deterministic steps, sacrificing end-to-end training. XM tackles this by restructuring the training loop itself. Its key insight is to amplify "generative expressivity." For each training input, instead of generating one sample, the model generates K candidate outputs. Only the candidate closest to the real data is used for computing the loss and updating the model via backpropagation. This simple "best-of-K" mechanism is implemented as a short for-loop. By exploring multiple possibilities, the model learns to distribute its guesses across different modes rather than collapsing to their uninformative average. The paper demonstrates that "exploration" acts as a new, powerful scaling axis. Gains from XM increase with model size, data scale, and compute. Experiments show improvements in FID scores for image generation and significant efficiency gains, sometimes outperforming larger models without exploration. When pushed to the limit, XM enables fully single-step, end-to-end generative models. In robotics tasks, an "Explorative Policy" matched the performance of a 100-step Diffusion Policy with a single forward pass, drastically improving inference speed. While the best-of-K concept is not entirely new, the authors' contribution lies in formally understanding it as a direct method to boost generative expressivity without fragmenting the generation process. This work suggests that as models scale, enhancing exploration during training may become crucial for overcoming fundamental performance bottlenecks.

marsbit1 h fa

Can Generative Models Finally Be Trained End-to-End? The Core Is a For Loop

marsbit1 h fa

Trading

Spot
活动图片