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Vitalik: The Diamond iO exploration program "stealth" new paradigm may drive privacy computing into a new stage

Ethereum co-founder Vitalik Buterin published a new article introducing a novel cryptographic obfuscation technology called Diamond iO. This technology aims to address the extremely low efficiency of traditional indistinguishable obfuscation schemes, allowing programs to run while hiding internal logic and critical data. Although traditional iO technology possesses strong privacy protection capabilities, its operational costs are prohibitively high, making it nearly impractical. Diamond iO, by adopting more aggressive new cryptographic assumptions, enhances computational efficiency from "universe-level time consumption" to "planet-level time consumption," bringing it closer to practical application.Diamond iO is built on technologies such as Attribute-Based Encryption (ABE) and Fully Homomorphic Encryption (FHE). By encrypting programs, it allows users to execute the encrypted programs and obtain correct outputs without being able to view the internal code or hidden keys. This solution introduces a new input encoding mechanism and conditional decryption method, reducing computational complexity while ensuring the program logic remains hidden. Its core application scenarios include protecting programs that contain private keys, enabling secure software licensing, constructing trustless cryptographic services, and supporting blockchain and artificial intelligence systems with stronger privacy protection.However, Diamond iO is still in the early research stage, and its security relies on new cryptographic assumptions, including All-Product LWE and Evasive LWE, which require further research validation. At the same time, the technology still faces efficiency challenges such as high computational overhead and circuit depth limitations. Researchers believe that by optimizing underlying hash functions, improving homomorphic encryption schemes, and reducing security parameter requirements, Diamond iO is expected to become an important direction for advancing practical program obfuscation technology in the future.

NVIDIA releases quantum computing AI calibration model, promoting the fusion of AI and quantum

NVIDIA released the open-source AI model NVIDIA Ising Calibration 1.5, designed for the automatic analysis of quantum processor (QPU) diagnostic data, and to autonomously determine device calibration schemes, achieving automation of the quantum computer calibration process. NVIDIA stated that Ising Calibration 1.5 is a visual language model (VLM) specifically designed for quantum computing calibration scenarios, capable of understanding experimental data from quantum chips and performing "zero-shot" analysis in the absence of historical cases, while also enabling context learning (ICL) with relevant experimental samples to help continuously optimize the operational state of quantum devices.In the QCalEval quantum calibration benchmark test, Ising Calibration 1.5 averaged about 10% ahead of similarly sized open-source models in zero-shot inference capability, and when using relevant experimental cases for context learning, it showed an approximately 86.5% performance improvement over the previous generation model, surpassing multiple open-source models and approaching the level of top closed-source large models. The model has 31 billion parameters and supports operation on NVIDIA Grace Blackwell and Vera Rubin data center GPUs. It also launched an NVFP4 quantized version, which can be deployed on a single consumer-grade GPU or NVIDIA DGX Spark, significantly lowering the usage threshold for quantum laboratories.NVIDIA claims that the training data for Ising Calibration 1.5 comes from various qubit architectures, including superconducting qubits, quantum dots, ions, neutral atoms, and helium surface electrons, providing calibration capabilities for different types of quantum computing devices. Industry experts believe that automated calibration is one of the key bottlenecks in the scalable development of quantum computing. NVIDIA's launch of this AI-driven quantum calibration tool signifies that AI models are beginning to extend from traditional computing domains into the quantum hardware control layer, potentially becoming an important component of the future quantum computing industry infrastructure.

NVIDIA invests in OpenAI to co-establish a new AI laboratory, providing large-scale GPU computing power support

According to a report by the WSJ, Nvidia has invested in the AI laboratory Safe Superintelligence (SSI), founded by former OpenAI chief scientist Ilya Sutskever. The two parties have reached a long-term cooperation agreement aimed at expanding SSI's computing resources while helping Nvidia secure important clients in the AI field. Both companies stated that Nvidia made a "large-scale" investment after understanding some of SSI's research progress, but did not disclose the specific amount.As part of the collaboration, SSI will receive a significant amount of Nvidia's flagship GPU resources, with its computing power expected to increase by an order of magnitude. Previously, SSI primarily relied on TPU chips provided by Google for AI research and development. This collaboration indicates that Nvidia is further expanding its AI chip ecosystem and binding future computing power demands through investments in top AI laboratories.SSI was established in 2024 by Ilya Sutskever, with the goal of developing "Safe Superintelligence." The company has previously raised about $2 billion in funding, with investors including Andreessen Horowitz and Sequoia Capital, and reached a valuation of approximately $30 billion last year. This is not Nvidia's first investment in an AI company founded by former core members of OpenAI. In March of this year, Nvidia also invested in Thinking Machines Lab, founded by former OpenAI chief technology officer Mira Murati, whose first AI model is trained on Nvidia hardware.Ilya Sutskever is one of the important researchers in the field of modern artificial intelligence, having co-founded OpenAI and promoted the development of large model technologies such as ChatGPT. However, after leaving OpenAI, he began to question the approach of solely relying on expanding data and computing power to drive AI progress, turning instead to explore new directions in superintelligence research.
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