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Toronto-based startup Xaba, focused on developing 'synthetic brains' for industrial robots via a no-code approach, announced a $6 million seed funding round led by Hitachi Ventures. The funding will accelerate deployment of its AI-powered robots and cognitive industrial control systems. Image note: Image generated by AI, licensed from MidjourneyX.

A recent joint study by Google, Carnegie Mellon University, and MultiOn explores the application of synthetic data in training large language models. According to Epoch AI, a research institution focused on AI development, currently available high-quality text training data totals around 300 trillion tokens. However, with the rapid advancement of large models like ChatGPT, the demand for training data is growing exponentially, projected to exhaust existing resources by 2026. Therefore, synthetic data is becoming increasingly crucial.

Diffusion models have garnered significant attention in the field of AI video generation due to their superior performance. However, their inherent iterative denoising nature leads to a time-consuming and computationally expensive generation process, significantly hindering their widespread adoption. Recently, a research team from Beihang University, the University of Hong Kong, and the Shanghai AI Laboratory jointly released AccVideo, an innovative technology. This method, employing a novel and efficient distillation approach combined with a synthetic dataset, successfully boosts the generation speed of video diffusion models by an impressive 8.5 times.

Nvidia, the leading AI chipmaker, has reportedly acquired San Diego-based startup Gretel, a company specializing in a platform for generating synthetic AI training data. Wired reports the acquisition was a nine-figure deal, exceeding Gretel's recent $320 million valuation, though specific terms remain undisclosed. Founded in 2019 by Alex Watson and Laszl...

The Cyberspace Administration of China (CAC), Ministry of Industry and Information Technology, Ministry of Public Security, and the State Administration of Radio and Television have jointly formulated the "Regulations on Identifying AI-Generated Synthetic Content." This new regulation aims to address societal issues stemming from the proliferation of AI-generated content, particularly the spread of misinformation affecting public rights and interests. The regulations mandate prominent labeling of all AI-generated synthetic content, including text, audio, images, and videos. This measure will effectively protect citizens, legal persons, and other organizations.

In a recent live conversation, Tesla and SpaceX CEO Elon Musk stated that the real-world data available for training artificial intelligence models is nearly exhausted. He was in discussion with Mark Penn, Chairman of Stagwell. Musk mentioned, "We have basically consumed all the accumulated knowledge of humanity... for AI training data. This phenomenon essentially occurred last year." Musk's viewpoint aligns with that of former OpenAI Chief Scientist Ilya Sutskever.

On December 24, the Shanghai Municipal Government Office announced the 'Work Plan for the Development of Medical Artificial Intelligence in Shanghai (2025-2027)'. The plan clarifies the main tasks and goals for the next three years, aiming to respond to the national strategic deployment of 'AI + Action'. By promoting deep integration of medical innovation and artificial intelligence, it seeks to transform Shanghai into a globally influential hub for innovation, application demonstration, and industrial development in medical AI technology.

According to Xinhua News Agency, the Madrid Zoo Aquarium recently announced that a unique AI and synthetic biology-developed zookeeper, Irenia, has joined the giant panda support team, providing relevant information for the pandas 'Jinxi' and 'Zhuyu,' who arrived from China in April this year. Irenia is a digital persona that previously appeared on the zoo's TikTok channel, offering educational insights about giant pandas. It is now featured on a large screen behind the panda exhibit, providing real-time information to visitors.

The National Internet Information Office recently released the 'Measures for Labeling AI-Generated Synthetic Content (Draft for Comments)', aimed at regulating the labeling of AI-generated synthetic content, safeguarding national security and public interests, and protecting the legitimate rights and interests of citizens, legal persons, and other organizations. This measure is formulated based on the 'Cybersecurity Law of the People's Republic of China', 'Regulations on the Administration of Algorithms for Internet Information Services', 'Regulations on the Administration of Deep Synthesis in Internet Information Services', and the 'Interim Measures for the Administration of Generative Artificial Intelligence Services', among other laws and regulations. The deadline for feedback is the 20th.

YouTube is developing new tools to help creators manage and protect their voices and images, especially in the context of generative AI. The new tools include 'Synthetic Singing Recognition Technology' aimed at automatically detecting content that uses generative AI to mimic creators' singing voices. This technology will be integrated into YouTube's copyright identification system, with pilot testing planned for next year. Additionally, YouTube is also developing another technology to recognize deepfake videos to protect creators' images. In order to prevent unauthorized YouTube...

Meta AI's latest project, Llama 3, focuses on training with synthetic data rather than human-written answers to optimize model performance in areas such as code generation, mathematical reasoning, multilingual processing, and long text handling. Llama 3 generates synthetic data for code generation through three methods, employs research methods for mathematical reasoning, and collects high-quality human annotations for multilingual pre-training. Furthermore, Llama 3's training is enhanced using Brave Search, Wolfram Alpha, and Python interpreter.

Evo is a genomic foundation model capable of multi-modal and multi-scale learning. Evo can generate genomic-scale DNA sequences. Evo goes beyond single molecules and short sequences, allowing for the design of synthetic multi-component biological systems. Evo can predict essential genes in bacteria or phages at the whole genome scale.

Large language models have garnered significant attention in the AI community, showcasing remarkable capabilities. The Apple research team reveals WRAP technology, which utilizes synthetic data for pre-training large models. WRAP enhances model performance, rewrites network documentation, and improves pre-training effectiveness. By using high-quality synthetic data to accelerate training, overall performance is increased, paving new paths.

Google will participate in the Global Partnership on AI (GPAI) summit, committing to make machine learning and artificial intelligence more beneficial for users. The company will take steps to address synthetic media, including labeling modified or synthesized content on Search and YouTube. Google has invested $1 million to establish a multidisciplinary Responsible AI Center at the Indian Institute of Technology Madras. Google is dedicated to the development of responsible artificial intelligence and is taking action to combat the deepfake challenge.

Research shows that the MimicGen system can generate large-scale training data for robots. By leveraging digital twin technology, it replicates human operation data in a simulated environment. Using fewer than 200 human demonstrations, it autonomously generates 50,000 training data points. Synthetic data and simulation are crucial for AI development, providing an infinite source of training data. MimicGen is suitable for long-sequence tasks and high-precision tasks, such as grasping and assembling.