Llama 2 Defeats GPT-4! Meta Enables Large Models to Self-Reward and Iteratively Improve, Reinforcing the Finality of Synthetic Data for LLMs

Google Research Shows Synthetic Data Improves Large Model Logical Reasoning Eightfold

Google Research: Synthetic Data Boosts Large Model Math Reasoning Eightfold

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.

AccVideo Achieves High-Quality Video Generation with Synthetic Data, Boosting Speed by 8.5x

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 Makes Major Acquisition: Purchases Synthetic Data Startup Gretel to Accelerate Generative AI Push

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...

OpenAI Chat Playground Upgraded to Prompts Playground for Better Prompt Testing and Iteration

Elon Musk Warns: Real World Data for AI Training is Almost Exhausted

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.

Keling Version 1.6 Launched: Significant Enhancement in Physical Realism

Synthetic Data is Toxic! Meta Team Confirms: 1% of Data Can Completely Collapse Large Models

Writer Uses Synthetic Data to Reduce AI Model Training Costs, Challenging Industry Giants

Meta AI Researcher: The Text on the Internet is All 'Garbage,' Llama 3 is Complete Synthetic Data

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.