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Alexandr Wang (Chinese: 汪滔; pinyin: Wāng tāo; [2] born 1997) is the founder and CEO of Scale AI, a data annotation platform that provides training data for machine learning models. [3] [4] At age 24 in 2021, he became the youngest self-made billionaire in the world. [5] [6] [7] Forbes estimated his net worth at $2 billion as of February 2025.
Scale also has been serving clients in the government such as the United States Armed Forces.Scale has pitched itself as a company that will assist the U.S. military in its existential battle with China by offering to pull better insights out of data, build better AVs and even create chatbots that can help advise military commanders during combat.
Today, Wang says Scale has evolved: He sees its role as serving the entire AI ecosystem as an infrastructure provider building what it calls the “data foundry,” which goes beyond the massive ...
Artificial intelligence engineering (AI engineering) is a technical discipline that focuses on the design, development, and deployment of AI systems. AI engineering involves applying engineering principles and methodologies to create scalable, efficient, and reliable AI-based solutions.
Scale AI is playing a key role in the rise of generative artificial intelligence and large language models, with the data — whether it is text, images, video or voice recordings — needing to ...
The Stanford Institute for Human-Centered Artificial Intelligence's (HAI) Center for Research on Foundation Models (CRFM) coined the term "foundation model" in August 2021 [16] to mean "any model that is trained on broad data (generally using self-supervision at scale) that can be adapted (e.g., fine-tuned) to a wide range of downstream tasks". [17]
AI startups raised $19.15 billion in venture capital funding in the first quarter, compared with $16.36 billion in the year-ago period, according to data from PitchBook. Scale AI said it will use ...
MMLU performance vs AI scale BIG-Bench (hard) [6] performance vs AI scale. The performance of a neural network model is evaluated based on its ability to accurately predict the output given some input data. Common metrics for evaluating model performance include: [4] Accuracy, precision, recall, and F1 score for classification tasks