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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.
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.
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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.
Below is a list of notable companies that primarily focuses on artificial intelligence (AI). Companies that simply makes use of AI but have a different primary focus are not included. Companies that simply makes use of AI but have a different primary focus are not included.
Scale AI has landed $1 billion in new funding that values the buzzy six-year-old startup at $14 billion, placing it in an exclusive club of companies that have been able to surf the generative AI ...
The Cerebras Wafer Scale Engine (WSE) is a single, wafer-scale integrated processor that includes compute, memory and interconnect fabric. The WSE-1 powers the Cerebras CS-1, Cerebras’ first-generation AI computer. [27] It is a 19-inch rack-mounted appliance designed for AI training and inference workloads in a datacenter. [13]
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]