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Rabbit CEO Jesse Lyu on launching the R1, future of AI hardware, and going viral at CES | E1885
This Week in Startups
The Advantage of Large Actions Models over LLMs and APIs
Large Action Models (LAMs) are preferable over Large Language Models (LLMs) like GPT-4 because LAMs are designed to complete tasks rather than just understanding language. Unlike LLMs that require significant GPU resources from the cloud, LAMs can be developed without massive funding, allowing even startups to create their own models. Moreover, APIs are not always available or do not fully replicate the features of applications, making them less reliable for comprehensive task completion. Large Action Models address these issues by using a neurosymbolic approach. By recording human interactions with software and feeding this data to LAMs, it facilitates the development of a universal solution for action-triggering across various applications irrespective of their platform. This approach has been iteratively built over two and a half years through the collection of human-software interaction data.
Maxims:
Large Action Models offer a task-oriented alternative to resource-intensive Large Language Models and incomplete APIs.
Neurosymbolic Large Action Models can train to perform tasks universally across platforms by analyzing direct human interaction with software.
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