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In the realm of real-time applications, Large Language Models (LLMs) have long dominated language-centric tasks, while tools like OpenCV have excelled in the visual domain. However, the future (maybe) lies in the fusion of LLMs and deep learning, giving birth to the revolutionary concept of Large Action Models (LAMs). Imagine a world where AI not only comprehends language but mimics human actions on technology interfaces. For example, the Rabbit r1 device presented at CES 2024, driven by an AI operating system and LAM, brings this vision to life. It executes complex commands, leveraging GUIs with unprecedented ease. In this presentation, join me on a journey as a software engineer tinkering with WebRTC, Janus, and LLM/LAMs. Together, we’ll evaluate the current state of these AI technologies, unraveling the potential they hold for shaping the future of real-time applications.
Unveiling the Tech Salsa of LAMs with Janus in Real-Time Applications
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Foundation models are machine learning models which are easily capable of performing variable tasks on large and huge datasets. FMs have managed to get a lot of attention due to this feature of handling large datasets. It can do text generation, video editing to protein folding and robotics. In case we believe that FMs can help the hospitals and patients in any way, we need to perform some important evaluations, tests to test these assumptions. In this review, we take a walk through Fms and their evaluation regimes assumed clinical value. To clarify on this topic, we reviewed no less than 80 clinical FMs built from the EMR data. We added all the models trained on structured and unstructured data. We are referring to this combination of structured and unstructured EMR data or clinical data.
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