Embodied AI

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Action Learning: Embodied AI where AI algorithms and agents learn through interactions with their environments from an egocentric perception similar to humans, rather than learning from datasets of images, videos or text curated primarily from the Internet. This involves working with real-world physical systems, such as robots.

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Embodiment Hypothesis: intelligence emerges in the interaction of an agent with an environment

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Embodied AI has a wide range of applications:

• Real-world physical systems, such as Robotics, where AI algorithms and agents learn through interactions with their environments from an egocentric perception similar to humans. There are several examples of robots that use Embodied AI. Mobile robots are one example of physically embodied agents that use AI algorithms and agents to learn through interactions with their environments from an egocentric perception similar to humans. Another example is Google’s PaLM-E, a multimodal embodied visual-language model that integrates vision and language for robotic control. It can perform a variety of tasks without the need for retraining.
• Psychology - Mental Health care, where it offers new opportunities to increase and improve the provision of care. This includes advances such as ‘virtual psychotherapists’ and social robots in dementia care and autism disorder. From an ethical perspective, important benefits of embodied AI applications in mental health include new modes of treatment, opportunities to engage hard-to-reach populations, better patient response, and freeing up time for physicians.
• Education:Augmented intelligence systems offer promising avenues for managing the complexity of education by integrating the strengths of disembodied AI to detect complex patterns of student behavior from multimodal data streams, with the strengths of humans to meaningfully interpret embodied interactions in service of consequential decision making. This can help achieve a balance between complexity, interpretability, and accountability for allocating education resources to children.
• Agriculture: AI on farms include self-driving tractors and combine harvesters, robot swarms for crop inspection and autonomous sprayers. Indoor farming companies like Plenty and AppHarvest are also using AI and computer vision to collect data on crops and adjust the environment for optimal nutrition and flavor]