Difference between revisions of "Embodied AI"
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| − | <b>Embodied AI</b> is an emerging field 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. The embodiment hypothesis is the idea that “intelligence emerges in the interaction of an agent with an environment and as a result of sensorimotor activity” | + | <b>Embodied AI</b> is an emerging field 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. The embodiment hypothesis is the idea that “intelligence emerges in the interaction of an agent with an environment and as a result of sensorimotor activity” 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 humans3. So while you can have | ||
| + | * 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. | ||
| + | * 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. | ||
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<youtube>pPxft5kYcYI</youtube> | <youtube>pPxft5kYcYI</youtube> | ||
<youtube>Iu2S1wWJ2T4</youtube> | <youtube>Iu2S1wWJ2T4</youtube> | ||
<youtube>Iu2S1wWJ2T4</youtube> | <youtube>Iu2S1wWJ2T4</youtube> | ||
Revision as of 08:04, 20 May 2023
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- ALFRED
- AlfWorld
Embodied AI is an emerging field 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. The embodiment hypothesis is the idea that “intelligence emerges in the interaction of an agent with an environment and as a result of sensorimotor activity” 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 humans3. So while you can have
- 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.
- 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.
