Embodied AI - Revision history

BPeat at 21:02, 28 April 2024

2024-04-28T21:02:37Z

BPeat at 19:03, 8 October 2023

2023-10-08T19:03:37Z

BPeat at 00:17, 9 September 2023

2023-09-09T00:17:44Z

BPeat at 14:14, 20 May 2023

2023-05-20T14:14:47Z

BPeat at 14:12, 20 May 2023

2023-05-20T14:12:00Z

BPeat at 14:10, 20 May 2023

2023-05-20T14:10:58Z

BPeat at 13:55, 20 May 2023

2023-05-20T13:55:56Z

BPeat at 13:54, 20 May 2023

2023-05-20T13:54:13Z

BPeat at 13:51, 20 May 2023

2023-05-20T13:51:15Z

BPeat at 13:41, 20 May 2023

2023-05-20T13:41:43Z

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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|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.
+* [[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 [[Ethic|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]

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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.
+* 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|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.

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 [https://www.bing.com/news/search?q=Embodied+ai+interaction+environment+ai&qft=interval%3d%228%22 ...Bing News]
-* [[Singularity]] ... [[Artificial Consciousness / Sentience|Sentience]] ... [[Artificial General Intelligence (AGI)| AGI]] ... [[Inside Out - Curious Optimistic Reasoning| Curious Reasoning]] ... [[Emergence]] ... [[Moonshots]] ... [[Explainable / Interpretable AI|Explainable AI]] ...  [[Algorithm Administration#Automated Learning|Automated Learning]]
+* [[Artificial General Intelligence (AGI) to Singularity]] ... [[Inside Out - Curious Optimistic Reasoning| Curious Reasoning]] ... [[Emergence]] ... [[Moonshots]] ... [[Explainable / Interpretable AI|Explainable AI]] ...  [[Algorithm Administration#Automated Learning|Automated Learning]]
 * [[ALFRED]]  ... Action Learning From Realistic Environments and Directives
 * [[AlfWorld]]

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 * [[Singularity]] ... [[Artificial Consciousness / Sentience|Sentience]] ... [[Artificial General Intelligence (AGI)| AGI]] ... [[Inside Out - Curious Optimistic Reasoning| Curious Reasoning]] ... [[Emergence]] ... [[Moonshots]] ... [[Explainable / Interpretable AI|Explainable AI]] ...  [[Algorithm Administration#Automated Learning|Automated Learning]]
-* [[ALFRED]]
+* [[ALFRED]]  ... Action Learning From Realistic Environments and Directives
 * [[AlfWorld]]
 * [[Learning Techniques]]

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 * [[AlfWorld]]
 * [[Learning Techniques]]
 [[Learning Techniques| Action Learning]]: <b>Embodied AI</b> 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 [[Robotics|robots]].

← Older revision		Revision as of 14:10, 20 May 2023
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	* [[Learning Techniques]]		* [[Learning Techniques]]

−	[[Learning Techniques\| ~~Interactive~~ Learning]]: <b>Embodied AI</b> where AI algorithms and agents learn through ~~engagements~~ 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 [[Robotics\|robots]].	+	[[Learning Techniques\| Action Learning]]: <b>Embodied AI</b> 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 [[Robotics\|robots]].

← Older revision		Revision as of 13:55, 20 May 2023
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	* [[Learning Techniques]]		* [[Learning Techniques]]

−	~~<b>Embodied AI</b> using~~ [[Learning Techniques\|<b>~~Interactive Learning~~</b>]] AI algorithms and agents learn through engagements 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 [[Robotics\|robots]].	+	[[Learning Techniques\| Interactive Learning]]: <b>Embodied AI</b> where AI algorithms and agents learn through engagements 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 [[Robotics\|robots]].

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 * [[Learning Techniques]]
-<b>Embodied AI</b> using [[Learning Techniques|<b>Interactive Learning</b>]] 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 [[Robotics|robots]].
+<b>Embodied AI</b> using [[Learning Techniques|<b>Interactive Learning</b>]] AI algorithms and agents learn through engagements 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 [[Robotics|robots]].
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