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Autonomous AI (older ANI) vs. newer Agentic AI | Autonomous AI (older ANI) vs. newer Agentic AI | ||
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* '''Agentic AI''' = | * '''Agentic AI''' = semi-autonomously working AI | ||
: The word '''agentic''' is meant to emphasize that the '''AI''' - unlike a mere GenAI - executes given tasks '''more like a capable servant'''. | : The word '''agentic''' is meant to emphasize that the '''AI''' - unlike a mere GenAI - executes given tasks '''more like a capable servant'''. | ||
: Agentic AIs are essentially LLMs with extended write access that can use external tools via MCP. By design, agentic AI systems (including their cloud-hosted LLM) '''still lack sufficient alignment'''. In other words, they are effectively beta software and can be dangerous both for one's own production environments and for others. | : Agentic AIs are essentially LLMs with extended write access that can use external tools via MCP. By design, agentic AI systems (including their cloud-hosted LLM) '''still lack sufficient alignment'''. In other words, they are effectively beta software and can be dangerous both for one's own production environments and for others. | ||
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* '''Physical AI''' = Physical Artificial Intelligence. Basically AI used in robots, including self-driving cars. | * '''Physical AI''' = Physical Artificial Intelligence. Basically AI used in robots, including self-driving cars. | ||
:: The general idea is: Like humans or other real organisms, AIs benefit from having an "inner world" to improve understanding and reasoning. The use of large language models (LLMs) is optional but can be a useful design choice to assist humans in directing such systems. | :: The general idea is: Like humans or other real organisms, AIs benefit from having an "inner world" to improve understanding and reasoning. The use of large language models (LLMs) is optional but can be a useful design choice to assist humans in directing such systems. | ||
:: Training from ''first-hand sensor input'' is obvious but real world actions can be dangerous and are - because realtime - ''slow'' in context of the computer age. Therefore, AIs are alternatively pre-trained in a simulation where the robot is represented by a digital twin. ''Real world training will be kept for fine-tuning.''' Modern physical AIs are in overall multimodal. | :: Training from ''first-hand sensor input'' is obvious but real world actions can be dangerous and are - because realtime - ''slow'' in context of the computer age. Therefore, AIs are alternatively pre-trained in a simulation where the robot is represented by a digital twin. '''Real world training will be kept for fine-tuning.''' Modern physical AIs are in overall multimodal. | ||
:: Alternatively, physical AIs are trained from video. As this posses ''second-hand sensor input'', the reasoning capabilities are less potent.<!--// commenting out company specific information for reasons ... // | :: Alternatively, physical AIs are trained from video. As this posses ''second-hand sensor input'', the reasoning capabilities are less potent.<!--// commenting out company specific information for reasons ... // | ||
::: In 2026 Sam Altman said OpenAI's next breakthrough is expected within two years, possibly meaning such hybrid approach which either gives him a something close to a world model, if not the real thing. The knowledge gained from Sora is rumored to flow into that. Google is working on a (''native'') "general purpose world model" instead. So, Genie 3 will - when it is released - probably perform better.--> | ::: In 2026 Sam Altman said OpenAI's next breakthrough is expected within two years, possibly meaning such hybrid approach which either gives him a something close to a world model, if not the real thing. The knowledge gained from Sora is rumored to flow into that. Google is working on a (''native'') "general purpose world model" instead. So, Genie 3 will - when it is released - probably perform better.--> | ||
:: Training with motion capture data is quickly done - and often falls short in generating stable locomotion as additional training would be needed.<!--Embodied AI--> | :: Training with motion capture data is quickly done - and often falls short in generating stable locomotion as additional training would be needed.<!--Embodied AI--> | ||
* '''World model''' = World models get trained on multimodal data, especially videos. | * '''World model''' = World models get trained on multimodal data, especially videos and simulation data. | ||
:: These models build an internal world and can '''better understand spatial inputs and forecast physics'''. Therefore they are '''also named predictive intelligence''' and are '''suited for''' applications like video synthesis, 3D simulations, animations and robotic motion planning therefore '''physical AI'''. | :: These models build an internal world and can '''better understand spatial inputs and forecast physics'''. Therefore they are '''also named predictive intelligence''' and are '''suited for''' applications like video synthesis, 3D simulations, animations and robotic motion planning therefore '''physical AI'''. | ||
::: See also: | ::: See also: | ||