8,700
edits
Talk:Restless Souls/Technology: Difference between revisions
m
no edit summary
Paradox-01 (talk | contribs) |
Paradox-01 (talk | contribs) mNo edit summary |
||
| Line 566: | Line 566: | ||
:: After the obvious slowdown in advancement through scaling, chain of thought (COT) was introduced. It is also known under the marketing term "reasoning [AI]". (See: Gemini 2.0 Flash Thinking and ChatGPT o3, the later one especially [https://techxplore.com/news/2024-12-ai-human-general-intelligence.html trained to cheat … erm ... score high in the ARC-AGI test].) | :: After the obvious slowdown in advancement through scaling, chain of thought (COT) was introduced. It is also known under the marketing term "reasoning [AI]". (See: Gemini 2.0 Flash Thinking and ChatGPT o3, the later one especially [https://techxplore.com/news/2024-12-ai-human-general-intelligence.html trained to cheat … erm ... score high in the ARC-AGI test].) | ||
:: This pushes ANI somewhat more into direction of AGI with the drawback of slower response times and higher energy costs.--> | :: This pushes ANI somewhat more into direction of AGI with the drawback of slower response times and higher energy costs.--> | ||
* '''Physical AI''' = Physical Artificial Intelligence. Basically AI used in robots. Since direct training would be dangerous, slow and therefore ineffective, the AI gets pre-trained in a simulation where the robot is represented by a digital twin. By the nature of this construction, multimodal learning (MML) is predestined for robots. Like humans (or other real organisms), AIs should include an "inner world" for better understanding. The use of LLM is optional but seems to be a good design choice for walking further into direction of AGI. <!--Expecting [[wp:Symbolic_artificial_intelligence#Neuro-symbolic_AI:_integrating_neural_and_symbolic_approaches|some kind of hybrid approaches]] within physical AIs later when all other current ''cash cow'' approaches reached their end.--> | * '''Physical AI''' = Physical Artificial Intelligence. Basically AI used in robots. Since direct training would be dangerous, slow and therefore ineffective, the AI gets pre-trained in a simulation where the robot is represented by a digital twin. By the nature of this construction, multimodal learning (MML) is predestined for robots. Like humans (or other real organisms), AIs should include an "inner world" for better understanding. The use of LLM is optional but seems to be a good design choice for walking further into direction of AGI. <!--Expecting [[wp:Symbolic_artificial_intelligence#Neuro-symbolic_AI:_integrating_neural_and_symbolic_approaches|some kind of hybrid approaches]] within physical AIs later when all other current ''cash cow'' approaches reached their end.--><!--Embodied AI--> | ||
<!-- | |||
Autonomous AI (alte NAI) vs. Agentic AI | |||
World model AI | |||
* https://deepmind.google/blog/genie-3-a-new-frontier-for-world-models/ | |||
--> | |||
* '''AGI''' = Artificial General Intelligence, also "strong AI" ('''on par with human thinking''', ''a real AI'' capable to fully self-improve and drive its own development) | * '''AGI''' = Artificial General Intelligence, also "strong AI" ('''on par with human thinking''', ''a real AI'' capable to fully self-improve and drive its own development) | ||
:: In discussions AGI is often equated with super intelligence. The argument is that as soon as AGI is achieved ASI is just around the corner. | :: In discussions AGI is often equated with super intelligence. The argument is that as soon as AGI is achieved ASI is just around the corner. | ||