8,773
edits
Talk:Restless Souls/Technology: Difference between revisions
Talk:Restless Souls/Technology (view source)
Revision as of 10:39, 12 March 2026
, Thursday at 10:39no edit summary
Paradox-01 (talk | contribs) mNo edit summary |
Paradox-01 (talk | contribs) mNo edit summary |
||
| Line 561: | Line 561: | ||
** The new transformer architecture (GPT). | ** The new transformer architecture (GPT). | ||
** The actual data-holding model consists of its parameters and weights, which encode learned patterns. This is a Large Language Model (LLM) or a Large Multimodal Model (LMM). The model learns statistical patterns in text, images, or other media, and its outputs arise through generalization, applying learned correlations rather than retrieving data verbatim. | ** The actual data-holding model consists of its parameters and weights, which encode learned patterns. This is a Large Language Model (LLM) or a Large Multimodal Model (LMM). The model learns statistical patterns in text, images, or other media, and its outputs arise through generalization, applying learned correlations rather than retrieving data verbatim. | ||
*** In some cases, if original training data appears exactly in outputs, it is considered | *** In some cases, if original training data appears exactly in outputs, it is considered memorized - meaning the model reproduced a pattern it encountered multiple times during training, such as passages from a widely available book. | ||
*** Unique training data can also sometimes be returned. Even if a piece of data appeared only once, if it is highly distinctive or contextually reinforced by similar patterns, the model may reproduce it with surprising fidelity. This occurs because the model has overfitted locally to these rare signals, making them more "retrievable" than typical generalized content. | *** Unique training data can also sometimes be returned. Even if a piece of data appeared only once, if it is highly distinctive or contextually reinforced by similar patterns, the model may reproduce it with surprising fidelity. This occurs because the model has overfitted locally to these rare signals, making them more "retrievable" than typical generalized content. | ||
** Reinforcement learning from human feedback (RLHF) and (its successor RLAIF) can be named as another important feature that added a reward model for higher quality and alignment. | ** Reinforcement learning from human feedback (RLHF) and (its successor RLAIF) can be named as another important feature that added a reward model for higher quality and alignment. | ||
| Line 577: | Line 577: | ||
:::: https://deepmind.google/blog/genie-3-a-new-frontier-for-world-models/ | :::: https://deepmind.google/blog/genie-3-a-new-frontier-for-world-models/ | ||
:::: https://www.nvidia.com/en-us/glossary/world-models/ | :::: https://www.nvidia.com/en-us/glossary/world-models/ | ||
* '''Physical AI''' = Physical Artificial Intelligence. Basically AI used in robots. | * '''Physical AI''' = Physical Artificial Intelligence. Basically AI used in robots, including self-driving cars. | ||
:: Since direct training can 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 to | :: Since direct training can 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 to help humans directing such systems. <!--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--> | ||
::: An older approach for physical AIs was direct training in real environments and therefore learning from real sensory inputs. As this method is slower it will become out of fashion, at least as primary training. '''Real world training will be kept for fine-tuning.''' | ::: An older approach for physical AIs was direct training in real environments and therefore learning from real sensory inputs. As this method is slower it will become out of fashion, at least as primary training. '''Real world training will be kept for fine-tuning.''' | ||
* '''Symbolic AI and neuro-symbolic AI''' | * '''Symbolic AI and neuro-symbolic AI''' | ||