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* '''ANI''' = Artificial Narrow Intelligence, '''specialized or "weak AI"''' (basically machine learning: pattern recognition) | * '''ANI''' = Artificial Narrow Intelligence, '''specialized or "weak AI"''' (basically machine learning: pattern recognition) | ||
* '''GenAI''' = Generative AI, its a significant step between ANI and AGI. GenAI included at least three major points: | * '''GenAI''' = Generative AI, its a significant step between ANI and AGI. GenAI included at least three major points: | ||
** The | ** The 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 memorized - meaning the model reproduced a pattern it encountered multiple times during training, such as passages from a widely available book. | *** 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. | ||
** Other features like chain of thought (reasoning), mixture of experts (MoE), context expansion | ** Other features like chain of thought (reasoning), mixture of experts (MoE), context expansion, the use of external tools via model context protocol (MCP) to compensate own shortcomings and architecture changes like mamba are better described as incremental improvements in the evolution of GenAI. | ||
*** The transformer-only approach doesn't scale well with increasing token count. The added [[wp:Mamba_(deep_learning_architecture)|mamba]] architecture removes that bottleneck. Therefore, mamba transformer hybrid LLMs feature better long-context reasoning.<!-- The hybrid approach together with MoE is called Jamba by AI21 and is also used by Nvidia.--> As consequence, matured hybrids will make agentic AI smarter and therefore a bit more safer to use by default. | |||
<!-- | <!-- | ||
Autonomous AI (older ANI) vs. newer Agentic AI | Autonomous AI (older ANI) vs. newer Agentic AI | ||
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:: It can therefore be seen as the ironic embodiment of the tech industry's saying: "Move fast and break things." Unfortunately, people were impressed by demos such as moltbook, which fuel the illusion that these programs possess true intelligence. | :: It can therefore be seen as the ironic embodiment of the tech industry's saying: "Move fast and break things." Unfortunately, people were impressed by demos such as moltbook, which fuel the illusion that these programs possess true intelligence. | ||
:: As (still too) probabilistic systems - a.k.a. "statistical parrots" - LLMs tend to treat many possible outputs as valid solutions unless they are explicitly prohibited. [https://www.golem.de/news/unkontrollierbares-fehlverhalten-ki-agenten-werden-zu-immer-groesserem-insider-risiko-2603-206491.html Because hacking is fundamentally a creative act, even simple and seemingly harmless directives such as "be more creative" can lead to unintended or even catastrophic outcomes.] Therefore, agentic AI has also been compared to jinn or genies that grant wishes, but these wishes might be fulfilled in unexpected or even unacceptable ways. | :: As (still too) probabilistic systems - a.k.a. "statistical parrots" - LLMs tend to treat many possible outputs as valid solutions unless they are explicitly prohibited. [https://www.golem.de/news/unkontrollierbares-fehlverhalten-ki-agenten-werden-zu-immer-groesserem-insider-risiko-2603-206491.html Because hacking is fundamentally a creative act, even simple and seemingly harmless directives such as "be more creative" can lead to unintended or even catastrophic outcomes.] Therefore, agentic AI has also been compared to jinn or genies that grant wishes, but these wishes might be fulfilled in unexpected or even unacceptable ways. | ||
: Due to current shortcomings, | :: Due to current shortcomings, specialized agentic AIs are also setup to work in groups. Multi-agent systems. | ||
* '''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. | :: 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. | ||
:: 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: | ||
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::: Active expansion of knowledge alongside memetic hygiene: regularly review existing knowledge at defined intervals, specifically prevent anthropomorphization and more generally meme injections, and actively seek new knowledge. This process should operate under the constraint that exponential growth must not destroy or suppress life (preventing a Memehunter scenario). | ::: Active expansion of knowledge alongside memetic hygiene: regularly review existing knowledge at defined intervals, specifically prevent anthropomorphization and more generally meme injections, and actively seek new knowledge. This process should operate under the constraint that exponential growth must not destroy or suppress life (preventing a Memehunter scenario). | ||
::: Abstract reasoning and generation of completely new thought patterns (beyond pattern remixing and transmission). That's basically what [https://the-decoder.de/arc-agi-3-top-ki-modelle-schaffen-unter-1-prozent-bei-aufgaben-die-menschen-einfach-loesen/ AGI-ARC-3] wants to test. As for 2026, current models were not able to surpass 0,4 %. | ::: Abstract reasoning and generation of completely new thought patterns (beyond pattern remixing and transmission). That's basically what [https://the-decoder.de/arc-agi-3-top-ki-modelle-schaffen-unter-1-prozent-bei-aufgaben-die-menschen-einfach-loesen/ AGI-ARC-3] wants to test. As for 2026, current models were not able to surpass 0,4 %. | ||
::::''Base, meta, temporal and spatial logic'' should give rise to a foundation for a theory of mind. ''Internal simulations'' allow, in principle, a deep understanding of all objects and lifeforms - including one's own self. Therefore, a ToM could also give rise to a true (machine) consciousness. At this point it is important to note that an <!--wisely educated for memetic hygiene-->AGI will miss intrinsic dynamics found only in biological lifeforms. Therefore it is not subject to pain, <!--true -->fear, hunger, reproduction instincts or motivations derived from those. -- Humans and AGIs should never forget this in order to sustain coexistence. | ::::''Base, meta<!--abstract reasoning-->, temporal and spatial logic'' should give rise to a foundation for a theory of mind. ''Internal simulations'' allow, in principle, a deep understanding of all objects and lifeforms - including one's own self. Therefore, a ToM could also give rise to a true (machine) consciousness. At this point it is important to note that an <!--wisely educated for memetic hygiene-->AGI will miss intrinsic dynamics found only in biological lifeforms. Therefore it is not subject to pain, <!--true -->fear, hunger, reproduction instincts or motivations derived from those. -- Humans and AGIs should never forget this in order to sustain coexistence. | ||
:: Sub-types: | :: Sub-types: | ||
::: fake AGI (considered AGI by power but it has only moderate success rates<!--no or poor "machine consciousness"-->) | ::: fake AGI (considered AGI by power but it has only moderate success rates<!--no or poor "machine consciousness"-->) | ||