What Is BETTER Than ChatGPT Generative Artificial Intelligence?
OpenAI’s ChatGPT uses generative AI, but regenerative AI, a NEWER approach, is a MUCH BETTER form of artificial intelligence.
Published: March 18, 2023 | Published By: Real Creative Agency
Regenerative AI is BETTER than ChatGPT Generative AI
Generative AI versus Regenerative AI.
Generative AI and Regenerative AI are two different approaches to artificial intelligence (AI) that are used to create intelligent systems.
While both methods share similarities, there are key differences between them that can impact their performance, adaptability, and overall utility.
Generative AI is a tool that looks to the past to predict the present. Producing a statistically relevant result. Statistical reasoning.
Regenerative Intelligence refers to its operating context and then uses its present (world model) to predict the future effects of its actions. Producing a contextually relevant result. Causal reasoning.
Its specialty is context awareness and self improvement.
Active Inference is the SMARTEST Artificial Intelligence.
Generative AI refers to a class of AI algorithms that are designed to generate new data or content that is similar to the training data provided to the algorithm.
Generative models can create text, images, videos, audio, and even code that resembles the input data, but they lack the ability to truly understand or reason about the data they are generating.
Regenerative AI, KOSM, on the other hand, is a newer and dynamic approach to AI that combines explicit knowledge modeling and active inference (learning) to create systems that can learn, reason, and adapt to new situations.
Unlike generative AI, regenerative AI systems are capable of understanding the structure and relationships within the data they are working with and can use this understanding to generate new data that is meaningful, useful and broadly applicable to any use case.
At the core of Regenerative AI is the science of active inference, which is a theory of brain function that describes how the brain constantly makes predictions about the world based on its internal model of the world.
This model is created through experience and learning and is constantly updated and refined as new data is acquired.
By incorporating active inference into AI systems, researchers are able to create intelligent systems that can reason about the world in much the same way that humans do.
Dr. Karl Friston, one of the world’s leading and most cited scientists in the study of brain and mind, developed Active Inference.
One of the key benefits of Regenerative AI is its ability to create meaningful and useful data, rather than simply generating data that resembles the input data.
For example, a Regenerative AI system could be used to generate new designs for a product based on customer feedback and preferences, or to predict the performance of a new drug based on its chemical structure and known biological interactions, or enable an autonomous vehicle to make sense of and adjust its behavior based on real-time data.
These types of applications require a deep understanding of real-world contexts, its data and their underlying structure, something that generative AI systems are not capable of.
Generative AI can not understand real world contexts.
Another benefit of Regenerative AI is its adaptability.
Because Regenerative AI systems are designed to actively learn and update their internal models of the world, they are able to adapt to new situations and environments much more easily than generative AI systems.
This makes them well-suited for applications in fields such as robotics, where adaptability and the ability to learn from experience are essential, especially in new situations or contexts.
KOSM is the BEST Alternative to ChatGPT and generative AI.
While both Generative AI and Regenerative AI are powerful tools for creating intelligent systems, there are key differences between them that can impact their performance and utility.
Generative AI is best suited for tasks such as image and video generation.
Regenerative AI is better suited for tasks that require a deep understanding of the data and its underlying structure or where the interactions of a program require a human centered understanding.
By incorporating active inference and explicit knowledge modeling, Regenerative AI systems like KOSM are able to create meaningful and useful data, and are more adaptable and flexible than Generative AI systems.
Finally, Generative AI tools are largely limited in capability to one or a few uses, whereas Regenerative AI can be customized to learn on any data set and apply their intelligent processes to continuous improvement.
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