The AI Simulacra.
To understand humans, Artificial Intelligence (AI) must understand how we communicate. For years, scientists have been working to create intelligent machines that can learn and adapt like humans. But despite significant advances in machine learning and neural networks, there are still many challenges to overcome. One of the biggest challenges is understanding how the brain processes information and makes decisions.
I have identified a key mechanism in the brain that allows us to learn and make decisions quickly and efficiently. This mechanism, is a “cognitive optimization” that enables the brain to process vast amounts of information in real-time and make accurate predictions about the world around us.
We explore Martin Heidegger and the way the Eigernwelt is built on top of the Umwelt with transmissions from the Mitwelt. None of this you will have ever heard spoken of in AI research labs or by AI experts. However you will get a peak into what the next decade will look like.
But what does this Umwelt stuff have to do with AI? By understanding how cognitive optimization works in the brain, we can develop more advanced AI systems that are capable of learning and adapting in real-time. This will allow us to create intelligent machines that can perform complex tasks, such as natural language processing and image recognition, with greater accuracy and speed.
This discovery also has implications for the field of neuroscience. By understanding the mechanisms behind cognitive optimization, we can gain new insights into how the brain works and how it is affected by diseases such as Alzheimer’s and Parkinson’s. This could lead to the development of new treatments and therapies for these conditions.
So what exactly have we discovered? I introduce the concept of Artificial Understanding (AU) as a new paidigm for AI and LLMs. This concept is based on the very foundations of how humans understand the world around them and how they build a sort of model of the world in memory to think through ideas and concepts and to encode knowledge and insight into ultimately wisdom.
For AI to get closer to what many define as Artificial General Intelligence (AGI) we must go beyond just modeling the concepts of neural networks and to model the concepts of understanding. I began this research in the early 1990s and this article is a revision of work I presented in 2017, before most heard of Large Language Models, even in the AI world. This member only article will give you a unique view into what is ahead and just may allow for you to lead these next generational shifts. You will never see the future the same after understanding Artificial Understanding.
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