Spatial Web and the Era of AI - Part 1 | KB #10 - Spatial Web AI Podcast

Spatial Web and the Era of AI - Part 1  #futureofai #artificialintelligence by Denise Holt

Deep Learning Language Models vs. Cognitive Science

The pioneering goal of Artificial Intelligence has been to understand how humans think. The original idea was to merge intellectual and computer contributions to learn about cognition.

 

In the 1990’s, a shift took place from a knowledge-driven AI approach to a data-driven AI approach, replacing the original objectives with a type of Machine Learning called Deep Learning, capable of analyzing large amounts of data, drawing conclusions from the results.

 

Deep Learning is a predictive machine model that operates off of pattern recognition. Some people believe that if you simply feed the model more and more data, then the AI will begin to evolve on its own, eventually reaching AGI (Artificial General Intelligence), the ‘Holy Grail’ of AI. 

 

This theory, however, is viewed as being deeply flawed because these AI machines are not capable of “awareness” or the ability to “reason.” With Machine Learning/Deep Learning AI, there is no “thinking taking place.” 

 

These predictive machines are void of any actual intelligence. 

 

Scaling into bigger models by adding more and more parameters until these models consume the entire internet, will only prove useful to a point.

 

A larger data bank will not be able to solve for recognizing toxicity within the data structures, nor will it enable the ability to navigate sensitive data, permissioned information, protected identities, or intellectual property. A larger data bank does not enable reasoning or abstract thinking.

 

For AI to achieve the ultimate goal of AGI we need to be able to construct cognitive models of the world and map ‘meaning’ onto the data. We need a large database of abstract knowledge that can be interpreted by a machine imparting a level of ‘awareness’.

Newton vs. Einstein

Model Based AI for Active Inference is an Artificial Intelligence methodology that possesses all the ingredients required to achieve the breakthrough to AGI by surpassing all of the fundamental limitations of current Machine Learning/Deep Learning AI.

 

The difference between Machine Learning AI and Active Inference AI is as stark as the jump from Newton’s Laws of Universal Gravitation to Einstein’s Theory of Relativity.

 

In the late 1800’s, physicists believed that we had already discovered the laws that govern motion and gravity within our physical universe. Little did they know how naïve Isaac Newton’s ideas were, until Albert Einstein opened mankind’s eyes to spacetime and the totality of existence and reality.

 

This is what is happening with AI right now.

 

It’s simply not possible to get to AGI (Artificial General Intelligence) with a machine learning model, but AGI is inevitable with Active Inference.

 

 

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Special thanks to Dan Mapes, President and Co-Founder, VERSES AI and Director of The Spatial Web Foundation. If you’d like to know more about The Spatial Web, I highly recommend a helpful introductory book written by Dan and his VERSES Co-Founder, Gabriel Rene, titled, “The Spatial Web,” with a dedication “to all future generations.”

 

Listen to more episodes in my Knowledge Bank Playlist to learn everything you need to know to stay ahead of this rapidly accelerating technology.

 

Check out more at, SpatialWebAI and Spatial Web Foundation

 

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