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Mounir Shita

Research Scientist & Futurist (Nature of Intelligence & AGI)

Mounir S., also known as Mounir Samuel, is a self-employed individual who engages in discussions about language, privilege, and social issues. He has participated in various platforms, including podcasts, where he explores the intersections of language and societal constructs. His insights often focus on how language shapes reality and influences perceptions of privilege and exclusion.1

In addition to his discussions on language, Mounir Samuel is involved in storytelling and branding, aiming to breathe life into brands through narrative techniques. His work reflects a commitment to understanding and articulating complex social themes.1

For further details, you can find him on LinkedIn under the username "mounirshita."

Highlights

Jun 23 · linkedin

Another sniplet from my revised 2nd edition where I highlight the difference between static post-trained AI models vs how an AGI (or human) create their dynamic models in real-time.

#agi #artificialintelligence

Jun 13 · linkedin

The difference between Causality Hierarchy as the core AI learning model vs mainstream deep neural network learning algorithms according to Gemini Advanced (see image). I fed it Chapter 5 of "Science of Intelligence 2nd Edition" and asked it to compare the learning aspects in the Theory of General Intelligence with mainstream AI learning.

The Causality Hierarchy is inherently grounded in the physical world because it's built upon the fundamental principle of cause and effect. An AI system employing the Causality Hierarchy learns by observing and interacting with the environment, building a model of how actions lead to consequences in the real world. This grounding in physical causality allows the AI to develop a deeper understanding of how the world works, enabling it to make more informed and effective decisions.

Deep learning networks, on the other hand, are primarily based on statistical patterns extracted from large datasets. While these patterns might correlate with real-world phenomena, the connection is often indirect and lacks a deep understanding of the underlying causal mechanisms.

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Mounir Shita
Mounir Shita, photo 1
Mounir Shita, photo 2
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Location

Portland, Oregon, United States