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Richard A Aragon

TuringsSolutions

https://www.turingssolutions.com

AI & ML interests

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Articles

The Myth of Running Out of Data: Why Infinite Math Makes AI Training Limitless

Aug 8

• 1

Simulating Monte Carlo Algorithms With Gaussian Probability

Jul 29

• 1

Utilizing Gaussian Probability Space to Simulate Monte Carlo Algorithms with Particle Swarm Optimization

Jul 27

Navigating Complexity with Elegance: The P-FAF Approach to Fractal Word Embeddings

Feb 22

Humor Understanding Multi-task Optimization & Ranking

Feb 9

• 1

Probabilistic Fractal Activation Function (P-FAF) and Its Advantages Over Traditional Word Vectorization

Feb 8

• 5

Organizations

Posts 32

Post

1189

Want a simple overview of 'AI Geometry' in video format? Then this video is for you. The description includes any further resources you need to understand it from there.

AI geometry is a new way of thinking about geometry that is based on the idea that AI models do not think spatially. It is made up of four main concepts: nodes, edges, structures, and transformations. Nodes represent identity and sameness, edges represent difference and relationships, structures represent clusters and categories, and transformations represent contextual shifts. AI geometry is still in its early stages of development, but it has the potential to revolutionize the way we think about AI and geometry.

https://youtu.be/7Ap6ji5_VTg

Post

3843

Are you familiar with the difference between discrete learning and predictive learning? This distinction is exactly why LLM models are not designed to perform and execute function calls, they are not the right shape for it. LLM models are prediction machines. Function calling requires discrete learning machines. Fortunately, you can easily couple an LLM model with a discrete learning algorithm. It is beyond easy to do, you simply need to know the math to do it. Want to dive deeper into this subject? Check out this video.

https://youtu.be/wBRem2p8iPM

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