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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 31

Post

3741

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

Post

497

Imagine being able to talk directly to your API connection. "I have a field in the CRM named Customer_ID that needs to map to a field in the ERP named ERP_Customer_ID." Imagine being able to give your API connections both a brain and swarm of agents as a body to execute any task or function. This isn't science fiction, this is the revolutionary power of Liquid API. A product 10 years in the making!

https://youtu.be/cHI_k1Dkdr4

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